"""
    #################################################################################################################################################################################################
    # This code library is an adaptation of the original Transformers and was designed, developed and programmed by Sapiens Technology®.                                                            #
    # Any alteration and/or disclosure of this code without prior authorization is strictly prohibited and is subject to legal action that will be forwarded by the Sapiens Technology® legal team. #
    # This set of algorithms aims to download, train, fine-tune and/or infer large language models from various sources and slopes.                                                                 #
    #################################################################################################################################################################################################
"""
import math
from typing import Dict, List, Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from ...activations import ACT2FN
from ...generation import GenerationMixin
from ...modeling_outputs import (BaseModelOutput, BaseModelOutputWithPooling, CausalLMOutputWithCrossAttentions, Seq2SeqLMOutput, Seq2SeqModelOutput)
from ...modeling_utils import PreTrainedModel
from ...pytorch_utils import ALL_LAYERNORM_LAYERS
from ...utils import (DUMMY_INPUTS, DUMMY_MASK, add_start_docstrings, add_start_docstrings_to_model_forward, is_torch_fx_proxy, logging, replace_return_docstrings)
from .configuration_pix2struct import Pix2StructConfig, Pix2StructTextConfig, Pix2StructVisionConfig
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = "Pix2StructConfig"
class Pix2StructLayerNorm(nn.Module):
    def __init__(self, hidden_size, eps=1e-6):
        super().__init__()
        self.weight = nn.Parameter(torch.ones(hidden_size))
        self.variance_epsilon = eps
    def forward(self, hidden_states):
        variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
        if self.weight.dtype in [torch.float16, torch.bfloat16]: hidden_states = hidden_states.to(self.weight.dtype)
        return self.weight * hidden_states
try:
    from apex.normalization import FusedRMSNorm
    Pix2StructLayerNorm = FusedRMSNorm
    logger.info("Discovered apex.normalization.FusedRMSNorm - will use it instead of Pix2StructLayerNorm")
except ImportError: pass
except Exception:
    logger.warning("Discovered apex but it failed to load, falling back to Pix2StructLayerNorm")
    pass
ALL_LAYERNORM_LAYERS.append(Pix2StructLayerNorm)
class Pix2StructVisionEmbeddings(nn.Module):
    def __init__(self, config: Pix2StructConfig) -> None:
        super().__init__()
        self.patch_projection = nn.Linear(config.patch_embed_hidden_size, config.hidden_size)
        self.row_embedder = nn.Embedding(config.seq_len, config.hidden_size)
        self.column_embedder = nn.Embedding(config.seq_len, config.hidden_size)
        self.dropout = nn.Dropout(config.dropout_rate)
    def forward(self, flattened_patches: torch.Tensor) -> torch.Tensor:
        row_indices = flattened_patches[:, :, 0].long()
        col_indices = flattened_patches[:, :, 1].long()
        flattened_patches = flattened_patches[:, :, 2:]
        embeddings = self.patch_projection(flattened_patches)
        row_embeddings = self.row_embedder(row_indices)
        col_embeddings = self.column_embedder(col_indices)
        embeddings = embeddings + row_embeddings + col_embeddings
        embeddings = self.dropout(embeddings)
        return embeddings
class Pix2StructVisionAttention(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.hidden_size = config.hidden_size
        self.key_value_proj_dim = config.d_kv
        self.n_heads = config.num_attention_heads
        self.dropout = config.attention_dropout
        self.inner_dim = self.n_heads * self.key_value_proj_dim
        self.query = nn.Linear(self.hidden_size, self.inner_dim, bias=False)
        self.key = nn.Linear(self.hidden_size, self.inner_dim, bias=False)
        self.value = nn.Linear(self.hidden_size, self.inner_dim, bias=False)
        self.output = nn.Linear(self.inner_dim, self.hidden_size, bias=False)
        self.gradient_checkpointing = False
    def forward(self, hidden_states, attention_mask=None, position_bias=None, layer_head_mask=None, output_attentions=False):
        batch_size, seq_length = hidden_states.shape[:2]
        def to_projection_shape(states): return states.contiguous().view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2)
        query_states = to_projection_shape(self.query(hidden_states))
        key_states = to_projection_shape(self.key(hidden_states))
        value_states = to_projection_shape(self.value(hidden_states))
        scores = torch.matmul(query_states, key_states.transpose(3, 2))
        if position_bias is None:
            position_bias = torch.zeros((1, self.n_heads, seq_length, seq_length), device=scores.device, dtype=scores.dtype)
            if self.gradient_checkpointing and self.training: position_bias.requires_grad = True
            if attention_mask is None: attention_mask = torch.ones((batch_size, seq_length), device=scores.device, dtype=scores.dtype)
            if attention_mask.dim() == 2: position_bias = position_bias + attention_mask[:, None, None, :].to(position_bias.device)
            else: position_bias = position_bias + attention_mask.to(position_bias.device)
            position_bias = 1 - position_bias
        position_bias_masked = position_bias.masked_fill(position_bias == 1, torch.finfo(scores.dtype).min)
        scores += position_bias_masked
        scores = torch.max(scores, torch.tensor(torch.finfo(scores.dtype).min))
        attn_weights = nn.functional.softmax(scores, dim=-1, dtype=torch.float32).type_as(scores)
        attn_weights = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)
        if layer_head_mask is not None: attn_weights = attn_weights * layer_head_mask
        attn_output = torch.matmul(attn_weights, value_states)
        attn_output = attn_output.transpose(1, 2).contiguous().view(batch_size, -1, self.inner_dim)
        attn_output = self.output(attn_output)
        outputs = (attn_output,) + (position_bias,)
        if output_attentions: outputs = outputs + (attn_weights,)
        return outputs
class Pix2StructVisionMlp(nn.Module):
    def __init__(self, config: Pix2StructVisionConfig):
        super().__init__()
        self.wi_0 = nn.Linear(config.hidden_size, config.d_ff, bias=False)
        self.wi_1 = nn.Linear(config.hidden_size, config.d_ff, bias=False)
        self.wo = nn.Linear(config.d_ff, config.hidden_size, bias=False)
        self.dropout = nn.Dropout(config.dropout_rate)
        self.act = ACT2FN[config.dense_act_fn]
    def forward(self, hidden_states):
        hidden_gelu = self.act(self.wi_0(hidden_states))
        hidden_linear = self.wi_1(hidden_states)
        hidden_states = hidden_gelu * hidden_linear
        hidden_states = self.dropout(hidden_states)
        if (isinstance(self.wo.weight, torch.Tensor) and hidden_states.dtype != self.wo.weight.dtype and self.wo.weight.dtype != torch.int8): hidden_states = hidden_states.to(self.wo.weight.dtype)
        hidden_states = self.wo(hidden_states)
        return hidden_states
class Pix2StructVisionLayer(nn.Module):
    def __init__(self, config: Pix2StructConfig) -> None:
        super().__init__()
        self.chunk_size_feed_forward = config.chunk_size_feed_forward
        self.seq_len_dim = 1
        self.attention = Pix2StructVisionAttention(config)
        self.mlp = Pix2StructVisionMlp(config)
        self.pre_mlp_layer_norm = Pix2StructLayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.pre_attention_layer_norm = Pix2StructLayerNorm(config.hidden_size, eps=config.layer_norm_eps)
    def forward(self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None,
    output_attentions: bool = False) -> Union[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor]]:
        residual = hidden_states
        hidden_states = self.pre_attention_layer_norm(hidden_states)
        self_attention_outputs = self.attention(hidden_states, attention_mask=attention_mask, layer_head_mask=head_mask, output_attentions=output_attentions)
        attention_output = self_attention_outputs[0]
        outputs = self_attention_outputs[1:]
        hidden_states = attention_output + residual
        layer_output = self.pre_mlp_layer_norm(hidden_states)
        layer_output = self.mlp(layer_output) + hidden_states
        outputs = (layer_output,) + outputs
        return outputs
class Pix2StructVisionEncoder(nn.Module):
    def __init__(self, config: Pix2StructConfig) -> None:
        super().__init__()
        self.config = config
        self.layer = nn.ModuleList([Pix2StructVisionLayer(config) for _ in range(config.num_hidden_layers)])
        self.gradient_checkpointing = False
    def forward(self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, output_attentions: bool = False,
    output_hidden_states: bool = False, return_dict: bool = True) -> Union[tuple, BaseModelOutput]:
        all_hidden_states = () if output_hidden_states else None
        all_self_attentions = () if output_attentions else None
        for i, layer_module in enumerate(self.layer):
            if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,)
            layer_head_mask = head_mask[i] if head_mask is not None else None
            if self.gradient_checkpointing and self.training: layer_outputs = self._gradient_checkpointing_func(layer_module.__call__, hidden_states, attention_mask, layer_head_mask, output_attentions)
            else: layer_outputs = layer_module(hidden_states, attention_mask, layer_head_mask, output_attentions)
            hidden_states = layer_outputs[0]
            if output_attentions: all_self_attentions = all_self_attentions + (layer_outputs[1],)
        if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,)
        if not return_dict: return tuple(v for v in [hidden_states, all_hidden_states, all_self_attentions] if v is not None)
        return BaseModelOutput(last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_self_attentions)
class Pix2StructPreTrainedModel(PreTrainedModel):
    config_class = Pix2StructConfig
    @property
    def dummy_inputs(self):
        input_ids = torch.tensor(DUMMY_INPUTS)
        input_mask = torch.tensor(DUMMY_MASK)
        dummy_inputs = {"decoder_input_ids": input_ids, "input_ids": input_ids, "decoder_attention_mask": input_mask}
        return dummy_inputs
    def _init_weights(self, module):
        factor = self.config.initializer_factor
        if isinstance(module, Pix2StructLayerNorm): module.weight.data.fill_(factor * 1.0)
        elif isinstance(module, Pix2StructTextDenseGatedActDense):
            hidden_size = (self.config.text_config.hidden_size if isinstance(self.config, Pix2StructConfig) else self.config.hidden_size)
            d_ff = self.config.text_config.d_ff if isinstance(self.config, Pix2StructConfig) else self.config.d_ff
            module.wi_0.weight.data.normal_(mean=0.0, std=factor * ((hidden_size) ** -0.5))
            if hasattr(module.wi_0, "bias") and module.wi_0.bias is not None: module.wi_0.bias.data.zero_()
            module.wi_1.weight.data.normal_(mean=0.0, std=factor * ((hidden_size) ** -0.5))
            if hasattr(module.wi_1, "bias") and module.wi_1.bias is not None: module.wi_1.bias.data.zero_()
            module.wo.weight.data.normal_(mean=0.0, std=factor * ((d_ff) ** -0.5))
            if hasattr(module.wo, "bias") and module.wo.bias is not None: module.wo.bias.data.zero_()
        elif isinstance(module, Pix2StructTextAttention):
            hidden_size = (self.config.text_config.hidden_size if isinstance(self.config, Pix2StructConfig) else self.config.hidden_size)
            key_value_proj_dim = (self.config.text_config.d_kv if isinstance(self.config, Pix2StructConfig) else self.config.hidden_size)
            n_heads = (self.config.text_config.num_heads if isinstance(self.config, Pix2StructConfig) else self.config.num_heads)
            module.query.weight.data.normal_(mean=0.0, std=factor * ((hidden_size * key_value_proj_dim) ** -0.5))
            module.key.weight.data.normal_(mean=0.0, std=factor * (hidden_size**-0.5))
            module.value.weight.data.normal_(mean=0.0, std=factor * (hidden_size**-0.5))
            module.output.weight.data.normal_(mean=0.0, std=factor * ((n_heads * key_value_proj_dim) ** -0.5))
            if module.has_relative_attention_bias: module.relative_attention_bias.weight.data.normal_(mean=0.0, std=factor * ((hidden_size) ** -0.5))
        elif isinstance(module, nn.Embedding):
            hidden_size = (self.config.text_config.hidden_size if isinstance(self.config, Pix2StructConfig) else self.config.hidden_size)
            module.weight.data.normal_(mean=0.0, std=factor * ((hidden_size) ** -0.5))
            if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_()
        elif isinstance(module, Pix2StructTextModel):
            hidden_size = (self.config.text_config.hidden_size if isinstance(self.config, Pix2StructConfig) else self.config.hidden_size)
            module.lm_head.weight.data.normal_(mean=0.0, std=factor * ((hidden_size) ** -0.5))
        elif isinstance(module, (nn.Linear, nn.Conv2d)):
            module.weight.data = nn.init.trunc_normal_(module.weight.data.to(torch.float32), mean=0.0, std=self.config.initializer_range).to(module.weight.dtype)
            if module.bias is not None: module.bias.data.zero_()
        elif isinstance(module, Pix2StructLayerNorm):
            if module.weight is not None: module.weight.data.fill_(1.0)
        elif isinstance(module, nn.Embedding):
            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
            if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_()
    def _shift_right(self, input_ids):
        decoder_start_token_id = self.config.decoder_start_token_id
        pad_token_id = self.config.pad_token_id
        if decoder_start_token_id is None: raise ValueError("self.model.config.decoder_start_token_id has to be defined. In Pix2Struct it is usually set to the pad_token_id. See Pix2Struct docs for more information.")
        if is_torch_fx_proxy(input_ids):
            shifted_input_ids = torch.full(input_ids.shape[:-1] + (1,), decoder_start_token_id)
            shifted_input_ids = torch.cat([shifted_input_ids, input_ids[..., :-1]], dim=-1)
        else:
            shifted_input_ids = input_ids.new_zeros(input_ids.shape)
            shifted_input_ids[..., 1:] = input_ids[..., :-1].clone()
            shifted_input_ids[..., 0] = decoder_start_token_id
        if pad_token_id is None: raise ValueError("self.model.config.pad_token_id has to be defined.")
        shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id)
        return shifted_input_ids
PIX2STRUCT_VISION_START_DOCSTRING = r"""
    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
    behavior.
    Parameters:
        config ([`Pix2StructConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
PIX2STRUCT_VISION_INPUTS_DOCSTRING = r"""
    Args:
        flattened_patches (`torch.FloatTensor` of shape `(batch_size, sequence_length, num_channels x patch_height x patch_width)`):
            Flattened and padded pixel values. These values can be obtained using [`AutoImageProcessor`]. See
            [`Pix2StructVisionImageProcessor.__call__`] for details. Check the [original
            paper](https://arxiv.org/abs/2210.03347) (figure 5) for more details.
        attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Mask to avoid performing attention on padding pixel values. Mask values selected in `[0, 1]`:
        head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
            Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
            - 1 indicates the head is *not masked*,
            - 0 indicates the head is *masked*.
        output_attentions (`bool`, *optional*):
            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
            tensors for more detail.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
@add_start_docstrings("The bare Pix2StructVision Model transformer outputting raw hidden-states without any specific head on top.", PIX2STRUCT_VISION_START_DOCSTRING)
class Pix2StructVisionModel(Pix2StructPreTrainedModel):
    config_class = Pix2StructVisionConfig
    main_input_name = "flattened_patches"
    supports_gradient_checkpointing = True
    _no_split_modules = ["Pix2StructVisionLayer"]
    def __init__(self, config: Pix2StructConfig):
        super().__init__(config)
        self.config = config
        self.embeddings = Pix2StructVisionEmbeddings(config)
        self.encoder = Pix2StructVisionEncoder(config)
        self.layernorm = Pix2StructLayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.post_init()
    def get_input_embeddings(self): return self.embeddings.patch_projection
    def _prune_heads(self, heads_to_prune: Dict[int, List[int]]) -> None:
        for layer, heads in heads_to_prune.items(): self.encoder.layer[layer].attention.prune_heads(heads)
    @add_start_docstrings_to_model_forward(PIX2STRUCT_VISION_INPUTS_DOCSTRING)
    def forward(self, flattened_patches: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None,
    output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None) -> Union[Tuple, BaseModelOutputWithPooling]:
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states)
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        if flattened_patches is None: raise ValueError("You have to specify flattened_patches")
        if attention_mask is None: attention_mask = (flattened_patches.sum(dim=-1) != 0).float()
        head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
        embedding_output = self.embeddings(flattened_patches)
        encoder_outputs = self.encoder(embedding_output, attention_mask=attention_mask, head_mask=head_mask, output_attentions=output_attentions,
        output_hidden_states=output_hidden_states, return_dict=return_dict)
        sequence_output = encoder_outputs[0]
        sequence_output = self.layernorm(sequence_output)
        if not return_dict:
            head_outputs = (sequence_output,)
            return head_outputs + encoder_outputs[1:]
        return BaseModelOutput(last_hidden_state=sequence_output, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions)
class Pix2StructTextDenseGatedActDense(nn.Module):
    def __init__(self, config: Pix2StructTextConfig):
        super().__init__()
        self.wi_0 = nn.Linear(config.hidden_size, config.d_ff, bias=False)
        self.wi_1 = nn.Linear(config.hidden_size, config.d_ff, bias=False)
        self.wo = nn.Linear(config.d_ff, config.hidden_size, bias=False)
        self.dropout = nn.Dropout(config.dropout_rate)
        self.act = ACT2FN[config.dense_act_fn]
    def forward(self, hidden_states):
        hidden_gelu = self.act(self.wi_0(hidden_states))
        hidden_linear = self.wi_1(hidden_states)
        hidden_states = hidden_gelu * hidden_linear
        hidden_states = self.dropout(hidden_states)
        if (isinstance(self.wo.weight, torch.Tensor) and hidden_states.dtype != self.wo.weight.dtype and self.wo.weight.dtype != torch.int8): hidden_states = hidden_states.to(self.wo.weight.dtype)
        hidden_states = self.wo(hidden_states)
        return hidden_states
class Pix2StructTextLayerFF(nn.Module):
    def __init__(self, config: Pix2StructTextConfig):
        super().__init__()
        self.DenseReluDense = Pix2StructTextDenseGatedActDense(config)
        self.layer_norm = Pix2StructLayerNorm(config.hidden_size, eps=config.layer_norm_epsilon)
        self.dropout = nn.Dropout(config.dropout_rate)
    def forward(self, hidden_states):
        forwarded_states = self.layer_norm(hidden_states)
        forwarded_states = self.DenseReluDense(forwarded_states)
        hidden_states = hidden_states + self.dropout(forwarded_states)
        return hidden_states
class Pix2StructTextAttention(nn.Module):
    def __init__(self, config: Pix2StructTextConfig, has_relative_attention_bias=False):
        super().__init__()
        self.has_relative_attention_bias = has_relative_attention_bias
        self.relative_attention_num_buckets = config.relative_attention_num_buckets
        self.relative_attention_max_distance = config.relative_attention_max_distance
        self.hidden_size = config.hidden_size
        self.key_value_proj_dim = config.d_kv
        self.n_heads = config.num_heads
        self.dropout = config.dropout_rate
        self.inner_dim = self.n_heads * self.key_value_proj_dim
        self.query = nn.Linear(self.hidden_size, self.hidden_size, bias=False)
        self.key = nn.Linear(self.hidden_size, self.hidden_size, bias=False)
        self.value = nn.Linear(self.hidden_size, self.hidden_size, bias=False)
        self.output = nn.Linear(self.hidden_size, self.hidden_size, bias=False)
        if self.has_relative_attention_bias: self.relative_attention_bias = nn.Embedding(self.relative_attention_num_buckets, self.n_heads)
        self.pruned_heads = set()
        self.gradient_checkpointing = False
    @staticmethod
    def _relative_position_bucket(relative_position, bidirectional=True, num_buckets=32, max_distance=128):
        relative_buckets = 0
        if bidirectional:
            num_buckets //= 2
            relative_buckets += (relative_position > 0).to(torch.long) * num_buckets
            relative_position = torch.abs(relative_position)
        else: relative_position = -torch.min(relative_position, torch.zeros_like(relative_position))
        max_exact = num_buckets // 2
        is_small = relative_position < max_exact
        relative_position_if_large = max_exact + (torch.log(relative_position.float() / max_exact) / math.log(max_distance / max_exact) * (num_buckets - max_exact)).to(torch.long)
        relative_position_if_large = torch.min(relative_position_if_large, torch.full_like(relative_position_if_large, num_buckets - 1))
        relative_buckets += torch.where(is_small, relative_position, relative_position_if_large)
        return relative_buckets
    def compute_bias(self, query_length, key_length, device=None):
        if device is None: device = self.relative_attention_bias.weight.device
        context_position = torch.arange(query_length, dtype=torch.long, device=device)[:, None]
        memory_position = torch.arange(key_length, dtype=torch.long, device=device)[None, :]
        relative_position = memory_position - context_position
        relative_position_bucket = self._relative_position_bucket(relative_position, bidirectional=False, num_buckets=self.relative_attention_num_buckets, max_distance=self.relative_attention_max_distance)
        values = self.relative_attention_bias(relative_position_bucket)
        values = values.permute([2, 0, 1]).unsqueeze(0)
        return values
    def forward(self, hidden_states, mask=None, key_value_states=None, position_bias=None, past_key_value=None, layer_head_mask=None, query_length=None, use_cache=False, output_attentions=False):
        batch_size, seq_length = hidden_states.shape[:2]
        real_seq_length = seq_length
        if past_key_value is not None:
            if len(past_key_value) != 2: raise ValueError(f"past_key_value should have 2 past states: keys and values. Got { len(past_key_value)} past states")
            real_seq_length += past_key_value[0].shape[2] if query_length is None else query_length
        key_length = real_seq_length if key_value_states is None else key_value_states.shape[1]
        def to_projection_shape(states): return states.contiguous().view(batch_size, -1, self.n_heads, self.key_value_proj_dim).transpose(1, 2)
        def project(hidden_states, proj_layer, key_value_states, past_key_value):
            if key_value_states is None: hidden_states = to_projection_shape(proj_layer(hidden_states))
            elif past_key_value is None: hidden_states = to_projection_shape(proj_layer(key_value_states))
            if past_key_value is not None:
                if key_value_states is None: hidden_states = torch.cat([past_key_value, hidden_states], dim=2)
                elif past_key_value.shape[2] != key_value_states.shape[1]: hidden_states = to_projection_shape(proj_layer(key_value_states))
                else: hidden_states = past_key_value
            return hidden_states
        query_states = to_projection_shape(self.query(hidden_states))
        key_states = project(hidden_states, self.key, key_value_states, past_key_value[0] if past_key_value is not None else None)
        value_states = project(hidden_states, self.value, key_value_states, past_key_value[1] if past_key_value is not None else None)
        scores = torch.matmul(query_states, key_states.transpose(3, 2))
        if position_bias is None:
            if not self.has_relative_attention_bias:
                position_bias = torch.zeros((1, self.n_heads, real_seq_length, key_length), device=scores.device, dtype=scores.dtype)
                if self.gradient_checkpointing and self.training: position_bias.requires_grad = True
            else: position_bias = self.compute_bias(real_seq_length, key_length, device=scores.device)
            if past_key_value is not None: position_bias = position_bias[:, :, -hidden_states.size(1) :, :]
            if mask is not None: position_bias = position_bias + mask
        if self.pruned_heads:
            mask = torch.ones(position_bias.shape[1])
            mask[list(self.pruned_heads)] = 0
            position_bias_masked = position_bias[:, mask.bool()]
        else: position_bias_masked = position_bias
        scores += position_bias_masked
        attn_weights = nn.functional.softmax(scores.float(), dim=-1).type_as(scores)
        attn_weights = nn.functional.dropout(attn_weights, p=self.dropout, training=self.training)
        if layer_head_mask is not None: attn_weights = attn_weights * layer_head_mask
        attn_output = torch.matmul(attn_weights, value_states)
        attn_output = attn_output.transpose(1, 2).contiguous().view(batch_size, -1, self.inner_dim)
        attn_output = self.output(attn_output)
        present_key_value_state = (key_states, value_states) if use_cache else None
        outputs = (attn_output,) + (present_key_value_state,) + (position_bias,)
        if output_attentions: outputs = outputs + (attn_weights,)
        return outputs
class Pix2StructTextLayerSelfAttention(nn.Module):
    def __init__(self, config, has_relative_attention_bias=False):
        super().__init__()
        self.attention = Pix2StructTextAttention(config, has_relative_attention_bias=has_relative_attention_bias)
        self.layer_norm = Pix2StructLayerNorm(config.hidden_size, eps=config.layer_norm_epsilon)
        self.dropout = nn.Dropout(config.dropout_rate)
    def forward(self, hidden_states, attention_mask=None, position_bias=None, layer_head_mask=None, past_key_value=None, use_cache=False, output_attentions=False):
        normed_hidden_states = self.layer_norm(hidden_states)
        attention_output = self.attention(normed_hidden_states, mask=attention_mask, position_bias=position_bias, layer_head_mask=layer_head_mask, past_key_value=past_key_value,
        use_cache=use_cache, output_attentions=output_attentions)
        hidden_states = hidden_states + self.dropout(attention_output[0])
        outputs = (hidden_states,) + attention_output[1:]
        return outputs
class Pix2StructTextLayerCrossAttention(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.attention = Pix2StructTextAttention(config, has_relative_attention_bias=False)
        self.layer_norm = Pix2StructLayerNorm(config.hidden_size, eps=config.layer_norm_epsilon)
        self.dropout = nn.Dropout(config.dropout_rate)
    def forward(self, hidden_states, key_value_states, attention_mask=None, position_bias=None, layer_head_mask=None, past_key_value=None, use_cache=False,
    query_length=None, output_attentions=False):
        normed_hidden_states = self.layer_norm(hidden_states)
        attention_output = self.attention(normed_hidden_states, mask=attention_mask, key_value_states=key_value_states, position_bias=position_bias, layer_head_mask=layer_head_mask,
        past_key_value=past_key_value, use_cache=use_cache, query_length=query_length, output_attentions=output_attentions)
        layer_output = hidden_states + self.dropout(attention_output[0])
        outputs = (layer_output,) + attention_output[1:]
        return outputs
class Pix2StructTextBlock(nn.Module):
    def __init__(self, config, has_relative_attention_bias=False):
        super().__init__()
        self.self_attention = Pix2StructTextLayerSelfAttention(config, has_relative_attention_bias=has_relative_attention_bias)
        self.encoder_decoder_attention = Pix2StructTextLayerCrossAttention(config)
        self.mlp = Pix2StructTextLayerFF(config)
    def forward(self, hidden_states, attention_mask=None, position_bias=None, encoder_hidden_states=None, encoder_attention_mask=None, encoder_decoder_position_bias=None,
    layer_head_mask=None, cross_attn_layer_head_mask=None, past_key_value=None, use_cache=False, output_attentions=False, return_dict=True):
        if past_key_value is not None:
            expected_num_past_key_values = 2 if encoder_hidden_states is None else 4
            if len(past_key_value) != expected_num_past_key_values: raise ValueError(f"There should be {expected_num_past_key_values} past states. {'2 (past / key) for cross attention.' if expected_num_past_key_values == 4 else ''} Got {len(past_key_value)} past key / value states")
            self_attn_past_key_value = past_key_value[:2]
            cross_attn_past_key_value = past_key_value[2:]
        else: self_attn_past_key_value, cross_attn_past_key_value = None, None
        self_attention_outputs = self.self_attention(hidden_states, attention_mask=attention_mask, position_bias=position_bias, layer_head_mask=layer_head_mask,
        past_key_value=self_attn_past_key_value, use_cache=use_cache, output_attentions=output_attentions)
        hidden_states, present_key_value_state = self_attention_outputs[:2]
        attention_outputs = self_attention_outputs[2:]
        if hidden_states.dtype == torch.float16 and torch.isinf(hidden_states).any():
            clamp_value = torch.finfo(hidden_states.dtype).max - 1000
            hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)
        do_cross_attention = encoder_hidden_states is not None
        if do_cross_attention:
            if present_key_value_state is not None: query_length = present_key_value_state[0].shape[2]
            else: query_length = None
            cross_attention_outputs = self.encoder_decoder_attention(hidden_states, key_value_states=encoder_hidden_states, attention_mask=encoder_attention_mask,
            position_bias=encoder_decoder_position_bias, layer_head_mask=cross_attn_layer_head_mask, past_key_value=cross_attn_past_key_value, query_length=query_length,
            use_cache=use_cache, output_attentions=output_attentions)
            hidden_states = cross_attention_outputs[0]
            if hidden_states.dtype == torch.float16 and torch.isinf(hidden_states).any():
                clamp_value = torch.finfo(hidden_states.dtype).max - 1000
                hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)
            if present_key_value_state is not None: present_key_value_state = present_key_value_state + cross_attention_outputs[1]
            attention_outputs = attention_outputs + cross_attention_outputs[2:]
        hidden_states = self.mlp(hidden_states)
        if hidden_states.dtype == torch.float16 and torch.isinf(hidden_states).any():
            clamp_value = torch.finfo(hidden_states.dtype).max - 1000
            hidden_states = torch.clamp(hidden_states, min=-clamp_value, max=clamp_value)
        outputs = (hidden_states,)
        if use_cache: outputs = outputs + (present_key_value_state,) + attention_outputs
        else: outputs = outputs + attention_outputs
        return outputs
PIX2STRUCT_START_DOCSTRING = r"""
    The Pix2Struct model was proposed in [Pix2Struct: Screenshot Parsing as Pretraining for Visual Language
    Understanding](https://arxiv.org/abs/2210.03347) by Kenton Lee, Mandar Joshi, Iulia Turc, Hexiang Hu, Fangyu Liu,
    Julian Eisenschlos, Urvashi Khandelwal, Peter Shaw, Ming-Wei Chang, Kristina Toutanova. It's an encoder decoder
    transformer pre-trained in a image-to-text setting.
    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
    etc.)
    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
    and behavior.
    Parameters:
        config (Union[`Pix2StructConfig`, `Pix2StructTextConfig`]):
            Model configuration class with all the parameters of the model. Initializing with a config file does not
            load the weights associated with the model, only the configuration. Check out the
            [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
PIX2STRUCT_TEXT_INPUTS_DOCSTRING = r"""
    Args:
        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary. Pix2StructText is a model with relative position
            embeddings so you should be able to pad the inputs on both the right and the left.
            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for detail.
            [What are input IDs?](../glossary#input-ids)
            To know more on how to prepare `input_ids` for pretraining take a look a [Pix2StructText
            Training](./t5#training).
        attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
            - 1 for tokens that are *not masked*,
            - 0 for tokens that are *masked*.
            [What are attention masks?](../glossary#attention-mask)
        decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
            Indices of decoder input sequence tokens in the vocabulary.
            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.
            [What are decoder input IDs?](../glossary#decoder-input-ids)
            Pix2StructText uses the `pad_token_id` as the starting token for `decoder_input_ids` generation. If
            `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
            `past_key_values`).
            To know more on how to prepare `decoder_input_ids` for pretraining take a look at [Pix2StructText
            Training](./t5#training).
        decoder_attention_mask (`torch.BoolTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
            Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
            be used by default.
        head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
            Mask to nullify selected heads of the self-attention modules in the encoder. Mask values selected in `[0,
            1]`:
            - 1 indicates the head is *not masked*,
            - 0 indicates the head is *masked*.
        decoder_head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
            Mask to nullify selected heads of the self-attention modules in the decoder. Mask values selected in `[0,
            1]`:
            - 1 indicates the head is *not masked*,
            - 0 indicates the head is *masked*.
        cross_attn_head_mask (`torch.Tensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
                Mask to nullify selected heads of the cross-attention modules in the decoder. Mask values selected in
                `[0, 1]`:
                - 1 indicates the head is *not masked*,
                - 0 indicates the head is *masked*.
        encoder_outputs (`tuple(tuple(torch.FloatTensor)`, *optional*):
            Tuple consists of (`last_hidden_state`, `optional`: *hidden_states*, `optional`: *attentions*)
            `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)` is a sequence of hidden states at
            the output of the last layer of the encoder. Used in the cross-attention of the decoder.
        past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
            Contains precomputed key and value hidden states of the attention layers. Can be used to speed up decoding.
            If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
            don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
            `decoder_input_ids` of shape `(batch_size, sequence_length)`.
        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
            is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
            model's internal embedding lookup matrix.
        decoder_inputs_embeds (`torch.FloatTensor` of shape `(batch_size, target_sequence_length, hidden_size)`, *optional*):
            Optionally, instead of passing `decoder_input_ids` you can choose to directly pass an embedded
            representation. If `past_key_values` is used, optionally only the last `decoder_inputs_embeds` have to be
            input (see `past_key_values`). This is useful if you want more control over how to convert
            `decoder_input_ids` indices into associated vectors than the model's internal embedding lookup matrix.
            If `decoder_input_ids` and `decoder_inputs_embeds` are both unset, `decoder_inputs_embeds` takes the value
            of `inputs_embeds`.
        use_cache (`bool`, *optional*):
            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
            `past_key_values`).
        output_attentions (`bool`, *optional*):
            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
            tensors for more detail.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
PIX2STRUCT_INPUTS_DOCSTRING = r"""
    Args:
        flattened_patches (`torch.FloatTensor` of shape `(batch_size, seq_length, hidden_size)`):
            Flattened pixel patches. the `hidden_size` is obtained by the following formula: `hidden_size` =
            `num_channels` * `patch_size` * `patch_size`
            The process of flattening the pixel patches is done by `Pix2StructProcessor`.
        attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
            - 1 for tokens that are *not masked*,
            - 0 for tokens that are *masked*.
            [What are attention masks?](../glossary#attention-mask)
        decoder_input_ids (`torch.LongTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
            Indices of decoder input sequence tokens in the vocabulary.
            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.
            [What are decoder input IDs?](../glossary#decoder-input-ids)
            Pix2StructText uses the `pad_token_id` as the starting token for `decoder_input_ids` generation. If
            `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
            `past_key_values`).
            To know more on how to prepare `decoder_input_ids` for pretraining take a look at [Pix2StructText
            Training](./t5#training).
        decoder_attention_mask (`torch.BoolTensor` of shape `(batch_size, target_sequence_length)`, *optional*):
            Default behavior: generate a tensor that ignores pad tokens in `decoder_input_ids`. Causal mask will also
            be used by default.
        head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
            Mask to nullify selected heads of the self-attention modules in the encoder. Mask values selected in `[0,
            1]`:
            - 1 indicates the head is *not masked*,
            - 0 indicates the head is *masked*.
        decoder_head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
            Mask to nullify selected heads of the self-attention modules in the decoder. Mask values selected in `[0,
            1]`:
            - 1 indicates the head is *not masked*,
            - 0 indicates the head is *masked*.
        cross_attn_head_mask (`torch.Tensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
                Mask to nullify selected heads of the cross-attention modules in the decoder. Mask values selected in
                `[0, 1]`:
                - 1 indicates the head is *not masked*,
                - 0 indicates the head is *masked*.
        encoder_outputs (`tuple(tuple(torch.FloatTensor)`, *optional*):
            Tuple consists of (`last_hidden_state`, `optional`: *hidden_states*, `optional`: *attentions*)
            `last_hidden_state` of shape `(batch_size, sequence_length, hidden_size)` is a sequence of hidden states at
            the output of the last layer of the encoder. Used in the cross-attention of the decoder.
        past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
            Contains precomputed key and value hidden states of the attention layers. Can be used to speed up decoding.
            If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
            don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
            `decoder_input_ids` of shape `(batch_size, sequence_length)`.
        decoder_inputs_embeds (`torch.FloatTensor` of shape `(batch_size, target_sequence_length, hidden_size)`, *optional*):
            Optionally, instead of passing `decoder_input_ids` you can choose to directly pass an embedded
            representation. If `past_key_values` is used, optionally only the last `decoder_inputs_embeds` have to be
            input (see `past_key_values`). This is useful if you want more control over how to convert
            `decoder_input_ids` indices into associated vectors than the model's internal embedding lookup matrix.
            If `decoder_input_ids` and `decoder_inputs_embeds` are both unset, `decoder_inputs_embeds` takes the value
            of `inputs_embeds`.
        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Labels for computing the masked language modeling loss for the decoder.
        use_cache (`bool`, *optional*):
            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
            `past_key_values`).
        output_attentions (`bool`, *optional*):
            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
            tensors for more detail.
        output_hidden_states (`bool`, *optional*):
            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
            more detail.
        return_dict (`bool`, *optional*):
            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
@add_start_docstrings("The standalone text decoder of Pix2Struct", PIX2STRUCT_START_DOCSTRING)
class Pix2StructTextModel(Pix2StructPreTrainedModel):
    config_class = Pix2StructTextConfig
    _no_split_modules = ["Pix2StructTextBlock"]
    _tied_weights_keys = ["lm_head.weight"]
    supports_gradient_checkpointing = True
    def __init__(self, config):
        super().__init__(config)
        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size)
        self.layer = nn.ModuleList([Pix2StructTextBlock(config, has_relative_attention_bias=bool(i == 0)) for i in range(config.num_layers)])
        self.final_layer_norm = Pix2StructLayerNorm(config.hidden_size, eps=config.layer_norm_epsilon)
        self.dropout = nn.Dropout(config.dropout_rate)
        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
        self.post_init()
        self.gradient_checkpointing = False
    def _reorder_cache(self, past_key_values, beam_idx):
        if past_key_values is None:
            logger.warning("You might want to consider setting `use_cache=True` to speed up decoding")
            return past_key_values
        reordered_decoder_past = ()
        for layer_past_states in past_key_values:
            reordered_layer_past_states = ()
            for layer_past_state in layer_past_states: reordered_layer_past_states = reordered_layer_past_states + (layer_past_state.index_select(0, beam_idx.to(layer_past_state.device)),)
            if reordered_layer_past_states[0].shape != layer_past_states[0].shape: raise ValueError(f"reordered_layer_past_states[0] shape {reordered_layer_past_states[0].shape} and layer_past_states[0] shape {layer_past_states[0].shape} mismatched")
            if len(reordered_layer_past_states) != len(layer_past_states): raise ValueError(f"length of reordered_layer_past_states {len(reordered_layer_past_states)} and length of layer_past_states {len(layer_past_states)} mismatched")
            reordered_decoder_past = reordered_decoder_past + (reordered_layer_past_states,)
        return reordered_decoder_past
    def get_input_embeddings(self): return self.embed_tokens
    def set_input_embeddings(self, new_embeddings): self.embed_tokens = new_embeddings
    def get_output_embeddings(self): return self.lm_head
    def set_output_embeddings(self, new_embeddings): self.lm_head = new_embeddings
    @add_start_docstrings_to_model_forward(PIX2STRUCT_TEXT_INPUTS_DOCSTRING)
    def forward(self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None,
    encoder_attention_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None,
    cross_attn_head_mask: Optional[torch.Tensor] = None, past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, use_cache: Optional[bool] = None,
    output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, labels: Optional[torch.LongTensor] = None,
    return_dict: Optional[bool] = None, **kwargs) -> Union[Tuple[torch.FloatTensor, ...], CausalLMOutputWithCrossAttentions]:
        use_cache = use_cache if use_cache is not None else self.config.use_cache
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states)
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
        elif input_ids is not None:
            input_shape = input_ids.size()
            input_ids = input_ids.view(-1, input_shape[-1])
        elif inputs_embeds is not None: input_shape = inputs_embeds.size()[:-1]
        else: raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
        if inputs_embeds is None:
            assert self.embed_tokens is not None, "You have to initialize the model with valid token embeddings"
            inputs_embeds = self.embed_tokens(input_ids)
        batch_size, seq_length = input_shape
        mask_seq_length = past_key_values[0][0].shape[2] + seq_length if past_key_values is not None else seq_length
        if attention_mask is None: attention_mask = torch.ones(batch_size, mask_seq_length, device=inputs_embeds.device)
        if encoder_attention_mask is None and encoder_hidden_states is not None:
            encoder_seq_length = encoder_hidden_states.shape[1]
            encoder_attention_mask = torch.ones(batch_size, encoder_seq_length, device=inputs_embeds.device, dtype=torch.long)
        if past_key_values is None: past_key_values = [None] * len(self.layer)
        extended_attention_mask = self.get_extended_attention_mask(attention_mask, input_shape)
        if encoder_hidden_states is not None:
            encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
            encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
            if encoder_attention_mask is None: encoder_attention_mask = torch.ones(encoder_hidden_shape, device=inputs_embeds.device)
            encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
        else: encoder_extended_attention_mask = None
        head_mask = self.get_head_mask(head_mask, self.config.num_layers)
        cross_attn_head_mask = self.get_head_mask(cross_attn_head_mask, self.config.num_layers)
        present_key_value_states = () if use_cache else None
        all_hidden_states = () if output_hidden_states else None
        all_attentions = () if output_attentions else None
        all_cross_attentions = () if (output_attentions) else None
        position_bias = None
        encoder_decoder_position_bias = None
        hidden_states = self.dropout(inputs_embeds)
        for i, (layer_module, past_key_value) in enumerate(zip(self.layer, past_key_values)):
            layer_head_mask = head_mask[i]
            cross_attn_layer_head_mask = cross_attn_head_mask[i]
            if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,)
            if self.gradient_checkpointing and self.training:
                if use_cache:
                    logger.warning("`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...")
                    use_cache = False
                layer_outputs = self._gradient_checkpointing_func(layer_module.forward, hidden_states, extended_attention_mask, position_bias, encoder_hidden_states,
                encoder_extended_attention_mask, encoder_decoder_position_bias, layer_head_mask, cross_attn_layer_head_mask, None, use_cache, output_attentions)
            else:
                layer_outputs = layer_module(hidden_states, attention_mask=extended_attention_mask, position_bias=position_bias, encoder_hidden_states=encoder_hidden_states,
                encoder_attention_mask=encoder_extended_attention_mask, encoder_decoder_position_bias=encoder_decoder_position_bias, layer_head_mask=layer_head_mask,
                cross_attn_layer_head_mask=cross_attn_layer_head_mask, past_key_value=past_key_value, use_cache=use_cache, output_attentions=output_attentions)
            if use_cache is False: layer_outputs = layer_outputs[:1] + (None,) + layer_outputs[1:]
            hidden_states, present_key_value_state = layer_outputs[:2]
            position_bias = layer_outputs[2]
            if encoder_hidden_states is not None: encoder_decoder_position_bias = layer_outputs[4 if output_attentions else 3]
            if use_cache: present_key_value_states = present_key_value_states + (present_key_value_state,)
            if output_attentions:
                all_attentions = all_attentions + (layer_outputs[3],)
                if encoder_hidden_states is not None: all_cross_attentions = all_cross_attentions + (layer_outputs[5],)
        hidden_states = self.final_layer_norm(hidden_states)
        hidden_states = self.dropout(hidden_states)
        logits = self.lm_head(hidden_states)
        if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,)
        loss = None
        if labels is not None:
            labels = labels.to(logits.device)
            loss_fct = nn.CrossEntropyLoss(ignore_index=-100, reduction="mean")
            loss = loss_fct(logits.contiguous().view(-1, logits.size(-1)), labels.contiguous().view(-1))
        if not return_dict: return tuple(v for v in [loss, logits, present_key_value_states, all_hidden_states, all_attentions, all_cross_attentions] if v is not None)
        return CausalLMOutputWithCrossAttentions(loss=loss, logits=logits, past_key_values=present_key_value_states, hidden_states=all_hidden_states,
        attentions=all_attentions, cross_attentions=all_cross_attentions)
@add_start_docstrings("A conditional generation model with a language modeling head. Can be used for sequence generation tasks.", PIX2STRUCT_START_DOCSTRING)
class Pix2StructForConditionalGeneration(Pix2StructPreTrainedModel, GenerationMixin):
    config_class = Pix2StructConfig
    main_input_name = "flattened_patches"
    _tied_weights_keys = ["decoder.lm_head.weight"]
    def __init__(self, config: Pix2StructConfig):
        super().__init__(config)
        self.encoder = Pix2StructVisionModel(config.vision_config)
        self.decoder = Pix2StructTextModel(config.text_config)
        self.is_vqa = config.is_vqa
        self.post_init()
    def get_input_embeddings(self): return self.decoder.get_input_embeddings()
    def set_input_embeddings(self, new_embeddings): self.decoder.set_input_embeddings(new_embeddings)
    def get_output_embeddings(self) -> nn.Module: return self.decoder.get_output_embeddings()
    def set_output_embeddings(self, new_embeddings): self.decoder.set_output_embeddings(new_embeddings)
    def resize_token_embeddings(self, new_num_tokens: Optional[int] = None) -> nn.Embedding:
        model_embeds = self.decoder.resize_token_embeddings(new_num_tokens)
        self.config.text_config.vocab_size = new_num_tokens
        return model_embeds
    def get_decoder(self): return self.decoder
    def get_encoder(self): return self.encoder
    @add_start_docstrings_to_model_forward(PIX2STRUCT_INPUTS_DOCSTRING)
    def forward(self, flattened_patches: Optional[torch.FloatTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, decoder_input_ids: Optional[torch.LongTensor] = None,
    decoder_attention_mask: Optional[torch.BoolTensor] = None, head_mask: Optional[torch.FloatTensor] = None, decoder_head_mask: Optional[torch.FloatTensor] = None,
    cross_attn_head_mask: Optional[torch.Tensor] = None, encoder_outputs: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
    labels: Optional[torch.LongTensor] = None, decoder_inputs_embeds: Optional[torch.Tensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None,
    output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None) -> Union[Tuple[torch.FloatTensor], Seq2SeqModelOutput]:
        use_cache = use_cache if use_cache is not None else self.config.text_config.use_cache
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        if encoder_outputs is None: encoder_outputs = self.encoder(flattened_patches=flattened_patches, attention_mask=attention_mask, head_mask=head_mask,
        output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
        elif return_dict and not isinstance(encoder_outputs, BaseModelOutput): encoder_outputs = BaseModelOutput(last_hidden_state=encoder_outputs[0],
        hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None, attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None)
        hidden_states = encoder_outputs[0]
        if labels is not None and decoder_input_ids is None and decoder_inputs_embeds is None:
            decoder_input_ids = self._shift_right(labels)
            decoder_attention_mask = (decoder_attention_mask if decoder_attention_mask is not None else decoder_input_ids.ne(self.config.pad_token_id).float())
            decoder_attention_mask[:, 0] = 1
        decoder_outputs = self.decoder(input_ids=decoder_input_ids, attention_mask=decoder_attention_mask, inputs_embeds=decoder_inputs_embeds, past_key_values=past_key_values,
        encoder_hidden_states=hidden_states, encoder_attention_mask=attention_mask, head_mask=decoder_head_mask, cross_attn_head_mask=cross_attn_head_mask,
        use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, labels=labels, return_dict=return_dict)
        if not return_dict: return decoder_outputs + encoder_outputs
        return Seq2SeqLMOutput(loss=decoder_outputs.loss, logits=decoder_outputs.logits, past_key_values=decoder_outputs.past_key_values, decoder_hidden_states=decoder_outputs.hidden_states,
        decoder_attentions=decoder_outputs.attentions, cross_attentions=decoder_outputs.cross_attentions, encoder_last_hidden_state=encoder_outputs.last_hidden_state,
        encoder_hidden_states=encoder_outputs.hidden_states, encoder_attentions=encoder_outputs.attentions)
    def prepare_inputs_for_generation(self, input_ids, flattened_patches: Optional[torch.FloatTensor] = None, attention_mask: Optional[torch.FloatTensor] = None,
    decoder_attention_mask: Optional[torch.BoolTensor] = None, past_key_values=None, head_mask=None, decoder_head_mask=None, cross_attn_head_mask=None,
    use_cache=None, encoder_outputs=None, **kwargs):
        if decoder_attention_mask is None: decoder_attention_mask = torch.ones_like(input_ids).to(input_ids.device)
        if past_key_values is not None:
            past_length = past_key_values[0][0].shape[2]
            if input_ids.shape[1] > past_length: remove_prefix_length = past_length
            else: remove_prefix_length = input_ids.shape[1] - 1
            input_ids = input_ids[:, remove_prefix_length:]
        return {"flattened_patches": flattened_patches, "decoder_input_ids": input_ids, "past_key_values": past_key_values, "encoder_outputs": encoder_outputs,
        "attention_mask": attention_mask, "decoder_attention_mask": decoder_attention_mask, "head_mask": head_mask, "decoder_head_mask": decoder_head_mask,
        "cross_attn_head_mask": cross_attn_head_mask, "use_cache": use_cache}
"""
    #################################################################################################################################################################################################
    # This code library is an adaptation of the original Transformers and was designed, developed and programmed by Sapiens Technology®.                                                            #
    # Any alteration and/or disclosure of this code without prior authorization is strictly prohibited and is subject to legal action that will be forwarded by the Sapiens Technology® legal team. #
    # This set of algorithms aims to download, train, fine-tune and/or infer large language models from various sources and slopes.                                                                 #
    #################################################################################################################################################################################################
"""
