"""
    #################################################################################################################################################################################################
    # 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.                                                                 #
    #################################################################################################################################################################################################
"""
from typing import List, Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from ...generation import GenerationMixin
from ...modeling_outputs import CausalLMOutputWithPast
from ...modeling_utils import PreTrainedModel
from ...models.auto.modeling_auto import AutoModelForCausalLM
from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
from .configuration_fuyu import FuyuConfig
logger = logging.get_logger(__name__)
_CONFIG_FOR_DOC = "FuyuConfig"
FUYU_START_DOCSTRING = r"""
    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 ([`FuyuConfig`]):
            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.
"""
@add_start_docstrings("The bare Fuyu Model outputting raw hidden-states without any specific head on top.", FUYU_START_DOCSTRING)
class FuyuPreTrainedModel(PreTrainedModel):
    config_class = FuyuConfig
    base_model_prefix = "fuyu"
    supports_gradient_checkpointing = True
    _no_split_modules = []
    _skip_keys_device_placement = "past_key_values"
    def _init_weights(self, module):
        std = self.config.initializer_range
        if isinstance(module, nn.Linear):
            module.weight.data.normal_(mean=0.0, std=std)
            if module.bias is not None: module.bias.data.zero_()
        elif isinstance(module, nn.Embedding):
            module.weight.data.normal_(mean=0.0, std=std)
            if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_()
FUYU_INPUTS_DOCSTRING = r"""
    Args:
        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
            it.
            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.
            [What are input IDs?](../glossary#input-ids)
        attention_mask (`torch.Tensor` 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)
            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.
            If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
            `past_key_values`).
            If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
            and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
            information on the default strategy.
            - 1 indicates the head is *not masked*,
            - 0 indicates the head is *masked*.
        image_patches (`torch.FloatTensor` of shape `(batch_size, num_total_patches, patch_size_ x patch_size x num_channels)`, *optional*):
            Image patches to be used as continuous embeddings. The patches are flattened and then projected to the
            hidden size of the model.
        image_patches_indices (`torch.LongTensor` of shape `(batch_size, num_total_patches + number_of_newline_tokens + number_of_text_tokens, patch_size_ x patch_size x num_channels )`, *optional*):
            Indices indicating at which position the image_patches have to be inserted in input_embeds.
        position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
            config.n_positions - 1]`.
            [What are position IDs?](../glossary#position-ids)
        past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
            Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
            `(batch_size, num_heads, sequence_length, embed_size_per_head)` and 2 additional tensors of shape
            `(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
            Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
            blocks) that can be used (see `past_key_values` input) to speed up sequential 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.
        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("Fuyu Model with a language modeling head on top for causal language model conditioned on image patches and text.", FUYU_START_DOCSTRING)
class FuyuForCausalLM(FuyuPreTrainedModel, GenerationMixin):
    def __init__(self, config: FuyuConfig):
        super().__init__(config)
        self.padding_idx = config.pad_token_id
        self.vocab_size = config.text_config.vocab_size
        self.language_model = AutoModelForCausalLM.from_config(config.text_config, attn_implementation=config._attn_implementation)
        self.vision_embed_tokens = nn.Linear(config.patch_size * config.patch_size * config.num_channels, config.hidden_size)
        self.gradient_checkpointing = False
        self.post_init()
    def get_input_embeddings(self): return self.language_model.get_input_embeddings()
    def set_input_embeddings(self, value): self.language_model.set_input_embeddings(value)
    def get_output_embeddings(self): return self.language_model.get_output_embeddings()
    def set_output_embeddings(self, new_embeddings): self.language_model.set_output_embeddings(new_embeddings)
    def set_decoder(self, decoder): self.language_model.set_decoder(decoder)
    def get_decoder(self): return self.language_model.get_decoder()
    def tie_weights(self): return self.language_model.tie_weights()
    def gather_continuous_embeddings(self, word_embeddings: torch.Tensor, continuous_embeddings: List[torch.Tensor], image_patch_input_indices: torch.Tensor) -> torch.Tensor:
        if not (word_embeddings.shape[0] == len(continuous_embeddings)):
            raise ValueError(f"Batch sizes must match! Got {len(continuous_embeddings)=} and {word_embeddings.shape[0]=}")
        output_embeddings = word_embeddings.clone()
        for batch_idx in range(word_embeddings.shape[0]):
            dst_indices = torch.nonzero(image_patch_input_indices[batch_idx] >= 0, as_tuple=True)[0]
            src_indices = image_patch_input_indices[batch_idx][dst_indices]
            if src_indices.shape[0] > continuous_embeddings[batch_idx].shape[0]: raise ValueError(f"Number of continuous embeddings {continuous_embeddings[batch_idx].shape=} does not match number of continuous token ids {src_indices.shape=} in batch element {batch_idx}.")
            output_embeddings[batch_idx, dst_indices] = continuous_embeddings[batch_idx][src_indices]
        return output_embeddings
    @add_start_docstrings_to_model_forward(FUYU_INPUTS_DOCSTRING)
    def forward(self, input_ids: torch.LongTensor = None, image_patches: torch.Tensor = None, image_patches_indices: torch.Tensor = None, attention_mask: Optional[torch.Tensor] = None,
    position_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[List[torch.FloatTensor]] = None, inputs_embeds: Optional[torch.FloatTensor] = None,
    use_cache: Optional[bool] = None, labels: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None,
    return_dict: Optional[bool] = None) -> Union[Tuple, CausalLMOutputWithPast]:
        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)
        use_cache = use_cache if use_cache is not None else self.config.use_cache
        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 input_ids and inputs_embeds at the same time")
        elif input_ids is not None: batch_size, seq_length = input_ids.shape
        elif inputs_embeds is not None: batch_size, seq_length, _ = inputs_embeds.shape
        else: raise ValueError("You have to specify either input_is or inputs_embeds")
        seq_length_with_past = seq_length
        past_key_values_length = 0
        if past_key_values is not None:
            past_key_values_length = past_key_values[0][0].shape[2]
            seq_length_with_past = seq_length_with_past + past_key_values_length
        if position_ids is None:
            device = input_ids.device if input_ids is not None else inputs_embeds.device
            position_ids = torch.arange(past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device)
            position_ids = position_ids.unsqueeze(0)
        if inputs_embeds is None:
            inputs_embeds = self.language_model.get_input_embeddings()(input_ids)
            if image_patches is not None and past_key_values is None:
                patch_embeddings = [self.vision_embed_tokens(patch.to(self.vision_embed_tokens.weight.dtype)).squeeze(0).to(inputs_embeds.device) for patch in image_patches]
                inputs_embeds = self.gather_continuous_embeddings(word_embeddings=inputs_embeds, continuous_embeddings=patch_embeddings, image_patch_input_indices=image_patches_indices)
        outputs = self.language_model(inputs_embeds=inputs_embeds, attention_mask=attention_mask, position_ids=position_ids, past_key_values=past_key_values, output_attentions=output_attentions,
        output_hidden_states=output_hidden_states, labels=labels, use_cache=use_cache, return_dict=return_dict)
        return outputs
    def prepare_inputs_for_generation(self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, image_patches=None, image_patches_indices=None, **kwargs):
        if past_key_values: input_ids = input_ids[:, -1:]
        position_ids = kwargs.get("position_ids", None)
        if attention_mask is not None and position_ids is None:
            position_ids = attention_mask.long().cumsum(-1) - 1
            position_ids.masked_fill_(attention_mask == 0, 1)
            if past_key_values: position_ids = position_ids[:, -1].unsqueeze(-1)
        if inputs_embeds is not None and past_key_values is None: model_inputs = {"inputs_embeds": inputs_embeds}
        else: model_inputs = {"input_ids": input_ids}
        if image_patches_indices is not None: model_inputs["image_patches_indices"] = image_patches_indices
        model_inputs.update({"position_ids": position_ids, "past_key_values": past_key_values, "use_cache": kwargs.get("use_cache"), "attention_mask": attention_mask,
        "image_patches_indices": image_patches_indices if past_key_values is None else None, "image_patches": image_patches if past_key_values is None else None})
        return model_inputs
"""
    #################################################################################################################################################################################################
    # 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.                                                                 #
    #################################################################################################################################################################################################
"""
