"""
    #################################################################################################################################################################################################
    # 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 Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACT2FN
from ...modeling_outputs import (BaseModelOutputWithPastAndCrossAttentions, MaskedLMOutput, MultipleChoiceModelOutput, QuestionAnsweringModelOutput,
SequenceClassifierOutput, TokenClassifierOutput)
from ...modeling_utils import PreTrainedModel
from ...pytorch_utils import apply_chunking_to_forward, find_pruneable_heads_and_indices, prune_linear_layer
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_nystromformer import NystromformerConfig
logger = logging.get_logger(__name__)
_CHECKPOINT_FOR_DOC = "uw-madison/nystromformer-512"
_CONFIG_FOR_DOC = "NystromformerConfig"
class NystromformerEmbeddings(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=config.pad_token_id)
        self.position_embeddings = nn.Embedding(config.max_position_embeddings + 2, config.hidden_size)
        self.token_type_embeddings = nn.Embedding(config.type_vocab_size, config.hidden_size)
        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)
        self.register_buffer("position_ids", torch.arange(config.max_position_embeddings).expand((1, -1)) + 2, persistent=False)
        self.position_embedding_type = getattr(config, "position_embedding_type", "absolute")
        self.register_buffer("token_type_ids", torch.zeros(self.position_ids.size(), dtype=torch.long, device=self.position_ids.device), persistent=False)
    def forward(self, input_ids=None, token_type_ids=None, position_ids=None, inputs_embeds=None):
        if input_ids is not None: input_shape = input_ids.size()
        else: input_shape = inputs_embeds.size()[:-1]
        seq_length = input_shape[1]
        if position_ids is None: position_ids = self.position_ids[:, :seq_length]
        if token_type_ids is None:
            if hasattr(self, "token_type_ids"):
                buffered_token_type_ids = self.token_type_ids[:, :seq_length]
                buffered_token_type_ids_expanded = buffered_token_type_ids.expand(input_shape[0], seq_length)
                token_type_ids = buffered_token_type_ids_expanded
            else: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device)
        if inputs_embeds is None: inputs_embeds = self.word_embeddings(input_ids)
        token_type_embeddings = self.token_type_embeddings(token_type_ids)
        embeddings = inputs_embeds + token_type_embeddings
        if self.position_embedding_type == "absolute":
            position_embeddings = self.position_embeddings(position_ids)
            embeddings += position_embeddings
        embeddings = self.LayerNorm(embeddings)
        embeddings = self.dropout(embeddings)
        return embeddings
class NystromformerSelfAttention(nn.Module):
    def __init__(self, config, position_embedding_type=None):
        super().__init__()
        if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"): raise ValueError(f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention heads ({config.num_attention_heads})")
        self.num_attention_heads = config.num_attention_heads
        self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
        self.all_head_size = self.num_attention_heads * self.attention_head_size
        self.num_landmarks = config.num_landmarks
        self.seq_len = config.segment_means_seq_len
        self.conv_kernel_size = config.conv_kernel_size
        if config.inv_coeff_init_option: self.init_option = config["inv_init_coeff_option"]
        else: self.init_option = "original"
        self.query = nn.Linear(config.hidden_size, self.all_head_size)
        self.key = nn.Linear(config.hidden_size, self.all_head_size)
        self.value = nn.Linear(config.hidden_size, self.all_head_size)
        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)
        self.position_embedding_type = position_embedding_type or getattr(config, "position_embedding_type", "absolute")
        if self.conv_kernel_size is not None:
            self.conv = nn.Conv2d(in_channels=self.num_attention_heads, out_channels=self.num_attention_heads, kernel_size=(self.conv_kernel_size, 1),
            padding=(self.conv_kernel_size // 2, 0), bias=False, groups=self.num_attention_heads)
    def iterative_inv(self, mat, n_iter=6):
        identity = torch.eye(mat.size(-1), device=mat.device)
        key = mat
        if self.init_option == "original": value = 1 / torch.max(torch.sum(key, dim=-2)) * key.transpose(-1, -2)
        else: value = 1 / torch.max(torch.sum(key, dim=-2), dim=-1).values[:, :, None, None] * key.transpose(-1, -2)
        for _ in range(n_iter):
            key_value = torch.matmul(key, value)
            value = torch.matmul(0.25 * value, 13 * identity - torch.matmul(key_value, 15 * identity - torch.matmul(key_value, 7 * identity - key_value)))
        return value
    def transpose_for_scores(self, layer):
        new_layer_shape = layer.size()[:-1] + (self.num_attention_heads, self.attention_head_size)
        layer = layer.view(*new_layer_shape)
        return layer.permute(0, 2, 1, 3)
    def forward(self, hidden_states, attention_mask=None, output_attentions=False):
        mixed_query_layer = self.query(hidden_states)
        key_layer = self.transpose_for_scores(self.key(hidden_states))
        value_layer = self.transpose_for_scores(self.value(hidden_states))
        query_layer = self.transpose_for_scores(mixed_query_layer)
        query_layer = query_layer / math.sqrt(math.sqrt(self.attention_head_size))
        key_layer = key_layer / math.sqrt(math.sqrt(self.attention_head_size))
        if self.num_landmarks == self.seq_len:
            attention_scores = torch.matmul(query_layer, key_layer.transpose(-1, -2))
            if attention_mask is not None: attention_scores = attention_scores + attention_mask
            attention_probs = nn.functional.softmax(attention_scores, dim=-1)
            context_layer = torch.matmul(attention_probs, value_layer)
        else:
            q_landmarks = query_layer.reshape(-1, self.num_attention_heads, self.num_landmarks, self.seq_len // self.num_landmarks, self.attention_head_size).mean(dim=-2)
            k_landmarks = key_layer.reshape(-1, self.num_attention_heads, self.num_landmarks, self.seq_len // self.num_landmarks, self.attention_head_size).mean(dim=-2)
            kernel_1 = torch.nn.functional.softmax(torch.matmul(query_layer, k_landmarks.transpose(-1, -2)), dim=-1)
            kernel_2 = torch.nn.functional.softmax(torch.matmul(q_landmarks, k_landmarks.transpose(-1, -2)), dim=-1)
            attention_scores = torch.matmul(q_landmarks, key_layer.transpose(-1, -2))
            if attention_mask is not None: attention_scores = attention_scores + attention_mask
            kernel_3 = nn.functional.softmax(attention_scores, dim=-1)
            attention_probs = torch.matmul(kernel_1, self.iterative_inv(kernel_2))
            new_value_layer = torch.matmul(kernel_3, value_layer)
            context_layer = torch.matmul(attention_probs, new_value_layer)
        if self.conv_kernel_size is not None: context_layer += self.conv(value_layer)
        context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
        new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
        context_layer = context_layer.view(*new_context_layer_shape)
        outputs = (context_layer, attention_probs) if output_attentions else (context_layer,)
        return outputs
class NystromformerSelfOutput(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)
    def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
        hidden_states = self.dense(hidden_states)
        hidden_states = self.dropout(hidden_states)
        hidden_states = self.LayerNorm(hidden_states + input_tensor)
        return hidden_states
class NystromformerAttention(nn.Module):
    def __init__(self, config, position_embedding_type=None):
        super().__init__()
        self.self = NystromformerSelfAttention(config, position_embedding_type=position_embedding_type)
        self.output = NystromformerSelfOutput(config)
        self.pruned_heads = set()
    def prune_heads(self, heads):
        if len(heads) == 0: return
        heads, index = find_pruneable_heads_and_indices(heads, self.self.num_attention_heads, self.self.attention_head_size, self.pruned_heads)
        self.self.query = prune_linear_layer(self.self.query, index)
        self.self.key = prune_linear_layer(self.self.key, index)
        self.self.value = prune_linear_layer(self.self.value, index)
        self.output.dense = prune_linear_layer(self.output.dense, index, dim=1)
        self.self.num_attention_heads = self.self.num_attention_heads - len(heads)
        self.self.all_head_size = self.self.attention_head_size * self.self.num_attention_heads
        self.pruned_heads = self.pruned_heads.union(heads)
    def forward(self, hidden_states, attention_mask=None, output_attentions=False):
        self_outputs = self.self(hidden_states, attention_mask, output_attentions)
        attention_output = self.output(self_outputs[0], hidden_states)
        outputs = (attention_output,) + self_outputs[1:]
        return outputs
class NystromformerIntermediate(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.hidden_size, config.intermediate_size)
        if isinstance(config.hidden_act, str): self.intermediate_act_fn = ACT2FN[config.hidden_act]
        else: self.intermediate_act_fn = config.hidden_act
    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        hidden_states = self.dense(hidden_states)
        hidden_states = self.intermediate_act_fn(hidden_states)
        return hidden_states
class NystromformerOutput(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.intermediate_size, config.hidden_size)
        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)
    def forward(self, hidden_states: torch.Tensor, input_tensor: torch.Tensor) -> torch.Tensor:
        hidden_states = self.dense(hidden_states)
        hidden_states = self.dropout(hidden_states)
        hidden_states = self.LayerNorm(hidden_states + input_tensor)
        return hidden_states
class NystromformerLayer(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.chunk_size_feed_forward = config.chunk_size_feed_forward
        self.seq_len_dim = 1
        self.attention = NystromformerAttention(config)
        self.add_cross_attention = config.add_cross_attention
        self.intermediate = NystromformerIntermediate(config)
        self.output = NystromformerOutput(config)
    def forward(self, hidden_states, attention_mask=None, output_attentions=False):
        self_attention_outputs = self.attention(hidden_states, attention_mask, output_attentions=output_attentions)
        attention_output = self_attention_outputs[0]
        outputs = self_attention_outputs[1:]
        layer_output = apply_chunking_to_forward(self.feed_forward_chunk, self.chunk_size_feed_forward, self.seq_len_dim, attention_output)
        outputs = (layer_output,) + outputs
        return outputs
    def feed_forward_chunk(self, attention_output):
        intermediate_output = self.intermediate(attention_output)
        layer_output = self.output(intermediate_output, attention_output)
        return layer_output
class NystromformerEncoder(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.config = config
        self.layer = nn.ModuleList([NystromformerLayer(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):
        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,)
            if self.gradient_checkpointing and self.training: layer_outputs = self._gradient_checkpointing_func(layer_module.__call__, hidden_states, attention_mask, output_attentions)
            else: layer_outputs = layer_module(hidden_states, attention_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 BaseModelOutputWithPastAndCrossAttentions(last_hidden_state=hidden_states, hidden_states=all_hidden_states, attentions=all_self_attentions)
class NystromformerPredictionHeadTransform(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
        if isinstance(config.hidden_act, str): self.transform_act_fn = ACT2FN[config.hidden_act]
        else: self.transform_act_fn = config.hidden_act
        self.LayerNorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
    def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
        hidden_states = self.dense(hidden_states)
        hidden_states = self.transform_act_fn(hidden_states)
        hidden_states = self.LayerNorm(hidden_states)
        return hidden_states
class NystromformerLMPredictionHead(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.transform = NystromformerPredictionHeadTransform(config)
        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
        self.bias = nn.Parameter(torch.zeros(config.vocab_size))
        self.decoder.bias = self.bias
    def _tie_weights(self): self.decoder.bias = self.bias
    def forward(self, hidden_states):
        hidden_states = self.transform(hidden_states)
        hidden_states = self.decoder(hidden_states)
        return hidden_states
class NystromformerOnlyMLMHead(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.predictions = NystromformerLMPredictionHead(config)
    def forward(self, sequence_output: torch.Tensor) -> torch.Tensor:
        prediction_scores = self.predictions(sequence_output)
        return prediction_scores
class NystromformerPreTrainedModel(PreTrainedModel):
    config_class = NystromformerConfig
    base_model_prefix = "nystromformer"
    supports_gradient_checkpointing = True
    def _init_weights(self, module):
        if isinstance(module, (nn.Linear, nn.Conv2d)):
            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
            if module.bias is not None: module.bias.data.zero_()
        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_()
        elif isinstance(module, nn.LayerNorm):
            module.bias.data.zero_()
            module.weight.data.fill_(1.0)
NYSTROMFORMER_START_DOCSTRING = r"""
    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
    it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
    behavior.
    Parameters:
        config ([`NystromformerConfig`]): 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.
"""
NYSTROMFORMER_INPUTS_DOCSTRING = r"""
    Args:
        input_ids (`torch.LongTensor` of shape `(0)`):
            Indices of input sequence tokens in the vocabulary.
            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.
            [What are input IDs?](../glossary#input-ids)
        attention_mask (`torch.FloatTensor` of shape `(0)`, *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)
        token_type_ids (`torch.LongTensor` of shape `(0)`, *optional*):
            Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0,
            1]`:
            - 0 corresponds to a *sentence A* token,
            - 1 corresponds to a *sentence B* token.
            [What are token type IDs?](../glossary#token-type-ids)
        position_ids (`torch.LongTensor` of shape `(0)`, *optional*):
            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
            config.max_position_embeddings - 1]`.
            [What are position IDs?](../glossary#position-ids)
        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*.
        inputs_embeds (`torch.FloatTensor` of shape `(0, 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.
        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 Nyströmformer Model transformer outputting raw hidden-states without any specific head on top.", NYSTROMFORMER_START_DOCSTRING)
class NystromformerModel(NystromformerPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.config = config
        self.embeddings = NystromformerEmbeddings(config)
        self.encoder = NystromformerEncoder(config)
        self.post_init()
    def get_input_embeddings(self): return self.embeddings.word_embeddings
    def set_input_embeddings(self, value): self.embeddings.word_embeddings = value
    def _prune_heads(self, heads_to_prune):
        for layer, heads in heads_to_prune.items(): self.encoder.layer[layer].attention.prune_heads(heads)
    @add_start_docstrings_to_model_forward(NYSTROMFORMER_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    def forward(self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None,
    position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None,
    output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPastAndCrossAttentions]:
        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 input_ids and inputs_embeds at the same time")
        elif input_ids is not None:
            self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask)
            input_shape = input_ids.size()
        elif inputs_embeds is not None: input_shape = inputs_embeds.size()[:-1]
        else: raise ValueError("You have to specify either input_ids or inputs_embeds")
        batch_size, seq_length = input_shape
        device = input_ids.device if input_ids is not None else inputs_embeds.device
        if attention_mask is None: attention_mask = torch.ones(((batch_size, seq_length)), device=device)
        if token_type_ids is None:
            if hasattr(self.embeddings, "token_type_ids"):
                buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length]
                buffered_token_type_ids_expanded = buffered_token_type_ids.expand(batch_size, seq_length)
                token_type_ids = buffered_token_type_ids_expanded
            else: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
        extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape)
        head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
        embedding_output = self.embeddings(input_ids=input_ids, position_ids=position_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds)
        encoder_outputs = self.encoder(embedding_output, attention_mask=extended_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]
        if not return_dict: return (sequence_output,) + encoder_outputs[1:]
        return BaseModelOutputWithPastAndCrossAttentions(last_hidden_state=sequence_output, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions,
        cross_attentions=encoder_outputs.cross_attentions)
@add_start_docstrings("Nyströmformer Model with a `language modeling` head on top.", NYSTROMFORMER_START_DOCSTRING)
class NystromformerForMaskedLM(NystromformerPreTrainedModel):
    _tied_weights_keys = ["cls.predictions.decoder"]
    def __init__(self, config):
        super().__init__(config)
        self.nystromformer = NystromformerModel(config)
        self.cls = NystromformerOnlyMLMHead(config)
        self.post_init()
    def get_output_embeddings(self): return self.cls.predictions.decoder
    def set_output_embeddings(self, new_embeddings):
        self.cls.predictions.decoder = new_embeddings
        self.cls.predictions.bias = new_embeddings.bias
    @add_start_docstrings_to_model_forward(NYSTROMFORMER_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    def forward(self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None,
    position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None,
    labels: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None,
    return_dict: Optional[bool] = None) -> Union[Tuple[torch.Tensor], MaskedLMOutput]:
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        outputs = self.nystromformer(input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask,
        inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
        sequence_output = outputs[0]
        prediction_scores = self.cls(sequence_output)
        masked_lm_loss = None
        if labels is not None:
            loss_fct = CrossEntropyLoss()
            masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
        if not return_dict:
            output = (prediction_scores,) + outputs[1:]
            return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output
        return MaskedLMOutput(loss=masked_lm_loss, logits=prediction_scores, hidden_states=outputs.hidden_states, attentions=outputs.attentions)
class NystromformerClassificationHead(nn.Module):
    def __init__(self, config):
        super().__init__()
        self.dense = nn.Linear(config.hidden_size, config.hidden_size)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)
        self.out_proj = nn.Linear(config.hidden_size, config.num_labels)
        self.config = config
    def forward(self, features, **kwargs):
        x = features[:, 0, :]
        x = self.dropout(x)
        x = self.dense(x)
        x = ACT2FN[self.config.hidden_act](x)
        x = self.dropout(x)
        x = self.out_proj(x)
        return x
@add_start_docstrings("Nyströmformer Model transformer with a sequence classification/regression head on top (a linear layer on top of the pooled output) e.g. for GLUE tasks.", NYSTROMFORMER_START_DOCSTRING)
class NystromformerForSequenceClassification(NystromformerPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.num_labels = config.num_labels
        self.nystromformer = NystromformerModel(config)
        self.classifier = NystromformerClassificationHead(config)
        self.post_init()
    @add_start_docstrings_to_model_forward(NYSTROMFORMER_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    def forward(self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None,
    position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None,
    labels: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None,
    return_dict: Optional[bool] = None) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]:
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        outputs = self.nystromformer(input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask,
        inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
        sequence_output = outputs[0]
        logits = self.classifier(sequence_output)
        loss = None
        if labels is not None:
            if self.config.problem_type is None:
                if self.num_labels == 1: self.config.problem_type = "regression"
                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): self.config.problem_type = "single_label_classification"
                else: self.config.problem_type = "multi_label_classification"
            if self.config.problem_type == "regression":
                loss_fct = MSELoss()
                if self.num_labels == 1: loss = loss_fct(logits.squeeze(), labels.squeeze())
                else: loss = loss_fct(logits, labels)
            elif self.config.problem_type == "single_label_classification":
                loss_fct = CrossEntropyLoss()
                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
            elif self.config.problem_type == "multi_label_classification":
                loss_fct = BCEWithLogitsLoss()
                loss = loss_fct(logits, labels)
        if not return_dict:
            output = (logits,) + outputs[1:]
            return ((loss,) + output) if loss is not None else output
        return SequenceClassifierOutput(loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions)
@add_start_docstrings("Nyströmformer Model with a multiple choice classification head on top (a linear layer on top of the pooled output and a softmax) e.g. for RocStories/SWAG tasks.", NYSTROMFORMER_START_DOCSTRING)
class NystromformerForMultipleChoice(NystromformerPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.nystromformer = NystromformerModel(config)
        self.pre_classifier = nn.Linear(config.hidden_size, config.hidden_size)
        self.classifier = nn.Linear(config.hidden_size, 1)
        self.post_init()
    @add_start_docstrings_to_model_forward(NYSTROMFORMER_INPUTS_DOCSTRING.format("batch_size, num_choices, sequence_length"))
    def forward(self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None,
    position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None,
    output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None) -> Union[Tuple[torch.Tensor], MultipleChoiceModelOutput]:
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1]
        input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None
        attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None
        token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None
        position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None
        inputs_embeds = (inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1)) if inputs_embeds is not None else None)
        outputs = self.nystromformer(input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask,
        inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
        hidden_state = outputs[0]
        pooled_output = hidden_state[:, 0]
        pooled_output = self.pre_classifier(pooled_output)
        pooled_output = nn.ReLU()(pooled_output)
        logits = self.classifier(pooled_output)
        reshaped_logits = logits.view(-1, num_choices)
        loss = None
        if labels is not None:
            loss_fct = CrossEntropyLoss()
            loss = loss_fct(reshaped_logits, labels)
        if not return_dict:
            output = (reshaped_logits,) + outputs[1:]
            return ((loss,) + output) if loss is not None else output
        return MultipleChoiceModelOutput(loss=loss, logits=reshaped_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions)
@add_start_docstrings("Nyströmformer Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g. for Named-Entity-Recognition (NER) tasks.", NYSTROMFORMER_START_DOCSTRING)
class NystromformerForTokenClassification(NystromformerPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.num_labels = config.num_labels
        self.nystromformer = NystromformerModel(config)
        self.dropout = nn.Dropout(config.hidden_dropout_prob)
        self.classifier = nn.Linear(config.hidden_size, config.num_labels)
        self.post_init()
    @add_start_docstrings_to_model_forward(NYSTROMFORMER_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    def forward(self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None,
    position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None,
    output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None) -> Union[Tuple[torch.Tensor], TokenClassifierOutput]:
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        outputs = self.nystromformer(input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask,
        inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
        sequence_output = outputs[0]
        sequence_output = self.dropout(sequence_output)
        logits = self.classifier(sequence_output)
        loss = None
        if labels is not None:
            loss_fct = CrossEntropyLoss()
            loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
        if not return_dict:
            output = (logits,) + outputs[1:]
            return ((loss,) + output) if loss is not None else output
        return TokenClassifierOutput(loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions)
@add_start_docstrings("Nyströmformer Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear layers on top of the hidden-states output to compute `span start logits` and `span end logits`).", NYSTROMFORMER_START_DOCSTRING)
class NystromformerForQuestionAnswering(NystromformerPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        config.num_labels = 2
        self.num_labels = config.num_labels
        self.nystromformer = NystromformerModel(config)
        self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels)
        self.post_init()
    @add_start_docstrings_to_model_forward(NYSTROMFORMER_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    def forward(self, input_ids: Optional[torch.LongTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None,
    position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None,
    start_positions: Optional[torch.LongTensor] = None, end_positions: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None,
    output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None) -> Union[Tuple[torch.Tensor], QuestionAnsweringModelOutput]:
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        outputs = self.nystromformer(input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask,
        inputs_embeds=inputs_embeds, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
        sequence_output = outputs[0]
        logits = self.qa_outputs(sequence_output)
        start_logits, end_logits = logits.split(1, dim=-1)
        start_logits = start_logits.squeeze(-1)
        end_logits = end_logits.squeeze(-1)
        total_loss = None
        if start_positions is not None and end_positions is not None:
            if len(start_positions.size()) > 1: start_positions = start_positions.squeeze(-1)
            if len(end_positions.size()) > 1: end_positions = end_positions.squeeze(-1)
            ignored_index = start_logits.size(1)
            start_positions = start_positions.clamp(0, ignored_index)
            end_positions = end_positions.clamp(0, ignored_index)
            loss_fct = CrossEntropyLoss(ignore_index=ignored_index)
            start_loss = loss_fct(start_logits, start_positions)
            end_loss = loss_fct(end_logits, end_positions)
            total_loss = (start_loss + end_loss) / 2
        if not return_dict:
            output = (start_logits, end_logits) + outputs[1:]
            return ((total_loss,) + output) if total_loss is not None else output
        return QuestionAnsweringModelOutput(loss=total_loss, start_logits=start_logits, end_logits=end_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions)
"""
    #################################################################################################################################################################################################
    # 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.                                                                 #
    #################################################################################################################################################################################################
"""
