


from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_snake_case				=			{
    "configuration_retribert": ["RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "RetriBertConfig"],
    "tokenization_retribert": ["RetriBertTokenizer"],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _snake_case				=			["RetriBertTokenizerFast"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _snake_case				=			[
          "RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "RetriBertModel",
          "RetriBertPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_retribert import RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RetriBertConfig
      from .tokenization_retribert import RetriBertTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_retribert_fast import RetriBertTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_retribert import (
                RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                RetriBertModel,
                RetriBertPreTrainedModel,
            )

else:
      import sys

      _snake_case				=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)











"""simple docstring"""




import logging

import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.albert.modeling_albert import (
    ALBERT_INPUTS_DOCSTRING,
    ALBERT_START_DOCSTRING,
    AlbertModel,
    AlbertPreTrainedModel,
    AlbertTransformer,
)


UpperCamelCase											=     logging.getLogger(__name__)




class  lowercase_     (_UpperCAmelCase  ):






      def       lowerCamelCase__     (     self  ,  a_  ,  a_  ,  a_=None  ,  a_=None						)						->Optional[int]:







        '''simple docstring'''


        if current_layer == 0:
          _a					      =						self.embedding_hidden_mapping_in(a_						)
        else:
          _a					      =						hidden_states[0]

        _a					      =						int(self.config.num_hidden_layers / self.config.num_hidden_groups						)

        # Index of the hidden group
        _a					      =						int(current_layer / (self.config.num_hidden_layers / self.config.num_hidden_groups)						)

        _a					      =						self.albert_layer_groups[group_idx](
            a_  ,  a_  ,  head_mask[group_idx * layers_per_group : (group_idx + 1) * layers_per_group]  ,  )
        _a					      =						layer_group_output[0]

        return (hidden_states,)







@add_start_docstrings(
    '''The bare ALBERT Model transformer with PABEE outputting raw hidden-states without any specific head on top.''',     _UpperCAmelCase,     )
class  lowercase_     (_UpperCAmelCase  ):






      def __init__(     self  ,  a_						)						->Dict:







        '''simple docstring'''


        super().__init__(a_						)

        _a					      =						AlbertTransformerWithPabee(a_						)

        self.init_weights()
        _a					      =						0
        _a					      =						0
        _a					      =						0

        _a					      =						0






      def       lowerCamelCase__     (     self  ,  a_						)						->Optional[int]:







        '''simple docstring'''


        _a					      =						threshold






      def       lowerCamelCase__     (     self  ,  a_						)						->Union[str, Any]:







        '''simple docstring'''


        _a					      =						patience






      def       lowerCamelCase__     (     self						)						->int:







        '''simple docstring'''


        _a					      =						0
        _a					      =						0






      def       lowerCamelCase__     (     self						)						->Tuple:







        '''simple docstring'''


        _a					      =						self.inference_layers_num / self.inference_instances_num
        _a					      =						(
            f'''*** Patience = {self.patience} Avg. Inference Layers = {avg_inf_layers:.2f} Speed Up ='''
            f''' {1 - avg_inf_layers / self.config.num_hidden_layers:.2f} ***'''
        )
        print(a_						)






      @add_start_docstrings_to_model_forward(a_						)
      def       lowerCamelCase__     (     self  ,  a_=None  ,  a_=None  ,  a_=None  ,  a_=None  ,  a_=None  ,  a_=None  ,  a_=None  ,  a_=None  ,  a_=False  ,  )						->int:







        '''simple docstring'''


        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:
          _a					      =						input_ids.size()
        elif inputs_embeds is not None:
          _a					      =						inputs_embeds.size()[:-1]
        else:
          raise ValueError("You have to specify either input_ids or inputs_embeds"						)

        _a					      =						input_ids.device if input_ids is not None else inputs_embeds.device

        if attention_mask is None:
          _a					      =						torch.ones(a_  ,  device=a_						)
        if token_type_ids is None:
          _a					      =						torch.zeros(a_  ,  dtype=torch.long  ,  device=a_						)

        _a					      =						attention_mask.unsqueeze(1						).unsqueeze(2						)
        _a					      =						extended_attention_mask.to(dtype=self.dtype						)  # fp16 compatibility
        _a					      =						(1.0 - extended_attention_mask) * -10_000.0
        _a					      =						self.get_head_mask(a_  ,  self.config.num_hidden_layers						)

        _a					      =						self.embeddings(
            a_  ,  position_ids=a_  ,  token_type_ids=a_  ,  inputs_embeds=a_						)
        _a					      =						embedding_output

        if self.training:
          _a					      =						[]
          for i in range(self.config.num_hidden_layers						):
            _a					      =						self.encoder.adaptive_forward(
                a_  ,  current_layer=a_  ,  attention_mask=a_  ,  head_mask=a_  ,  )

            _a					      =						self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]						)						)
            _a					      =						output_layers[i](output_dropout(a_						)						)
            res.append(a_						)
        elif self.patience == 0:  # Use all layers for inference
          _a					      =						self.encoder(a_  ,  a_  ,  head_mask=a_						)
          _a					      =						self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]						)						)
          _a					      =						[output_layers[self.config.num_hidden_layers - 1](a_						)]
        else:
          _a					      =						0
          _a					      =						None
          _a					      =						0
          for i in range(self.config.num_hidden_layers						):
            calculated_layer_num += 1
            _a					      =						self.encoder.adaptive_forward(
                a_  ,  current_layer=a_  ,  attention_mask=a_  ,  head_mask=a_  ,  )

            _a					      =						self.pooler_activation(self.pooler(encoder_outputs[0][:, 0]						)						)
            _a					      =						output_layers[i](a_						)
            if regression:
              _a					      =						logits.detach()
              if patient_result is not None:
                _a					      =						patient_result.detach()
              if (patient_result is not None) and torch.abs(patient_result - labels						) < self.regression_threshold:
                patient_counter += 1
              else:
                _a					      =						0
            else:
              _a					      =						logits.detach().argmax(dim=1						)
              if patient_result is not None:
                _a					      =						patient_result.detach().argmax(dim=1						)
              if (patient_result is not None) and torch.all(labels.eq(a_						)						):
                patient_counter += 1
              else:
                _a					      =						0

            _a					      =						logits
            if patient_counter == self.patience:
              break
          _a					      =						[patient_result]
          self.inference_layers_num += calculated_layer_num
          self.inference_instances_num += 1

        return res







@add_start_docstrings(
    '''Albert Model transformer with PABEE and a sequence classification/regression head on top (a linear layer on top of
    the pooled output) e.g. for GLUE tasks. ''',     _UpperCAmelCase,     )
class  lowercase_     (_UpperCAmelCase  ):






      def __init__(     self  ,  a_						)						->Dict:







        '''simple docstring'''


        super().__init__(a_						)
        _a					      =						config.num_labels

        _a					      =						AlbertModelWithPabee(a_						)
        _a					      =						nn.Dropout(config.classifier_dropout_prob						)
        _a					      =						nn.ModuleList(
            [nn.Linear(config.hidden_size  ,  self.config.num_labels						) for _ in range(config.num_hidden_layers						)]						)

        self.init_weights()






      @add_start_docstrings_to_model_forward(a_						)
      def       lowerCamelCase__     (     self  ,  a_=None  ,  a_=None  ,  a_=None  ,  a_=None  ,  a_=None  ,  a_=None  ,  a_=None  ,  )						->List[Any]:







        '''simple docstring'''


        _a					      =						self.albert(
            input_ids=a_  ,  attention_mask=a_  ,  token_type_ids=a_  ,  position_ids=a_  ,  head_mask=a_  ,  inputs_embeds=a_  ,  output_dropout=self.dropout  ,  output_layers=self.classifiers  ,  regression=self.num_labels == 1  ,  )

        _a					      =						(logits[-1],)

        if labels is not None:
          _a					      =						None
          _a					      =						0
          for ix, logits_item in enumerate(a_						):
            if self.num_labels == 1:
              #  We are doing regression
              _a					      =						MSELoss()
              _a					      =						loss_fct(logits_item.view(-1						)  ,  labels.view(-1						)						)
            else:
              _a					      =						CrossEntropyLoss()
              _a					      =						loss_fct(logits_item.view(-1  ,  self.num_labels						)  ,  labels.view(-1						)						)
            if total_loss is None:
              _a					      =						loss
            else:
              total_loss += loss * (ix + 1)
            total_weights += ix + 1
          _a					      =						(total_loss / total_weights,) + outputs

        return outputs


