






"""simple docstring"""




import argparse
import collections
from pathlib import Path

import torch
from torch.serialization import default_restore_location

from transformers import BertConfig, DPRConfig, DPRContextEncoder, DPRQuestionEncoder, DPRReader


UpperCamelCase											=     collections.namedtuple(
    """CheckpointState""", ["""model_dict""", """optimizer_dict""", """scheduler_dict""", """offset""", """epoch""", """encoder_params"""]
)






def 							lowerCAmelCase			(   UpperCamelCase_:				str       )							->				CheckpointState:







  '''simple docstring'''







  print(f'''Reading saved model from {model_file}'''       )
  _a					      =						torch.load(UpperCamelCase_  ,			map_location=lambda UpperCamelCase_  ,			UpperCamelCase_       : default_restore_location(UpperCamelCase_  ,			"cpu"       )       )
  return CheckpointState(**UpperCamelCase_       )




class  lowercase_     :






      def __init__(     self  ,  a_						)						->Dict:







        '''simple docstring'''


        _a					      =						src_file






      def       lowerCamelCase__     (     self						)						->Any:







        '''simple docstring'''


        raise NotImplementedError






      @staticmethod
      def       lowerCamelCase__     (     a_  ,  *a_  ,  **a_						)						->"DPRState":







        '''simple docstring'''


        if comp_type.startswith("c"						):
          return DPRContextEncoderState(*a_  ,  **a_						)
        if comp_type.startswith("q"						):
          return DPRQuestionEncoderState(*a_  ,  **a_						)
        if comp_type.startswith("r"						):
          return DPRReaderState(*a_  ,  **a_						)
        else:
          raise ValueError("Component type must be either 'ctx_encoder', 'question_encoder' or 'reader'."						)







class  lowercase_     (_UpperCAmelCase  ):






      def       lowerCamelCase__     (     self						)						->str:







        '''simple docstring'''


        _a					      =						DPRContextEncoder(DPRConfig(**BertConfig.get_config_dict("bert-base-uncased"						)[0]						)						)
        print(f'''Loading DPR biencoder from {self.src_file}'''						)
        _a					      =						load_states_from_checkpoint(self.src_file						)
        _a      ,						_a					      =						model.ctx_encoder, "ctx_model."
        # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
        _a					      =						{"bert_model.embeddings.position_ids": model.ctx_encoder.bert_model.embeddings.position_ids}
        for key, value in saved_state.model_dict.items():
          if key.startswith(a_						):
            _a					      =						key[len(a_						) :]
            if not key.startswith("encode_proj."						):
              _a					      =						"bert_model." + key
            _a					      =						value
        encoder.load_state_dict(a_						)
        return model







class  lowercase_     (_UpperCAmelCase  ):






      def       lowerCamelCase__     (     self						)						->Tuple:







        '''simple docstring'''


        _a					      =						DPRQuestionEncoder(DPRConfig(**BertConfig.get_config_dict("bert-base-uncased"						)[0]						)						)
        print(f'''Loading DPR biencoder from {self.src_file}'''						)
        _a					      =						load_states_from_checkpoint(self.src_file						)
        _a      ,						_a					      =						model.question_encoder, "question_model."
        # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
        _a					      =						{"bert_model.embeddings.position_ids": model.question_encoder.bert_model.embeddings.position_ids}
        for key, value in saved_state.model_dict.items():
          if key.startswith(a_						):
            _a					      =						key[len(a_						) :]
            if not key.startswith("encode_proj."						):
              _a					      =						"bert_model." + key
            _a					      =						value
        encoder.load_state_dict(a_						)
        return model







class  lowercase_     (_UpperCAmelCase  ):






      def       lowerCamelCase__     (     self						)						->Optional[Any]:







        '''simple docstring'''


        _a					      =						DPRReader(DPRConfig(**BertConfig.get_config_dict("bert-base-uncased"						)[0]						)						)
        print(f'''Loading DPR reader from {self.src_file}'''						)
        _a					      =						load_states_from_checkpoint(self.src_file						)
        # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
        _a					      =						{
            "encoder.bert_model.embeddings.position_ids": model.span_predictor.encoder.bert_model.embeddings.position_ids
        }
        for key, value in saved_state.model_dict.items():
          if key.startswith("encoder."						) and not key.startswith("encoder.encode_proj"						):
            _a					      =						"encoder.bert_model." + key[len("encoder."						) :]
          _a					      =						value
        model.span_predictor.load_state_dict(a_						)
        return model







def 							lowerCAmelCase			(   UpperCamelCase_:				str  ,			UpperCamelCase_:				Path  ,			UpperCamelCase_:				Path       )							->				Dict:







  '''simple docstring'''







  _a					      =						Path(UpperCamelCase_       )
  dest_dir.mkdir(exist_ok=UpperCamelCase_       )

  _a					      =						DPRState.from_type(UpperCamelCase_  ,			src_file=UpperCamelCase_       )
  _a					      =						dpr_state.load_dpr_model()
  model.save_pretrained(UpperCamelCase_       )
  model.from_pretrained(UpperCamelCase_       )  # sanity check


if __name__ == "__main__":
     UpperCamelCase											=     argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         """--type""", type=str, help="""Type of the component to convert: 'ctx_encoder', 'question_encoder' or 'reader'."""
     )
     parser.add_argument(
         """--src""",
         type=str,
         help=(
             """Path to the dpr checkpoint file. They can be downloaded from the official DPR repo"""
             """ https://github.com/facebookresearch/DPR. Note that in the official repo, both encoders are stored in the"""
             """ 'retriever' checkpoints."""
         ),
     )
     parser.add_argument("""--dest""", type=str, default=None, help="""Path to the output PyTorch model directory.""")
     UpperCamelCase											=     parser.parse_args()

     UpperCamelCase											=     Path(args.src)
     UpperCamelCase											=     f"converted-{src_file.name}" if args.dest is None else args.dest
     UpperCamelCase											=     Path(dest_dir)
     assert src_file.exists()
     assert (
         args.type is not None
     ), "Please specify the component type of the DPR model to convert: 'ctx_encoder', 'question_encoder' or 'reader'."
     convert(args.type, src_file, dest_dir)










"""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


