






"""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


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







def 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE       )       ->						CheckpointState:
    """simple docstring"""

    print(F'Reading saved model from {model_file}'       )
    UpperCamelCase__								=     torch.load(SCREAMING_SNAKE_CASE			,						map_location=lambda SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       : default_restore_location(SCREAMING_SNAKE_CASE			,						'cpu'       )       )
    return CheckpointState(**SCREAMING_SNAKE_CASE       )
class  __lowerCamelCase     :

   def __init__(       self			,					snake_case_   )		->							Optional[Any]:
       UpperCamelCase__								=     src_file

   def     SCREAMING_SNAKE_CASE__			(       self   )		->							Tuple:
       raise NotImplementedError







   @staticmethod
   def     SCREAMING_SNAKE_CASE__			(       snake_case_			,					*snake_case_			,					**snake_case_   )		->							"DPRState":
       if comp_type.startswith('c'   ):
           return DPRContextEncoderState(*snake_case_			,					**snake_case_   )
       if comp_type.startswith('q'   ):
           return DPRQuestionEncoderState(*snake_case_			,					**snake_case_   )
       if comp_type.startswith('r'   ):
           return DPRReaderState(*snake_case_			,					**snake_case_   )
       else:
           raise ValueError('Component type must be either \'ctx_encoder\', \'question_encoder\' or \'reader\'.'   )







class  __lowerCamelCase     (	_a	):

   def     SCREAMING_SNAKE_CASE__			(       self   )		->							List[str]:
       UpperCamelCase__								=     DPRContextEncoder(DPRConfig(**BertConfig.get_config_dict('bert-base-uncased'   )[0]   )   )
       print(F'Loading DPR biencoder from {self.src_file}'   )
       UpperCamelCase__								=     load_states_from_checkpoint(self.src_file   )
       UpperCamelCase__							,    UpperCamelCase__								=     model.ctx_encoder, 'ctx_model.'
       # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
       UpperCamelCase__								=     {'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(snake_case_   ):
               UpperCamelCase__								=     key[len(snake_case_   ) :]
               if not key.startswith('encode_proj.'   ):
                   UpperCamelCase__								=     'bert_model.' + key
               UpperCamelCase__								=     value
       encoder.load_state_dict(snake_case_   )
       return model







class  __lowerCamelCase     (	_a	):

   def     SCREAMING_SNAKE_CASE__			(       self   )		->							str:
       UpperCamelCase__								=     DPRQuestionEncoder(DPRConfig(**BertConfig.get_config_dict('bert-base-uncased'   )[0]   )   )
       print(F'Loading DPR biencoder from {self.src_file}'   )
       UpperCamelCase__								=     load_states_from_checkpoint(self.src_file   )
       UpperCamelCase__							,    UpperCamelCase__								=     model.question_encoder, 'question_model.'
       # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
       UpperCamelCase__								=     {'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(snake_case_   ):
               UpperCamelCase__								=     key[len(snake_case_   ) :]
               if not key.startswith('encode_proj.'   ):
                   UpperCamelCase__								=     'bert_model.' + key
               UpperCamelCase__								=     value
       encoder.load_state_dict(snake_case_   )
       return model







class  __lowerCamelCase     (	_a	):

   def     SCREAMING_SNAKE_CASE__			(       self   )		->							List[Any]:
       UpperCamelCase__								=     DPRReader(DPRConfig(**BertConfig.get_config_dict('bert-base-uncased'   )[0]   )   )
       print(F'Loading DPR reader from {self.src_file}'   )
       UpperCamelCase__								=     load_states_from_checkpoint(self.src_file   )
       # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
       UpperCamelCase__								=     {
           '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'   ):
               UpperCamelCase__								=     'encoder.bert_model.' + key[len('encoder.'   ) :]
           UpperCamelCase__								=     value
       model.span_predictor.load_state_dict(snake_case_   )
       return model







def 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       )       ->						Optional[Any]:
    """simple docstring"""

    UpperCamelCase__								=     Path(SCREAMING_SNAKE_CASE       )
    dest_dir.mkdir(exist_ok=SCREAMING_SNAKE_CASE       )

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


if __name__ == "__main__":
  A__       :			Optional[int]=						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.""")
  A__       :			Optional[Any]=						parser.parse_args()

  A__       :			Tuple=						Path(args.src)
  A__       :			Optional[int]=						F"""converted-{src_file.name}""" if args.dest is None else args.dest
  A__       :			List[str]=						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 functools
import operator

from ...configuration_utils import PretrainedConfig
from ...utils import logging


A__       :			int=						logging.get_logger(__name__)

A__       :			Optional[int]=						{
    """facebook/hubert-base-ls960""": """https://huggingface.co/facebook/hubert-base-ls960/resolve/main/config.json""",
    # See all Hubert models at https://huggingface.co/models?filter=hubert
}
class  __lowerCamelCase     (	_a	):
   a   :					Dict			    ="""hubert"""

   def __init__(       self			,					snake_case_=32			,					snake_case_=768			,					snake_case_=12			,					snake_case_=12			,					snake_case_=3072			,					snake_case_="gelu"			,					snake_case_=0.1			,					snake_case_=0.1			,					snake_case_=0.1			,					snake_case_=True			,					snake_case_=0.0			,					snake_case_=0.1			,					snake_case_=0.1			,					snake_case_=0.02			,					snake_case_=1E-5			,					snake_case_="group"			,					snake_case_="gelu"			,					snake_case_=(512, 512, 512, 512, 512, 512, 512)			,					snake_case_=(5, 2, 2, 2, 2, 2, 2)			,					snake_case_=(10, 3, 3, 3, 3, 2, 2)			,					snake_case_=False			,					snake_case_=128			,					snake_case_=16			,					snake_case_=False			,					snake_case_=True			,					snake_case_=0.05			,					snake_case_=10			,					snake_case_=2			,					snake_case_=0.0			,					snake_case_=10			,					snake_case_=0			,					snake_case_="sum"			,					snake_case_=False			,					snake_case_=False			,					snake_case_=256			,					snake_case_=0			,					snake_case_=1			,					snake_case_=2			,					**snake_case_			,					)		->							Any:
       super().__init__(**snake_case_			,					pad_token_id=snake_case_			,					bos_token_id=snake_case_			,					eos_token_id=snake_case_   )
       UpperCamelCase__								=     hidden_size
       UpperCamelCase__								=     feat_extract_norm
       UpperCamelCase__								=     feat_extract_activation
       UpperCamelCase__								=     list(snake_case_   )
       UpperCamelCase__								=     list(snake_case_   )
       UpperCamelCase__								=     list(snake_case_   )
       UpperCamelCase__								=     conv_bias
       UpperCamelCase__								=     num_conv_pos_embeddings
       UpperCamelCase__								=     num_conv_pos_embedding_groups
       UpperCamelCase__								=     len(self.conv_dim   )
       UpperCamelCase__								=     num_hidden_layers
       UpperCamelCase__								=     intermediate_size
       UpperCamelCase__								=     hidden_act
       UpperCamelCase__								=     num_attention_heads
       UpperCamelCase__								=     hidden_dropout
       UpperCamelCase__								=     attention_dropout
       UpperCamelCase__								=     activation_dropout
       UpperCamelCase__								=     feat_proj_layer_norm
       UpperCamelCase__								=     feat_proj_dropout
       UpperCamelCase__								=     final_dropout
       UpperCamelCase__								=     layerdrop
       UpperCamelCase__								=     layer_norm_eps
       UpperCamelCase__								=     initializer_range
       UpperCamelCase__								=     vocab_size
       UpperCamelCase__								=     do_stable_layer_norm
       UpperCamelCase__								=     use_weighted_layer_sum
       UpperCamelCase__								=     classifier_proj_size

       if (
           (len(self.conv_stride   ) != self.num_feat_extract_layers)
           or (len(self.conv_kernel   ) != self.num_feat_extract_layers)
           or (len(self.conv_dim   ) != self.num_feat_extract_layers)
       ):
           raise ValueError(
               'Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='
               ' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='
               F' {len(self.conv_dim   )}`, `len(config.conv_stride) = {len(self.conv_stride   )}`,'
               F' `len(config.conv_kernel) = {len(self.conv_kernel   )}`.'   )

       # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
       UpperCamelCase__								=     apply_spec_augment
       UpperCamelCase__								=     mask_time_prob
       UpperCamelCase__								=     mask_time_length
       UpperCamelCase__								=     mask_time_min_masks
       UpperCamelCase__								=     mask_feature_prob
       UpperCamelCase__								=     mask_feature_length
       UpperCamelCase__								=     mask_feature_min_masks

       # ctc loss
       UpperCamelCase__								=     ctc_loss_reduction
       UpperCamelCase__								=     ctc_zero_infinity







   @property
   def     SCREAMING_SNAKE_CASE__			(       self   )		->							Dict:
       return functools.reduce(operator.mul			,					self.conv_stride			,					1   )




