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


_SCREAMING_SNAKE_CASE:    Optional[Any]     						=							collections.namedtuple(
    '''CheckpointState''', ['''model_dict''', '''optimizer_dict''', '''scheduler_dict''', '''offset''', '''epoch''', '''encoder_params''']
)






def  UpperCAmelCase__(    lowerCamelCase_	):



			"""simple docstring"""





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

class 						_SCREAMING_SNAKE_CASE				:






		"""simple docstring"""







		def __init__(      self     :					Union[str, Any]	,       __SCREAMING_SNAKE_CASE     :					Path ):






					"""simple docstring"""
					_snake_case														=   src_file



		def 					_a					(      self     :					Dict ):






					"""simple docstring"""
					raise NotImplementedError







		@staticmethod
		def 					_a					(      __SCREAMING_SNAKE_CASE     :					str	,       *__SCREAMING_SNAKE_CASE     :					Dict	,       **__SCREAMING_SNAKE_CASE     :					Tuple ):






					"""simple docstring"""





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

class 						_SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE				):






		"""simple docstring"""







		def 					_a					(      self     :					Optional[int] ):






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

class 						_SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE				):






		"""simple docstring"""







		def 					_a					(      self     :					int ):






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

class 						_SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE				):






		"""simple docstring"""







		def 					_a					(      self     :					List[str] ):






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






def  UpperCAmelCase__(    lowerCamelCase_				,						lowerCamelCase_				,						lowerCamelCase_	):



			"""simple docstring"""





			_snake_case														=   Path(__SCREAMING_SNAKE_CASE	)
			dest_dir.mkdir(exist_ok=__SCREAMING_SNAKE_CASE	)

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


if __name__ == "__main__":
			_SCREAMING_SNAKE_CASE:    Tuple     						=							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.''')
			_SCREAMING_SNAKE_CASE:    Dict     						=							parser.parse_args()

			_SCREAMING_SNAKE_CASE:    Tuple     						=							Path(args.src)
			_SCREAMING_SNAKE_CASE:    Dict     						=							F"converted-{src_file.name}" if args.dest is None else args.dest
			_SCREAMING_SNAKE_CASE:    Union[str, Any]     						=							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'''
A__ :		dict[str, float]					={
    "kilogram": 1,
    "gram": pow(10, 3),
    "milligram": pow(10, 6),
    "metric-ton": pow(10, -3),
    "long-ton": 0.0_0_0_9_8_4_2_0_7_3,
    "short-ton": 0.0_0_1_1_0_2_3_1_2_2,
    "pound": 2.2_0_4_6_2_4_4_2_0_2,
    "stone": 0.1_5_7_4_7_3_1_7_2_8,
    "ounce": 3_5.2_7_3_9_9_0_7_2_3,
    "carrat": 5_000,
    "atomic-mass-unit": 6.0_2_2_1_3_6_6_5_2E2_6,
}

A__ :		dict[str, float]					={
    "kilogram": 1,
    "gram": pow(10, -3),
    "milligram": pow(10, -6),
    "metric-ton": pow(10, 3),
    "long-ton": 1_0_1_6.0_4_6_0_8,
    "short-ton": 9_0_7.1_8_4,
    "pound": 0.4_5_3_5_9_2,
    "stone": 6.3_5_0_2_9,
    "ounce": 0.0_2_8_3_4_9_5,
    "carrat": 0.0_0_0_2,
    "atomic-mass-unit": 1.6_6_0_5_4_0_1_9_9E-2_7,
}







def 							A_  (    __SCREAMING_SNAKE_CASE    :	str      ,  __SCREAMING_SNAKE_CASE    :	str      ,  __SCREAMING_SNAKE_CASE    :	float			)					->							float:





  """simple docstring"""

  if to_type not in KILOGRAM_CHART or from_type not in WEIGHT_TYPE_CHART:
    __A		:  Tuple       				=		(
        F"Invalid 'from_type' or 'to_type' value: {from_type!r}, {to_type!r}\n"
        F"Supported values are: {', '.join(__SCREAMING_SNAKE_CASE			)}"
    )
    raise ValueError(__SCREAMING_SNAKE_CASE			)
  return value * KILOGRAM_CHART[to_type] * WEIGHT_TYPE_CHART[from_type]


if __name__ == "__main__":
     import doctest

     doctest.testmod()



