
'''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


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







def 		SCREAMING_SNAKE_CASE__					(							_SCREAMING_SNAKE_CASE  ):
		print(f'Reading saved model from {model_file}'  )
		lowerCAmelCase_   :       str    							=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 							_snake_case     :
	"""simple docstring"""





	def __init__(						self				: Dict					,						UpperCamelCase_				: Path      ):
			lowerCAmelCase_   :       List[str]    							=src_file




	def 	__A      (						self				: Optional[Any]      ):
			raise NotImplementedError





	@staticmethod
	def 	__A      (						UpperCamelCase_				: str					,						*UpperCamelCase_				: Union[str, Any]					,						**UpperCamelCase_				: List[str]      ):
			if comp_type.startswith('''c'''      ):
					return DPRContextEncoderState(*UpperCamelCase_					,						**UpperCamelCase_      )
			if comp_type.startswith('''q'''      ):
					return DPRQuestionEncoderState(*UpperCamelCase_					,						**UpperCamelCase_      )
			if comp_type.startswith('''r'''      ):
					return DPRReaderState(*UpperCamelCase_					,						**UpperCamelCase_      )
			else:
					raise ValueError('''Component type must be either \'ctx_encoder\', \'question_encoder\' or \'reader\'.'''      )







class 							_snake_case     (  lowerCAmelCase_ ):
	"""simple docstring"""





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







class 							_snake_case     (  lowerCAmelCase_ ):
	"""simple docstring"""





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







class 							_snake_case     (  lowerCAmelCase_ ):
	"""simple docstring"""





	def 	__A      (						self				: Dict      ):
			lowerCAmelCase_   :       Optional[int]    							=DPRReader(DPRConfig(**BertConfig.get_config_dict('''bert-base-uncased'''      )[0]      )      )
			print(F'Loading DPR reader from {self.src_file}'      )
			lowerCAmelCase_   :       List[str]    							=load_states_from_checkpoint(self.src_file      )
			# Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
			lowerCAmelCase_   :       Union[str, Any]    							={
			    '''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'''      ):
							lowerCAmelCase_   :       Tuple    							='''encoder.bert_model.''' + key[len('''encoder.'''      ) :]
					lowerCAmelCase_   :       str    							=value
			model.span_predictor.load_state_dict(UpperCamelCase_      )
			return model







def 		SCREAMING_SNAKE_CASE__					(							_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ,					_SCREAMING_SNAKE_CASE  ):
		lowerCAmelCase_   :       int    							=Path(_SCREAMING_SNAKE_CASE  )
		dest_dir.mkdir(exist_ok=_SCREAMING_SNAKE_CASE  )

		lowerCAmelCase_   :       str    							=DPRState.from_type(_SCREAMING_SNAKE_CASE  ,					src_file=_SCREAMING_SNAKE_CASE  )
		lowerCAmelCase_   :       Tuple    							=dpr_state.load_dpr_model()
		model.save_pretrained(_SCREAMING_SNAKE_CASE  )
		model.from_pretrained(_SCREAMING_SNAKE_CASE  )  # sanity check


if __name__ == "__main__":
							__lowercase	=    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.''')
							__lowercase	=    parser.parse_args()

							__lowercase	=    Path(args.src)
							__lowercase	=    f'converted-{src_file.name}' if args.dest is None else args.dest
							__lowercase	=    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'''






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


__lowercase	=    {'''configuration_gptj''': ['''GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTJConfig''', '''GPTJOnnxConfig''']}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowercase	=    [
							    '''GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''GPTJForCausalLM''',
							    '''GPTJForQuestionAnswering''',
							    '''GPTJForSequenceClassification''',
							    '''GPTJModel''',
							    '''GPTJPreTrainedModel''',
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowercase	=    [
							    '''TFGPTJForCausalLM''',
							    '''TFGPTJForQuestionAnswering''',
							    '''TFGPTJForSequenceClassification''',
							    '''TFGPTJModel''',
							    '''TFGPTJPreTrainedModel''',
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowercase	=    [
							    '''FlaxGPTJForCausalLM''',
							    '''FlaxGPTJModel''',
							    '''FlaxGPTJPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_gptj import GPTJ_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTJConfig, GPTJOnnxConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_gptj import (
														    GPTJ_PRETRAINED_MODEL_ARCHIVE_LIST,
														    GPTJForCausalLM,
														    GPTJForQuestionAnswering,
														    GPTJForSequenceClassification,
														    GPTJModel,
														    GPTJPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_gptj import (
														    TFGPTJForCausalLM,
														    TFGPTJForQuestionAnswering,
														    TFGPTJForSequenceClassification,
														    TFGPTJModel,
														    TFGPTJPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel, FlaxGPTJPreTrainedModel

else:
							import sys

							__lowercase	=    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






