
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


snake_case_      :					List[str]				      =     collections.namedtuple(
    'CheckpointState', ['model_dict', 'optimizer_dict', 'scheduler_dict', 'offset', 'epoch', 'encoder_params']
)



def      __UpperCAmelCase    (  snake_case_							:		str				):



	'''simple docstring'''



	print(F'Reading saved model from {model_file}'				)
	UpperCAmelCase:					int 						=     torch.load(lowerCAmelCase__				,					map_location=lambda snake_case_				,					snake_case_				: default_restore_location(lowerCAmelCase__				,					"cpu"				)				)
	return CheckpointState(**lowerCAmelCase__				)





class 		__lowerCamelCase     :



							def __init__(     self	,				__snake_case							)						->     List[Any]:






								"""simple docstring"""



								UpperCAmelCase:					Dict 						=     src_file



							def     A__ (     self							)						->     List[Any]:






								"""simple docstring"""



								raise NotImplementedError




							@staticmethod
							def     A__ (     __snake_case	,				*__snake_case	,				**__snake_case							)						->     Optional[int]:






								"""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 		__lowerCamelCase     (   lowercase_		):



							def     A__ (     self							)						->     List[str]:






								"""simple docstring"""



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





class 		__lowerCamelCase     (   lowercase_		):



							def     A__ (     self							)						->     str:






								"""simple docstring"""



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





class 		__lowerCamelCase     (   lowercase_		):



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






								"""simple docstring"""



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



def      __UpperCAmelCase    (  snake_case_							:		str				,					snake_case_							:		Path				,					snake_case_							:		Path				):



	'''simple docstring'''



	UpperCAmelCase:					str 						=     Path(lowerCAmelCase__				)
	dest_dir.mkdir(exist_ok=lowerCAmelCase__				)

	UpperCAmelCase:					List[str] 						=     DPRState.from_type(lowerCAmelCase__				,					src_file=lowerCAmelCase__				)
	UpperCAmelCase:					int 						=     dpr_state.load_dpr_model()
	model.save_pretrained(lowerCAmelCase__				)
	model.from_pretrained(lowerCAmelCase__				)  # sanity check


if __name__ == "__main__":
	snake_case_      :					Optional[Any]				      =     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.')
	snake_case_      :					Dict				      =     parser.parse_args()

	snake_case_      :					Union[str, Any]				      =     Path(args.src)
	snake_case_      :					Any				      =     f"""converted-{src_file.name}""" if args.dest is None else args.dest
	snake_case_      :					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"""







import inspect

from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
     import jax







@require_flax
class  UpperCamelCase__  :




      """simple docstring"""




      def 	SCREAMING_SNAKE_CASE__					( self							: Optional[int]			):
        lowerCAmelCase_		,lowerCAmelCase_  :							Any								=					self.prepare_init_args_and_inputs_for_common()

        lowerCAmelCase_  :							List[str]								=					self.model_class(**SCREAMING_SNAKE_CASE_			)
        lowerCAmelCase_  :							Any								=					model.init(inputs_dict['prng_key']       ,     inputs_dict['sample']			)
        jax.lax.stop_gradient(SCREAMING_SNAKE_CASE_			)

        lowerCAmelCase_  :							Tuple								=					model.apply(SCREAMING_SNAKE_CASE_       ,     inputs_dict['sample']			)

        if isinstance(SCREAMING_SNAKE_CASE_       ,     SCREAMING_SNAKE_CASE_			):
          lowerCAmelCase_  :							str								=					output.sample

        self.assertIsNotNone(SCREAMING_SNAKE_CASE_			)
        lowerCAmelCase_  :							Any								=					inputs_dict['sample'].shape
        self.assertEqual(output.shape       ,     SCREAMING_SNAKE_CASE_       ,     'Input and output shapes do not match'			)



      def 	SCREAMING_SNAKE_CASE__					( self							: List[str]			):
        lowerCAmelCase_		,lowerCAmelCase_  :							Union[str, Any]								=					self.prepare_init_args_and_inputs_for_common()

        lowerCAmelCase_  :							Tuple								=					1_6
        lowerCAmelCase_  :							Dict								=					(1_6, 3_2)

        lowerCAmelCase_  :							Any								=					self.model_class(**SCREAMING_SNAKE_CASE_			)
        lowerCAmelCase_  :							Optional[Any]								=					model.init(inputs_dict['prng_key']       ,     inputs_dict['sample']			)
        jax.lax.stop_gradient(SCREAMING_SNAKE_CASE_			)

        lowerCAmelCase_  :							Optional[Any]								=					model.apply(SCREAMING_SNAKE_CASE_       ,     inputs_dict['sample']			)

        if isinstance(SCREAMING_SNAKE_CASE_       ,     SCREAMING_SNAKE_CASE_			):
          lowerCAmelCase_  :							Dict								=					output.sample

        self.assertIsNotNone(SCREAMING_SNAKE_CASE_			)
        lowerCAmelCase_  :							Optional[Any]								=					inputs_dict['sample'].shape
        self.assertEqual(output.shape       ,     SCREAMING_SNAKE_CASE_       ,     'Input and output shapes do not match'			)





      def 	SCREAMING_SNAKE_CASE__					( self							: List[Any]			):
        lowerCAmelCase_  :							List[str]								=					'kwargs' in inspect.signature(self.model_class.__init__			).parameters
        lowerCAmelCase_  :							Optional[Any]								=					len(self.model_class._deprecated_kwargs			) > 0

        if has_kwarg_in_model_class and not has_deprecated_kwarg:
          raise ValueError(
              F"{self.model_class} has `**kwargs` in its __init__ method but has not defined any deprecated kwargs"
              ' under the `_deprecated_kwargs` class attribute. Make sure to either remove `**kwargs` if there are'
              ' no deprecated arguments or add the deprecated argument with `_deprecated_kwargs ='
              ' [<deprecated_argument>]`'			)

        if not has_kwarg_in_model_class and has_deprecated_kwarg:
          raise ValueError(
              F"{self.model_class} doesn't have `**kwargs` in its __init__ method but has defined deprecated kwargs"
              ' under the `_deprecated_kwargs` class attribute. Make sure to either add the `**kwargs` argument to'
              F" {self.model_class}.__init__ if there are deprecated arguments or remove the deprecated argument"
              ' from `_deprecated_kwargs = [<deprecated_argument>]`'			)





