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



def     _lowerCamelCase(__UpperCamelCase       )				-> CheckpointState:
	print(F'''Reading saved model from {model_file}'''       )
	_lowerCAmelCase								=torch.load(__UpperCamelCase    , map_location=lambda __UpperCamelCase    , __UpperCamelCase       : default_restore_location(__UpperCamelCase    , """cpu"""       )       )
	return CheckpointState(**__UpperCamelCase       )







class 		lowerCamelCase__					:





			'''simple docstring'''



			def __init__(							self     ,     __UpperCAmelCase  ) ->     int:
				_lowerCAmelCase								=src_file



			def 					_lowerCAmelCase		(							self  ) ->     Optional[Any]:
				raise NotImplementedError



			@staticmethod
			def 					_lowerCAmelCase		(							__UpperCAmelCase     ,     *__UpperCAmelCase     ,     **__UpperCAmelCase  ) ->     "DPRState":
				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 		lowerCamelCase__					(					__magic_name__				):





			'''simple docstring'''



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







class 		lowerCamelCase__					(					__magic_name__				):





			'''simple docstring'''



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







class 		lowerCamelCase__					(					__magic_name__				):





			'''simple docstring'''



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







def     _lowerCamelCase(__UpperCamelCase    , __UpperCamelCase    , __UpperCamelCase       )				-> Union[str, Any]:
	_lowerCAmelCase								=Path(__UpperCamelCase       )
	dest_dir.mkdir(exist_ok=__UpperCamelCase       )

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


if __name__ == "__main__":
			__A       						=       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       						=       parser.parse_args()

			__A       						=       Path(args.src)
			__A       						=       F"""converted-{src_file.name}""" if args.dest is None else args.dest
			__A       						=       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 unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
		import torch

		from transformers import CamembertModel
@require_torch
@require_sentencepiece
@require_tokenizers
class snake_case					(     unittest.TestCase):







		@slow
		def        a_       (    self					:				int						)				->  Tuple:





						'''simple docstring'''







						_A         =	CamembertModel.from_pretrained("camembert-base"						)
						model.to(a__						)

						_A         =	torch.tensor(
						    [[5, 1_21, 11, 6_60, 16, 7_30, 2_55_43, 1_10, 83, 6]]			,      device=a__			,      dtype=torch.long			,      )  # J'aime le camembert !
						with torch.no_grad():
										_A         =	model(a__						)["last_hidden_state"]
						_A         =	torch.Size((1, 10, 7_68)						)
						self.assertEqual(output.shape			,      a__						)
						# compare the actual values for a slice.
						_A         =	torch.tensor(
						    [[[-0.0_2_5_4, 0.0_2_3_5, 0.1_0_2_7], [0.0_6_0_6, -0.1_8_1_1, -0.0_4_1_8], [-0.1_5_6_1, -0.1_1_2_7, 0.2_6_8_7]]]			,      device=a__			,      dtype=torch.float			,      )
						# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
						# camembert.eval()
						# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

						self.assertTrue(torch.allclose(output[:, :3, :3]			,      a__			,      atol=1E-4						)						)