


'''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


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







def       snake_case_       (  SCREAMING_SNAKE_CASE__					):







				"""simple docstring"""





				print(f"""Reading saved model from {model_file}"""					)
				_SCREAMING_SNAKE_CASE			:	Optional[int]							=				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    lowercase__  :





							'''simple docstring'''


							def __init__(   self   , __snake_case  ):
											_SCREAMING_SNAKE_CASE			:	List[str]							=				src_file






							def UpperCAmelCase_     (   self  ):
											raise NotImplementedError






							@staticmethod
							def UpperCAmelCase_     (   __snake_case   , *__snake_case   , **__snake_case  ):
											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    lowercase__  (						_snake_case ):





							'''simple docstring'''


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





class    lowercase__  (						_snake_case ):





							'''simple docstring'''


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





class    lowercase__  (						_snake_case ):





							'''simple docstring'''


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







def       snake_case_       (  SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__							,				SCREAMING_SNAKE_CASE__					):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE			:	List[Any]							=				Path(SCREAMING_SNAKE_CASE__					)
				dest_dir.mkdir(exist_ok=SCREAMING_SNAKE_CASE__					)

				_SCREAMING_SNAKE_CASE			:	List[Any]							=				DPRState.from_type(SCREAMING_SNAKE_CASE__							,				src_file=SCREAMING_SNAKE_CASE__					)
				_SCREAMING_SNAKE_CASE			:	List[str]							=				dpr_state.load_dpr_model()
				model.save_pretrained(SCREAMING_SNAKE_CASE__					)
				model.from_pretrained(SCREAMING_SNAKE_CASE__					)  # sanity check


if __name__ == "__main__":
				UpperCAmelCase_		:       List[str]							       =		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.')
				UpperCAmelCase_		:       List[str]							       =		parser.parse_args()

				UpperCAmelCase_		:       Optional[int]							       =		Path(args.src)
				UpperCAmelCase_		:       int							       =		F"converted-{src_file.name}" if args.dest is None else args.dest
				UpperCAmelCase_		:       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'''


import os
import tempfile
import unittest
from typing import List

from transformers.models.esm.tokenization_esm import VOCAB_FILES_NAMES, EsmTokenizer
from transformers.testing_utils import require_tokenizers
from transformers.tokenization_utils import PreTrainedTokenizer
from transformers.tokenization_utils_base import PreTrainedTokenizerBase





@require_tokenizers
class    lowercase__  (						unittest.TestCase ):





							'''simple docstring'''


							A_							:				Optional[Any]				     =  EsmTokenizer






							def UpperCAmelCase_     (   self  ):
											super().setUp()
											_SCREAMING_SNAKE_CASE			:	List[Any]							=				tempfile.mkdtemp()
											# fmt: off
											_SCREAMING_SNAKE_CASE			:	List[str]							=				["<cls>", "<pad>", "<eos>", "<unk>", "L", "A", "G", "V", "S", "E", "R", "T", "I", "D", "P", "K", "Q", "N", "F", "Y", "M", "H", "W", "C", "X", "B", "U", "Z", "O", ".", "-", "<null_1>", "<mask>"]  # noqa: E501
											# fmt: on
											_SCREAMING_SNAKE_CASE			:	Any							=				os.path.join(self.tmpdirname   , VOCAB_FILES_NAMES["""vocab_file"""]  )
											with open(self.vocab_file   , """w"""   , encoding="""utf-8"""  ) as vocab_writer:
															vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]  )  )






							def UpperCAmelCase_     (   self   , **__snake_case  ):
											return [self.get_tokenizer(**__snake_case  )]






							def UpperCAmelCase_     (   self   , **__snake_case  ):
											return self.tokenizer_class.from_pretrained(self.tmpdirname   , **__snake_case  )






							def UpperCAmelCase_     (   self  ):
											_SCREAMING_SNAKE_CASE			:	List[str]							=				self.tokenizer_class(self.vocab_file  )

											_SCREAMING_SNAKE_CASE			:	int							=				tokenizer.tokenize("""LAGVS"""  )
											self.assertListEqual(__snake_case   , ["""L""", """A""", """G""", """V""", """S"""]  )
											self.assertListEqual(tokenizer.convert_tokens_to_ids(__snake_case  )   , [4, 5, 6, 7, 8]  )






							def UpperCAmelCase_     (   self  ):
											_SCREAMING_SNAKE_CASE			:	int							=				self.tokenizer_class(self.vocab_file  )

											_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				"""LAGVS"""
											self.assertListEqual(tokenizer.encode(__snake_case  )   , [0, 4, 5, 6, 7, 8, 2]  )






							def UpperCAmelCase_     (   self  ):
											_SCREAMING_SNAKE_CASE			:	Any							=				self.tokenizer_class(self.vocab_file  )

											_SCREAMING_SNAKE_CASE			:	List[Any]							=				["""LAGVS""", """WCB"""]
											_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				tokenizer(__snake_case   , padding=__snake_case  )["""input_ids"""]

											self.assertListEqual(__snake_case   , [[0, 4, 5, 6, 7, 8, 2], [0, 22, 23, 25, 2]]  )






							def UpperCAmelCase_     (   self  ):
											_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				self.tokenizer_class(self.vocab_file  )

											_SCREAMING_SNAKE_CASE			:	str							=				["""LAGVS""", """WCB"""]
											_SCREAMING_SNAKE_CASE			:	Optional[int]							=				tokenizer(__snake_case   , padding=__snake_case  )["""input_ids"""]

											self.assertListEqual(__snake_case   , [[0, 4, 5, 6, 7, 8, 2], [0, 22, 23, 25, 2, 1, 1]]  )






							def UpperCAmelCase_     (   self  ):
											_SCREAMING_SNAKE_CASE			:	str							=				self.get_tokenizers(fast=__snake_case  )
											for tokenizer in tokenizers:
															with self.subTest(f"""{tokenizer.__class__.__name__}"""  ):
																			_SCREAMING_SNAKE_CASE			:	Optional[int]							=				"""<unk>"""
																			_SCREAMING_SNAKE_CASE			:	Optional[int]							=				"""<mask>"""

																			_SCREAMING_SNAKE_CASE			:	Union[str, Any]							=				tokenizer.tokenize(__snake_case  )
																			_SCREAMING_SNAKE_CASE			:	Optional[Any]							=				tokenizer.tokenize(__snake_case  )

																			self.assertEqual(len(__snake_case  )   , 1  )
																			self.assertEqual(len(__snake_case  )   , 1  )
																			self.assertEqual(token_a[0]   , __snake_case  )
																			self.assertEqual(token_a[0]   , __snake_case  )




