


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


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






def 		a__       (							A__							):
			print(F'''Reading saved model from {model_file}'''							)
			SCREAMING_SNAKE_CASE_						:			List[str]       						= torch.load(A__,	map_location=lambda A__,	A__							: default_restore_location(A__,	'cpu'							)							)
			return CheckpointState(**A__							)







class      __lowercase  :





							"""simple docstring"""







							def __init__(	self       ,		lowerCAmelCase__    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			List[Any]       						= src_file






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										raise NotImplementedError






							@staticmethod
							def  UpperCamelCase__			(	lowerCAmelCase__       ,		*lowerCAmelCase__       ,		**lowerCAmelCase__    ):





										"""simple docstring"""




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







class      __lowercase  (__SCREAMING_SNAKE_CASE   ):





							"""simple docstring"""







							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			Optional[int]       						= DPRContextEncoder(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_						:			Dict       						= model.ctx_encoder, 'ctx_model.'
										# Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
										SCREAMING_SNAKE_CASE_						:			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(lowerCAmelCase__    ):
																SCREAMING_SNAKE_CASE_						:			Optional[int]       						= key[len(lowerCAmelCase__    ) :]
																if not key.startswith('encode_proj.'    ):
																			SCREAMING_SNAKE_CASE_						:			Tuple       						= 'bert_model.' + key
																SCREAMING_SNAKE_CASE_						:			Tuple       						= value
										encoder.load_state_dict(lowerCAmelCase__    )
										return model







class      __lowercase  (__SCREAMING_SNAKE_CASE   ):





							"""simple docstring"""







							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			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_						:			Optional[int]       						= model.question_encoder, 'question_model.'
										# Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
										SCREAMING_SNAKE_CASE_						:			List[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(lowerCAmelCase__    ):
																SCREAMING_SNAKE_CASE_						:			Optional[int]       						= key[len(lowerCAmelCase__    ) :]
																if not key.startswith('encode_proj.'    ):
																			SCREAMING_SNAKE_CASE_						:			Tuple       						= 'bert_model.' + key
																SCREAMING_SNAKE_CASE_						:			Optional[int]       						= value
										encoder.load_state_dict(lowerCAmelCase__    )
										return model







class      __lowercase  (__SCREAMING_SNAKE_CASE   ):





							"""simple docstring"""







							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			str       						= 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[str]       						= {
										    '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_						:			int       						= value
										model.span_predictor.load_state_dict(lowerCAmelCase__    )
										return model







def 		a__       (							A__,	A__,	A__							):
			SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= Path(A__							)
			dest_dir.mkdir(exist_ok=A__							)

			SCREAMING_SNAKE_CASE_						:			Any       						= DPRState.from_type(A__,	src_file=A__							)
			SCREAMING_SNAKE_CASE_						:			str       						= dpr_state.load_dpr_model()
			model.save_pretrained(A__							)
			model.from_pretrained(A__							)  # sanity check


if __name__ == "__main__":
				lowerCAmelCase__       :	Union[str, 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.')
				lowerCAmelCase__       :	List[Any]     				=parser.parse_args()

				lowerCAmelCase__       :	Any     				=Path(args.src)
				lowerCAmelCase__       :	Dict     				=F"""converted-{src_file.name}""" if args.dest is None else args.dest
				lowerCAmelCase__       :	List[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)









# as due to their complexity multi-gpu tests could impact other tests, and to aid debug we have those in a separate module.

import os
import sys

from transformers.testing_utils import TestCasePlus, execute_subprocess_async, get_gpu_count, require_torch_gpu, slow

from .utils import load_json







class      __lowercase  (__SCREAMING_SNAKE_CASE   ):





							"""simple docstring"""







							@classmethod
							def  UpperCamelCase__			(	cls    ):





										"""simple docstring"""




										return cls






							@slow
							@require_torch_gpu
							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			List[str]       						= self.get_auto_remove_tmp_dir()
										SCREAMING_SNAKE_CASE_						:			Tuple       						= F'''
            --model_name Helsinki-NLP/opus-mt-en-ro
            --save_dir {output_dir}
            --data_dir {self.test_file_dir_str}/test_data/wmt_en_ro
            --num_beams 2
            --task translation
        '''.split()

										# we want this test to run even if there is only one GPU, but if there are more we use them all
										SCREAMING_SNAKE_CASE_						:			Dict       						= get_gpu_count()
										SCREAMING_SNAKE_CASE_						:			int       						= F'''
            -m torch.distributed.launch
            --nproc_per_node={n_gpu}
            {self.test_file_dir}/run_distributed_eval.py
        '''.split()
										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= [sys.executable] + distributed_args + args
										execute_subprocess_async(lowerCAmelCase__       ,		env=self.get_env()    )

										SCREAMING_SNAKE_CASE_						:			List[str]       						= os.path.join(lowerCAmelCase__       ,		'test_bleu.json'    )
										SCREAMING_SNAKE_CASE_						:			Optional[int]       						= load_json(lowerCAmelCase__    )
										# print(metrics)
										self.assertGreaterEqual(metrics['bleu']       ,		2_5    )





