


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



def 				SCREAMING_SNAKE_CASE__(						_UpperCamelCase					: List[Any]      )						->    List[str]:





   '''simple docstring'''







   print(F'Reading saved model from {model_file}'      )
   UpperCamelCase__         =  torch.load(A__			,						map_location=lambda _UpperCamelCase			,						_UpperCamelCase      : default_restore_location(A__			,						"cpu"      )      )
   return CheckpointState(**A__      )







class        UpperCAmelCase       :

 def __init__(       self       :		int,    a_       :		str    ):





    """simple docstring"""



    UpperCamelCase__         =  src_file

 def 						lowercase_  (       self       :		Dict    ):





    """simple docstring"""



    raise NotImplementedError

 @staticmethod
 def 						lowercase_  (       a_       :		Tuple,    *a_       :		List[Any],    **a_       :		Dict    ):





    """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        UpperCAmelCase       (				__SCREAMING_SNAKE_CASE):

 def 						lowercase_  (       self       :		Optional[int]    ):





    """simple docstring"""



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







class        UpperCAmelCase       (				__SCREAMING_SNAKE_CASE):

 def 						lowercase_  (       self       :		int    ):





    """simple docstring"""



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







class        UpperCAmelCase       (				__SCREAMING_SNAKE_CASE):

 def 						lowercase_  (       self       :		str    ):





    """simple docstring"""



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







def 				SCREAMING_SNAKE_CASE__(						_UpperCamelCase					: Optional[int]			,						_UpperCamelCase					: Optional[Any]			,						_UpperCamelCase					: List[Any]      )						->    Optional[int]:





   '''simple docstring'''







   UpperCamelCase__         =  Path(A__      )
   dest_dir.mkdir(exist_ok=A__      )

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


if __name__ == "__main__":
       __lowercase:						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.")
       __lowercase:						List[Any]  		=   parser.parse_args()

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





