


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 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   str							):
     print(F'Reading saved model from {model_file}'							)
     __UpperCamelCase       				=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        UpperCAmelCase__			:



    """simple docstring"""






    def __init__(     self   ,  A_       )    ->			str:
         __UpperCamelCase       				=src_file






    def _a   (     self       )    ->			Any:
         raise NotImplementedError






    @staticmethod
    def _a   (     A_   ,  *A_   ,  **A_       )    ->			"DPRState":
         if comp_type.startswith('c'       ):
              return DPRContextEncoderState(*A_   ,  **A_       )
         if comp_type.startswith('q'       ):
              return DPRQuestionEncoderState(*A_   ,  **A_       )
         if comp_type.startswith('r'       ):
              return DPRReaderState(*A_   ,  **A_       )
         else:
              raise ValueError('Component type must be either \'ctx_encoder\', \'question_encoder\' or \'reader\'.'       )






class        UpperCAmelCase__			(   A_						):



    """simple docstring"""






    def _a   (     self       )    ->			List[str]:
         __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				, __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(A_       ):
                   __UpperCamelCase       				=key[len(A_       ) :]
                   if not key.startswith('encode_proj.'       ):
                        __UpperCamelCase       				='bert_model.' + key
                   __UpperCamelCase       				=value
         encoder.load_state_dict(A_       )
         return model






class        UpperCAmelCase__			(   A_						):



    """simple docstring"""






    def _a   (     self       )    ->			Dict:
         __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				, __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(A_       ):
                   __UpperCamelCase       				=key[len(A_       ) :]
                   if not key.startswith('encode_proj.'       ):
                        __UpperCamelCase       				='bert_model.' + key
                   __UpperCamelCase       				=value
         encoder.load_state_dict(A_       )
         return model






class        UpperCAmelCase__			(   A_						):



    """simple docstring"""






    def _a   (     self       )    ->			Tuple:
         __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(A_       )
         return model

def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   str     ,	SCREAMING_SNAKE_CASE__     :   Path     ,	SCREAMING_SNAKE_CASE__     :   Path							):
     __UpperCamelCase       				=Path(SCREAMING_SNAKE_CASE__							)
     dest_dir.mkdir(exist_ok=SCREAMING_SNAKE_CASE__							)

     __UpperCamelCase       				=DPRState.from_type(SCREAMING_SNAKE_CASE__     ,	src_file=SCREAMING_SNAKE_CASE__							)
     __UpperCamelCase       				=dpr_state.load_dpr_model()
     model.save_pretrained(SCREAMING_SNAKE_CASE__							)
     model.from_pretrained(SCREAMING_SNAKE_CASE__							)  # 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)









import argparse
import json

from transformers import GPTNeoConfig, GPTNeoForCausalLM, load_tf_weights_in_gpt_neo
from transformers.utils import logging


logging.set_verbosity_info()

def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   List[Any]     ,	SCREAMING_SNAKE_CASE__     :   Union[str, Any]     ,	SCREAMING_SNAKE_CASE__     :   Any							):
     # Initialise PyTorch model
     __UpperCamelCase       				=json.load(open(SCREAMING_SNAKE_CASE__     ,	'r'							)							)
     __UpperCamelCase       				=GPTNeoConfig(
         hidden_size=config_json['n_embd']     ,	num_layers=config_json['n_layer']     ,	num_heads=config_json['n_head']     ,	attention_types=config_json['attention_types']     ,	max_position_embeddings=config_json['n_positions']     ,	resid_dropout=config_json['res_dropout']     ,	embed_dropout=config_json['embed_dropout']     ,	attention_dropout=config_json['attn_dropout']     ,	)
     print(F'Building PyTorch model from configuration: {config}'							)
     __UpperCamelCase       				=GPTNeoForCausalLM(SCREAMING_SNAKE_CASE__							)

     # Load weights from tf checkpoint
     load_tf_weights_in_gpt_neo(SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__							)

     # Save pytorch-model
     print(F'Save PyTorch model to {pytorch_dump_path}'							)
     model.save_pretrained(SCREAMING_SNAKE_CASE__							)


if __name__ == "__main__":
   _A									=		argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
   )
   parser.add_argument(
       '--config_file',
       default=None,
       type=str,
       required=True,
       help=(
           'The config json file corresponding to the pre-trained mesh-tf model. \n'
           'This specifies the model architecture.'
       ),
   )
   parser.add_argument(
       '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
   )
   _A									=		parser.parse_args()
   convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)





