





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


def 			lowerCamelCase_		(					lowerCAmelCase:		str )->						CheckpointState:
  print(F"""Reading saved model from {model_file}""" )
  _snake_case      : int														=		torch.load(lowerCAmelCase			,							map_location=lambda lowerCAmelCase			,							lowerCAmelCase : default_restore_location(lowerCAmelCase			,							'cpu' ) )
  return CheckpointState(**lowerCAmelCase )



class 				_lowerCAmelCase			:


  '''simple docstring'''







  def __init__(					self					:    List[str]					, UpperCamelCase					:    Path							):


    '''simple docstring'''





    _snake_case      : Tuple														=		src_file







  def UpperCamelCase_       (					self					:    Any							):


    '''simple docstring'''





    raise NotImplementedError







  @staticmethod
  def UpperCamelCase_       (					UpperCamelCase					:    str					, *UpperCamelCase					:    Dict					, **UpperCamelCase					:    Optional[Any]							):


    '''simple docstring'''





    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			(	UpperCAmelCase_							):


  '''simple docstring'''







  def UpperCamelCase_       (					self					:    int							):


    '''simple docstring'''





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




class 				_lowerCAmelCase			(	UpperCAmelCase_							):


  '''simple docstring'''







  def UpperCamelCase_       (					self					:    Union[str, Any]							):


    '''simple docstring'''





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




class 				_lowerCAmelCase			(	UpperCAmelCase_							):


  '''simple docstring'''







  def UpperCamelCase_       (					self					:    int							):


    '''simple docstring'''





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




def 			lowerCamelCase_		(					lowerCAmelCase:		str			,							lowerCAmelCase:		Path			,							lowerCAmelCase:		Path )->						int:
  _snake_case      : Dict														=		Path(lowerCAmelCase )
  dest_dir.mkdir(exist_ok=lowerCAmelCase )

  _snake_case      : List[Any]														=		DPRState.from_type(lowerCAmelCase			,							src_file=lowerCAmelCase )
  _snake_case      : Dict														=		dpr_state.load_dpr_model()
  model.save_pretrained(lowerCAmelCase )
  model.from_pretrained(lowerCAmelCase )  # sanity check


if __name__ == "__main__":
      lowerCAmelCase_        =			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_        =			parser.parse_args()

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

from transformers import ImageGPTConfig, ImageGPTForCausalLM, load_tf_weights_in_imagegpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()


def 			lowerCamelCase_		(					lowerCAmelCase:		Optional[Any]			,							lowerCAmelCase:		Tuple			,							lowerCAmelCase:		List[Any] )->						Dict:
  # Construct configuration depending on size
  _snake_case      : Tuple														=		{'small': (5_12, 8, 24), 'medium': (10_24, 8, 36), 'large': (15_36, 16, 48)}
  _snake_case							,    _snake_case							,    _snake_case      : Optional[Any]														=		MODELS[model_size]  # set model hyperparameters
  _snake_case      : Union[str, Any]														=		ImageGPTConfig(n_embd=lowerCAmelCase			,							n_layer=lowerCAmelCase			,							n_head=lowerCAmelCase )
  _snake_case      : int														=		ImageGPTForCausalLM(lowerCAmelCase )

  # Load weights from numpy
  load_tf_weights_in_imagegpt(lowerCAmelCase			,							lowerCAmelCase			,							lowerCAmelCase )

  # Save pytorch-model
  _snake_case      : List[Any]														=		pytorch_dump_folder_path + '/' + WEIGHTS_NAME
  _snake_case      : Optional[Any]														=		pytorch_dump_folder_path + '/' + CONFIG_NAME
  print(F"""Save PyTorch model to {pytorch_weights_dump_path}""" )
  torch.save(model.state_dict()			,							lowerCAmelCase )
  print(F"""Save configuration file to {pytorch_config_dump_path}""" )
  with open(lowerCAmelCase			,							'w'			,							encoding='utf-8' ) as f:
    f.write(config.to_json_string() )


if __name__ == "__main__":
      lowerCAmelCase_        =			argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--imagegpt_checkpoint_path""",
          default=None,
          type=str,
          required=True,
          help="""Path to the TensorFlow checkpoint path.""",
      )
      parser.add_argument(
          """--model_size""",
          default=None,
          type=str,
          required=True,
          help="""Size of the model (can be either 'small', 'medium' or 'large').""",
      )
      parser.add_argument(
          """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
      )
      lowerCAmelCase_        =			parser.parse_args()
      convert_imagegpt_checkpoint_to_pytorch(
          args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
      )




