





import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__A      		=     logging.get_logger(__name__)

__A      		=     {
    '''microsoft/git-base''': '''https://huggingface.co/microsoft/git-base/resolve/main/config.json''',
}



class   lowercase_   (  _snake_case					):
      UpperCamelCase_      :		Optional[int]										=    "git_vision_model"



      def __init__(      self				:    Optional[int]      , A__				:    Optional[Any]=768      , A__				:    Optional[Any]=3072      , A__				:    str=12      , A__				:    Any=12      , A__				:    Tuple=3      , A__				:    Optional[int]=224      , A__				:    Optional[int]=16      , A__				:    Optional[Any]="quick_gelu"      , A__				:    List[Any]=1e-5      , A__				:    str=0.0      , A__				:    Optional[Any]=0.02      , **A__				:    List[Any]      , )   ->					Union[str, Any]:
        super().__init__(**UpperCamelCase__		)

        _snake_case						=							hidden_size
        _snake_case						=							intermediate_size
        _snake_case						=							num_hidden_layers
        _snake_case						=							num_attention_heads
        _snake_case						=							num_channels
        _snake_case						=							patch_size
        _snake_case						=							image_size
        _snake_case						=							initializer_range
        _snake_case						=							attention_dropout
        _snake_case						=							layer_norm_eps
        _snake_case						=							hidden_act







      @classmethod
      def 						UpperCamelCase_		(      cls				:    Union[str, Any]      , A__				:    List[Any]      , **A__				:    int		)   ->					"PretrainedConfig":
        cls._set_token_in_kwargs(UpperCamelCase__		)

        _snake_case,					_snake_case						=							cls.get_config_dict(UpperCamelCase__      , **UpperCamelCase__		)

        # get the vision config dict if we are loading from GITConfig
        if config_dict.get('''model_type'''		) == "git":
          _snake_case						=							config_dict['''vision_config''']

        if "model_type" in config_dict and hasattr(cls      , '''model_type'''		) and config_dict["model_type"] != cls.model_type:
          logger.warning(
              f"""You are using a model of type {config_dict['model_type']} to instantiate a model of type """
              f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""		)

        return cls.from_dict(UpperCamelCase__      , **UpperCamelCase__		)



class   lowercase_   (  _snake_case					):
      UpperCamelCase_      :		Union[str, Any]										=    "git"



      def __init__(      self				:    Any      , A__				:    Any=None      , A__				:    Dict=30522      , A__				:    List[str]=768      , A__				:    Union[str, Any]=6      , A__				:    Dict=12      , A__				:    int=3072      , A__				:    Any="gelu"      , A__				:    List[str]=0.1      , A__				:    List[str]=0.1      , A__				:    Optional[Any]=1024      , A__				:    List[Any]=0.02      , A__				:    Optional[Any]=1e-12      , A__				:    str=0      , A__				:    Optional[Any]="absolute"      , A__				:    Dict=True      , A__				:    Tuple=False      , A__				:    Optional[int]=101      , A__				:    List[str]=102      , A__				:    List[Any]=None      , **A__				:    Dict      , )   ->					Union[str, Any]:
        super().__init__(bos_token_id=UpperCamelCase__      , eos_token_id=UpperCamelCase__      , pad_token_id=UpperCamelCase__      , **UpperCamelCase__		)

        if vision_config is None:
          _snake_case						=							{}
          logger.info('''vision_config is None. initializing the GitVisionConfig with default values.'''		)

        _snake_case						=							GitVisionConfig(**UpperCamelCase__		)
        _snake_case						=							vocab_size
        _snake_case						=							hidden_size
        _snake_case						=							num_hidden_layers
        _snake_case						=							num_attention_heads
        _snake_case						=							hidden_act
        _snake_case						=							intermediate_size
        _snake_case						=							hidden_dropout_prob
        _snake_case						=							attention_probs_dropout_prob
        _snake_case						=							max_position_embeddings
        _snake_case						=							initializer_range
        _snake_case						=							layer_norm_eps
        _snake_case						=							position_embedding_type
        _snake_case						=							use_cache
        _snake_case						=							tie_word_embeddings
        _snake_case						=							num_image_with_embedding

        _snake_case						=							bos_token_id
        _snake_case						=							eos_token_id







      def 						UpperCamelCase_		(      self				:    List[Any]		)   ->					Optional[int]:
        _snake_case						=							copy.deepcopy(self.__dict__		)
        _snake_case						=							self.vision_config.to_dict()
        _snake_case						=							self.__class__.model_type
        return output




import argparse

import torch

from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert


logging.set_verbosity_info()



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase					)							->     Optional[Any]:



  """simple docstring"""






  _snake_case						=							AlbertConfig.from_json_file(_UpperCamelCase					)
  print(F"""Building PyTorch model from configuration: {config}"""					)
  _snake_case						=							AlbertForPreTraining(_UpperCamelCase					)

  # Load weights from tf checkpoint
  load_tf_weights_in_albert(_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase					)

  # Save pytorch-model
  print(F"""Save PyTorch model to {pytorch_dump_path}"""					)
  torch.save(model.state_dict()			,	_UpperCamelCase					)


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(
     '''--albert_config_file''',
     default=None,
     type=str,
     required=True,
     help=(
         '''The config json file corresponding to the pre-trained ALBERT 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.albert_config_file, args.pytorch_dump_path)

