





import argparse

import torch

from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging


logging.set_verbosity_info()





def lowerCAmelCase__(__snake_case			,__snake_case			,__snake_case				)							-> List[Any]:







	'''simple docstring'''







	lowerCamelCase__														=						RemBertConfig.from_json_file(A__				)
	print('''Building PyTorch model from configuration: {}'''.format(str(A__				)				)				)
	lowerCamelCase__														=						RemBertModel(A__				)

	# Load weights from tf checkpoint
	load_tf_weights_in_rembert(A__			,A__			,A__				)

	# Save pytorch-model
	print('''Save PyTorch model to {}'''.format(A__				)				)
	torch.save(model.state_dict()			,A__				)


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(
			    "--rembert_config_file",
			    default=None,
			    type=str,
			    required=True,
			    help=(
			        "The config json file corresponding to the pre-trained RemBERT 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_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)







import copy
import os
from typing import Union

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


lowerCAmelCase__			:      Tuple								=logging.get_logger(__name__)

lowerCAmelCase__			:      Optional[int]								={
    'microsoft/git-base': 'https://huggingface.co/microsoft/git-base/resolve/main/config.json',
}







class 	__lowercase    (__SCREAMING_SNAKE_CASE  ):





							"""simple docstring"""







							_UpperCAmelCase      =						"""git_vision_model"""






							def __init__(      self			,						lowerCAmelCase__=7_6_8			,						lowerCAmelCase__=3_0_7_2			,						lowerCAmelCase__=1_2			,						lowerCAmelCase__=1_2			,						lowerCAmelCase__=3			,						lowerCAmelCase__=2_2_4			,						lowerCAmelCase__=1_6			,						lowerCAmelCase__="quick_gelu"			,						lowerCAmelCase__=1E-5			,						lowerCAmelCase__=0.0			,						lowerCAmelCase__=0.02			,						**lowerCAmelCase__			,						):





										"""simple docstring"""




										super().__init__(**lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					hidden_size
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					intermediate_size
										SCREAMING_SNAKE_CASE_		:     Any					=					num_hidden_layers
										SCREAMING_SNAKE_CASE_		:     Dict					=					num_attention_heads
										SCREAMING_SNAKE_CASE_		:     Tuple					=					num_channels
										SCREAMING_SNAKE_CASE_		:     Tuple					=					patch_size
										SCREAMING_SNAKE_CASE_		:     Any					=					image_size
										SCREAMING_SNAKE_CASE_		:     Dict					=					initializer_range
										SCREAMING_SNAKE_CASE_		:     List[str]					=					attention_dropout
										SCREAMING_SNAKE_CASE_		:     Any					=					layer_norm_eps
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					hidden_act






							@classmethod
							def 					UpperCamelCase__     (      cls			,						lowerCAmelCase__			,						**lowerCAmelCase__       ):





										"""simple docstring"""




										cls._set_token_in_kwargs(lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     List[str]					=					cls.get_config_dict(lowerCAmelCase__			,						**lowerCAmelCase__       )

										# get the vision config dict if we are loading from GITConfig
										if config_dict.get('model_type'       ) == "git":
													SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					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(lowerCAmelCase__			,						**lowerCAmelCase__       )







class 	__lowercase    (__SCREAMING_SNAKE_CASE  ):





							"""simple docstring"""







							_UpperCAmelCase      =						"""git"""






							def __init__(      self			,						lowerCAmelCase__=None			,						lowerCAmelCase__=3_0_5_2_2			,						lowerCAmelCase__=7_6_8			,						lowerCAmelCase__=6			,						lowerCAmelCase__=1_2			,						lowerCAmelCase__=3_0_7_2			,						lowerCAmelCase__="gelu"			,						lowerCAmelCase__=0.1			,						lowerCAmelCase__=0.1			,						lowerCAmelCase__=1_0_2_4			,						lowerCAmelCase__=0.02			,						lowerCAmelCase__=1E-12			,						lowerCAmelCase__=0			,						lowerCAmelCase__="absolute"			,						lowerCAmelCase__=True			,						lowerCAmelCase__=False			,						lowerCAmelCase__=1_0_1			,						lowerCAmelCase__=1_0_2			,						lowerCAmelCase__=None			,						**lowerCAmelCase__			,						):





										"""simple docstring"""




										super().__init__(bos_token_id=lowerCAmelCase__			,						eos_token_id=lowerCAmelCase__			,						pad_token_id=lowerCAmelCase__			,						**lowerCAmelCase__       )

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

										SCREAMING_SNAKE_CASE_		:     str					=					GitVisionConfig(**lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     Dict					=					vocab_size
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					hidden_size
										SCREAMING_SNAKE_CASE_		:     Tuple					=					num_hidden_layers
										SCREAMING_SNAKE_CASE_		:     Any					=					num_attention_heads
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					hidden_act
										SCREAMING_SNAKE_CASE_		:     Any					=					intermediate_size
										SCREAMING_SNAKE_CASE_		:     Tuple					=					hidden_dropout_prob
										SCREAMING_SNAKE_CASE_		:     str					=					attention_probs_dropout_prob
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					max_position_embeddings
										SCREAMING_SNAKE_CASE_		:     int					=					initializer_range
										SCREAMING_SNAKE_CASE_		:     int					=					layer_norm_eps
										SCREAMING_SNAKE_CASE_		:     List[str]					=					position_embedding_type
										SCREAMING_SNAKE_CASE_		:     List[str]					=					use_cache
										SCREAMING_SNAKE_CASE_		:     Any					=					tie_word_embeddings
										SCREAMING_SNAKE_CASE_		:     Dict					=					num_image_with_embedding

										SCREAMING_SNAKE_CASE_		:     List[str]					=					bos_token_id
										SCREAMING_SNAKE_CASE_		:     str					=					eos_token_id






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					copy.deepcopy(self.__dict__       )
										SCREAMING_SNAKE_CASE_		:     str					=					self.vision_config.to_dict()
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					self.__class__.model_type
										return output





