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


lowercase						:      Optional[Any]									= logging.get_logger(__name__)

lowercase						:      Optional[int]									= {
    'microsoft/swinv2-tiny-patch4-window8-256': (
        'https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json'
    ),
}




class 				lowerCamelCase__							(       __lowercase):




	'''simple docstring'''




	_A			=  'swinv2'

	_A			=  {
	    'num_attention_heads': 'num_heads',
	    'num_hidden_layers': 'num_layers',
	}







	def __init__(      self	:Union[str, Any]  ,     a	:Dict=2_2_4  ,     a	:Dict=4  ,     a	:List[Any]=3  ,     a	:Optional[Any]=9_6  ,     a	:int=[2, 2, 6, 2]  ,     a	:int=[3, 6, 1_2, 2_4]  ,     a	:Tuple=7  ,     a	:List[Any]=4.0  ,     a	:Union[str, Any]=True  ,     a	:Optional[Any]=0.0  ,     a	:int=0.0  ,     a	:str=0.1  ,     a	:str="gelu"  ,     a	:List[str]=False  ,     a	:Dict=0.02  ,     a	:Dict=1E-5  ,     a	:Optional[int]=3_2  ,     **a	:Optional[Any]  ,     )	->				Optional[Any]:
								super().__init__(**a		)

								__UpperCamelCase			:     List[str]         =      image_size
								__UpperCamelCase			:     Dict         =      patch_size
								__UpperCamelCase			:     List[str]         =      num_channels
								__UpperCamelCase			:     Any         =      embed_dim
								__UpperCamelCase			:     Dict         =      depths
								__UpperCamelCase			:     Dict         =      len(a		)
								__UpperCamelCase			:     Dict         =      num_heads
								__UpperCamelCase			:     Tuple         =      window_size
								__UpperCamelCase			:     int         =      mlp_ratio
								__UpperCamelCase			:     Optional[Any]         =      qkv_bias
								__UpperCamelCase			:     Optional[Any]         =      hidden_dropout_prob
								__UpperCamelCase			:     str         =      attention_probs_dropout_prob
								__UpperCamelCase			:     List[Any]         =      drop_path_rate
								__UpperCamelCase			:     Optional[int]         =      hidden_act
								__UpperCamelCase			:     List[Any]         =      use_absolute_embeddings
								__UpperCamelCase			:     List[str]         =      layer_norm_eps
								__UpperCamelCase			:     Tuple         =      initializer_range
								__UpperCamelCase			:     Any         =      encoder_stride
								# we set the hidden_size attribute in order to make Swinv2 work with VisionEncoderDecoderModel
								# this indicates the channel dimension after the last stage of the model
								__UpperCamelCase			:     Dict         =      int(embed_dim * 2 ** (len(a		) - 1)		)
								__UpperCamelCase			:     Tuple         =      (0, 0, 0, 0)

import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Dict		,						_lowerCamelCase				:    Tuple		,						_lowerCamelCase				:    Union[str, Any])      ->					List[Any]:






							'''simple docstring'''





							__UpperCamelCase			:     Tuple         =      BertConfig.from_json_file(_lowerCamelCase)
							print(F'Building PyTorch model from configuration: {config}')
							__UpperCamelCase			:     List[Any]         =      BertForPreTraining(_lowerCamelCase)

							# Load weights from tf checkpoint
							load_tf_weights_in_bert(_lowerCamelCase		,						_lowerCamelCase		,						_lowerCamelCase)

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


if __name__ == "__main__":
		lowercase						:      Any									= 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(
		    '--bert_config_file',
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        'The config json file corresponding to the pre-trained BERT 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.'
		)
		lowercase						:      Tuple									= parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)