

"""simple docstring"""


import argparse

import torch

from transformers import OpenAIGPTConfig, OpenAIGPTModel, load_tf_weights_in_openai_gpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()


def  UpperCAmelCase__	(snake_case__							: int		,       snake_case__							: Tuple		,       snake_case__							: Any   ):
  """simple docstring"""

  if openai_config_file == "":
    _snake_case							:		Optional[int]								=  OpenAIGPTConfig()
  else:
    _snake_case							:		Union[str, Any]								=  OpenAIGPTConfig.from_json_file(snake_case__   )
  _snake_case							:		Tuple								=  OpenAIGPTModel(snake_case__   )

  # Load weights from numpy
  load_tf_weights_in_openai_gpt(snake_case__		,       snake_case__		,       snake_case__   )

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


if __name__ == "__main__":
     A_      =    argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '''--openai_checkpoint_folder_path''',
         default=None,
         type=str,
         required=True,
         help='''Path to the TensorFlow checkpoint path.''',
     )
     parser.add_argument(
         '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
     )
     parser.add_argument(
         '''--openai_config_file''',
         default='''''',
         type=str,
         help=(
             '''An optional config json file corresponding to the pre-trained OpenAI model. \n'''
             '''This specifies the model architecture.'''
         ),
     )
     A_      =    parser.parse_args()
     convert_openai_checkpoint_to_pytorch(
         args.openai_checkpoint_folder_path, args.openai_config_file, args.pytorch_dump_folder_path
     )








"""simple docstring"""


from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


A_      =    logging.get_logger(__name__)

A_      =    {
    '''microsoft/swin-tiny-patch4-window7-224''': (
        '''https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json'''
    ),
    # See all Swin models at https://huggingface.co/models?filter=swin
}






class  lowercase(					__a	,      __a			):







 '''simple docstring'''





 lowercase__			 = "swin"

 lowercase__			 = {
     "num_attention_heads": "num_heads",
     "num_hidden_layers": "num_layers",
 }


 def __init__(    self:		Any,				a_:		List[str]=224,				a_:		List[Any]=4,				a_:		List[Any]=3,				a_:		Dict=96,				a_:		List[str]=[2, 2, 6, 2],				a_:		int=[3, 6, 12, 24],				a_:		int=7,				a_:		str=4.0,				a_:		Optional[Any]=True,				a_:		Dict=0.0,				a_:		List[Any]=0.0,				a_:		List[str]=0.1,				a_:		Union[str, Any]="gelu",				a_:		Dict=False,				a_:		Union[str, Any]=0.02,				a_:		Optional[int]=1E-5,				a_:		Optional[int]=32,				a_:		Tuple=None,				a_:		Union[str, Any]=None,				**a_:		Any,				):




   '''simple docstring'''



   super().__init__(**a_		)

   _snake_case							:		Any								=  image_size
   _snake_case							:		List[Any]								=  patch_size
   _snake_case							:		Tuple								=  num_channels
   _snake_case							:		str								=  embed_dim
   _snake_case							:		Union[str, Any]								=  depths
   _snake_case							:		int								=  len(a_		)
   _snake_case							:		Union[str, Any]								=  num_heads
   _snake_case							:		List[str]								=  window_size
   _snake_case							:		str								=  mlp_ratio
   _snake_case							:		Union[str, Any]								=  qkv_bias
   _snake_case							:		Dict								=  hidden_dropout_prob
   _snake_case							:		str								=  attention_probs_dropout_prob
   _snake_case							:		Union[str, Any]								=  drop_path_rate
   _snake_case							:		Optional[int]								=  hidden_act
   _snake_case							:		str								=  use_absolute_embeddings
   _snake_case							:		Tuple								=  layer_norm_eps
   _snake_case							:		List[Any]								=  initializer_range
   _snake_case							:		Optional[Any]								=  encoder_stride
   # we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
   # this indicates the channel dimension after the last stage of the model
   _snake_case							:		Any								=  int(embed_dim * 2 ** (len(a_		) - 1)		)
   _snake_case							:		Any								=  ["""stem"""] + [f"stage{idx}" for idx in range(1,				len(a_		) + 1		)]
   _snake_case		, _snake_case							:		List[str]								=  get_aligned_output_features_output_indices(
       out_features=a_,				out_indices=a_,				stage_names=self.stage_names		)







class  lowercase(					__a			):







 '''simple docstring'''





 lowercase__			 = version.parse("1.11"			)


 @property
 def 			UpperCamelCase_       (    self:		Any		):




   '''simple docstring'''



   return OrderedDict(
       [
           ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
       ]		)


 @property
 def 			UpperCamelCase_       (    self:		List[str]		):




   '''simple docstring'''



   return 1E-4


