






"""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


__A		:       str  				=		logging.get_logger(__name__)

__A		:       Any  				=		{
    '''microsoft/beit-base-patch16-224-pt22k''': (
        '''https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json'''
    ),
    # See all BEiT models at https://huggingface.co/models?filter=beit
}





class 					_UpperCAmelCase       ( _A							):
					SCREAMING_SNAKE_CASE_		:				str							    =   "beit"




					def __init__(				self     :						Dict    ,						A     :						Union[str, Any]=81_92    ,						A     :						str=7_68    ,						A     :						Optional[int]=12    ,						A     :						Union[str, Any]=12    ,						A     :						Union[str, Any]=30_72    ,						A     :						str="gelu"    ,						A     :						Dict=0.0    ,						A     :						List[Any]=0.0    ,						A     :						Optional[int]=0.02    ,						A     :						Union[str, Any]=1e-12    ,						A     :						Any=2_24    ,						A     :						int=16    ,						A     :						Dict=3    ,						A     :						Tuple=False    ,						A     :						Optional[Any]=False    ,						A     :						List[str]=False    ,						A     :						List[Any]=False    ,						A     :						List[Any]=0.1    ,						A     :						List[Any]=0.1    ,						A     :						str=True    ,						A     :						Tuple=[3, 5, 7, 11]    ,						A     :						Dict=[1, 2, 3, 6]    ,						A     :						str=True    ,						A     :						Tuple=0.4    ,						A     :						List[Any]=2_56    ,						A     :						str=1    ,						A     :						Union[str, Any]=False    ,						A     :						List[str]=2_55    ,						**A     :						Optional[Any]    ,						)  ->  Any:
							super().__init__(**A   )

							lowercase_ : int							       =   vocab_size
							lowercase_ : Optional[Any]							       =   hidden_size
							lowercase_ : int							       =   num_hidden_layers
							lowercase_ : Optional[int]							       =   num_attention_heads
							lowercase_ : Union[str, Any]							       =   intermediate_size
							lowercase_ : Optional[int]							       =   hidden_act
							lowercase_ : Optional[int]							       =   hidden_dropout_prob
							lowercase_ : int							       =   attention_probs_dropout_prob
							lowercase_ : Any							       =   initializer_range
							lowercase_ : List[str]							       =   layer_norm_eps

							lowercase_ : str							       =   image_size
							lowercase_ : Dict							       =   patch_size
							lowercase_ : Any							       =   num_channels
							lowercase_ : Any							       =   use_mask_token
							lowercase_ : Any							       =   use_absolute_position_embeddings
							lowercase_ : str							       =   use_relative_position_bias
							lowercase_ : Dict							       =   use_shared_relative_position_bias
							lowercase_ : List[str]							       =   layer_scale_init_value
							lowercase_ : List[Any]							       =   drop_path_rate
							lowercase_ : Union[str, Any]							       =   use_mean_pooling
							# decode head attributes (semantic segmentation)
							lowercase_ : Any							       =   out_indices
							lowercase_ : Tuple							       =   pool_scales
							# auxiliary head attributes (semantic segmentation)
							lowercase_ : Optional[int]							       =   use_auxiliary_head
							lowercase_ : List[str]							       =   auxiliary_loss_weight
							lowercase_ : Optional[int]							       =   auxiliary_channels
							lowercase_ : Tuple							       =   auxiliary_num_convs
							lowercase_ : str							       =   auxiliary_concat_input
							lowercase_ : Any							       =   semantic_loss_ignore_index





class 					_UpperCAmelCase       ( _A							):
					SCREAMING_SNAKE_CASE_		:				str							    =   version.parse("1.11"							)




					@property
					def   A  (				self     :						int   )  ->  Mapping[str, Mapping[int, str]]:
							return OrderedDict(
							    [
							        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
							    ]   )






					@property
					def   A  (				self     :						Dict   )  ->  float:
							return 1e-4








"""simple docstring"""






import argparse

import torch

from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()






def      snake_case__   (		__lowerCamelCase				:    List[Any]							,		__lowerCamelCase				:    List[str]							,		__lowerCamelCase				:    List[Any]	):
			"""simple docstring"""
			# Construct model
			if gpta_config_file == "":
						lowerCamelCase__		:		Dict			=GPTaConfig()
			else:
						lowerCamelCase__		:		Tuple			=GPTaConfig.from_json_file(__lowerCamelCase	)
			lowerCamelCase__		:		Optional[Any]			=GPTaModel(__lowerCamelCase	)

			# Load weights from numpy
			load_tf_weights_in_gpta(__lowerCamelCase							,		__lowerCamelCase							,		__lowerCamelCase	)

			# Save pytorch-model
			lowerCamelCase__		:		List[str]			=pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
			lowerCamelCase__		:		int			=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__":
		_lowercase : Optional[Any]      						=						argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    "--gpt2_checkpoint_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(
		    "--gpt2_config_file",
		    default="",
		    type=str,
		    help=(
		        "An optional config json file corresponding to the pre-trained OpenAI model. \n"
		        "This specifies the model architecture."
		    ),
		)
		_lowercase : Any      						=						parser.parse_args()
		convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)




