





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


_lowerCamelCase							=     logging.get_logger(__name__)

_lowerCamelCase							=     {
    'MCG-NJU/videomae-base': 'https://huggingface.co/MCG-NJU/videomae-base/resolve/main/config.json',
}




class 					UpperCamelCase_    (       UpperCamelCase__      ):
							lowerCamelCase_          =   "videomae"


							def __init__(							self      :Dict							,			__A      :List[str]=224							,			__A      :List[Any]=16							,			__A      :int=3							,			__A      :Optional[Any]=16							,			__A      :Optional[int]=2							,			__A      :Optional[Any]=768							,			__A      :List[str]=12							,			__A      :Optional[Any]=12							,			__A      :List[str]=3072							,			__A      :Tuple="gelu"							,			__A      :Optional[int]=0.0							,			__A      :Union[str, Any]=0.0							,			__A      :int=0.0_2							,			__A      :Union[str, Any]=1E-12							,			__A      :Optional[Any]=True							,			__A      :Union[str, Any]=True							,			__A      :Optional[int]=6							,			__A      :Tuple=384							,			__A      :str=4							,			__A      :List[str]=1536							,			__A      :Dict=True							,			**__A      :int							,			)				->				Optional[int]:



									"""simple docstring"""



									super().__init__(**__A						)

									SCREAMING_SNAKE_CASE__								= image_size
									SCREAMING_SNAKE_CASE__								= patch_size
									SCREAMING_SNAKE_CASE__								= num_channels
									SCREAMING_SNAKE_CASE__								= num_frames
									SCREAMING_SNAKE_CASE__								= tubelet_size

									SCREAMING_SNAKE_CASE__								= hidden_size
									SCREAMING_SNAKE_CASE__								= num_hidden_layers
									SCREAMING_SNAKE_CASE__								= num_attention_heads
									SCREAMING_SNAKE_CASE__								= intermediate_size
									SCREAMING_SNAKE_CASE__								= hidden_act
									SCREAMING_SNAKE_CASE__								= hidden_dropout_prob
									SCREAMING_SNAKE_CASE__								= attention_probs_dropout_prob
									SCREAMING_SNAKE_CASE__								= initializer_range
									SCREAMING_SNAKE_CASE__								= layer_norm_eps
									SCREAMING_SNAKE_CASE__								= qkv_bias
									SCREAMING_SNAKE_CASE__								= use_mean_pooling

									SCREAMING_SNAKE_CASE__								= decoder_num_attention_heads
									SCREAMING_SNAKE_CASE__								= decoder_hidden_size
									SCREAMING_SNAKE_CASE__								= decoder_num_hidden_layers
									SCREAMING_SNAKE_CASE__								= decoder_intermediate_size
									SCREAMING_SNAKE_CASE__								= norm_pix_loss






import argparse

import torch

from transformers import ImageGPTConfig, ImageGPTForCausalLM, load_tf_weights_in_imagegpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()






def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							Any   ,				UpperCamelCase__:							List[Any]   ,				UpperCamelCase__:							Union[str, Any]      ):
		# Construct configuration depending on size
		SCREAMING_SNAKE_CASE__								= {"""small""": (512, 8, 24), """medium""": (1_024, 8, 36), """large""": (1_536, 16, 48)}
		SCREAMING_SNAKE_CASE__		,  SCREAMING_SNAKE_CASE__		,  SCREAMING_SNAKE_CASE__								= MODELS[model_size]  # set model hyperparameters
		SCREAMING_SNAKE_CASE__								= ImageGPTConfig(n_embd=UpperCamelCase__   ,				n_layer=UpperCamelCase__   ,				n_head=UpperCamelCase__      )
		SCREAMING_SNAKE_CASE__								= ImageGPTForCausalLM(UpperCamelCase__      )

		# Load weights from numpy
		load_tf_weights_in_imagegpt(UpperCamelCase__   ,				UpperCamelCase__   ,				UpperCamelCase__      )

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


if __name__ == "__main__":
	_lowerCamelCase							=     argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--imagegpt_checkpoint_path',
	    default=None,
	    type=str,
	    required=True,
	    help='Path to the TensorFlow checkpoint path.',
	)
	parser.add_argument(
	    '--model_size',
	    default=None,
	    type=str,
	    required=True,
	    help='Size of the model (can be either \'small\', \'medium\' or \'large\').',
	)
	parser.add_argument(
	    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
	)
	_lowerCamelCase							=     parser.parse_args()
	convert_imagegpt_checkpoint_to_pytorch(
	    args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
	)