
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      __UpperCAmelCase    (  snake_case_							:		Optional[Any]				,					snake_case_							:		Optional[int]				,					snake_case_							:		List[Any]				):



	'''simple docstring'''



	UpperCAmelCase:					List[str] 						=     {"small": (5_1_2, 8, 2_4), "medium": (1_0_2_4, 8, 3_6), "large": (1_5_3_6, 1_6, 4_8)}
	UpperCAmelCase						,   UpperCAmelCase						,   UpperCAmelCase:					str 						=     MODELS[model_size]  # set model hyperparameters
	UpperCAmelCase:					str 						=     ImageGPTConfig(n_embd=snake_case_				,					n_layer=snake_case_				,					n_head=snake_case_				)
	UpperCAmelCase:					Union[str, Any] 						=     ImageGPTForCausalLM(snake_case_				)

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

	# Save pytorch-model
	UpperCAmelCase:					int 						=     pytorch_dump_folder_path + "/" + WEIGHTS_NAME
	UpperCAmelCase:					List[str] 						=     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__":
	snake_case_      :					Union[str, Any]				      =     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.'
	)
	snake_case_      :					str				      =     parser.parse_args()
	convert_imagegpt_checkpoint_to_pytorch(
	    args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
	)


import torch
from torch import autograd





class 		__lowerCamelCase     (   autograd.Function		):



							@staticmethod
							def     A__ (     __snake_case	,				__snake_case	,				__snake_case	,				__snake_case							)						->     Dict:






								"""simple docstring"""



								UpperCAmelCase:					Optional[Any] 						=     inputs.numel()
								UpperCAmelCase:					Tuple 						=     int(0.0_05 * nb_elems							) + 1
								if sigmoid:
									UpperCAmelCase:					int 						=     (torch.sigmoid(__snake_case							) > threshold).type(inputs.type()							)
								else:
									UpperCAmelCase:					List[Any] 						=     (inputs > threshold).type(inputs.type()							)
								if mask.sum() < nb_min:
									# We limit the pruning so that at least 0.5% (half a percent) of the weights are remaining
									UpperCAmelCase:					List[Any] 						=     inputs.flatten().kthvalue(max(nb_elems - nb_min	,				1							)							).values
									UpperCAmelCase:					Optional[int] 						=     (inputs > k_threshold).type(inputs.type()							)
								return mask




							@staticmethod
							def     A__ (     __snake_case	,				__snake_case							)						->     Any:






								"""simple docstring"""



								return gradOutput, None, None





class 		__lowerCamelCase     (   autograd.Function		):



							@staticmethod
							def     A__ (     __snake_case	,				__snake_case	,				__snake_case							)						->     Any:






								"""simple docstring"""



								UpperCAmelCase:					Tuple 						=     inputs.clone()
								UpperCAmelCase						,   UpperCAmelCase:					List[Any] 						=     inputs.flatten().sort(descending=__snake_case							)
								UpperCAmelCase:					Optional[Any] 						=     int(threshold * inputs.numel()							)

								# flat_out and mask access the same memory.
								UpperCAmelCase:					Optional[int] 						=     mask.flatten()
								UpperCAmelCase:					Union[str, Any] 						=     0
								UpperCAmelCase:					int 						=     1
								return mask




							@staticmethod
							def     A__ (     __snake_case	,				__snake_case							)						->     Optional[Any]:






								"""simple docstring"""



								return gradOutput, None





class 		__lowerCamelCase     (   lowercase		):



							@staticmethod
							def     A__ (     __snake_case	,				__snake_case							)						->     str:






								"""simple docstring"""



								UpperCAmelCase:					Union[str, Any] 						=     inputs.clone()
								UpperCAmelCase						,   UpperCAmelCase:					Any 						=     inputs.abs().flatten().sort(descending=__snake_case							)
								UpperCAmelCase:					str 						=     int(threshold * inputs.numel()							)

								# flat_out and mask access the same memory.
								UpperCAmelCase:					Optional[int] 						=     mask.flatten()
								UpperCAmelCase:					Union[str, Any] 						=     0
								UpperCAmelCase:					List[str] 						=     1
								return mask
