


import torch
from torch import autograd




class      SCREAMING_SNAKE_CASE__  (			autograd.Function      ):





	'''simple docstring'''



	@staticmethod
	def 				A							(						lowercase			:					Optional[int]    ,						lowercase			:					str    ,						lowercase			:					List[str]    ,						lowercase			:					Optional[Any]			):


				'''simple docstring'''


				_snake_case						=    inputs.numel()
				_snake_case						=    int(0.005 * nb_elems			) + 1
				if sigmoid:
							_snake_case						=    (torch.sigmoid(lowercase			) > threshold).type(inputs.type()			)
				else:
							_snake_case						=    (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
							_snake_case						=    inputs.flatten().kthvalue(max(nb_elems - nb_min    ,						1			)			).values
							_snake_case						=    (inputs > k_threshold).type(inputs.type()			)
				return mask





	@staticmethod
	def 				A							(						lowercase			:					int    ,						lowercase			:					List[Any]			):


				'''simple docstring'''


				return gradOutput, None, None




class      SCREAMING_SNAKE_CASE__  (			autograd.Function      ):





	'''simple docstring'''



	@staticmethod
	def 				A							(						lowercase			:					Union[str, Any]    ,						lowercase			:					Union[str, Any]    ,						lowercase			:					Any			):


				'''simple docstring'''


				_snake_case						=    inputs.clone()
				_snake_case						=    inputs.flatten().sort(descending=lowercase			)
				_snake_case						=    int(threshold * inputs.numel()			)

				# flat_out and mask access the same memory.
				_snake_case						=    mask.flatten()
				_snake_case						=    0
				_snake_case						=    1
				return mask





	@staticmethod
	def 				A							(						lowercase			:					Dict    ,						lowercase			:					Dict			):


				'''simple docstring'''


				return gradOutput, None




class      SCREAMING_SNAKE_CASE__  (			UpperCAmelCase      ):





	'''simple docstring'''



	@staticmethod
	def 				A							(						lowercase			:					Optional[Any]    ,						lowercase			:					Tuple			):


				'''simple docstring'''


				_snake_case						=    inputs.clone()
				_snake_case						=    inputs.abs().flatten().sort(descending=lowercase			)
				_snake_case						=    int(threshold * inputs.numel()			)

				# flat_out and mask access the same memory.
				_snake_case						=    mask.flatten()
				_snake_case						=    0
				_snake_case						=    1
				return mask

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  a_	( __lowercase     :			Optional[Any] ,      __lowercase     :			Union[str, Any] ,      __lowercase     :			str				)				->						Any:
			# Construct configuration depending on size
			_snake_case						=    {'small': (512, 8, 24), 'medium': (1_024, 8, 36), 'large': (1_536, 16, 48)}
			_snake_case			,      _snake_case			,      _snake_case						=    MODELS[model_size]  # set model hyperparameters
			_snake_case						=    ImageGPTConfig(n_embd=__lowercase ,      n_layer=__lowercase ,      n_head=__lowercase				)
			_snake_case						=    ImageGPTForCausalLM(__lowercase				)

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

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


if __name__ == "__main__":
			_lowerCamelCase :       List[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.'''
			)
			_lowerCamelCase :       Optional[Any]     					=    parser.parse_args()
			convert_imagegpt_checkpoint_to_pytorch(
			    args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
			)