


from typing import List, Union

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


_lowerCamelCase :       List[str]     					=    logging.get_logger(__name__)

_lowerCamelCase :       Optional[Any]     					=    {
    '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/config.json''',
    # See all LED models at https://huggingface.co/models?filter=led
}




class      SCREAMING_SNAKE_CASE__  (			UpperCAmelCase      ):





	'''simple docstring'''



	_UpperCAmelCase :	List[str]					     =      "led"
	_UpperCAmelCase :	Optional[int]					     =      {
	    "num_attention_heads": "encoder_attention_heads",
	    "hidden_size": "d_model",
	    "attention_probs_dropout_prob": "attention_dropout",
	    "initializer_range": "init_std",
	}



	def __init__(						self			:					int    ,						lowercase			:					List[str]=50_265    ,						lowercase			:					Dict=16_384    ,						lowercase			:					Union[str, Any]=1_024    ,						lowercase			:					str=12    ,						lowercase			:					Any=4_096    ,						lowercase			:					Optional[Any]=16    ,						lowercase			:					Optional[Any]=12    ,						lowercase			:					Any=4_096    ,						lowercase			:					Dict=16    ,						lowercase			:					int=0.0    ,						lowercase			:					List[str]=0.0    ,						lowercase			:					Optional[int]=True    ,						lowercase			:					Optional[Any]=True    ,						lowercase			:					Any="gelu"    ,						lowercase			:					Any=1_024    ,						lowercase			:					Union[str, Any]=0.1    ,						lowercase			:					Optional[Any]=0.0    ,						lowercase			:					List[str]=0.0    ,						lowercase			:					int=0.02    ,						lowercase			:					List[str]=2    ,						lowercase			:					Optional[Any]=0.0    ,						lowercase			:					str=1    ,						lowercase			:					Union[str, Any]=0    ,						lowercase			:					str=2    ,						lowercase			:					Union[List[int], int] = 512    ,						**lowercase			:					Optional[Any]    ,						):


				'''simple docstring'''


				_snake_case						=    vocab_size
				_snake_case						=    max_encoder_position_embeddings
				_snake_case						=    max_decoder_position_embeddings
				_snake_case						=    d_model
				_snake_case						=    encoder_ffn_dim
				_snake_case						=    encoder_layers
				_snake_case						=    encoder_attention_heads
				_snake_case						=    decoder_ffn_dim
				_snake_case						=    decoder_layers
				_snake_case						=    decoder_attention_heads
				_snake_case						=    dropout
				_snake_case						=    attention_dropout
				_snake_case						=    activation_dropout
				_snake_case						=    activation_function
				_snake_case						=    init_std
				_snake_case						=    encoder_layerdrop
				_snake_case						=    decoder_layerdrop
				_snake_case						=    classifier_dropout
				_snake_case						=    use_cache
				_snake_case						=    encoder_layers
				_snake_case						=    attention_window

				super().__init__(
				    pad_token_id=lowercase    ,						bos_token_id=lowercase    ,						eos_token_id=lowercase    ,						is_encoder_decoder=lowercase    ,						decoder_start_token_id=lowercase    ,						**lowercase    ,						)

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
			)