"""simple docstring"""






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_						( lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase	):
				# Construct configuration depending on size
				UpperCAmelCase__					=							{'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__					=							MODELS[model_size]  # set model hyperparameters
				UpperCAmelCase__					=							ImageGPTConfig(n_embd=lowerCamelCase ,      n_layer=lowerCamelCase ,      n_head=lowerCamelCase	)
				UpperCAmelCase__					=							ImageGPTForCausalLM(lowerCamelCase	)

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

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




"""simple docstring"""






from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


lowerCAmelCase__  :  str											=     logging.get_logger(__name__)

lowerCAmelCase__  :  Union[str, Any]											=     {
    'uclanlp/plbart-base': 'https://huggingface.co/uclanlp/plbart-base/resolve/main/config.json',
    # See all PLBART models at https://huggingface.co/models?filter=plbart
}
class     snake_case	(			__UpperCAmelCase    ):




					"""simple docstring"""

					snake_case__       =			"plbart"
					snake_case__       =			["past_key_values"]
					snake_case__       =			{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}







					def __init__(    self       :     str   ,lowerCamelCase__       :     List[Any]=50_005   ,lowerCamelCase__       :     Optional[Any]=1_024   ,lowerCamelCase__       :     List[str]=6   ,lowerCamelCase__       :     Tuple=3_072   ,lowerCamelCase__       :     Optional[int]=12   ,lowerCamelCase__       :     Optional[int]=6   ,lowerCamelCase__       :     Optional[int]=3_072   ,lowerCamelCase__       :     Any=12   ,lowerCamelCase__       :     str=0.0   ,lowerCamelCase__       :     str=0.0   ,lowerCamelCase__       :     Union[str, Any]=True   ,lowerCamelCase__       :     str=True   ,lowerCamelCase__       :     Optional[int]="gelu"   ,lowerCamelCase__       :     Optional[Any]=768   ,lowerCamelCase__       :     int=0.1   ,lowerCamelCase__       :     int=0.1   ,lowerCamelCase__       :     Union[str, Any]=0.0   ,lowerCamelCase__       :     List[str]=0.0_2   ,lowerCamelCase__       :     Optional[int]=0.0   ,lowerCamelCase__       :     Dict=True   ,lowerCamelCase__       :     List[Any]=1   ,lowerCamelCase__       :     List[str]=0   ,lowerCamelCase__       :     Any=2   ,lowerCamelCase__       :     Any=2   ,**lowerCamelCase__       :     Dict   ,):
									UpperCAmelCase__					=							vocab_size
									UpperCAmelCase__					=							max_position_embeddings
									UpperCAmelCase__					=							d_model
									UpperCAmelCase__					=							encoder_ffn_dim
									UpperCAmelCase__					=							encoder_layers
									UpperCAmelCase__					=							encoder_attention_heads
									UpperCAmelCase__					=							decoder_ffn_dim
									UpperCAmelCase__					=							decoder_layers
									UpperCAmelCase__					=							decoder_attention_heads
									UpperCAmelCase__					=							dropout
									UpperCAmelCase__					=							attention_dropout
									UpperCAmelCase__					=							activation_dropout
									UpperCAmelCase__					=							activation_function
									UpperCAmelCase__					=							init_std
									UpperCAmelCase__					=							encoder_layerdrop
									UpperCAmelCase__					=							decoder_layerdrop
									UpperCAmelCase__					=							classifier_dropout
									UpperCAmelCase__					=							use_cache
									UpperCAmelCase__					=							encoder_layers
									UpperCAmelCase__					=							scale_embedding  # scale factor will be sqrt(d_model) if True
									super().__init__(
									    pad_token_id=lowerCamelCase__   ,bos_token_id=lowerCamelCase__   ,eos_token_id=lowerCamelCase__   ,is_encoder_decoder=lowerCamelCase__   ,forced_eos_token_id=lowerCamelCase__   ,**lowerCamelCase__   ,)






class     snake_case	(			__UpperCAmelCase    ):




					"""simple docstring"""

					@property
					def 		__lowerCAmelCase						(    self       :     int      ):
									return OrderedDict(
									    [
									        ('input_ids', {0: 'batch', 1: 'sequence'}),
									        ('attention_mask', {0: 'batch', 1: 'sequence'}),
									    ]      )







					@property
					def 		__lowerCAmelCase						(    self       :     Any      ):
									if self.use_past:
													return OrderedDict(
													    [
													        ('last_hidden_state', {0: 'batch', 1: 'sequence'}),
													        ('past_keys', {0: 'batch', 2: 'sequence'}),
													        ('encoder_last_hidden_state', {0: 'batch', 1: 'sequence'}),
													    ]      )
									else:
													return OrderedDict(
													    [
													        ('last_hidden_state', {0: 'batch', 1: 'sequence'}),
													        ('encoder_last_hidden_state', {0: 'batch', 1: 'sequence'}),
													    ]      )



