





'''simple docstring'''




from collections import OrderedDict
from typing import Mapping

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


__UpperCAmelCase         =   logging.get_logger(__name__)

__UpperCAmelCase         =   {
    '''google/mobilebert-uncased''': '''https://huggingface.co/google/mobilebert-uncased/resolve/main/config.json'''
}




class       UpperCamelCase__			(			lowercase_	):




					"""simple docstring"""
					SCREAMING_SNAKE_CASE__						=					MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP
					SCREAMING_SNAKE_CASE__						=					'''mobilebert'''


					def __init__(		self							:	List[Any]     ,		lowerCamelCase_							:	Union[str, Any]=3_05_22     ,		lowerCamelCase_							:	int=5_12     ,		lowerCamelCase_							:	int=24     ,		lowerCamelCase_							:	Dict=4     ,		lowerCamelCase_							:	List[Any]=5_12     ,		lowerCamelCase_							:	Any="relu"     ,		lowerCamelCase_							:	Tuple=0.0     ,		lowerCamelCase_							:	int=0.1     ,		lowerCamelCase_							:	str=5_12     ,		lowerCamelCase_							:	List[str]=2     ,		lowerCamelCase_							:	str=0.02     ,		lowerCamelCase_							:	Any=1e-12     ,		lowerCamelCase_							:	str=0     ,		lowerCamelCase_							:	int=1_28     ,		lowerCamelCase_							:	List[Any]=True     ,		lowerCamelCase_							:	Dict=True     ,		lowerCamelCase_							:	Union[str, Any]=1_28     ,		lowerCamelCase_							:	Any=False     ,		lowerCamelCase_							:	Optional[int]=True     ,		lowerCamelCase_							:	Union[str, Any]=4     ,		lowerCamelCase_							:	List[Any]="no_norm"     ,		lowerCamelCase_							:	Tuple=True     ,		lowerCamelCase_							:	Optional[Any]=None     ,		**lowerCamelCase_							:	Dict     ,		):

							'''simple docstring'''



							super().__init__(pad_token_id=lowerCamelCase_     ,		**lowerCamelCase_		)

							SCREAMING_SNAKE_CASE				:							Tuple						=	vocab_size
							SCREAMING_SNAKE_CASE				:							Tuple						=	hidden_size
							SCREAMING_SNAKE_CASE				:							Dict						=	num_hidden_layers
							SCREAMING_SNAKE_CASE				:							Optional[int]						=	num_attention_heads
							SCREAMING_SNAKE_CASE				:							List[Any]						=	hidden_act
							SCREAMING_SNAKE_CASE				:							Optional[int]						=	intermediate_size
							SCREAMING_SNAKE_CASE				:							List[str]						=	hidden_dropout_prob
							SCREAMING_SNAKE_CASE				:							List[Any]						=	attention_probs_dropout_prob
							SCREAMING_SNAKE_CASE				:							Dict						=	max_position_embeddings
							SCREAMING_SNAKE_CASE				:							Dict						=	type_vocab_size
							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	initializer_range
							SCREAMING_SNAKE_CASE				:							Dict						=	layer_norm_eps
							SCREAMING_SNAKE_CASE				:							int						=	embedding_size
							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	trigram_input
							SCREAMING_SNAKE_CASE				:							int						=	use_bottleneck
							SCREAMING_SNAKE_CASE				:							List[Any]						=	intra_bottleneck_size
							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	use_bottleneck_attention
							SCREAMING_SNAKE_CASE				:							str						=	key_query_shared_bottleneck
							SCREAMING_SNAKE_CASE				:							Optional[Any]						=	num_feedforward_networks
							SCREAMING_SNAKE_CASE				:							Tuple						=	normalization_type
							SCREAMING_SNAKE_CASE				:							Optional[int]						=	classifier_activation

							if self.use_bottleneck:
									SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	intra_bottleneck_size
							else:
									SCREAMING_SNAKE_CASE				:							List[Any]						=	hidden_size

							SCREAMING_SNAKE_CASE				:							List[str]						=	classifier_dropout







class       UpperCamelCase__			(			lowercase_	):




					"""simple docstring"""


					@property
					def lowerCamelCase_	(		self							:	str		):

							'''simple docstring'''



							if self.task == "multiple-choice":
									SCREAMING_SNAKE_CASE				:							Any						=	{0: """batch""", 1: """choice""", 2: """sequence"""}
							else:
									SCREAMING_SNAKE_CASE				:							List[str]						=	{0: """batch""", 1: """sequence"""}
							return OrderedDict(
							    [
							        ("""input_ids""", dynamic_axis),
							        ("""attention_mask""", dynamic_axis),
							        ("""token_type_ids""", dynamic_axis),
							    ]		)










'''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_					):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE				:							Optional[int]						=	{"""small""": (5_12, 8, 24), """medium""": (10_24, 8, 36), """large""": (15_36, 16, 48)}
		SCREAMING_SNAKE_CASE,    SCREAMING_SNAKE_CASE,    SCREAMING_SNAKE_CASE				:							Tuple						=	MODELS[model_size]  # set model hyperparameters
		SCREAMING_SNAKE_CASE				:							Any						=	ImageGPTConfig(n_embd=lowerCamelCase_	,	n_layer=lowerCamelCase_	,	n_head=lowerCamelCase_					)
		SCREAMING_SNAKE_CASE				:							str						=	ImageGPTForCausalLM(lowerCamelCase_					)

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

		# Save pytorch-model
		SCREAMING_SNAKE_CASE				:							Any						=	pytorch_dump_folder_path + """/""" + WEIGHTS_NAME
		SCREAMING_SNAKE_CASE				:							Optional[int]						=	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__":
							__UpperCAmelCase         =   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."""
							)
							__UpperCAmelCase         =   parser.parse_args()
							convert_imagegpt_checkpoint_to_pytorch(
							    args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
							)



