





'''simple docstring'''




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


__UpperCAmelCase         =   logging.get_logger(__name__)

__UpperCAmelCase         =   {
    """ZinengTang/tvlt-base""": """https://huggingface.co/ZinengTang/tvlt-base/blob/main/config.json""",
}




class       UpperCamelCase__			(			lowercase_	):




					"""simple docstring"""
					SCREAMING_SNAKE_CASE__						=					'''tvlt'''


					def __init__(		self							:	Optional[Any]     ,		lowerCamelCase_							:	Tuple=2_24     ,		lowerCamelCase_							:	List[str]=20_48     ,		lowerCamelCase_							:	str=1_28     ,		lowerCamelCase_							:	Dict=[16, 16]     ,		lowerCamelCase_							:	Tuple=[16, 16]     ,		lowerCamelCase_							:	Tuple=3     ,		lowerCamelCase_							:	Optional[Any]=1     ,		lowerCamelCase_							:	Union[str, Any]=8     ,		lowerCamelCase_							:	Any=7_68     ,		lowerCamelCase_							:	str=12     ,		lowerCamelCase_							:	List[str]=12     ,		lowerCamelCase_							:	int=30_72     ,		lowerCamelCase_							:	List[Any]="gelu"     ,		lowerCamelCase_							:	str=0.0     ,		lowerCamelCase_							:	Any=0.0     ,		lowerCamelCase_							:	Optional[int]=0.02     ,		lowerCamelCase_							:	List[Any]=1e-6     ,		lowerCamelCase_							:	Tuple=True     ,		lowerCamelCase_							:	Optional[Any]=False     ,		lowerCamelCase_							:	Optional[Any]=16     ,		lowerCamelCase_							:	Optional[int]=5_12     ,		lowerCamelCase_							:	Tuple=8     ,		lowerCamelCase_							:	Optional[Any]=20_48     ,		lowerCamelCase_							:	Any=0.75     ,		lowerCamelCase_							:	List[str]=0.15     ,		lowerCamelCase_							:	Union[str, Any]="frame-level"     ,		lowerCamelCase_							:	Any=True     ,		lowerCamelCase_							:	List[str]=True     ,		lowerCamelCase_							:	Any="classification"     ,		**lowerCamelCase_							:	List[str]     ,		):

							'''simple docstring'''



							super().__init__(**lowerCamelCase_		)

							if audio_mask_type not in ("frame-level", "patch_level"):
									raise ValueError(
									    """audio_mask_type must be one of two acceptable strategies - {'frame_level', 'patch-level') """
									    f'''got {audio_mask_type}'''		)

							SCREAMING_SNAKE_CASE				:							Any						=	image_size
							SCREAMING_SNAKE_CASE				:							Tuple						=	spectrogram_length
							SCREAMING_SNAKE_CASE				:							Dict						=	frequency_length
							SCREAMING_SNAKE_CASE				:							Tuple						=	image_patch_size
							SCREAMING_SNAKE_CASE				:							Tuple						=	audio_patch_size
							SCREAMING_SNAKE_CASE				:							str						=	num_image_channels
							SCREAMING_SNAKE_CASE				:							Any						=	num_audio_channels
							SCREAMING_SNAKE_CASE				:							Optional[Any]						=	num_frames

							SCREAMING_SNAKE_CASE				:							Optional[Any]						=	hidden_size
							SCREAMING_SNAKE_CASE				:							Optional[int]						=	num_hidden_layers
							SCREAMING_SNAKE_CASE				:							int						=	num_attention_heads
							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	intermediate_size
							SCREAMING_SNAKE_CASE				:							str						=	hidden_act
							SCREAMING_SNAKE_CASE				:							str						=	hidden_dropout_prob
							SCREAMING_SNAKE_CASE				:							Dict						=	attention_probs_dropout_prob
							SCREAMING_SNAKE_CASE				:							Dict						=	initializer_range
							SCREAMING_SNAKE_CASE				:							Any						=	layer_norm_eps
							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	qkv_bias
							SCREAMING_SNAKE_CASE				:							int						=	use_mean_pooling

							SCREAMING_SNAKE_CASE				:							List[Any]						=	decoder_num_attention_heads
							SCREAMING_SNAKE_CASE				:							Optional[Any]						=	decoder_hidden_size
							SCREAMING_SNAKE_CASE				:							Optional[Any]						=	decoder_num_hidden_layers
							SCREAMING_SNAKE_CASE				:							Optional[int]						=	decoder_intermediate_size
							SCREAMING_SNAKE_CASE				:							Tuple						=	pixel_mask_ratio
							SCREAMING_SNAKE_CASE				:							Dict						=	audio_mask_ratio
							SCREAMING_SNAKE_CASE				:							Any						=	audio_mask_type

							SCREAMING_SNAKE_CASE				:							int						=	task_matching
							SCREAMING_SNAKE_CASE				:							Dict						=	task_mae
							SCREAMING_SNAKE_CASE				:							Tuple						=	loss_type










'''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
							)



