





'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__UpperCAmelCase         =   {
    """configuration_prophetnet""": ["""PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ProphetNetConfig"""],
    """tokenization_prophetnet""": ["""ProphetNetTokenizer"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCAmelCase         =   [
							    """PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """ProphetNetDecoder""",
							    """ProphetNetEncoder""",
							    """ProphetNetForCausalLM""",
							    """ProphetNetForConditionalGeneration""",
							    """ProphetNetModel""",
							    """ProphetNetPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_prophetnet import PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ProphetNetConfig
							from .tokenization_prophetnet import ProphetNetTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_prophetnet import (
														    PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
														    ProphetNetDecoder,
														    ProphetNetEncoder,
														    ProphetNetForCausalLM,
														    ProphetNetForConditionalGeneration,
														    ProphetNetModel,
														    ProphetNetPreTrainedModel,
														)

else:
							import sys

							__UpperCAmelCase         =   _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)










'''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
							)



