



"""simple docstring"""






import argparse

import torch

from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()






def      snake_case__   (		__lowerCamelCase				:    List[Any]							,		__lowerCamelCase				:    List[str]							,		__lowerCamelCase				:    List[Any]	):
			"""simple docstring"""
			# Construct model
			if gpta_config_file == "":
						lowerCamelCase__		:		Dict			=GPTaConfig()
			else:
						lowerCamelCase__		:		Tuple			=GPTaConfig.from_json_file(__lowerCamelCase	)
			lowerCamelCase__		:		Optional[Any]			=GPTaModel(__lowerCamelCase	)

			# Load weights from numpy
			load_tf_weights_in_gpta(__lowerCamelCase							,		__lowerCamelCase							,		__lowerCamelCase	)

			# Save pytorch-model
			lowerCamelCase__		:		List[str]			=pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
			lowerCamelCase__		:		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__":
		_lowercase : Optional[Any]      						=						argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    "--gpt2_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
		)
		parser.add_argument(
		    "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
		)
		parser.add_argument(
		    "--gpt2_config_file",
		    default="",
		    type=str,
		    help=(
		        "An optional config json file corresponding to the pre-trained OpenAI model. \n"
		        "This specifies the model architecture."
		    ),
		)
		_lowercase : Any      						=						parser.parse_args()
		convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)









"""simple docstring"""






from typing import Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import get_image_size, pad, rescale, to_channel_dimension_format
from ...image_utils import ChannelDimension, ImageInput, make_list_of_images, to_numpy_array, valid_images
from ...utils import TensorType, logging


_lowercase : Tuple      						=						logging.get_logger(__name__)

class        __SCREAMING_SNAKE_CASE	(      lowerCAmelCase_   ):


					'''simple docstring'''





					_a   			=							['pixel_values']



					def __init__(       self							:  Optional[Any],  lowerCamelCase							:  bool = True,  lowerCamelCase							:  Union[int, float] = 1 / 255,  lowerCamelCase							:  bool = True,  lowerCamelCase							:  int = 8,  **lowerCamelCase							:  Tuple,  )->					None:
								super().__init__(**lowerCamelCase     )

								lowerCamelCase__		:		int			=do_rescale
								lowerCamelCase__		:		Dict			=rescale_factor
								lowerCamelCase__		:		Union[str, Any]			=do_pad
								lowerCamelCase__		:		Union[str, Any]			=pad_size



					def 		snake_case       (       self							:  int,  lowerCamelCase							:  np.ndarray,  lowerCamelCase							:  float,  lowerCamelCase							:  Optional[Union[str, ChannelDimension]] = None,  **lowerCamelCase							:  int     )->					np.ndarray:
								return rescale(lowerCamelCase,  scale=lowerCamelCase,  data_format=lowerCamelCase,  **lowerCamelCase     )



					def 		snake_case       (       self							:  Optional[Any],  lowerCamelCase							:  np.ndarray,  lowerCamelCase							:  int,  lowerCamelCase							:  Optional[Union[str, ChannelDimension]] = None     )->					List[Any]:
								lowerCamelCase__	,   lowerCamelCase__		:		Optional[int]			=get_image_size(lowerCamelCase     )
								lowerCamelCase__		:		List[str]			=(old_height // size + 1) * size - old_height
								lowerCamelCase__		:		List[str]			=(old_width // size + 1) * size - old_width

								return pad(lowerCamelCase,  ((0, pad_height), (0, pad_width)),  mode='''symmetric''',  data_format=lowerCamelCase     )




					def 		snake_case       (       self							:  List[Any],  lowerCamelCase							:  ImageInput,  lowerCamelCase							:  Optional[bool] = None,  lowerCamelCase							:  Optional[float] = None,  lowerCamelCase							:  Optional[bool] = None,  lowerCamelCase							:  Optional[int] = None,  lowerCamelCase							:  Optional[Union[str, TensorType]] = None,  lowerCamelCase							:  Union[str, ChannelDimension] = ChannelDimension.FIRST,  **lowerCamelCase							:  Union[str, Any],  )->					Dict:
								lowerCamelCase__		:		List[str]			=do_rescale if do_rescale is not None else self.do_rescale
								lowerCamelCase__		:		Tuple			=rescale_factor if rescale_factor is not None else self.rescale_factor
								lowerCamelCase__		:		str			=do_pad if do_pad is not None else self.do_pad
								lowerCamelCase__		:		int			=pad_size if pad_size is not None else self.pad_size

								lowerCamelCase__		:		Optional[int]			=make_list_of_images(lowerCamelCase     )

								if not valid_images(lowerCamelCase     ):
											raise ValueError(
											    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
											    '''torch.Tensor, tf.Tensor or jax.ndarray.'''     )

								if do_rescale and rescale_factor is None:
											raise ValueError('''Rescale factor must be specified if do_rescale is True.'''     )

								# All transformations expect numpy arrays.
								lowerCamelCase__		:		Tuple			=[to_numpy_array(lowerCamelCase     ) for image in images]

								if do_rescale:
											lowerCamelCase__		:		Tuple			=[self.rescale(image=lowerCamelCase,  scale=lowerCamelCase     ) for image in images]

								if do_pad:
											lowerCamelCase__		:		Tuple			=[self.pad(lowerCamelCase,  size=lowerCamelCase     ) for image in images]

								lowerCamelCase__		:		int			=[to_channel_dimension_format(lowerCamelCase,  lowerCamelCase     ) for image in images]

								lowerCamelCase__		:		Dict			={'''pixel_values''': images}
								return BatchFeature(data=lowerCamelCase,  tensor_type=lowerCamelCase     )




