
from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowerCamelCase_  :	Union[str, Any] 				=					logging.get_logger(__name__)

class     _UpperCamelCase			(   _A					):






			'''simple docstring'''

			__UpperCamelCase :  Optional[int]    =	["""pixel_values"""]

			def __init__(    self	:    List[Any]  ,		snake_case_	:    bool = True  ,		snake_case_	:    int = 32  ,		snake_case_	:    str=PILImageResampling.BILINEAR  ,		snake_case_	:    bool = True  ,		**snake_case_	:    List[str]  ,		):
							UpperCamelCase_:							Optional[int]		=  do_resize
							UpperCamelCase_:							List[Any]		=  do_rescale
							UpperCamelCase_:							Optional[Any]		=  size_divisor
							UpperCamelCase_:							List[Any]		=  resample
							super().__init__(**snake_case_ )

			def  lowerCAmelCase__		(    self	:    List[str]  ,		snake_case_	:    np.ndarray  ,		snake_case_	:    int  ,		snake_case_	:    List[str]  ,		snake_case_	:    Optional[ChannelDimension] = None  ,		**snake_case_	:    List[str] ):
							UpperCamelCase_,				UpperCamelCase_:							Optional[Any]		=  get_image_size(snake_case_ )
							# Rounds the height and width down to the closest multiple of size_divisor
							UpperCamelCase_:							List[Any]		=  height // size_divisor * size_divisor
							UpperCamelCase_:							Union[str, Any]		=  width // size_divisor * size_divisor
							UpperCamelCase_:							Tuple		=  resize(snake_case_  ,		(new_h, new_w)  ,		resample=snake_case_  ,		data_format=snake_case_  ,		**snake_case_ )
							return image

			def  lowerCAmelCase__		(    self	:    List[Any]  ,		snake_case_	:    np.ndarray  ,		snake_case_	:    float  ,		snake_case_	:    Optional[ChannelDimension] = None  ,		**snake_case_	:    List[str] ):
							return rescale(image=snake_case_  ,		scale=snake_case_  ,		data_format=snake_case_  ,		**snake_case_ )




			def  lowerCAmelCase__		(    self	:    List[Any]  ,		snake_case_	:    Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]]  ,		snake_case_	:    Optional[bool] = None  ,		snake_case_	:    Optional[int] = None  ,		snake_case_	:    int=None  ,		snake_case_	:    Optional[bool] = None  ,		snake_case_	:    Optional[Union[TensorType, str]] = None  ,		snake_case_	:    ChannelDimension = ChannelDimension.FIRST  ,		**snake_case_	:    Union[str, Any]  ,		):
							UpperCamelCase_:							List[str]		=  do_resize if do_resize is not None else self.do_resize
							UpperCamelCase_:							int		=  do_rescale if do_rescale is not None else self.do_rescale
							UpperCamelCase_:							List[str]		=  size_divisor if size_divisor is not None else self.size_divisor
							UpperCamelCase_:							str		=  resample if resample is not None else self.resample

							if do_resize and size_divisor is None:
											raise ValueError("""size_divisor is required for resizing""" )

							UpperCamelCase_:							Any		=  make_list_of_images(snake_case_ )

							if not valid_images(snake_case_ ):
											raise ValueError("""Invalid image(s)""" )

							# All transformations expect numpy arrays.
							UpperCamelCase_:							Tuple		=  [to_numpy_array(snake_case_ ) for img in images]

							if do_resize:
											UpperCamelCase_:							str		=  [self.resize(snake_case_  ,		size_divisor=snake_case_  ,		resample=snake_case_ ) for image in images]

							if do_rescale:
											UpperCamelCase_:							Any		=  [self.rescale(snake_case_  ,		scale=1 / 255 ) for image in images]

							UpperCamelCase_:							Union[str, Any]		=  [to_channel_dimension_format(snake_case_  ,		snake_case_ ) for image in images]

							UpperCamelCase_:							List[str]		=  {"""pixel_values""": images}
							return BatchFeature(data=snake_case_  ,		tensor_type=snake_case_ )








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     A__   (  lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase  )					->		Union[str, Any]:
				# Construct model
				if gpta_config_file == "":
								UpperCamelCase_:							Union[str, Any]		=  GPTaConfig()
				else:
								UpperCamelCase_:							Optional[Any]		=  GPTaConfig.from_json_file(lowerCamelCase  )
				UpperCamelCase_:							List[Any]		=  GPTaModel(lowerCamelCase  )

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

				# Save pytorch-model
				UpperCamelCase_:							int		=  pytorch_dump_folder_path + """/""" + WEIGHTS_NAME
				UpperCamelCase_:							Any		=  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_  :	int 				=					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."""
							    ),
							)
							lowerCamelCase_  :	List[str] 				=					parser.parse_args()
							convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)






