



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


UpperCAmelCase__     = logging.get_logger(__name__)







class     __lowerCAmelCase					(    __lowercase		):
			UpperCamelCase           =  ['''pixel_values''']



			def __init__(		self     :						Optional[int]				,	A     :						bool = True				,	A     :						int = 32				,	A     :						Dict=PILImageResampling.BILINEAR				,	A     :						bool = True				,	**A     :						Union[str, Any]				,	)     ->					None:



						"""simple docstring"""






						_UpperCAmelCase	 =     do_resize
						_UpperCAmelCase	 =     do_rescale
						_UpperCAmelCase	 =     size_divisor
						_UpperCAmelCase	 =     resample
						super().__init__(**A)



			def 					_lowerCamelCase (		self     :						List[str]				,	A     :						np.ndarray				,	A     :						int				,	A     :						str				,	A     :						Optional[ChannelDimension] = None				,	**A     :						List[str])     ->					np.ndarray:



						"""simple docstring"""






						_UpperCAmelCase	 =     get_image_size(A)
						# Rounds the height and width down to the closest multiple of size_divisor
						_UpperCAmelCase	 =     height // size_divisor * size_divisor
						_UpperCAmelCase	 =     width // size_divisor * size_divisor
						_UpperCAmelCase	 =     resize(A				,	(new_h, new_w)				,	resample=A				,	data_format=A				,	**A)
						return image



			def 					_lowerCamelCase (		self     :						str				,	A     :						np.ndarray				,	A     :						float				,	A     :						Optional[ChannelDimension] = None				,	**A     :						Union[str, Any])     ->					np.ndarray:



						"""simple docstring"""






						return rescale(image=A				,	scale=A				,	data_format=A				,	**A)



			def 					_lowerCamelCase (		self     :						Union[str, Any]				,	A     :						Union["PIL.Image.Image", TensorType, List["PIL.Image.Image"], List[TensorType]]				,	A     :						Optional[bool] = None				,	A     :						Optional[int] = None				,	A     :						Tuple=None				,	A     :						Optional[bool] = None				,	A     :						Optional[Union[TensorType, str]] = None				,	A     :						ChannelDimension = ChannelDimension.FIRST				,	**A     :						Dict				,	)     ->					BatchFeature:



						"""simple docstring"""






						_UpperCAmelCase	 =     do_resize if do_resize is not None else self.do_resize
						_UpperCAmelCase	 =     do_rescale if do_rescale is not None else self.do_rescale
						_UpperCAmelCase	 =     size_divisor if size_divisor is not None else self.size_divisor
						_UpperCAmelCase	 =     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	 =     make_list_of_images(A)

						if not valid_images(A):
									raise ValueError('Invalid image(s)')

						# All transformations expect numpy arrays.
						_UpperCAmelCase	 =     [to_numpy_array(A) for img in images]

						if do_resize:
									_UpperCAmelCase	 =     [self.resize(A				,	size_divisor=A				,	resample=A) for image in images]

						if do_rescale:
									_UpperCAmelCase	 =     [self.rescale(A				,	scale=1 / 2_55) for image in images]

						_UpperCAmelCase	 =     [to_channel_dimension_format(A				,	A) for image in images]

						_UpperCAmelCase	 =     {"pixel_values": images}
						return BatchFeature(data=A				,	tensor_type=A)





import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()





def 							A			(  _UpperCAmelCase       :     Optional[Any]							,     _UpperCAmelCase       :     Union[str, Any]							,     _UpperCAmelCase       :     Optional[int]					)       ->				int:







			'''simple docstring'''
			# Initialise PyTorch model
			_UpperCAmelCase	 =     TaConfig.from_json_file(_UpperCAmelCase					)
			print(F"Building PyTorch model from configuration: {config}"					)
			_UpperCAmelCase	 =     TaForConditionalGeneration(_UpperCAmelCase					)

			# Load weights from tf checkpoint
			load_tf_weights_in_ta(_UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase					)

			# Save pytorch-model
			print(F"Save PyTorch model to {pytorch_dump_path}"					)
			model.save_pretrained(_UpperCAmelCase					)


if __name__ == "__main__":
							UpperCAmelCase__     = argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
							)
							parser.add_argument(
							    "--config_file",
							    default=None,
							    type=str,
							    required=True,
							    help=(
							        "The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture."
							    ),
							)
							parser.add_argument(
							    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
							)
							UpperCAmelCase__     = parser.parse_args()
							convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
