



from typing import Dict, List, Optional, Union

import numpy as np

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


if is_vision_available():
							import PIL


UpperCAmelCase__     = logging.get_logger(__name__)





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







			'''simple docstring'''
			_UpperCAmelCase	 =     b.T
			_UpperCAmelCase	 =     np.sum(np.square(lowerCamelCase_					)							,     axis=1					)
			_UpperCAmelCase	 =     np.sum(np.square(lowerCamelCase_					)							,     axis=0					)
			_UpperCAmelCase	 =     np.matmul(lowerCamelCase_							,     lowerCamelCase_					)
			_UpperCAmelCase	 =     aa[:, None] - 2 * ab + ba[None, :]
			return d





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







			'''simple docstring'''
			_UpperCAmelCase	 =     x.reshape(-1							,     3					)
			_UpperCAmelCase	 =     squared_euclidean_distance(lowerCamelCase_							,     lowerCamelCase_					)
			return np.argmin(lowerCamelCase_							,     axis=1					)







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



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



						"""simple docstring"""






						super().__init__(**UpperCAmelCase_)
						_UpperCAmelCase	 =     size if size is not None else {'height': 2_56, 'width': 2_56}
						_UpperCAmelCase	 =     get_size_dict(UpperCAmelCase_)
						_UpperCAmelCase	 =     np.array(UpperCAmelCase_) if clusters is not None else None
						_UpperCAmelCase	 =     do_resize
						_UpperCAmelCase	 =     size
						_UpperCAmelCase	 =     resample
						_UpperCAmelCase	 =     do_normalize
						_UpperCAmelCase	 =     do_color_quantize



			def 					_lowerCamelCase (		self     :						str				,	A     :						np.ndarray				,	A     :						Dict[str, int]				,	A     :						PILImageResampling = PILImageResampling.BILINEAR				,	A     :						Optional[Union[str, ChannelDimension]] = None				,	**A     :						Tuple				,	)     ->					Optional[Any]:



						"""simple docstring"""






						_UpperCAmelCase	 =     get_size_dict(UpperCAmelCase_)
						if "height" not in size or "width" not in size:
									raise ValueError(F"Size dictionary must contain both height and width keys. Got {size.keys()}")
						return resize(
						    UpperCAmelCase_				,	size=(size['height'], size['width'])				,	resample=UpperCAmelCase_				,	data_format=UpperCAmelCase_				,	**UpperCAmelCase_)



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



						"""simple docstring"""






						_UpperCAmelCase	 =     rescale(image=UpperCAmelCase_				,	scale=1 / 1_27.5				,	data_format=UpperCAmelCase_)
						_UpperCAmelCase	 =     image - 1
						return image



			def 					_lowerCamelCase (		self     :						Dict				,	A     :						ImageInput				,	A     :						bool = None				,	A     :						Dict[str, int] = None				,	A     :						PILImageResampling = None				,	A     :						bool = None				,	A     :						Optional[bool] = None				,	A     :						Optional[Union[List[List[int]], np.ndarray]] = None				,	A     :						Optional[Union[str, TensorType]] = None				,	A     :						Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST				,	**A     :						List[str]				,	)     ->					List[str]:



						"""simple docstring"""






						_UpperCAmelCase	 =     do_resize if do_resize is not None else self.do_resize
						_UpperCAmelCase	 =     size if size is not None else self.size
						_UpperCAmelCase	 =     get_size_dict(UpperCAmelCase_)
						_UpperCAmelCase	 =     resample if resample is not None else self.resample
						_UpperCAmelCase	 =     do_normalize if do_normalize is not None else self.do_normalize
						_UpperCAmelCase	 =     do_color_quantize if do_color_quantize is not None else self.do_color_quantize
						_UpperCAmelCase	 =     clusters if clusters is not None else self.clusters
						_UpperCAmelCase	 =     np.array(UpperCAmelCase_)

						_UpperCAmelCase	 =     make_list_of_images(UpperCAmelCase_)

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

						if do_resize and size is None or resample is None:
									raise ValueError('Size and resample must be specified if do_resize is True.')

						if do_color_quantize and clusters is None:
									raise ValueError('Clusters must be specified if do_color_quantize is True.')

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

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

						if do_normalize:
									_UpperCAmelCase	 =     [self.normalize(image=UpperCAmelCase_) for image in images]

						if do_color_quantize:
									_UpperCAmelCase	 =     [to_channel_dimension_format(UpperCAmelCase_				,	ChannelDimension.LAST) for image in images]
									# color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
									_UpperCAmelCase	 =     np.array(UpperCAmelCase_)
									_UpperCAmelCase	 =     color_quantize(UpperCAmelCase_				,	UpperCAmelCase_).reshape(images.shape[:-1])

									# flatten to (batch_size, height*width)
									_UpperCAmelCase	 =     images.shape[0]
									_UpperCAmelCase	 =     images.reshape(UpperCAmelCase_				,	-1)

									# We need to convert back to a list of images to keep consistent behaviour across processors.
									_UpperCAmelCase	 =     list(UpperCAmelCase_)
						else:
									_UpperCAmelCase	 =     [to_channel_dimension_format(UpperCAmelCase_				,	UpperCAmelCase_) for image in images]

						_UpperCAmelCase	 =     {'input_ids': images}
						return BatchFeature(data=UpperCAmelCase_				,	tensor_type=UpperCAmelCase_)





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)
