


"""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


_lowerCamelCase     =  logging.get_logger(__name__)


class 			lowerCamelCase_		(			lowercase   ):





				"""simple docstring"""







				_lowerCAmelCase      :      Optional[Any]      							=					["pixel_values"]



				def __init__(						self       ,		UpperCAmelCase__ = True       ,		UpperCAmelCase__ = 1 / 255       ,		UpperCAmelCase__ = True       ,		UpperCAmelCase__ = 8       ,		**UpperCAmelCase__       ,		):
								super().__init__(**UpperCAmelCase__     )

								SCREAMING_SNAKE_CASE__     							=				do_rescale
								SCREAMING_SNAKE_CASE__     							=				rescale_factor
								SCREAMING_SNAKE_CASE__     							=				do_pad
								SCREAMING_SNAKE_CASE__     							=				pad_size



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__ = None       ,		**UpperCAmelCase__     ):
								return rescale(UpperCAmelCase__       ,		scale=UpperCAmelCase__       ,		data_format=UpperCAmelCase__       ,		**UpperCAmelCase__     )



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__ = None     ):
								SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     							=				get_image_size(UpperCAmelCase__     )
								SCREAMING_SNAKE_CASE__     							=				(old_height // size + 1) * size - old_height
								SCREAMING_SNAKE_CASE__     							=				(old_width // size + 1) * size - old_width

								return pad(UpperCAmelCase__       ,		((0, pad_height), (0, pad_width))       ,		mode="symmetric"       ,		data_format=UpperCAmelCase__     )






				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__ = None       ,		UpperCAmelCase__ = None       ,		UpperCAmelCase__ = None       ,		UpperCAmelCase__ = None       ,		UpperCAmelCase__ = None       ,		UpperCAmelCase__ = ChannelDimension.FIRST       ,		**UpperCAmelCase__       ,		):
								SCREAMING_SNAKE_CASE__     							=				do_rescale if do_rescale is not None else self.do_rescale
								SCREAMING_SNAKE_CASE__     							=				rescale_factor if rescale_factor is not None else self.rescale_factor
								SCREAMING_SNAKE_CASE__     							=				do_pad if do_pad is not None else self.do_pad
								SCREAMING_SNAKE_CASE__     							=				pad_size if pad_size is not None else self.pad_size

								SCREAMING_SNAKE_CASE__     							=				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_rescale and rescale_factor is None:
												raise ValueError("Rescale factor must be specified if do_rescale is True."     )

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

								if do_rescale:
												SCREAMING_SNAKE_CASE__     							=				[self.rescale(image=UpperCAmelCase__       ,		scale=UpperCAmelCase__     ) for image in images]

								if do_pad:
												SCREAMING_SNAKE_CASE__     							=				[self.pad(UpperCAmelCase__       ,		size=UpperCAmelCase__     ) for image in images]

								SCREAMING_SNAKE_CASE__     							=				[to_channel_dimension_format(UpperCAmelCase__       ,		UpperCAmelCase__     ) for image in images]

								SCREAMING_SNAKE_CASE__     							=				{"pixel_values": images}
								return BatchFeature(data=UpperCAmelCase__       ,		tensor_type=UpperCAmelCase__     )




"""simple docstring"""






# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from argparse import ArgumentParser

from accelerate.commands.config import get_config_parser
from accelerate.commands.env import env_command_parser
from accelerate.commands.launch import launch_command_parser
from accelerate.commands.test import test_command_parser
from accelerate.commands.tpu import tpu_command_parser


def __lowercase     (      ):
				SCREAMING_SNAKE_CASE__     							=				ArgumentParser("Accelerate CLI tool"						,					usage="accelerate <command> [<args>]"						,					allow_abbrev=lowerCamelCase_  )
				SCREAMING_SNAKE_CASE__     							=				parser.add_subparsers(help="accelerate command helpers"  )

				# Register commands
				get_config_parser(subparsers=lowerCamelCase_  )
				env_command_parser(subparsers=lowerCamelCase_  )
				launch_command_parser(subparsers=lowerCamelCase_  )
				tpu_command_parser(subparsers=lowerCamelCase_  )
				test_command_parser(subparsers=lowerCamelCase_  )

				# Let's go
				SCREAMING_SNAKE_CASE__     							=				parser.parse_args()

				if not hasattr(lowerCamelCase_						,					"func"  ):
								parser.print_help()
								exit(1  )

				# Run
				args.func(lowerCamelCase_  )


if __name__ == "__main__":
	main()
