

"""simple docstring"""





from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
							import PIL

__UpperCamelCase  :      Any							      =		logging.get_logger(__name__)

def   __SCREAMING_SNAKE_CASE  (      A_ ):
	if isinstance(A_						,     (list, tuple) ) and isinstance(videos[0]						,     (list, tuple) ) and is_valid_image(videos[0][0] ):
		return videos

	elif isinstance(A_						,     (list, tuple) ) and is_valid_image(videos[0] ):
		return [videos]

	elif is_valid_image(A_ ):
		return [[videos]]

	raise ValueError(f'Could not make batched video from {videos}' )







class 				SCREAMING_SNAKE_CASE					(		a_       ):






					"""simple docstring"""



					lowercase__				 =       ["pixel_values"]







					def __init__(       self			:				Tuple   ,lowercase_			:				bool = True   ,lowercase_			:				Dict[str, int] = None   ,lowercase_			:				PILImageResampling = PILImageResampling.BILINEAR   ,lowercase_			:				bool = True   ,lowercase_			:				Dict[str, int] = None   ,lowercase_			:				bool = True   ,lowercase_			:				Union[int, float] = 1 / 2_5_5   ,lowercase_			:				bool = True   ,lowercase_			:				bool = True   ,lowercase_			:				Optional[Union[float, List[float]]] = None   ,lowercase_			:				Optional[Union[float, List[float]]] = None   ,**lowercase_			:				str   ,):
						super().__init__(**lowercase_		)
						lowerCAmelCase__	:						Union[str, Any]  				=	size if size is not None else {'''shortest_edge''': 2_5_6}
						lowerCAmelCase__	:						Any  				=	get_size_dict(lowercase_   ,default_to_square=lowercase_		)
						lowerCAmelCase__	:						Dict  				=	crop_size if crop_size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
						lowerCAmelCase__	:						List[str]  				=	get_size_dict(lowercase_   ,param_name='''crop_size'''		)

						lowerCAmelCase__	:						Union[str, Any]  				=	do_resize
						lowerCAmelCase__	:						Optional[Any]  				=	size
						lowerCAmelCase__	:						Any  				=	do_center_crop
						lowerCAmelCase__	:						int  				=	crop_size
						lowerCAmelCase__	:						List[Any]  				=	resample
						lowerCAmelCase__	:						Tuple  				=	do_rescale
						lowerCAmelCase__	:						List[Any]  				=	rescale_factor
						lowerCAmelCase__	:						str  				=	offset
						lowerCAmelCase__	:						Any  				=	do_normalize
						lowerCAmelCase__	:						Tuple  				=	image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
						lowerCAmelCase__	:						Optional[Any]  				=	image_std if image_std is not None else IMAGENET_STANDARD_STD







					def        __lowerCAmelCase (       self			:				Tuple   ,lowercase_			:				np.ndarray   ,lowercase_			:				Dict[str, int]   ,lowercase_			:				PILImageResampling = PILImageResampling.BILINEAR   ,lowercase_			:				Optional[Union[str, ChannelDimension]] = None   ,**lowercase_			:				Optional[Any]   ,):
						lowerCAmelCase__	:						Any  				=	get_size_dict(lowercase_   ,default_to_square=lowercase_		)
						if "shortest_edge" in size:
							lowerCAmelCase__	:						Any  				=	get_resize_output_image_size(lowercase_   ,size['''shortest_edge''']   ,default_to_square=lowercase_		)
						elif "height" in size and "width" in size:
							lowerCAmelCase__	:						Dict  				=	(size['''height'''], size['''width'''])
						else:
							raise ValueError(F'Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'		)
						return resize(lowercase_   ,size=lowercase_   ,resample=lowercase_   ,data_format=lowercase_   ,**lowercase_		)







					def        __lowerCAmelCase (       self			:				Optional[int]   ,lowercase_			:				np.ndarray   ,lowercase_			:				Dict[str, int]   ,lowercase_			:				Optional[Union[str, ChannelDimension]] = None   ,**lowercase_			:				Dict   ,):
						lowerCAmelCase__	:						List[Any]  				=	get_size_dict(lowercase_		)
						if "height" not in size or "width" not in size:
							raise ValueError(F'Size must have \'height\' and \'width\' as keys. Got {size.keys()}'		)
						return center_crop(lowercase_   ,size=(size['''height'''], size['''width'''])   ,data_format=lowercase_   ,**lowercase_		)







					def        __lowerCAmelCase (       self			:				int   ,lowercase_			:				np.ndarray   ,lowercase_			:				Union[int, float]   ,lowercase_			:				bool = True   ,lowercase_			:				Optional[Union[str, ChannelDimension]] = None   ,**lowercase_			:				Dict   ,):
						lowerCAmelCase__	:						int  				=	image.astype(np.floataa		)
						if offset:
							lowerCAmelCase__	:						Tuple  				=	image - (scale / 2)
						return rescale(lowercase_   ,scale=lowercase_   ,data_format=lowercase_   ,**lowercase_		)







					def        __lowerCAmelCase (       self			:				Any   ,lowercase_			:				np.ndarray   ,lowercase_			:				Union[float, List[float]]   ,lowercase_			:				Union[float, List[float]]   ,lowercase_			:				Optional[Union[str, ChannelDimension]] = None   ,**lowercase_			:				Dict   ,):
						return normalize(lowercase_   ,mean=lowercase_   ,std=lowercase_   ,data_format=lowercase_   ,**lowercase_		)







					def        __lowerCAmelCase (       self			:				Tuple   ,lowercase_			:				ImageInput   ,lowercase_			:				bool = None   ,lowercase_			:				Dict[str, int] = None   ,lowercase_			:				PILImageResampling = None   ,lowercase_			:				bool = None   ,lowercase_			:				Dict[str, int] = None   ,lowercase_			:				bool = None   ,lowercase_			:				float = None   ,lowercase_			:				bool = None   ,lowercase_			:				bool = None   ,lowercase_			:				Optional[Union[float, List[float]]] = None   ,lowercase_			:				Optional[Union[float, List[float]]] = None   ,lowercase_			:				Optional[ChannelDimension] = ChannelDimension.FIRST   ,):
						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_center_crop and crop_size is None:
							raise ValueError('''Crop size must be specified if do_center_crop is True.'''		)

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

						if do_normalize and (image_mean is None or image_std is None):
							raise ValueError('''Image mean and std must be specified if do_normalize is True.'''		)

						if offset and not do_rescale:
							raise ValueError('''For offset, do_rescale must also be set to True.'''		)

						# All transformations expect numpy arrays.
						lowerCAmelCase__	:						List[Any]  				=	to_numpy_array(lowercase_		)

						if do_resize:
							lowerCAmelCase__	:						Optional[int]  				=	self.resize(image=lowercase_   ,size=lowercase_   ,resample=lowercase_		)

						if do_center_crop:
							lowerCAmelCase__	:						Union[str, Any]  				=	self.center_crop(lowercase_   ,size=lowercase_		)

						if do_rescale:
							lowerCAmelCase__	:						Any  				=	self.rescale(image=lowercase_   ,scale=lowercase_   ,offset=lowercase_		)

						if do_normalize:
							lowerCAmelCase__	:						List[str]  				=	self.normalize(image=lowercase_   ,mean=lowercase_   ,std=lowercase_		)

						lowerCAmelCase__	:						Optional[Any]  				=	to_channel_dimension_format(lowercase_   ,lowercase_		)
						return image







					def        __lowerCAmelCase (       self			:				Tuple   ,lowercase_			:				ImageInput   ,lowercase_			:				bool = None   ,lowercase_			:				Dict[str, int] = None   ,lowercase_			:				PILImageResampling = None   ,lowercase_			:				bool = None   ,lowercase_			:				Dict[str, int] = None   ,lowercase_			:				bool = None   ,lowercase_			:				float = None   ,lowercase_			:				bool = None   ,lowercase_			:				bool = None   ,lowercase_			:				Optional[Union[float, List[float]]] = None   ,lowercase_			:				Optional[Union[float, List[float]]] = None   ,lowercase_			:				Optional[Union[str, TensorType]] = None   ,lowercase_			:				ChannelDimension = ChannelDimension.FIRST   ,**lowercase_			:				Optional[int]   ,):
						lowerCAmelCase__	:						int  				=	do_resize if do_resize is not None else self.do_resize
						lowerCAmelCase__	:						str  				=	resample if resample is not None else self.resample
						lowerCAmelCase__	:						Optional[int]  				=	do_center_crop if do_center_crop is not None else self.do_center_crop
						lowerCAmelCase__	:						Optional[Any]  				=	do_rescale if do_rescale is not None else self.do_rescale
						lowerCAmelCase__	:						List[str]  				=	rescale_factor if rescale_factor is not None else self.rescale_factor
						lowerCAmelCase__	:						str  				=	offset if offset is not None else self.offset
						lowerCAmelCase__	:						Tuple  				=	do_normalize if do_normalize is not None else self.do_normalize
						lowerCAmelCase__	:						int  				=	image_mean if image_mean is not None else self.image_mean
						lowerCAmelCase__	:						Optional[Any]  				=	image_std if image_std is not None else self.image_std

						lowerCAmelCase__	:						Optional[int]  				=	size if size is not None else self.size
						lowerCAmelCase__	:						Optional[int]  				=	get_size_dict(lowercase_   ,default_to_square=lowercase_		)
						lowerCAmelCase__	:						Any  				=	crop_size if crop_size is not None else self.crop_size
						lowerCAmelCase__	:						Dict  				=	get_size_dict(lowercase_   ,param_name='''crop_size'''		)

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

						lowerCAmelCase__	:						Tuple  				=	make_batched(lowercase_		)

						lowerCAmelCase__	:						Optional[int]  				=	[
						    [
						        self._preprocess_image(
						            image=lowercase_   ,do_resize=lowercase_   ,size=lowercase_   ,resample=lowercase_   ,do_center_crop=lowercase_   ,crop_size=lowercase_   ,do_rescale=lowercase_   ,rescale_factor=lowercase_   ,offset=lowercase_   ,do_normalize=lowercase_   ,image_mean=lowercase_   ,image_std=lowercase_   ,data_format=lowercase_   ,)
						        for img in video
						    ]
						    for video in videos
						]

						lowerCAmelCase__	:						Dict  				=	{'''pixel_values''': videos}
						return BatchFeature(data=lowercase_   ,tensor_type=lowercase_		)







"""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.

import argparse
import os

from accelerate.test_utils import execute_subprocess_async

def   __SCREAMING_SNAKE_CASE  (      A_=None ):
	if subparsers is not None:
		lowerCAmelCase__	:						Optional[Any]  				=	subparsers.add_parser('''test''' )
	else:
		lowerCAmelCase__	:						List[str]  				=	argparse.ArgumentParser('''Accelerate test command''' )

	parser.add_argument(
	    '''--config_file'''						,     default=A_						,     help=(
	        '''The path to use to store the config file. Will default to a file named default_config.yaml in the cache '''
	        '''location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '''
	        '''such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '''
	        '''with \'huggingface\'.'''
	    )						,     )

	if subparsers is not None:
		parser.set_defaults(func=A_ )
	return parser

def   __SCREAMING_SNAKE_CASE  (      A_ ):
	lowerCAmelCase__	:						Optional[int]  				=	os.path.sep.join(__file__.split(os.path.sep )[:-2] + ['''test_utils''', '''scripts''', '''test_script.py'''] )

	if args.config_file is None:
		lowerCAmelCase__	:						Optional[Any]  				=	script_name
	else:
		lowerCAmelCase__	:						Any  				=	f'--config_file={args.config_file} {script_name}'

	lowerCAmelCase__	:						List[Any]  				=	['''accelerate-launch'''] + test_args.split()
	lowerCAmelCase__	:						int  				=	execute_subprocess_async(A_						,     env=os.environ.copy() )
	if result.returncode == 0:
		print('''Test is a success! You are ready for your distributed training!''' )

def   __SCREAMING_SNAKE_CASE  (      ):
	lowerCAmelCase__	:						Any  				=	test_command_parser()
	lowerCAmelCase__	:						List[Any]  				=	parser.parse_args()
	test_command(A_ )


if __name__ == "__main__":
							main()




