



"""simple docstring"""


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 (
    get_resize_output_image_size,
    normalize,
    pad,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging
from ...utils.import_utils import is_vision_available


snake_case_								=			logging.get_logger(__name__)


if is_vision_available():
				import PIL







class  A_     (	SCREAMING_SNAKE_CASE_							):



						"""simple docstring"""
						__UpperCamelCase													=				["""pixel_values"""]






						def __init__(   self					:Tuple  ,    lowercase_					:bool = True  ,    lowercase_					:Dict[str, int] = None  ,    lowercase_					:PILImageResampling = PILImageResampling.BILINEAR  ,    lowercase_					:bool = True  ,    lowercase_					:bool = False  ,    lowercase_					:bool = True  ,    lowercase_					:bool = True  ,    lowercase_					:Union[int, float] = 1 / 2_55  ,    lowercase_					:bool = True  ,    lowercase_					:Optional[Union[float, List[float]]] = None  ,    lowercase_					:Optional[Union[float, List[float]]] = None  ,    **lowercase_					:Optional[int]  ,    )							->     None:
												super().__init__(**lowercase_   )

												UpperCAmelCase			 =					size if size is not None else {'height': 25_60, 'width': 19_20}
												if isinstance(lowercase_  ,    (tuple, list)   ):
																		# The previous feature extractor size parameter was in (width, height) format
																		UpperCAmelCase			 =					size[::-1]
												UpperCAmelCase			 =					get_size_dict(lowercase_   )

												UpperCAmelCase			 =					do_resize
												UpperCAmelCase			 =					size
												UpperCAmelCase			 =					resample
												UpperCAmelCase			 =					do_thumbnail
												UpperCAmelCase			 =					do_align_long_axis
												UpperCAmelCase			 =					do_pad
												UpperCAmelCase			 =					do_rescale
												UpperCAmelCase			 =					rescale_factor
												UpperCAmelCase			 =					do_normalize
												UpperCAmelCase			 =					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
												UpperCAmelCase			 =					image_std if image_std is not None else IMAGENET_STANDARD_STD






						def       UpperCAmelCase__	(   self					:List[str]  ,    lowercase_					:np.ndarray  ,    lowercase_					:Dict[str, int]  ,    lowercase_					:Optional[Union[str, ChannelDimension]] = None   )							->     np.ndarray:
												UpperCAmelCase ,       UpperCAmelCase			 =					get_image_size(lowercase_   )
												UpperCAmelCase ,       UpperCAmelCase			 =					size['height'], size['width']

												if (output_width < output_height and input_width > input_height) or (
												    output_width > output_height and input_width < input_height
												):
																		UpperCAmelCase			 =					np.rotaa(lowercase_  ,    3   )

												if data_format is not None:
																		UpperCAmelCase			 =					to_channel_dimension_format(lowercase_  ,    lowercase_   )

												return image






						def       UpperCAmelCase__	(   self					:List[Any]  ,    lowercase_					:np.ndarray  ,    lowercase_					:Dict[str, int]  ,    lowercase_					:bool = False  ,    lowercase_					:Optional[Union[str, ChannelDimension]] = None  ,    )							->     np.ndarray:
												UpperCAmelCase ,       UpperCAmelCase			 =					size['height'], size['width']
												UpperCAmelCase ,       UpperCAmelCase			 =					get_image_size(lowercase_   )

												UpperCAmelCase			 =					output_width - input_width
												UpperCAmelCase			 =					output_height - input_height

												if random_padding:
																		UpperCAmelCase			 =					np.random.randint(low=0  ,    high=delta_height + 1   )
																		UpperCAmelCase			 =					np.random.randint(low=0  ,    high=delta_width + 1   )
												else:
																		UpperCAmelCase			 =					delta_height // 2
																		UpperCAmelCase			 =					delta_width // 2

												UpperCAmelCase			 =					delta_height - pad_top
												UpperCAmelCase			 =					delta_width - pad_left

												UpperCAmelCase			 =					((pad_top, pad_bottom), (pad_left, pad_right))
												return pad(lowercase_  ,    lowercase_  ,    data_format=lowercase_   )






						def       UpperCAmelCase__	(   self					:Union[str, Any]  ,    *lowercase_					:Dict  ,    **lowercase_					:int   )							->     int:
												logger.info('pad is deprecated and will be removed in version 4.27. Please use pad_image instead.'   )
												return self.pad_image(*lowercase_  ,    **lowercase_   )






						def       UpperCAmelCase__	(   self					:Tuple  ,    lowercase_					:np.ndarray  ,    lowercase_					:Dict[str, int]  ,    lowercase_					:PILImageResampling = PILImageResampling.BICUBIC  ,    lowercase_					:Optional[Union[str, ChannelDimension]] = None  ,    **lowercase_					:int  ,    )							->     np.ndarray:
												UpperCAmelCase ,       UpperCAmelCase			 =					get_image_size(lowercase_   )
												UpperCAmelCase ,       UpperCAmelCase			 =					size['height'], size['width']

												# We always resize to the smallest of either the input or output size.
												UpperCAmelCase			 =					min(lowercase_  ,    lowercase_   )
												UpperCAmelCase			 =					min(lowercase_  ,    lowercase_   )

												if height == input_height and width == input_width:
																		return image

												if input_height > input_width:
																		UpperCAmelCase			 =					int(input_width * height / input_height   )
												elif input_width > input_height:
																		UpperCAmelCase			 =					int(input_height * width / input_width   )

												return resize(
												    lowercase_  ,    size=(height, width)  ,    resample=lowercase_  ,    reducing_gap=2.0  ,    data_format=lowercase_  ,    **lowercase_   )






						def       UpperCAmelCase__	(   self					:Union[str, Any]  ,    lowercase_					:np.ndarray  ,    lowercase_					:Dict[str, int]  ,    lowercase_					:PILImageResampling = PILImageResampling.BICUBIC  ,    lowercase_					:Optional[Union[str, ChannelDimension]] = None  ,    **lowercase_					:int  ,    )							->     np.ndarray:
												UpperCAmelCase			 =					get_size_dict(lowercase_   )
												UpperCAmelCase			 =					min(size['height']  ,    size['width']   )
												UpperCAmelCase			 =					get_resize_output_image_size(lowercase_  ,    size=lowercase_  ,    default_to_square=lowercase_   )
												UpperCAmelCase			 =					resize(lowercase_  ,    size=lowercase_  ,    resample=lowercase_  ,    data_format=lowercase_  ,    **lowercase_   )
												return resized_image






						def       UpperCAmelCase__	(   self					:Optional[Any]  ,    lowercase_					:np.ndarray  ,    lowercase_					:Union[int, float]  ,    lowercase_					:Optional[Union[str, ChannelDimension]] = None  ,    **lowercase_					:List[Any]  ,    )							->     List[str]:
												return rescale(lowercase_  ,    scale=lowercase_  ,    data_format=lowercase_  ,    **lowercase_   )






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






						def       UpperCAmelCase__	(   self					:Any  ,    lowercase_					:ImageInput  ,    lowercase_					:bool = None  ,    lowercase_					:Dict[str, int] = None  ,    lowercase_					:PILImageResampling = None  ,    lowercase_					:bool = None  ,    lowercase_					:bool = None  ,    lowercase_					:bool = None  ,    lowercase_					:bool = False  ,    lowercase_					:bool = None  ,    lowercase_					:float = 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_					:Optional[ChannelDimension] = ChannelDimension.FIRST  ,    **lowercase_					:List[str]  ,    )							->     PIL.Image.Image:
												UpperCAmelCase			 =					do_resize if do_resize is not None else self.do_resize
												UpperCAmelCase			 =					size if size is not None else self.size
												if isinstance(lowercase_  ,    (tuple, list)   ):
																		# Previous feature extractor had size in (width, height) format
																		UpperCAmelCase			 =					size[::-1]
												UpperCAmelCase			 =					get_size_dict(lowercase_   )
												UpperCAmelCase			 =					resample if resample is not None else self.resample
												UpperCAmelCase			 =					do_thumbnail if do_thumbnail is not None else self.do_thumbnail
												UpperCAmelCase			 =					do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
												UpperCAmelCase			 =					do_pad if do_pad is not None else self.do_pad
												UpperCAmelCase			 =					do_rescale if do_rescale is not None else self.do_rescale
												UpperCAmelCase			 =					rescale_factor if rescale_factor is not None else self.rescale_factor
												UpperCAmelCase			 =					do_normalize if do_normalize is not None else self.do_normalize
												UpperCAmelCase			 =					image_mean if image_mean is not None else self.image_mean
												UpperCAmelCase			 =					image_std if image_std is not None else self.image_std

												UpperCAmelCase			 =					make_list_of_images(lowercase_   )

												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.'   )

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

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

												if do_pad and size is None:
																		raise ValueError('Size must be specified if do_pad 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.'   )

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

												if do_align_long_axis:
																		UpperCAmelCase			 =					[self.align_long_axis(lowercase_  ,    size=lowercase_   ) for image in images]

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

												if do_thumbnail:
																		UpperCAmelCase			 =					[self.thumbnail(image=lowercase_  ,    size=lowercase_   ) for image in images]

												if do_pad:
																		UpperCAmelCase			 =					[self.pad_image(image=lowercase_  ,    size=lowercase_  ,    random_padding=lowercase_   ) for image in images]

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

												if do_normalize:
																		UpperCAmelCase			 =					[self.normalize(image=lowercase_  ,    mean=lowercase_  ,    std=lowercase_   ) for image in images]

												UpperCAmelCase			 =					[to_channel_dimension_format(lowercase_  ,    lowercase_   ) for image in images]

												UpperCAmelCase			 =					{'pixel_values': images}
												return BatchFeature(data=lowercase_  ,    tensor_type=lowercase_   )













# Copyright 2022 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 pathlib import Path

from .config_args import default_config_file, load_config_from_file
from .config_utils import SubcommandHelpFormatter


UpperCAmelCase_					:		int           = '''Update an existing config file with the latest defaults while maintaining the old configuration.'''

def   SCREAMING_SNAKE_CASE_				(				__magic_name__   :    str   )						->     Dict:






	"""simple docstring"""



	UpperCamelCase	:Optional[int]		=      args.config_file
	if config_file is None and Path(__magic_name__   ).exists():
		UpperCamelCase	:int		=      default_config_file
	elif not Path(__magic_name__   ).exists():
		raise ValueError(f"""The passed config file located at {config_file} doesn't exist."""   )
	UpperCamelCase	:Dict		=      load_config_from_file(__magic_name__   )

	if config_file.endswith(""".json"""   ):
		config.to_json_file(__magic_name__   )
	else:
		config.to_yaml_file(__magic_name__   )
	return config_file

def   SCREAMING_SNAKE_CASE_				(				__magic_name__   :    int      ,     __magic_name__   :    Optional[Any]   )						->     List[str]:






	"""simple docstring"""



	UpperCamelCase	:Union[str, Any]		=      parser.add_parser("""update"""      ,     parents=__magic_name__      ,     help=__magic_name__      ,     formatter_class=__magic_name__   )
	parser.add_argument(
	    """--config_file"""      ,     default=__magic_name__      ,     help=(
	        """The path to the config file to update. 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'."""
	    )      ,     )

	parser.set_defaults(func=__magic_name__   )
	return parser

def   SCREAMING_SNAKE_CASE_				(				__magic_name__   :    Union[str, Any]   )						->     Union[str, Any]:






	"""simple docstring"""



	UpperCamelCase	:Tuple		=      update_config(__magic_name__   )
	print(f"""Sucessfully updated the configuration file at {config_file}."""   )
