






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


UpperCAmelCase_					:		Optional[int]           = logging.get_logger(__name__)


if is_vision_available():
	import PIL

class        _SCREAMING_SNAKE_CASE			(     _a ):
	snake_case__				:	int	      =    ["""pixel_values"""]





	def __init__(						self							:   List[Any]					,    __lowerCamelCase							:   bool = True					,    __lowerCamelCase							:   Dict[str, int] = None					,    __lowerCamelCase							:   PILImageResampling = PILImageResampling.BILINEAR					,    __lowerCamelCase							:   bool = True					,    __lowerCamelCase							:   bool = False					,    __lowerCamelCase							:   bool = True					,    __lowerCamelCase							:   bool = True					,    __lowerCamelCase							:   Union[int, float] = 1 / 255					,    __lowerCamelCase							:   bool = True					,    __lowerCamelCase							:   Optional[Union[float, List[float]]] = None					,    __lowerCamelCase							:   Optional[Union[float, List[float]]] = None					,    **__lowerCamelCase							:   str					,    ):
		super().__init__(**__lowerCamelCase      )

		UpperCamelCase	:List[str]		=      size if size is not None else {"""height""": 2_560, """width""": 1_920}
		if isinstance(__lowerCamelCase					,    (tuple, list)      ):
			# The previous feature extractor size parameter was in (width, height) format
			UpperCamelCase	:Tuple		=      size[::-1]
		UpperCamelCase	:Any		=      get_size_dict(__lowerCamelCase      )

		UpperCamelCase	:Union[str, Any]		=      do_resize
		UpperCamelCase	:Optional[Any]		=      size
		UpperCamelCase	:List[str]		=      resample
		UpperCamelCase	:Optional[Any]		=      do_thumbnail
		UpperCamelCase	:List[str]		=      do_align_long_axis
		UpperCamelCase	:Tuple		=      do_pad
		UpperCamelCase	:Union[str, Any]		=      do_rescale
		UpperCamelCase	:Optional[Any]		=      rescale_factor
		UpperCamelCase	:Union[str, Any]		=      do_normalize
		UpperCamelCase	:Optional[int]		=      image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
		UpperCamelCase	:List[str]		=      image_std if image_std is not None else IMAGENET_STANDARD_STD





	def 		_A					(						self							:   List[str]					,    __lowerCamelCase							:   np.ndarray					,    __lowerCamelCase							:   Dict[str, int]					,    __lowerCamelCase							:   Optional[Union[str, ChannelDimension]] = None      ):
		UpperCamelCase	, UpperCamelCase	:int		=      get_image_size(__lowerCamelCase      )
		UpperCamelCase	, UpperCamelCase	:Union[str, Any]		=      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	:Any		=      np.rotaa(__lowerCamelCase					,    3      )

		if data_format is not None:
			UpperCamelCase	:Union[str, Any]		=      to_channel_dimension_format(__lowerCamelCase					,    __lowerCamelCase      )

		return image





	def 		_A					(						self							:   Dict					,    __lowerCamelCase							:   np.ndarray					,    __lowerCamelCase							:   Dict[str, int]					,    __lowerCamelCase							:   bool = False					,    __lowerCamelCase							:   Optional[Union[str, ChannelDimension]] = None					,    ):
		UpperCamelCase	, UpperCamelCase	:Optional[Any]		=      size["""height"""], size["""width"""]
		UpperCamelCase	, UpperCamelCase	:Optional[int]		=      get_image_size(__lowerCamelCase      )

		UpperCamelCase	:int		=      output_width - input_width
		UpperCamelCase	:Any		=      output_height - input_height

		if random_padding:
			UpperCamelCase	:Any		=      np.random.randint(low=0					,    high=delta_height + 1      )
			UpperCamelCase	:int		=      np.random.randint(low=0					,    high=delta_width + 1      )
		else:
			UpperCamelCase	:Union[str, Any]		=      delta_height // 2
			UpperCamelCase	:Optional[int]		=      delta_width // 2

		UpperCamelCase	:List[str]		=      delta_height - pad_top
		UpperCamelCase	:Union[str, Any]		=      delta_width - pad_left

		UpperCamelCase	:Any		=      ((pad_top, pad_bottom), (pad_left, pad_right))
		return pad(__lowerCamelCase					,    __lowerCamelCase					,    data_format=__lowerCamelCase      )





	def 		_A					(						self							:   Optional[Any]					,    *__lowerCamelCase							:   List[str]					,    **__lowerCamelCase							:   Dict      ):
		logger.info("""pad is deprecated and will be removed in version 4.27. Please use pad_image instead."""      )
		return self.pad_image(*__lowerCamelCase					,    **__lowerCamelCase      )





	def 		_A					(						self							:   Optional[Any]					,    __lowerCamelCase							:   np.ndarray					,    __lowerCamelCase							:   Dict[str, int]					,    __lowerCamelCase							:   PILImageResampling = PILImageResampling.BICUBIC					,    __lowerCamelCase							:   Optional[Union[str, ChannelDimension]] = None					,    **__lowerCamelCase							:   int					,    ):
		UpperCamelCase	, UpperCamelCase	:Union[str, Any]		=      get_image_size(__lowerCamelCase      )
		UpperCamelCase	, UpperCamelCase	:str		=      size["""height"""], size["""width"""]

		# We always resize to the smallest of either the input or output size.
		UpperCamelCase	:Dict		=      min(__lowerCamelCase					,    __lowerCamelCase      )
		UpperCamelCase	:Optional[int]		=      min(__lowerCamelCase					,    __lowerCamelCase      )

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

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

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





	def 		_A					(						self							:   List[Any]					,    __lowerCamelCase							:   np.ndarray					,    __lowerCamelCase							:   Dict[str, int]					,    __lowerCamelCase							:   PILImageResampling = PILImageResampling.BICUBIC					,    __lowerCamelCase							:   Optional[Union[str, ChannelDimension]] = None					,    **__lowerCamelCase							:   Tuple					,    ):
		UpperCamelCase	:str		=      get_size_dict(__lowerCamelCase      )
		UpperCamelCase	:Dict		=      min(size["""height"""]					,    size["""width"""]      )
		UpperCamelCase	:Optional[Any]		=      get_resize_output_image_size(__lowerCamelCase					,    size=__lowerCamelCase					,    default_to_square=__lowerCamelCase      )
		UpperCamelCase	:Dict		=      resize(__lowerCamelCase					,    size=__lowerCamelCase					,    resample=__lowerCamelCase					,    data_format=__lowerCamelCase					,    **__lowerCamelCase      )
		return resized_image





	def 		_A					(						self							:   Optional[int]					,    __lowerCamelCase							:   np.ndarray					,    __lowerCamelCase							:   Union[int, float]					,    __lowerCamelCase							:   Optional[Union[str, ChannelDimension]] = None					,    **__lowerCamelCase							:   Any					,    ):
		return rescale(__lowerCamelCase					,    scale=__lowerCamelCase					,    data_format=__lowerCamelCase					,    **__lowerCamelCase      )





	def 		_A					(						self							:   List[str]					,    __lowerCamelCase							:   np.ndarray					,    __lowerCamelCase							:   Union[float, List[float]]					,    __lowerCamelCase							:   Union[float, List[float]]					,    __lowerCamelCase							:   Optional[Union[str, ChannelDimension]] = None					,    **__lowerCamelCase							:   int					,    ):
		return normalize(__lowerCamelCase					,    mean=__lowerCamelCase					,    std=__lowerCamelCase					,    data_format=__lowerCamelCase					,    **__lowerCamelCase      )





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

		UpperCamelCase	:List[str]		=      make_list_of_images(__lowerCamelCase      )

		if not valid_images(__lowerCamelCase      ):
			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	:Optional[Any]		=      [to_numpy_array(__lowerCamelCase      ) for image in images]

		if do_align_long_axis:
			UpperCamelCase	:Union[str, Any]		=      [self.align_long_axis(__lowerCamelCase					,    size=__lowerCamelCase      ) for image in images]

		if do_resize:
			UpperCamelCase	:str		=      [self.resize(image=__lowerCamelCase					,    size=__lowerCamelCase					,    resample=__lowerCamelCase      ) for image in images]

		if do_thumbnail:
			UpperCamelCase	:Dict		=      [self.thumbnail(image=__lowerCamelCase					,    size=__lowerCamelCase      ) for image in images]

		if do_pad:
			UpperCamelCase	:Any		=      [self.pad_image(image=__lowerCamelCase					,    size=__lowerCamelCase					,    random_padding=__lowerCamelCase      ) for image in images]

		if do_rescale:
			UpperCamelCase	:int		=      [self.rescale(image=__lowerCamelCase					,    scale=__lowerCamelCase      ) for image in images]

		if do_normalize:
			UpperCamelCase	:Any		=      [self.normalize(image=__lowerCamelCase					,    mean=__lowerCamelCase					,    std=__lowerCamelCase      ) for image in images]

		UpperCamelCase	:Tuple		=      [to_channel_dimension_format(__lowerCamelCase					,    __lowerCamelCase      ) for image in images]

		UpperCamelCase	:Optional[Any]		=      {"""pixel_values""": images}
		return BatchFeature(data=__lowerCamelCase					,    tensor_type=__lowerCamelCase      )








# 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}."""   )
