





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


_lowerCamelCase       =	logging.get_logger(__name__)


if is_vision_available():
	import PIL




class      UpperCamelCase_				(   UpperCamelCase__			):
							lowerCamelCase_							=    ["pixel_values"]


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



									"""simple docstring"""



									super().__init__(**__A						)

									SCREAMING_SNAKE_CASE__								=					size if size is not None else {"""height""": 2560, """width""": 1920}
									if isinstance(__A							, (tuple, list)						):
											# The previous feature extractor size parameter was in (width, height) format
											SCREAMING_SNAKE_CASE__								=					size[::-1]
									SCREAMING_SNAKE_CASE__								=					get_size_dict(__A						)

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


							def    _snake_case     (							self			:Optional[int]							, __A			:np.ndarray							, __A			:Dict[str, int]							, __A			:Optional[Union[str, ChannelDimension]] = None						)      ->      np.ndarray:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__		,  SCREAMING_SNAKE_CASE__								=					get_image_size(__A						)
									SCREAMING_SNAKE_CASE__		,  SCREAMING_SNAKE_CASE__								=					size["""height"""], size["""width"""]

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

									if data_format is not None:
											SCREAMING_SNAKE_CASE__								=					to_channel_dimension_format(__A							, __A						)

									return image


							def    _snake_case     (							self			:Dict							, __A			:np.ndarray							, __A			:Dict[str, int]							, __A			:bool = False							, __A			:Optional[Union[str, ChannelDimension]] = None							, )      ->      np.ndarray:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__		,  SCREAMING_SNAKE_CASE__								=					size["""height"""], size["""width"""]
									SCREAMING_SNAKE_CASE__		,  SCREAMING_SNAKE_CASE__								=					get_image_size(__A						)

									SCREAMING_SNAKE_CASE__								=					output_width - input_width
									SCREAMING_SNAKE_CASE__								=					output_height - input_height

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

									SCREAMING_SNAKE_CASE__								=					delta_height - pad_top
									SCREAMING_SNAKE_CASE__								=					delta_width - pad_left

									SCREAMING_SNAKE_CASE__								=					((pad_top, pad_bottom), (pad_left, pad_right))
									return pad(__A							, __A							, data_format=__A						)


							def    _snake_case     (							self			:Union[str, Any]							, *__A			:Tuple							, **__A			:Dict						)      ->      Optional[int]:



									"""simple docstring"""



									logger.info("""pad is deprecated and will be removed in version 4.27. Please use pad_image instead."""						)
									return self.pad_image(*__A							, **__A						)


							def    _snake_case     (							self			:Optional[Any]							, __A			:np.ndarray							, __A			:Dict[str, int]							, __A			:PILImageResampling = PILImageResampling.BICUBIC							, __A			:Optional[Union[str, ChannelDimension]] = None							, **__A			:str							, )      ->      np.ndarray:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__		,  SCREAMING_SNAKE_CASE__								=					get_image_size(__A						)
									SCREAMING_SNAKE_CASE__		,  SCREAMING_SNAKE_CASE__								=					size["""height"""], size["""width"""]

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

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

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

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


							def    _snake_case     (							self			:str							, __A			:np.ndarray							, __A			:Dict[str, int]							, __A			:PILImageResampling = PILImageResampling.BICUBIC							, __A			:Optional[Union[str, ChannelDimension]] = None							, **__A			:List[str]							, )      ->      np.ndarray:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=					get_size_dict(__A						)
									SCREAMING_SNAKE_CASE__								=					min(size["""height"""]							, size["""width"""]						)
									SCREAMING_SNAKE_CASE__								=					get_resize_output_image_size(__A							, size=__A							, default_to_square=__A						)
									SCREAMING_SNAKE_CASE__								=					resize(__A							, size=__A							, resample=__A							, data_format=__A							, **__A						)
									return resized_image


							def    _snake_case     (							self			:Optional[Any]							, __A			:np.ndarray							, __A			:Union[int, float]							, __A			:Optional[Union[str, ChannelDimension]] = None							, **__A			:Any							, )      ->      int:



									"""simple docstring"""



									return rescale(__A							, scale=__A							, data_format=__A							, **__A						)


							def    _snake_case     (							self			:List[str]							, __A			:np.ndarray							, __A			:Union[float, List[float]]							, __A			:Union[float, List[float]]							, __A			:Optional[Union[str, ChannelDimension]] = None							, **__A			:int							, )      ->      np.ndarray:



									"""simple docstring"""



									return normalize(__A							, mean=__A							, std=__A							, data_format=__A							, **__A						)


							def    _snake_case     (							self			:Dict							, __A			:ImageInput							, __A			:bool = None							, __A			:Dict[str, int] = None							, __A			:PILImageResampling = None							, __A			:bool = None							, __A			:bool = None							, __A			:bool = None							, __A			:bool = False							, __A			:bool = None							, __A			:float = None							, __A			:bool = None							, __A			:Optional[Union[float, List[float]]] = None							, __A			:Optional[Union[float, List[float]]] = None							, __A			:Optional[Union[str, TensorType]] = None							, __A			:Optional[ChannelDimension] = ChannelDimension.FIRST							, **__A			:List[Any]							, )      ->      PIL.Image.Image:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								=					do_resize if do_resize is not None else self.do_resize
									SCREAMING_SNAKE_CASE__								=					size if size is not None else self.size
									if isinstance(__A							, (tuple, list)						):
											# Previous feature extractor had size in (width, height) format
											SCREAMING_SNAKE_CASE__								=					size[::-1]
									SCREAMING_SNAKE_CASE__								=					get_size_dict(__A						)
									SCREAMING_SNAKE_CASE__								=					resample if resample is not None else self.resample
									SCREAMING_SNAKE_CASE__								=					do_thumbnail if do_thumbnail is not None else self.do_thumbnail
									SCREAMING_SNAKE_CASE__								=					do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
									SCREAMING_SNAKE_CASE__								=					do_pad if do_pad is not None else self.do_pad
									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_normalize if do_normalize is not None else self.do_normalize
									SCREAMING_SNAKE_CASE__								=					image_mean if image_mean is not None else self.image_mean
									SCREAMING_SNAKE_CASE__								=					image_std if image_std is not None else self.image_std

									SCREAMING_SNAKE_CASE__								=					make_list_of_images(__A						)

									if not valid_images(__A						):
											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.
									SCREAMING_SNAKE_CASE__								=					[to_numpy_array(__A						) for image in images]

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

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

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

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

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

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

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

									SCREAMING_SNAKE_CASE__								=					{"""pixel_values""": images}
									return BatchFeature(data=__A							, tensor_type=__A						)
lowerCamelCase__   = '''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''

lowerCamelCase__   = [{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
lowerCamelCase__   = {
    '''{processor_class}''': '''FakeProcessorClass''',
    '''{model_class}''': '''FakeModelClass''',
    '''{object_class}''': '''FakeObjectClass''',
}




