


'''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 (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__lowercase     :						str	=     logging.get_logger(__name__)





class 							__UpperCamelCase    (		__lowerCAmelCase ):
	A_           =   ['''pixel_values''']




	def __init__(       self						,    __a = True						,    __a = None						,    __a = PILImageResampling.BICUBIC						,    __a = True						,    __a = True						,    __a = 1 / 255						,    __a = None						,    __a = True						,    __a = None						,    __a = None						,    **__a						,    ):

						'''simple docstring'''





						super().__init__(**lowerCAmelCase_							)
						__a      :	Any    =  size if size is not None else {'height': 224, 'width': 224}
						__a      :	Union[str, Any]    =  get_size_dict(lowerCAmelCase_							)
						__a      :	Any    =  crop_size if crop_size is not None else {'height': 224, 'width': 224}
						__a      :	Dict    =  get_size_dict(lowerCAmelCase_						,    default_to_square=lowerCAmelCase_						,    param_name='crop_size'							)

						__a      :	Optional[int]    =  do_resize
						__a      :	List[Any]    =  do_rescale
						__a      :	Union[str, Any]    =  do_normalize
						__a      :	Any    =  do_center_crop
						__a      :	Optional[Any]    =  crop_size
						__a      :	List[str]    =  size
						__a      :	List[Any]    =  resample
						__a      :	List[Any]    =  rescale_factor
						__a      :	Optional[Any]    =  image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
						__a      :	Tuple    =  image_std if image_std is not None else IMAGENET_DEFAULT_STD




	def 				__UpperCAmelCase							(       self						,    __a						,    __a						,    __a = PILImageResampling.BILINEAR						,    __a = None						,    **__a						,    ):

						'''simple docstring'''





						__a      :	Optional[int]    =  get_size_dict(lowerCAmelCase_							)

						if "shortest_edge" in size:
											__a      :	Any    =  get_resize_output_image_size(lowerCAmelCase_						,    size=size['shortest_edge']						,    default_to_square=lowerCAmelCase_							)
											# size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
						elif "height" in size and "width" in size:
											__a      :	str    =  (size['height'], size['width'])
						else:
											raise ValueError(f"""Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}"""							)
						return resize(lowerCAmelCase_						,    size=lowerCAmelCase_						,    resample=lowerCAmelCase_						,    data_format=lowerCAmelCase_						,    **lowerCAmelCase_							)




	def 				__UpperCAmelCase							(       self						,    __a						,    __a						,    __a = None						,    **__a						,    ):

						'''simple docstring'''





						__a      :	List[str]    =  get_size_dict(lowerCAmelCase_							)
						if "height" not in size or "width" not in size:
											raise ValueError(f"""The `size` parameter must contain the keys (height, width). Got {size.keys()}"""							)
						return center_crop(lowerCAmelCase_						,    size=(size['height'], size['width'])						,    data_format=lowerCAmelCase_						,    **lowerCAmelCase_							)




	def 				__UpperCAmelCase							(       self						,    __a						,    __a						,    __a = None						,    **__a							):

						'''simple docstring'''





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




	def 				__UpperCAmelCase							(       self						,    __a						,    __a						,    __a						,    __a = None						,    **__a						,    ):

						'''simple docstring'''





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





	def 				__UpperCAmelCase							(       self						,    __a						,    __a = None						,    __a = None						,    __a = None						,    __a = None						,    __a = None						,    __a = None						,    __a = None						,    __a = None						,    __a = None						,    __a = None						,    __a = None						,    __a = ChannelDimension.FIRST						,    **__a						,    ):

						'''simple docstring'''





						__a      :	Optional[Any]    =  do_resize if do_resize is not None else self.do_resize
						__a      :	Union[str, Any]    =  do_rescale if do_rescale is not None else self.do_rescale
						__a      :	Tuple    =  do_normalize if do_normalize is not None else self.do_normalize
						__a      :	Any    =  do_center_crop if do_center_crop is not None else self.do_center_crop
						__a      :	List[str]    =  crop_size if crop_size is not None else self.crop_size
						__a      :	int    =  get_size_dict(lowerCAmelCase_						,    param_name='crop_size'						,    default_to_square=lowerCAmelCase_							)
						__a      :	Any    =  resample if resample is not None else self.resample
						__a      :	str    =  rescale_factor if rescale_factor is not None else self.rescale_factor
						__a      :	Dict    =  image_mean if image_mean is not None else self.image_mean
						__a      :	Dict    =  image_std if image_std is not None else self.image_std

						__a      :	Optional[Any]    =  size if size is not None else self.size
						__a      :	List[Any]    =  get_size_dict(lowerCAmelCase_							)

						if not is_batched(lowerCAmelCase_							):
											__a      :	Tuple    =  [images]

						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_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.'							)

						# All transformations expect numpy arrays.
						__a      :	str    =  [to_numpy_array(lowerCAmelCase_							) for image in images]

						if do_resize:
											__a      :	int    =  [self.resize(image=lowerCAmelCase_						,    size=lowerCAmelCase_						,    resample=lowerCAmelCase_							) for image in images]

						if do_center_crop:
											__a      :	Optional[Any]    =  [self.center_crop(image=lowerCAmelCase_						,    size=lowerCAmelCase_							) for image in images]

						if do_rescale:
											__a      :	List[str]    =  [self.rescale(image=lowerCAmelCase_						,    scale=lowerCAmelCase_							) for image in images]

						if do_normalize:
											__a      :	Optional[Any]    =  [self.normalize(image=lowerCAmelCase_						,    mean=lowerCAmelCase_						,    std=lowerCAmelCase_							) for image in images]

						__a      :	Tuple    =  [to_channel_dimension_format(lowerCAmelCase_						,    lowerCAmelCase_							) for image in images]

						__a      :	Optional[Any]    =  {'pixel_values': images}
						return BatchFeature(data=lowerCAmelCase_						,    tensor_type=lowerCAmelCase_							)







'''simple docstring'''



# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run

import sys
import warnings
from os.path import abspath, dirname, join


# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
__lowercase     :						Dict	=     abspath(join(dirname(dirname(dirname(__file__))), 'src'))
sys.path.insert(1, git_repo_path)


# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='ignore', category=FutureWarning)






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       Optional[Any]       ):
					from transformers.testing_utils import pytest_addoption_shared

					pytest_addoption_shared(_SCREAMING_SNAKE_CASE       )






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       List[str]       ):
					from transformers.testing_utils import pytest_terminal_summary_main

					__a      :	Any    =  terminalreporter.config.getoption('--make-reports'       )
					if make_reports:
										pytest_terminal_summary_main(_SCREAMING_SNAKE_CASE						,				id=_SCREAMING_SNAKE_CASE       )



