"""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 convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
					import PIL


SCREAMING_SNAKE_CASE__      						=  logging.get_logger(__name__)



class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""
				_lowerCAmelCase     :    List[str]			  =						["""pixel_values"""]

				def __init__(				self		,    lowerCAmelCase = True		,    lowerCAmelCase = None		,    lowerCAmelCase = PILImageResampling.BICUBIC		,    lowerCAmelCase = True		,    lowerCAmelCase = 1 / 2_55		,    lowerCAmelCase = True		,    lowerCAmelCase = None		,    lowerCAmelCase = None		,    lowerCAmelCase = True		,    **lowerCAmelCase		,    ):






										"""simple docstring"""

										super().__init__(**lowerCAmelCase    )
										snake_case								=			size if size is not None else {'height': 3_84, 'width': 3_84}
										snake_case								=			get_size_dict(lowerCAmelCase		,    default_to_square=lowerCAmelCase    )

										snake_case								=			do_resize
										snake_case								=			size
										snake_case								=			resample
										snake_case								=			do_rescale
										snake_case								=			rescale_factor
										snake_case								=			do_normalize
										snake_case								=			image_mean if image_mean is not None else OPENAI_CLIP_MEAN
										snake_case								=			image_std if image_std is not None else OPENAI_CLIP_STD
										snake_case								=			do_convert_rgb

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase = PILImageResampling.BICUBIC		,    lowerCAmelCase = None		,    **lowerCAmelCase		,    ):






										"""simple docstring"""

										snake_case								=			get_size_dict(lowerCAmelCase		,    default_to_square=lowerCAmelCase    )
										if "height" not in size or "width" not in size:
																raise ValueError(F"""The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}"""    )
										snake_case								=			(size['height'], size['width'])
										return resize(lowerCAmelCase		,    size=lowerCAmelCase		,    resample=lowerCAmelCase		,    data_format=lowerCAmelCase		,    **lowerCAmelCase    )

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase = None		,    **lowerCAmelCase		,    ):






										"""simple docstring"""

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

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase = None		,    **lowerCAmelCase		,    ):






										"""simple docstring"""

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

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase = None		,    lowerCAmelCase = None		,    lowerCAmelCase = None		,    lowerCAmelCase = None		,    lowerCAmelCase = None		,    lowerCAmelCase = None		,    lowerCAmelCase = None		,    lowerCAmelCase = None		,    lowerCAmelCase = None		,    lowerCAmelCase = None		,    lowerCAmelCase = ChannelDimension.FIRST		,    **lowerCAmelCase		,    ):






										"""simple docstring"""

										snake_case								=			do_resize if do_resize is not None else self.do_resize
										snake_case								=			resample if resample is not None else self.resample
										snake_case								=			do_rescale if do_rescale is not None else self.do_rescale
										snake_case								=			rescale_factor if rescale_factor is not None else self.rescale_factor
										snake_case								=			do_normalize if do_normalize is not None else self.do_normalize
										snake_case								=			image_mean if image_mean is not None else self.image_mean
										snake_case								=			image_std if image_std is not None else self.image_std
										snake_case								=			do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

										snake_case								=			size if size is not None else self.size
										snake_case								=			get_size_dict(lowerCAmelCase		,    default_to_square=lowerCAmelCase    )

										snake_case								=			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 or resample is None:
																raise ValueError('Size and resample 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_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.'    )

										# PIL RGBA images are converted to RGB
										if do_convert_rgb:
																snake_case								=			[convert_to_rgb(lowerCAmelCase    ) for image in images]

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

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

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

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

										snake_case								=			[to_channel_dimension_format(lowerCAmelCase		,    lowerCAmelCase    ) for image in images]

										snake_case								=			BatchFeature(data={'pixel_values': images}		,    tensor_type=lowerCAmelCase    )

										return encoded_outputs



"""simple docstring"""
import unittest

from transformers.utils.backbone_utils import (
    BackboneMixin,
    get_aligned_output_features_output_indices,
    verify_out_features_out_indices,
)



class   lowerCAmelCase_    (			unittest.TestCase					):



				"""simple docstring"""

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			['a', 'b', 'c']

										# Defaults to last layer if both are None
										snake_case					,snake_case								=			get_aligned_output_features_output_indices(lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase    )
										self.assertEqual(lowerCAmelCase		,    ['c']    )
										self.assertEqual(lowerCAmelCase		,    [2]    )

										# Out indices set to match out features
										snake_case					,snake_case								=			get_aligned_output_features_output_indices(['a', 'c']		,    lowerCAmelCase		,    lowerCAmelCase    )
										self.assertEqual(lowerCAmelCase		,    ['a', 'c']    )
										self.assertEqual(lowerCAmelCase		,    [0, 2]    )

										# Out features set to match out indices
										snake_case					,snake_case								=			get_aligned_output_features_output_indices(lowerCAmelCase		,    [0, 2]		,    lowerCAmelCase    )
										self.assertEqual(lowerCAmelCase		,    ['a', 'c']    )
										self.assertEqual(lowerCAmelCase		,    [0, 2]    )

										# Out features selected from negative indices
										snake_case					,snake_case								=			get_aligned_output_features_output_indices(lowerCAmelCase		,    [-3, -1]		,    lowerCAmelCase    )
										self.assertEqual(lowerCAmelCase		,    ['a', 'c']    )
										self.assertEqual(lowerCAmelCase		,    [-3, -1]    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										with self.assertRaises(lowerCAmelCase    ):
																verify_out_features_out_indices(['a', 'b']		,    (0, 1)		,    lowerCAmelCase    )

										# Out features must be a list
										with self.assertRaises(lowerCAmelCase    ):
																verify_out_features_out_indices(('a', 'b')		,    (0, 1)		,    ['a', 'b']    )

										# Out features must be a subset of stage names
										with self.assertRaises(lowerCAmelCase    ):
																verify_out_features_out_indices(['a', 'b']		,    (0, 1)		,    ['a']    )

										# Out indices must be a list or tuple
										with self.assertRaises(lowerCAmelCase    ):
																verify_out_features_out_indices(lowerCAmelCase		,    0		,    ['a', 'b']    )

										# Out indices must be a subset of stage names
										with self.assertRaises(lowerCAmelCase    ):
																verify_out_features_out_indices(lowerCAmelCase		,    (0, 1)		,    ['a']    )

										# Out features and out indices must be the same length
										with self.assertRaises(lowerCAmelCase    ):
																verify_out_features_out_indices(['a', 'b']		,    (0,)		,    ['a', 'b', 'c']    )

										# Out features should match out indices
										with self.assertRaises(lowerCAmelCase    ):
																verify_out_features_out_indices(['a', 'b']		,    (0, 2)		,    ['a', 'b', 'c']    )

										# Out features and out indices should be in order
										with self.assertRaises(lowerCAmelCase    ):
																verify_out_features_out_indices(['b', 'a']		,    (0, 1)		,    ['a', 'b']    )

										# Check passes with valid inputs
										verify_out_features_out_indices(['a', 'b', 'd']		,    (0, 1, -1)		,    ['a', 'b', 'c', 'd']    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			BackboneMixin()

										snake_case								=			['a', 'b', 'c']
										snake_case								=			['a', 'c']
										snake_case								=			[0, 2]

										# Check that the output features and indices are set correctly
										self.assertEqual(backbone.out_features		,    ['a', 'c']    )
										self.assertEqual(backbone.out_indices		,    [0, 2]    )

										# Check out features and indices are updated correctly
										snake_case								=			['a', 'b']
										self.assertEqual(backbone.out_features		,    ['a', 'b']    )
										self.assertEqual(backbone.out_indices		,    [0, 1]    )

										snake_case								=			[-3, -1]
										self.assertEqual(backbone.out_features		,    ['a', 'c']    )
										self.assertEqual(backbone.out_indices		,    [-3, -1]    )


