
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,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
   import PIL


_A   =			logging.get_logger(__name__)







class 					lowerCamelCase		(  A_  ):
     UpperCAmelCase__    :							Union[str, Any]									=		["pixel_values"]





     def __init__(self				:   int    ,   _A				:   bool = True    ,   _A				:   Dict[str, int] = None    ,   _A				:   int = 0.9    ,   _A				:   PILImageResampling = PILImageResampling.BICUBIC    ,   _A				:   bool = True    ,   _A				:   Dict[str, int] = None    ,   _A				:   Union[int, float] = 1 / 2_5_5    ,   _A				:   bool = True    ,   _A				:   bool = True    ,   _A				:   Optional[Union[float, List[float]]] = None    ,   _A				:   Optional[Union[float, List[float]]] = None    ,   **_A				:   Dict    ,   )						->						None:
         super().__init__(**_A       )
         snake_case								= size if size is not None else {"shortest_edge": 2_2_4}
         snake_case								= get_size_dict(_A    ,   default_to_square=_A       )
         snake_case								= crop_size if crop_size is not None else {"height": 2_2_4, "width": 2_2_4}
         snake_case								= get_size_dict(_A    ,   param_name="crop_size"       )

         snake_case								= do_resize
         snake_case								= size
         snake_case								= crop_pct
         snake_case								= resample
         snake_case								= do_center_crop
         snake_case								= crop_size
         snake_case								= do_rescale
         snake_case								= rescale_factor
         snake_case								= do_normalize
         snake_case								= image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
         snake_case								= image_std if image_std is not None else IMAGENET_DEFAULT_STD





     def 		UpperCAmelCase(self				:   Optional[Any]    ,   _A				:   np.ndarray    ,   _A				:   Dict[str, int]    ,   _A				:   Optional[float] = None    ,   _A				:   PILImageResampling = PILImageResampling.BICUBIC    ,   _A				:   Optional[Union[str, ChannelDimension]] = None    ,   **_A				:   str    ,   )						->						np.ndarray:
         snake_case								= get_size_dict(_A    ,   default_to_square=_A       )
         if "shortest_edge" not in size and ("height" not in size or "width" not in size):
             raise ValueError(f'size must contain \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'       )
         if crop_pct is not None:
             if "shortest_edge" in size:
                 snake_case								= int(size["shortest_edge"] / crop_pct       )
             elif "height" in size and "width" in size:
                 if size["height"] == size["width"]:
                     snake_case								= int(size["height"] / crop_pct       )
                 else:
                     snake_case								= (int(size["height"] / crop_pct       ), int(size["width"] / crop_pct       ))
             else:
                 raise ValueError("Invalid size for resize: {}".format(_A       )       )

             snake_case								= get_resize_output_image_size(_A    ,   size=_A    ,   default_to_square=_A       )
         else:
             if "shortest_edge" in size:
                 snake_case								= get_resize_output_image_size(_A    ,   size=size["shortest_edge"]    ,   default_to_square=_A       )
             elif "height" in size and "width" in size:
                 snake_case								= (size["height"], size["width"])
             else:
                 raise ValueError("Invalid size for resize: {}".format(_A       )       )

         return resize(_A    ,   size=_A    ,   resample=_A    ,   data_format=_A    ,   **_A       )





     def 		UpperCAmelCase(self				:   Optional[int]    ,   _A				:   np.ndarray    ,   _A				:   Dict[str, int]    ,   _A				:   Optional[Union[str, ChannelDimension]] = None    ,   **_A				:   str    ,   )						->						np.ndarray:
         snake_case								= get_size_dict(_A       )
         if "height" not in size or "width" not in size:
             raise ValueError(f'size must contain \'height\' and \'width\' as keys. Got {size.keys()}'       )
         return center_crop(_A    ,   size=(size["height"], size["width"])    ,   data_format=_A    ,   **_A       )





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





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





     def 		UpperCAmelCase(self				:   Dict    ,   _A				:   ImageInput    ,   _A				:   bool = None    ,   _A				:   Dict[str, int] = None    ,   _A				:   int = None    ,   _A				:   PILImageResampling = None    ,   _A				:   bool = None    ,   _A				:   Dict[str, int] = None    ,   _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				:   ChannelDimension = ChannelDimension.FIRST    ,   **_A				:   List[str]    ,   )						->						PIL.Image.Image:
         snake_case								= do_resize if do_resize is not None else self.do_resize
         snake_case								= crop_pct if crop_pct is not None else self.crop_pct
         snake_case								= resample if resample is not None else self.resample
         snake_case								= do_center_crop if do_center_crop is not None else self.do_center_crop
         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								= size if size is not None else self.size
         snake_case								= get_size_dict(_A    ,   default_to_square=_A       )
         snake_case								= crop_size if crop_size is not None else self.crop_size
         snake_case								= get_size_dict(_A    ,   param_name="crop_size"       )

         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 or resample is None:
             raise ValueError("Size and resample must be specified if do_resize is True."       )

         if do_center_crop and crop_pct is None:
             raise ValueError("Crop_pct 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."       )

         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.
         snake_case								= [to_numpy_array(_A       ) for image in images]

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

         if do_center_crop:
             snake_case								= [self.center_crop(image=_A    ,   size=_A       ) for image in images]

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

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

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

         snake_case								= {"pixel_values": images}
         return BatchFeature(data=_A    ,   tensor_type=_A       )



import unittest

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







class 					lowerCamelCase		(  unittest.TestCase  ):





     def 		UpperCAmelCase(self				:   Tuple       )						->						List[str]:
         snake_case								= ["a", "b", "c"]

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

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

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

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





     def 		UpperCAmelCase(self				:   Optional[int]       )						->						str:
         # Stage names must be set
         with self.assertRaises(_A       ):
             verify_out_features_out_indices(["a", "b"]    ,   (0, 1)    ,   _A       )

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

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

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

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

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

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

         # Out features and out indices should be in order
         with self.assertRaises(_A       ):
             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 		UpperCAmelCase(self				:   List[str]       )						->						str:
         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]       )

