


from ..utils import DummyObject, requires_backends
class 						lowerCamelCase_							(					metaclass=lowerCAmelCase__					):



					'''simple docstring'''







					__UpperCAmelCase							  =					["flax", "transformers"]






					def __init__(					self			,							*snake_case_			,							**snake_case_    ) ->      Tuple:







								'''simple docstring'''

								requires_backends(self			,							['''flax''', '''transformers''']    )






					@classmethod
					def A   (					cls			,							*snake_case_			,							**snake_case_    ) ->      Optional[Any]:







								'''simple docstring'''

								requires_backends(cls			,							['''flax''', '''transformers''']    )






					@classmethod
					def A   (					cls			,							*snake_case_			,							**snake_case_    ) ->      Union[str, Any]:







								'''simple docstring'''

								requires_backends(cls			,							['''flax''', '''transformers''']    )




class 						lowerCamelCase_							(					metaclass=lowerCAmelCase__					):



					'''simple docstring'''







					__UpperCAmelCase							  =					["flax", "transformers"]






					def __init__(					self			,							*snake_case_			,							**snake_case_    ) ->      List[str]:







								'''simple docstring'''

								requires_backends(self			,							['''flax''', '''transformers''']    )






					@classmethod
					def A   (					cls			,							*snake_case_			,							**snake_case_    ) ->      Any:







								'''simple docstring'''

								requires_backends(cls			,							['''flax''', '''transformers''']    )






					@classmethod
					def A   (					cls			,							*snake_case_			,							**snake_case_    ) ->      Dict:







								'''simple docstring'''

								requires_backends(cls			,							['''flax''', '''transformers''']    )




class 						lowerCamelCase_							(					metaclass=lowerCAmelCase__					):



					'''simple docstring'''







					__UpperCAmelCase							  =					["flax", "transformers"]






					def __init__(					self			,							*snake_case_			,							**snake_case_    ) ->      Union[str, Any]:







								'''simple docstring'''

								requires_backends(self			,							['''flax''', '''transformers''']    )






					@classmethod
					def A   (					cls			,							*snake_case_			,							**snake_case_    ) ->      Dict:







								'''simple docstring'''

								requires_backends(cls			,							['''flax''', '''transformers''']    )






					@classmethod
					def A   (					cls			,							*snake_case_			,							**snake_case_    ) ->      Optional[Any]:







								'''simple docstring'''

								requires_backends(cls			,							['''flax''', '''transformers''']    )




class 						lowerCamelCase_							(					metaclass=lowerCAmelCase__					):



					'''simple docstring'''







					__UpperCAmelCase							  =					["flax", "transformers"]






					def __init__(					self			,							*snake_case_			,							**snake_case_    ) ->      List[str]:







								'''simple docstring'''

								requires_backends(self			,							['''flax''', '''transformers''']    )






					@classmethod
					def A   (					cls			,							*snake_case_			,							**snake_case_    ) ->      List[str]:







								'''simple docstring'''

								requires_backends(cls			,							['''flax''', '''transformers''']    )






					@classmethod
					def A   (					cls			,							*snake_case_			,							**snake_case_    ) ->      int:







								'''simple docstring'''

								requires_backends(cls			,							['''flax''', '''transformers''']    )






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_STANDARD_MEAN,
    IMAGENET_STANDARD_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			:			List[str]												=					logging.get_logger(__name__)
class 						lowerCamelCase_							(					lowerCAmelCase__					):



					'''simple docstring'''







					__UpperCAmelCase							  =					["pixel_values"]






					def __init__(					self			,							snake_case_ = True			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = PILImageResampling.BILINEAR			,							snake_case_ = True			,							snake_case_ = 1 / 2_5_5			,							snake_case_ = True			,							snake_case_ = None			,							snake_case_ = None			,							**snake_case_			,							) ->      None:







								'''simple docstring'''

								super().__init__(**snake_case_    )
								__lowercase			       = size if size is not None else {'''shortest_edge''': 3_8_4}
								__lowercase			       = get_size_dict(snake_case_			,							default_to_square=snake_case_    )

								__lowercase			       = do_resize
								__lowercase			       = size
								# Default value set here for backwards compatibility where the value in config is None
								__lowercase			       = crop_pct if crop_pct is not None else 2_2_4 / 2_5_6
								__lowercase			       = resample
								__lowercase			       = do_rescale
								__lowercase			       = rescale_factor
								__lowercase			       = do_normalize
								__lowercase			       = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
								__lowercase			       = image_std if image_std is not None else IMAGENET_STANDARD_STD






					def A   (					self			,							snake_case_			,							snake_case_			,							snake_case_			,							snake_case_ = PILImageResampling.BICUBIC			,							snake_case_ = None			,							**snake_case_			,							) ->      np.ndarray:







								'''simple docstring'''

								__lowercase			       = get_size_dict(snake_case_			,							default_to_square=snake_case_    )
								if "shortest_edge" not in size:
											raise ValueError(F'Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}'    )
								__lowercase			       = size['''shortest_edge''']

								if shortest_edge < 3_8_4:
											# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
											__lowercase			       = int(shortest_edge / crop_pct    )
											__lowercase			       = get_resize_output_image_size(snake_case_			,							size=snake_case_			,							default_to_square=snake_case_    )
											__lowercase			       = resize(image=snake_case_			,							size=snake_case_			,							resample=snake_case_			,							data_format=snake_case_			,							**snake_case_    )
											# then crop to (shortest_edge, shortest_edge)
											return center_crop(image=snake_case_			,							size=(shortest_edge, shortest_edge)			,							data_format=snake_case_			,							**snake_case_    )
								else:
											# warping (no cropping) when evaluated at 384 or larger
											return resize(
											    snake_case_			,							size=(shortest_edge, shortest_edge)			,							resample=snake_case_			,							data_format=snake_case_			,							**snake_case_    )






					def A   (					self			,							snake_case_			,							snake_case_			,							snake_case_ = None			,							**snake_case_			,							) ->      Optional[int]:







								'''simple docstring'''

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






					def A   (					self			,							snake_case_			,							snake_case_			,							snake_case_			,							snake_case_ = None			,							**snake_case_			,							) ->      np.ndarray:







								'''simple docstring'''

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






					def A   (					self			,							snake_case_			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = None			,							snake_case_ = ChannelDimension.FIRST			,							**snake_case_			,							) ->      PIL.Image.Image:







								'''simple docstring'''

								__lowercase			       = do_resize if do_resize is not None else self.do_resize
								__lowercase			       = crop_pct if crop_pct is not None else self.crop_pct
								__lowercase			       = resample if resample is not None else self.resample
								__lowercase			       = do_rescale if do_rescale is not None else self.do_rescale
								__lowercase			       = rescale_factor if rescale_factor is not None else self.rescale_factor
								__lowercase			       = do_normalize if do_normalize is not None else self.do_normalize
								__lowercase			       = image_mean if image_mean is not None else self.image_mean
								__lowercase			       = image_std if image_std is not None else self.image_std

								__lowercase			       = size if size is not None else self.size
								__lowercase			       = get_size_dict(snake_case_			,							default_to_square=snake_case_    )

								__lowercase			       = make_list_of_images(snake_case_    )

								if not valid_images(snake_case_    ):
											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_resize and size["shortest_edge"] < 3_8_4 and crop_pct is None:
											raise ValueError('''crop_pct must be specified if size < 384.'''    )

								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.
								__lowercase			       = [to_numpy_array(snake_case_    ) for image in images]

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

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

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

								__lowercase			       = [to_channel_dimension_format(snake_case_			,							snake_case_    ) for image in images]

								__lowercase			       = {'''pixel_values''': images}
								return BatchFeature(data=snake_case_			,							tensor_type=snake_case_    )


