
"""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_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowerCamelCase__        =		logging.get_logger(__name__)




class 			A__	(     _lowerCamelCase):
		A_						:	Optional[Any]           =     ['pixel_values']

		def __init__(				self	,		_SCREAMING_SNAKE_CASE = True	,		_SCREAMING_SNAKE_CASE = None	,		_SCREAMING_SNAKE_CASE = PILImageResampling.BILINEAR	,		_SCREAMING_SNAKE_CASE = True	,		_SCREAMING_SNAKE_CASE = None	,		_SCREAMING_SNAKE_CASE = True	,		_SCREAMING_SNAKE_CASE = 1 / 2_55	,		_SCREAMING_SNAKE_CASE = True	,		_SCREAMING_SNAKE_CASE = None	,		_SCREAMING_SNAKE_CASE = None	,		**_SCREAMING_SNAKE_CASE	,		):
				super().__init__(**_SCREAMING_SNAKE_CASE			)
				__lowerCAmelCase      :						List[Any]						      =				size if size is not None else {'shortest_edge': 2_56}
				__lowerCAmelCase      :						int						      =				get_size_dict(_SCREAMING_SNAKE_CASE	,		default_to_square=_SCREAMING_SNAKE_CASE			)
				__lowerCAmelCase      :						str						      =				crop_size if crop_size is not None else {'height': 2_24, 'width': 2_24}
				__lowerCAmelCase      :						str						      =				get_size_dict(_SCREAMING_SNAKE_CASE			)
				__lowerCAmelCase      :						Optional[int]						      =				do_resize
				__lowerCAmelCase      :						str						      =				size
				__lowerCAmelCase      :						int						      =				resample
				__lowerCAmelCase      :						str						      =				do_center_crop
				__lowerCAmelCase      :						Union[str, Any]						      =				crop_size
				__lowerCAmelCase      :						Dict						      =				do_rescale
				__lowerCAmelCase      :						str						      =				rescale_factor
				__lowerCAmelCase      :						str						      =				do_normalize
				__lowerCAmelCase      :						str						      =				image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
				__lowerCAmelCase      :						Union[str, Any]						      =				image_std if image_std is not None else IMAGENET_STANDARD_STD

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE = PILImageResampling.BICUBIC	,		_SCREAMING_SNAKE_CASE = None	,		**_SCREAMING_SNAKE_CASE	,		):
				__lowerCAmelCase      :						Optional[Any]						      =				get_size_dict(_SCREAMING_SNAKE_CASE	,		default_to_square=_SCREAMING_SNAKE_CASE			)
				if "shortest_edge" not in size:
						raise ValueError(f"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"			)
				__lowerCAmelCase      :						Dict						      =				get_resize_output_image_size(_SCREAMING_SNAKE_CASE	,		size=size['shortest_edge']	,		default_to_square=_SCREAMING_SNAKE_CASE			)
				return resize(_SCREAMING_SNAKE_CASE	,		size=_SCREAMING_SNAKE_CASE	,		resample=_SCREAMING_SNAKE_CASE	,		data_format=_SCREAMING_SNAKE_CASE	,		**_SCREAMING_SNAKE_CASE			)

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE = None	,		**_SCREAMING_SNAKE_CASE	,		):
				__lowerCAmelCase      :						int						      =				get_size_dict(_SCREAMING_SNAKE_CASE			)
				return center_crop(_SCREAMING_SNAKE_CASE	,		size=(size['height'], size['width'])	,		data_format=_SCREAMING_SNAKE_CASE	,		**_SCREAMING_SNAKE_CASE			)

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE = None	,		**_SCREAMING_SNAKE_CASE			):
				return rescale(_SCREAMING_SNAKE_CASE	,		scale=_SCREAMING_SNAKE_CASE	,		data_format=_SCREAMING_SNAKE_CASE	,		**_SCREAMING_SNAKE_CASE			)

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE = None	,		**_SCREAMING_SNAKE_CASE	,		):
				return normalize(_SCREAMING_SNAKE_CASE	,		mean=_SCREAMING_SNAKE_CASE	,		std=_SCREAMING_SNAKE_CASE	,		data_format=_SCREAMING_SNAKE_CASE	,		**_SCREAMING_SNAKE_CASE			)







		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE = None	,		_SCREAMING_SNAKE_CASE = None	,		_SCREAMING_SNAKE_CASE = None	,		_SCREAMING_SNAKE_CASE = None	,		_SCREAMING_SNAKE_CASE = None	,		_SCREAMING_SNAKE_CASE = None	,		_SCREAMING_SNAKE_CASE = None	,		_SCREAMING_SNAKE_CASE = None	,		_SCREAMING_SNAKE_CASE = None	,		_SCREAMING_SNAKE_CASE = None	,		_SCREAMING_SNAKE_CASE = None	,		_SCREAMING_SNAKE_CASE = ChannelDimension.FIRST	,		**_SCREAMING_SNAKE_CASE	,		):
				__lowerCAmelCase      :						str						      =				do_resize if do_resize is not None else self.do_resize
				__lowerCAmelCase      :						Optional[Any]						      =				size if size is not None else self.size
				__lowerCAmelCase      :						List[str]						      =				get_size_dict(_SCREAMING_SNAKE_CASE	,		default_to_square=_SCREAMING_SNAKE_CASE			)
				__lowerCAmelCase      :						Dict						      =				resample if resample is not None else self.resample
				__lowerCAmelCase      :						Optional[int]						      =				do_center_crop if do_center_crop is not None else self.do_center_crop
				__lowerCAmelCase      :						int						      =				crop_size if crop_size is not None else self.crop_size
				__lowerCAmelCase      :						List[str]						      =				get_size_dict(_SCREAMING_SNAKE_CASE			)
				__lowerCAmelCase      :						Union[str, Any]						      =				do_rescale if do_rescale is not None else self.do_rescale
				__lowerCAmelCase      :						Optional[int]						      =				rescale_factor if rescale_factor is not None else self.rescale_factor
				__lowerCAmelCase      :						List[str]						      =				do_normalize if do_normalize is not None else self.do_normalize
				__lowerCAmelCase      :						int						      =				image_mean if image_mean is not None else self.image_mean
				__lowerCAmelCase      :						Optional[int]						      =				image_std if image_std is not None else self.image_std

				__lowerCAmelCase      :						List[str]						      =				make_list_of_images(_SCREAMING_SNAKE_CASE			)

				if not valid_images(_SCREAMING_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:
						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.'			)

				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.
				__lowerCAmelCase      :						Optional[Any]						      =				[to_numpy_array(_SCREAMING_SNAKE_CASE			) for image in images]

				if do_resize:
						__lowerCAmelCase      :						List[str]						      =				[self.resize(image=_SCREAMING_SNAKE_CASE	,		size=_SCREAMING_SNAKE_CASE	,		resample=_SCREAMING_SNAKE_CASE			) for image in images]

				if do_center_crop:
						__lowerCAmelCase      :						Dict						      =				[self.center_crop(image=_SCREAMING_SNAKE_CASE	,		size=_SCREAMING_SNAKE_CASE			) for image in images]

				if do_rescale:
						__lowerCAmelCase      :						Union[str, Any]						      =				[self.rescale(image=_SCREAMING_SNAKE_CASE	,		scale=_SCREAMING_SNAKE_CASE			) for image in images]

				if do_normalize:
						__lowerCAmelCase      :						Any						      =				[self.normalize(image=_SCREAMING_SNAKE_CASE	,		mean=_SCREAMING_SNAKE_CASE	,		std=_SCREAMING_SNAKE_CASE			) for image in images]

				__lowerCAmelCase      :						Optional[int]						      =				[to_channel_dimension_format(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE			) for image in images]

				__lowerCAmelCase      :						List[str]						      =				{'pixel_values': images}
				return BatchFeature(data=_SCREAMING_SNAKE_CASE	,		tensor_type=_SCREAMING_SNAKE_CASE			)

"""simple docstring"""







# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works

from ...utils import deprecate
from ..controlnet.pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline  # noqa: F401


deprecate(
    """stable diffusion controlnet""",
    """0.22.0""",
    """Importing `FlaxStableDiffusionControlNetPipeline` from diffusers.pipelines.stable_diffusion.flax_pipeline_stable_diffusion_controlnet is deprecated. Please import `from diffusers import FlaxStableDiffusionControlNetPipeline` instead.""",
    standard_warn=False,
    stacklevel=3,
)