






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


__snake_case												=  logging.get_logger(__name__)







class 			UpperCAmelCase_						(					UpperCamelCase_ ):



      """simple docstring"""
      UpperCamelCase_				:					str           =['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 / 255    ,				SCREAMING_SNAKE_CASE_ = True    ,				SCREAMING_SNAKE_CASE_ = None    ,				SCREAMING_SNAKE_CASE_ = None    ,				**SCREAMING_SNAKE_CASE_    ,				)				->      List[Any]:
          super().__init__(**A_ )
          UpperCamelCase       :Optional[Any]								=   size if size is not None else {'''shortest_edge''': 256}
          UpperCamelCase       :int								=   get_size_dict(A_    ,				default_to_square=A_ )
          UpperCamelCase       :Dict								=   crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
          UpperCamelCase       :Tuple								=   get_size_dict(A_ )
          UpperCamelCase       :Union[str, Any]								=   do_resize
          UpperCamelCase       :Tuple								=   size
          UpperCamelCase       :List[Any]								=   resample
          UpperCamelCase       :List[str]								=   do_center_crop
          UpperCamelCase       :Optional[int]								=   crop_size
          UpperCamelCase       :Optional[int]								=   do_rescale
          UpperCamelCase       :Any								=   rescale_factor
          UpperCamelCase       :str								=   do_normalize
          UpperCamelCase       :List[Any]								=   image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
          UpperCamelCase       :Optional[Any]								=   image_std if image_std is not None else IMAGENET_STANDARD_STD
      def     UpperCAmelCase						(				self    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_ = PILImageResampling.BICUBIC    ,				SCREAMING_SNAKE_CASE_ = None    ,				**SCREAMING_SNAKE_CASE_    ,				)				->      Any:
          UpperCamelCase       :str								=   get_size_dict(A_    ,				default_to_square=A_ )
          if "shortest_edge" not in size:
              raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}''' )
          UpperCamelCase       :str								=   get_resize_output_image_size(A_    ,				size=size['''shortest_edge''']    ,				default_to_square=A_ )
          return resize(A_    ,				size=A_    ,				resample=A_    ,				data_format=A_    ,				**A_ )
      def     UpperCAmelCase						(				self    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_ = None    ,				**SCREAMING_SNAKE_CASE_    ,				)				->      Dict:
          UpperCamelCase       :Union[str, Any]								=   get_size_dict(A_ )
          return center_crop(A_    ,				size=(size['''height'''], size['''width'''])    ,				data_format=A_    ,				**A_ )
      def     UpperCAmelCase						(				self    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_ = None    ,				**SCREAMING_SNAKE_CASE_ )				->      Tuple:
          return rescale(A_    ,				scale=A_    ,				data_format=A_    ,				**A_ )
      def     UpperCAmelCase						(				self    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_ = None    ,				**SCREAMING_SNAKE_CASE_    ,				)				->      Optional[int]:
          return normalize(A_    ,				mean=A_    ,				std=A_    ,				data_format=A_    ,				**A_ )




      def     UpperCAmelCase						(				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_    ,				)				->      List[str]:
          UpperCamelCase       :Optional[Any]								=   do_resize if do_resize is not None else self.do_resize
          UpperCamelCase       :Dict								=   size if size is not None else self.size
          UpperCamelCase       :List[str]								=   get_size_dict(A_    ,				default_to_square=A_ )
          UpperCamelCase       :Union[str, Any]								=   resample if resample is not None else self.resample
          UpperCamelCase       :Tuple								=   do_center_crop if do_center_crop is not None else self.do_center_crop
          UpperCamelCase       :Any								=   crop_size if crop_size is not None else self.crop_size
          UpperCamelCase       :int								=   get_size_dict(A_ )
          UpperCamelCase       :Tuple								=   do_rescale if do_rescale is not None else self.do_rescale
          UpperCamelCase       :Optional[Any]								=   rescale_factor if rescale_factor is not None else self.rescale_factor
          UpperCamelCase       :Dict								=   do_normalize if do_normalize is not None else self.do_normalize
          UpperCamelCase       :Union[str, Any]								=   image_mean if image_mean is not None else self.image_mean
          UpperCamelCase       :List[str]								=   image_std if image_std is not None else self.image_std

          UpperCamelCase       :Dict								=   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:
              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.
          UpperCamelCase       :Any								=   [to_numpy_array(A_ ) for image in images]

          if do_resize:
              UpperCamelCase       :Optional[Any]								=   [self.resize(image=A_    ,				size=A_    ,				resample=A_ ) for image in images]

          if do_center_crop:
              UpperCamelCase       :List[str]								=   [self.center_crop(image=A_    ,				size=A_ ) for image in images]

          if do_rescale:
              UpperCamelCase       :Tuple								=   [self.rescale(image=A_    ,				scale=A_ ) for image in images]

          if do_normalize:
              UpperCamelCase       :int								=   [self.normalize(image=A_    ,				mean=A_    ,				std=A_ ) for image in images]

          UpperCamelCase       :Optional[int]								=   [to_channel_dimension_format(A_    ,				A_ ) for image in images]

          UpperCamelCase       :str								=   {'''pixel_values''': images}
          return BatchFeature(data=A_    ,				tensor_type=A_ )










import gc
import unittest

from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
						import jax
						import jax.numpy as jnp
						from flax.jax_utils import replicate
						from flax.training.common_utils import shard



@slow
@require_flax
class 				__snake_case						(     unittest.TestCase						):




							def    UpperCAmelCase__			(       self     :       Tuple):
									# clean up the VRAM after each test
									super().tearDown()
									gc.collect()







							def    UpperCAmelCase__			(       self     :       List[str]):
									lowerCAmelCase_			:     Any									= load_image(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
									    '''/sd2-inpaint/init_image.png''')
									lowerCAmelCase_			:     str									= load_image(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png''')

									lowerCAmelCase_			:     List[Any]									= '''xvjiarui/stable-diffusion-2-inpainting'''
									lowerCAmelCase_					,  lowerCAmelCase_			:     Optional[Any]									= FlaxStableDiffusionInpaintPipeline.from_pretrained(A_				,				safety_checker=A_)

									lowerCAmelCase_			:     List[str]									= '''Face of a yellow cat, high resolution, sitting on a park bench'''

									lowerCAmelCase_			:     List[Any]									= jax.random.PRNGKey(0)
									lowerCAmelCase_			:     str									= 5_0

									lowerCAmelCase_			:     List[Any]									= jax.device_count()
									lowerCAmelCase_			:     Union[str, Any]									= num_samples * [prompt]
									lowerCAmelCase_			:     str									= num_samples * [init_image]
									lowerCAmelCase_			:     Union[str, Any]									= num_samples * [mask_image]
									lowerCAmelCase_					,  lowerCAmelCase_					,  lowerCAmelCase_			:     Optional[Any]									= pipeline.prepare_inputs(A_				,				A_				,				A_)

									# shard inputs and rng
									lowerCAmelCase_			:     str									= replicate(A_)
									lowerCAmelCase_			:     str									= jax.random.split(A_				,				jax.device_count())
									lowerCAmelCase_			:     List[Any]									= shard(A_)
									lowerCAmelCase_			:     str									= shard(A_)
									lowerCAmelCase_			:     Tuple									= shard(A_)

									lowerCAmelCase_			:     int									= pipeline(
									    A_				,				A_				,				A_				,				A_				,				A_				,				A_				,				jit=A_)

									lowerCAmelCase_			:     Optional[int]									= output.images.reshape(A_				,				5_1_2				,				5_1_2				,				3)

									lowerCAmelCase_			:     List[str]									= images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

									lowerCAmelCase_			:     int									= jnp.asarray(jax.device_get(image_slice.flatten()))
									lowerCAmelCase_			:     str									= jnp.array(
									    [0.361_1307, 0.3764_9736, 0.375_7408, 0.3821_3953, 0.3929_5167, 0.384_1631, 0.4155_4978, 0.413_7475, 0.421_7084])
									print(F"""output_slice: {output_slice}""")
									assert jnp.abs(output_slice - expected_slice).max() < 1e-2





