


from typing import Dict, List, Optional, Tuple, 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_torch_available, is_torch_tensor, logging


if is_torch_available():
						import torch


__UpperCamelCase    		=       logging.get_logger(__name__)







class        lowerCamelCase__	(							UpperCAmelCase      ):






			"""simple docstring"""






			_UpperCamelCase		:					Optional[int]    =		['pixel_values']




			def __init__(							self      ,						snake_case = True      ,						snake_case = None      ,						snake_case = PILImageResampling.BILINEAR      ,						snake_case = True      ,						snake_case = None      ,						snake_case = True      ,						snake_case = 1 / 255      ,						snake_case = True      ,						snake_case = None      ,						snake_case = None      ,						**snake_case      ,						):

						'''simple docstring'''


						super().__init__(**snake_case     )
						UpperCamelCase__					=       size if size is not None else {"shortest_edge": 256}
						UpperCamelCase__					=       get_size_dict(snake_case      ,						default_to_square=snake_case     )
						UpperCamelCase__					=       crop_size if crop_size is not None else {"height": 224, "width": 224}
						UpperCamelCase__					=       get_size_dict(snake_case      ,						param_name="crop_size"     )
						UpperCamelCase__					=       do_resize
						UpperCamelCase__					=       size
						UpperCamelCase__					=       resample
						UpperCamelCase__					=       do_center_crop
						UpperCamelCase__					=       crop_size
						UpperCamelCase__					=       do_rescale
						UpperCamelCase__					=       rescale_factor
						UpperCamelCase__					=       do_normalize
						UpperCamelCase__					=       image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
						UpperCamelCase__					=       image_std if image_std is not None else IMAGENET_STANDARD_STD




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

						'''simple docstring'''


						UpperCamelCase__					=       get_size_dict(snake_case      ,						default_to_square=snake_case     )
						if "shortest_edge" not in size:
									raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''     )
						UpperCamelCase__					=       get_resize_output_image_size(snake_case      ,						size=size["shortest_edge"]      ,						default_to_square=snake_case     )
						return resize(snake_case      ,						size=snake_case      ,						resample=snake_case      ,						data_format=snake_case      ,						**snake_case     )




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

						'''simple docstring'''


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




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

						'''simple docstring'''


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




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

						'''simple docstring'''


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




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

						'''simple docstring'''


						UpperCamelCase__					=       do_resize if do_resize is not None else self.do_resize
						UpperCamelCase__					=       size if size is not None else self.size
						UpperCamelCase__					=       get_size_dict(snake_case      ,						default_to_square=snake_case     )
						UpperCamelCase__					=       resample if resample is not None else self.resample
						UpperCamelCase__					=       do_center_crop if do_center_crop is not None else self.do_center_crop
						UpperCamelCase__					=       crop_size if crop_size is not None else self.crop_size
						UpperCamelCase__					=       get_size_dict(snake_case      ,						param_name="crop_size"     )
						UpperCamelCase__					=       do_rescale if do_rescale is not None else self.do_rescale
						UpperCamelCase__					=       rescale_factor if rescale_factor is not None else self.rescale_factor
						UpperCamelCase__					=       do_normalize if do_normalize is not None else self.do_normalize
						UpperCamelCase__					=       image_mean if image_mean is not None else self.image_mean
						UpperCamelCase__					=       image_std if image_std is not None else self.image_std

						UpperCamelCase__					=       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:
									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__					=       [to_numpy_array(snake_case     ) for image in images]

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

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

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

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

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

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







			def 				snake_case__				(							self      ,						snake_case      ,						snake_case = None     ):

						'''simple docstring'''


						UpperCamelCase__					=       outputs.logits

						# Resize logits and compute semantic segmentation maps
						if target_sizes is not None:
									if len(snake_case     ) != len(snake_case     ):
												raise ValueError(
												    "Make sure that you pass in as many target sizes as the batch dimension of the logits"     )

									if is_torch_tensor(snake_case     ):
												UpperCamelCase__					=       target_sizes.numpy()

									UpperCamelCase__					=       []

									for idx in range(len(snake_case     )     ):
												UpperCamelCase__					=       torch.nn.functional.interpolate(
												    logits[idx].unsqueeze(dim=0     )      ,						size=target_sizes[idx]      ,						mode="bilinear"      ,						align_corners=snake_case     )
												UpperCamelCase__					=       resized_logits[0].argmax(dim=0     )
												semantic_segmentation.append(snake_case     )
						else:
									UpperCamelCase__					=       logits.argmax(dim=1     )
									UpperCamelCase__					=       [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]     )]

						return semantic_segmentation






import unittest

from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax

from .test_modeling_common_flax import FlaxModelTesterMixin


if is_flax_available():
						import jax







@require_flax
class        lowerCamelCase__	(							UpperCAmelCase					,	unittest.TestCase      ):






			"""simple docstring"""






			_UpperCamelCase		:					List[str]    =		FlaxAutoencoderKL




			@property
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       4
						UpperCamelCase__					=       3
						UpperCamelCase__					=       (32, 32)

						UpperCamelCase__					=       jax.random.PRNGKey(0     )
						UpperCamelCase__					=       jax.random.uniform(snake_case      ,						((batch_size, num_channels) + sizes)     )

						return {"sample": image, "prng_key": prng_key}







			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       {
						    "block_out_channels": [32, 64],
						    "in_channels": 3,
						    "out_channels": 3,
						    "down_block_types": ["DownEncoderBlock2D", "DownEncoderBlock2D"],
						    "up_block_types": ["UpDecoderBlock2D", "UpDecoderBlock2D"],
						    "latent_channels": 4,
						}
						UpperCamelCase__					=       self.dummy_input
						return init_dict, inputs_dict


