




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,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


UpperCAmelCase_						=logging.get_logger(__name__)


if is_vision_available():
		import PIL


class 							__UpperCamelCase			( __UpperCAmelCase  ):





				'''simple docstring'''



				__a							: List[str]										=["""pixel_values"""]



				def __init__( self       , UpperCAmelCase_ = True       , UpperCAmelCase_ = None       , UpperCAmelCase_ = PILImageResampling.BICUBIC       , UpperCAmelCase_ = True       , UpperCAmelCase_ = None       , UpperCAmelCase_ = True       , UpperCAmelCase_ = 1 / 2_55       , UpperCAmelCase_ = True       , UpperCAmelCase_ = None       , UpperCAmelCase_ = None       , UpperCAmelCase_ = True       , **UpperCAmelCase_       , ):
											super().__init__(**UpperCAmelCase_						)
											lowerCAmelCase   =			size if size is not None else {'''shortest_edge''': 2_24}
											lowerCAmelCase   =			get_size_dict(UpperCAmelCase_       , default_to_square=UpperCAmelCase_						)
											lowerCAmelCase   =			crop_size if crop_size is not None else {'''height''': 2_24, '''width''': 2_24}
											lowerCAmelCase   =			get_size_dict(UpperCAmelCase_       , default_to_square=UpperCAmelCase_       , param_name='''crop_size'''						)

											lowerCAmelCase   =			do_resize
											lowerCAmelCase   =			size
											lowerCAmelCase   =			resample
											lowerCAmelCase   =			do_center_crop
											lowerCAmelCase   =			crop_size
											lowerCAmelCase   =			do_rescale
											lowerCAmelCase   =			rescale_factor
											lowerCAmelCase   =			do_normalize
											lowerCAmelCase   =			image_mean if image_mean is not None else OPENAI_CLIP_MEAN
											lowerCAmelCase   =			image_std if image_std is not None else OPENAI_CLIP_STD
											lowerCAmelCase   =			do_convert_rgb



				def       __snake_case		( self       , UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_ = PILImageResampling.BICUBIC       , UpperCAmelCase_ = None       , **UpperCAmelCase_       , ):
											lowerCAmelCase   =			get_size_dict(UpperCAmelCase_       , default_to_square=UpperCAmelCase_						)
											if "shortest_edge" not in size:
																		raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}"""						)
											lowerCAmelCase   =			get_resize_output_image_size(UpperCAmelCase_       , size=size['''shortest_edge''']       , default_to_square=UpperCAmelCase_						)
											return resize(UpperCAmelCase_       , size=UpperCAmelCase_       , resample=UpperCAmelCase_       , data_format=UpperCAmelCase_       , **UpperCAmelCase_						)



				def       __snake_case		( self       , UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_ = None       , **UpperCAmelCase_       , ):
											lowerCAmelCase   =			get_size_dict(UpperCAmelCase_						)
											if "height" not in size or "width" not in size:
																		raise ValueError(F"""The `size` parameter must contain the keys (height, width). Got {size.keys()}"""						)
											return center_crop(UpperCAmelCase_       , size=(size['''height'''], size['''width'''])       , data_format=UpperCAmelCase_       , **UpperCAmelCase_						)



				def       __snake_case		( self       , UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_ = None       , **UpperCAmelCase_       , ):
											return rescale(UpperCAmelCase_       , scale=UpperCAmelCase_       , data_format=UpperCAmelCase_       , **UpperCAmelCase_						)



				def       __snake_case		( self       , UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_ = None       , **UpperCAmelCase_       , ):
											return normalize(UpperCAmelCase_       , mean=UpperCAmelCase_       , std=UpperCAmelCase_       , data_format=UpperCAmelCase_       , **UpperCAmelCase_						)






				def       __snake_case		( self       , UpperCAmelCase_       , UpperCAmelCase_ = None       , UpperCAmelCase_ = None       , UpperCAmelCase_ = None       , UpperCAmelCase_ = None       , UpperCAmelCase_ = None       , UpperCAmelCase_ = None       , UpperCAmelCase_ = None       , UpperCAmelCase_ = None       , UpperCAmelCase_ = None       , UpperCAmelCase_ = None       , UpperCAmelCase_ = None       , UpperCAmelCase_ = None       , UpperCAmelCase_ = ChannelDimension.FIRST       , **UpperCAmelCase_       , ):
											lowerCAmelCase   =			do_resize if do_resize is not None else self.do_resize
											lowerCAmelCase   =			size if size is not None else self.size
											lowerCAmelCase   =			get_size_dict(UpperCAmelCase_       , param_name='''size'''       , default_to_square=UpperCAmelCase_						)
											lowerCAmelCase   =			resample if resample is not None else self.resample
											lowerCAmelCase   =			do_center_crop if do_center_crop is not None else self.do_center_crop
											lowerCAmelCase   =			crop_size if crop_size is not None else self.crop_size
											lowerCAmelCase   =			get_size_dict(UpperCAmelCase_       , param_name='''crop_size'''       , default_to_square=UpperCAmelCase_						)
											lowerCAmelCase   =			do_rescale if do_rescale is not None else self.do_rescale
											lowerCAmelCase   =			rescale_factor if rescale_factor is not None else self.rescale_factor
											lowerCAmelCase   =			do_normalize if do_normalize is not None else self.do_normalize
											lowerCAmelCase   =			image_mean if image_mean is not None else self.image_mean
											lowerCAmelCase   =			image_std if image_std is not None else self.image_std
											lowerCAmelCase   =			do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

											lowerCAmelCase   =			make_list_of_images(UpperCAmelCase_						)

											if not valid_images(UpperCAmelCase_						):
																		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.'''						)

											# PIL RGBA images are converted to RGB
											if do_convert_rgb:
																		lowerCAmelCase   =			[convert_to_rgb(UpperCAmelCase_						) for image in images]

											# All transformations expect numpy arrays.
											lowerCAmelCase   =			[to_numpy_array(UpperCAmelCase_						) for image in images]

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

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

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

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

											lowerCAmelCase   =			[to_channel_dimension_format(UpperCAmelCase_       , UpperCAmelCase_						) for image in images]

											lowerCAmelCase   =			{'''pixel_values''': images}
											return BatchFeature(data=UpperCAmelCase_       , tensor_type=UpperCAmelCase_						)









import random
import unittest

import torch

from diffusers import IFInpaintingSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin


@skip_mps
class 							__UpperCamelCase			( __UpperCAmelCase     ,					__UpperCAmelCase     ,					unittest.TestCase  ):





				'''simple docstring'''



				__a							: Tuple										=IFInpaintingSuperResolutionPipeline
				__a							: Dict										=TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""width""", """height"""}
				__a							: int										=TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS.union({"""original_image"""}  )
				__a							: Union[str, Any]										=PipelineTesterMixin.required_optional_params - {"""latents"""}



				def       __snake_case		( self						):
											return self._get_superresolution_dummy_components()



				def       __snake_case		( self       , UpperCAmelCase_       , UpperCAmelCase_=0						):
											if str(UpperCAmelCase_						).startswith('''mps'''						):
																		lowerCAmelCase   =		torch.manual_seed(UpperCAmelCase_						)
											else:
																		lowerCAmelCase   =		torch.Generator(device=UpperCAmelCase_						).manual_seed(UpperCAmelCase_						)

											lowerCAmelCase   =		floats_tensor((1, 3, 16, 16)       , rng=random.Random(UpperCAmelCase_						)						).to(UpperCAmelCase_						)
											lowerCAmelCase   =		floats_tensor((1, 3, 32, 32)       , rng=random.Random(UpperCAmelCase_						)						).to(UpperCAmelCase_						)
											lowerCAmelCase   =		floats_tensor((1, 3, 32, 32)       , rng=random.Random(UpperCAmelCase_						)						).to(UpperCAmelCase_						)

											lowerCAmelCase   =		{
											    '''prompt''': '''A painting of a squirrel eating a burger''',
											    '''image''': image,
											    '''original_image''': original_image,
											    '''mask_image''': mask_image,
											    '''generator''': generator,
											    '''num_inference_steps''': 2,
											    '''output_type''': '''numpy''',
											}

											return inputs



				@unittest.skipIf(
				    torch_device != '''cuda''' or not is_xformers_available()       , reason='''XFormers attention is only available with CUDA and `xformers` installed'''       , )
				def       __snake_case		( self						):
											self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3						)



				def       __snake_case		( self						):
											self._test_save_load_optional_components()



				@unittest.skipIf(torch_device != '''cuda'''       , reason='''float16 requires CUDA'''						)
				def       __snake_case		( self						):
											# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
											super().test_save_load_floataa(expected_max_diff=1E-1						)



				def       __snake_case		( self						):
											self._test_attention_slicing_forward_pass(expected_max_diff=1E-2						)



				def       __snake_case		( self						):
											self._test_save_load_local()






				def       __snake_case		( self						):
											self._test_inference_batch_single_identical(
											    expected_max_diff=1E-2       , )



