




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 convert_to_rgb, 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


if is_vision_available():
			import PIL


UpperCAmelCase__				     = logging.get_logger(__name__)







class a (     _A  ):
						"""simple docstring"""

						UpperCamelCase_	:       int       	=		['pixel_values']






						def __init__( self :     List[Any]				,							lowerCamelCase__ :     Union[str, Any] = True				,							lowerCamelCase__ :     Union[str, Any] = None				,							lowerCamelCase__ :     List[str] = PILImageResampling.BICUBIC				,							lowerCamelCase__ :     Any = True				,							lowerCamelCase__ :     Optional[Any] = 1 / 255				,							lowerCamelCase__ :     Union[str, Any] = True				,							lowerCamelCase__ :     str = None				,							lowerCamelCase__ :     int = None				,							lowerCamelCase__ :     Tuple = True				,							**lowerCamelCase__ :     Optional[Any]				,							)    ->						None:
													"""simple docstring"""


													super().__init__(**lowerCamelCase__				)
													__lowercase      =    size if size is not None else {"height": 384, "width": 384}
													__lowercase      =    get_size_dict(lowerCamelCase__				,							default_to_square=lowerCamelCase__				)

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






						def     UpperCAmelCase_      ( self :     List[Any]				,							lowerCamelCase__ :     Any				,							lowerCamelCase__ :     List[Any]				,							lowerCamelCase__ :     int = PILImageResampling.BICUBIC				,							lowerCamelCase__ :     int = None				,							**lowerCamelCase__ :     Tuple				,							)    ->						np.ndarray:
													"""simple docstring"""


													__lowercase      =    get_size_dict(lowerCamelCase__				,							default_to_square=lowerCamelCase__				)
													if "height" not in size or "width" not in size:
																				raise ValueError(f'The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}'				)
													__lowercase      =    (size["height"], size["width"])
													return resize(lowerCamelCase__				,							size=lowerCamelCase__				,							resample=lowerCamelCase__				,							data_format=lowerCamelCase__				,							**lowerCamelCase__				)






						def     UpperCAmelCase_      ( self :     int				,							lowerCamelCase__ :     Any				,							lowerCamelCase__ :     Union[str, Any]				,							lowerCamelCase__ :     Optional[int] = None				,							**lowerCamelCase__ :     Union[str, Any]				,							)    ->						str:
													"""simple docstring"""


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






						def     UpperCAmelCase_      ( self :     List[Any]				,							lowerCamelCase__ :     int				,							lowerCamelCase__ :     List[str]				,							lowerCamelCase__ :     Union[str, Any]				,							lowerCamelCase__ :     List[str] = None				,							**lowerCamelCase__ :     List[Any]				,							)    ->						np.ndarray:
													"""simple docstring"""


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






						def     UpperCAmelCase_      ( self :     str				,							lowerCamelCase__ :     Dict				,							lowerCamelCase__ :     int = None				,							lowerCamelCase__ :     Optional[int] = None				,							lowerCamelCase__ :     Union[str, Any] = None				,							lowerCamelCase__ :     Dict = None				,							lowerCamelCase__ :     List[Any] = None				,							lowerCamelCase__ :     Optional[int] = None				,							lowerCamelCase__ :     Dict = None				,							lowerCamelCase__ :     str = None				,							lowerCamelCase__ :     int = None				,							lowerCamelCase__ :     Optional[Any] = None				,							lowerCamelCase__ :     List[str] = ChannelDimension.FIRST				,							**lowerCamelCase__ :     Tuple				,							)    ->						PIL.Image.Image:
													"""simple docstring"""


													__lowercase      =    do_resize if do_resize is not None else self.do_resize
													__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      =    do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

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

													__lowercase      =    make_list_of_images(lowerCamelCase__				)

													if not valid_images(lowerCamelCase__				):
																				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_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:
																				__lowercase      =    [convert_to_rgb(lowerCamelCase__				) for image in images]

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

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

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

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

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

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

													return encoded_outputs












import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()







class a (     __SCREAMING_SNAKE_CASE       ,						__SCREAMING_SNAKE_CASE       ,						unittest.TestCase  ):
						"""simple docstring"""

						UpperCamelCase_	:       Any       	=		CycleDiffusionPipeline
						UpperCamelCase_	:       List[str]       	=		TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
						    'negative_prompt',
						    'height',
						    'width',
						    'negative_prompt_embeds',
						}
						UpperCamelCase_	:       int       	=		PipelineTesterMixin.required_optional_params - {'latents'}
						UpperCamelCase_	:       Dict       	=		TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'source_prompt'}  )
						UpperCamelCase_	:       Dict       	=		IMAGE_TO_IMAGE_IMAGE_PARAMS
						UpperCamelCase_	:       Optional[Any]       	=		IMAGE_TO_IMAGE_IMAGE_PARAMS






						def     UpperCAmelCase_      ( self :     int				)    ->						Dict:
													"""simple docstring"""


													torch.manual_seed(0				)
													__lowercase      =    UNetaDConditionModel(
													    block_out_channels=(32, 64)				,							layers_per_block=2				,							sample_size=32				,							in_channels=4				,							out_channels=4				,							down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')				,							up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')				,							cross_attention_dim=32				,							)
													__lowercase      =    DDIMScheduler(
													    beta_start=0.0_0_0_8_5				,							beta_end=0.0_1_2				,							beta_schedule='''scaled_linear'''				,							num_train_timesteps=1_000				,							clip_sample=lowerCamelCase__				,							set_alpha_to_one=lowerCamelCase__				,							)
													torch.manual_seed(0				)
													__lowercase      =    AutoencoderKL(
													    block_out_channels=[32, 64]				,							in_channels=3				,							out_channels=3				,							down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']				,							up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']				,							latent_channels=4				,							)
													torch.manual_seed(0				)
													__lowercase      =    CLIPTextConfig(
													    bos_token_id=0				,							eos_token_id=2				,							hidden_size=32				,							intermediate_size=37				,							layer_norm_eps=1e-0_5				,							num_attention_heads=4				,							num_hidden_layers=5				,							pad_token_id=1				,							vocab_size=1_000				,							)
													__lowercase      =    CLIPTextModel(lowerCamelCase__				)
													__lowercase      =    CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''				)

													__lowercase      =    {
													    '''unet''': unet,
													    '''scheduler''': scheduler,
													    '''vae''': vae,
													    '''text_encoder''': text_encoder,
													    '''tokenizer''': tokenizer,
													    '''safety_checker''': None,
													    '''feature_extractor''': None,
													}
													return components






						def     UpperCAmelCase_      ( self :     Any				,							lowerCamelCase__ :     int				,							lowerCamelCase__ :     List[str]=0				)    ->						Any:
													"""simple docstring"""


													__lowercase      =    floats_tensor((1, 3, 32, 32)				,							rng=random.Random(lowerCamelCase__				)				).to(lowerCamelCase__				)
													__lowercase      =    image / 2 + 0.5
													if str(lowerCamelCase__				).startswith('''mps'''				):
																				__lowercase      =    torch.manual_seed(lowerCamelCase__				)
													else:
																				__lowercase      =    torch.Generator(device=lowerCamelCase__				).manual_seed(lowerCamelCase__				)
													__lowercase      =    {
													    '''prompt''': '''An astronaut riding an elephant''',
													    '''source_prompt''': '''An astronaut riding a horse''',
													    '''image''': image,
													    '''generator''': generator,
													    '''num_inference_steps''': 2,
													    '''eta''': 0.1,
													    '''strength''': 0.8,
													    '''guidance_scale''': 3,
													    '''source_guidance_scale''': 1,
													    '''output_type''': '''numpy''',
													}
													return inputs






						def     UpperCAmelCase_      ( self :     List[Any]				)    ->						Optional[Any]:
													"""simple docstring"""


													__lowercase      =    '''cpu'''  # ensure determinism for the device-dependent torch.Generator

													__lowercase      =    self.get_dummy_components()
													__lowercase      =    CycleDiffusionPipeline(**lowerCamelCase__				)
													__lowercase      =    pipe.to(lowerCamelCase__				)
													pipe.set_progress_bar_config(disable=lowerCamelCase__				)

													__lowercase      =    self.get_dummy_inputs(lowerCamelCase__				)
													__lowercase      =    pipe(**lowerCamelCase__				)
													__lowercase      =    output.images

													__lowercase      =    images[0, -3:, -3:, -1]

													assert images.shape == (1, 32, 32, 3)
													__lowercase      =    np.array([0.4_4_5_9, 0.4_9_4_3, 0.4_5_4_4, 0.6_6_4_3, 0.5_4_7_4, 0.4_3_2_7, 0.5_7_0_1, 0.5_9_5_9, 0.5_1_7_9]				)

													assert np.abs(image_slice.flatten() - expected_slice				).max() < 1e-2






						@unittest.skipIf(torch_device != '''cuda'''				,							'''This test requires a GPU'''				)
						def     UpperCAmelCase_      ( self :     str				)    ->						Optional[Any]:
													"""simple docstring"""


													__lowercase      =    self.get_dummy_components()
													for name, module in components.items():
																				if hasattr(lowerCamelCase__				,							'''half'''				):
																											__lowercase      =    module.half()
													__lowercase      =    CycleDiffusionPipeline(**lowerCamelCase__				)
													__lowercase      =    pipe.to(lowerCamelCase__				)
													pipe.set_progress_bar_config(disable=lowerCamelCase__				)

													__lowercase      =    self.get_dummy_inputs(lowerCamelCase__				)
													__lowercase      =    pipe(**lowerCamelCase__				)
													__lowercase      =    output.images

													__lowercase      =    images[0, -3:, -3:, -1]

													assert images.shape == (1, 32, 32, 3)
													__lowercase      =    np.array([0.3_5_0_6, 0.4_5_4_3, 0.4_4_6, 0.4_5_7_5, 0.5_1_9_5, 0.4_1_5_5, 0.5_2_7_3, 0.5_1_8, 0.4_1_1_6]				)

													assert np.abs(image_slice.flatten() - expected_slice				).max() < 1e-2






						@skip_mps
						def     UpperCAmelCase_      ( self :     str				)    ->						int:
													"""simple docstring"""


													return super().test_save_load_local()






						@unittest.skip('''non-deterministic pipeline'''				)
						def     UpperCAmelCase_      ( self :     int				)    ->						Union[str, Any]:
													"""simple docstring"""


													return super().test_inference_batch_single_identical()






						@skip_mps
						def     UpperCAmelCase_      ( self :     int				)    ->						Optional[int]:
													"""simple docstring"""


													return super().test_dict_tuple_outputs_equivalent()






						@skip_mps
						def     UpperCAmelCase_      ( self :     List[Any]				)    ->						List[str]:
													"""simple docstring"""


													return super().test_save_load_optional_components()






						@skip_mps
						def     UpperCAmelCase_      ( self :     Union[str, Any]				)    ->						Tuple:
													"""simple docstring"""


													return super().test_attention_slicing_forward_pass()







@slow
@require_torch_gpu
class a (     unittest.TestCase  ):
						"""simple docstring"""

						def     UpperCAmelCase_      ( self :     Optional[Any]				)    ->						Union[str, Any]:
													"""simple docstring"""


													super().tearDown()
													gc.collect()
													torch.cuda.empty_cache()






						def     UpperCAmelCase_      ( self :     Optional[int]				)    ->						List[str]:
													"""simple docstring"""


													__lowercase      =    load_image(
													    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
													    '''/cycle-diffusion/black_colored_car.png'''				)
													__lowercase      =    load_numpy(
													    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy'''				)
													__lowercase      =    init_image.resize((512, 512)				)

													__lowercase      =    '''CompVis/stable-diffusion-v1-4'''
													__lowercase      =    DDIMScheduler.from_pretrained(lowerCamelCase__				,							subfolder='''scheduler'''				)
													__lowercase      =    CycleDiffusionPipeline.from_pretrained(
													    lowerCamelCase__				,							scheduler=lowerCamelCase__				,							safety_checker=lowerCamelCase__				,							torch_dtype=torch.floataa				,							revision='''fp16'''				)

													pipe.to(lowerCamelCase__				)
													pipe.set_progress_bar_config(disable=lowerCamelCase__				)
													pipe.enable_attention_slicing()

													__lowercase      =    '''A black colored car'''
													__lowercase      =    '''A blue colored car'''

													__lowercase      =    torch.manual_seed(0				)
													__lowercase      =    pipe(
													    prompt=lowerCamelCase__				,							source_prompt=lowerCamelCase__				,							image=lowerCamelCase__				,							num_inference_steps=100				,							eta=0.1				,							strength=0.8_5				,							guidance_scale=3				,							source_guidance_scale=1				,							generator=lowerCamelCase__				,							output_type='''np'''				,							)
													__lowercase      =    output.images

													# the values aren't exactly equal, but the images look the same visually
													assert np.abs(image - expected_image				).max() < 5e-1






						def     UpperCAmelCase_      ( self :     Tuple				)    ->						List[str]:
													"""simple docstring"""


													__lowercase      =    load_image(
													    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
													    '''/cycle-diffusion/black_colored_car.png'''				)
													__lowercase      =    load_numpy(
													    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy'''				)
													__lowercase      =    init_image.resize((512, 512)				)

													__lowercase      =    '''CompVis/stable-diffusion-v1-4'''
													__lowercase      =    DDIMScheduler.from_pretrained(lowerCamelCase__				,							subfolder='''scheduler'''				)
													__lowercase      =    CycleDiffusionPipeline.from_pretrained(lowerCamelCase__				,							scheduler=lowerCamelCase__				,							safety_checker=lowerCamelCase__				)

													pipe.to(lowerCamelCase__				)
													pipe.set_progress_bar_config(disable=lowerCamelCase__				)
													pipe.enable_attention_slicing()

													__lowercase      =    '''A black colored car'''
													__lowercase      =    '''A blue colored car'''

													__lowercase      =    torch.manual_seed(0				)
													__lowercase      =    pipe(
													    prompt=lowerCamelCase__				,							source_prompt=lowerCamelCase__				,							image=lowerCamelCase__				,							num_inference_steps=100				,							eta=0.1				,							strength=0.8_5				,							guidance_scale=3				,							source_guidance_scale=1				,							generator=lowerCamelCase__				,							output_type='''np'''				,							)
													__lowercase      =    output.images

													assert np.abs(image - expected_image				).max() < 2e-2






