

import gc
import random
import unittest

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

import diffusers
from diffusers import (
    AutoencoderKL,
    EulerDiscreteScheduler,
    StableDiffusionLatentUpscalePipeline,
    StableDiffusionPipeline,
    UNetaDConditionModel,
)
from diffusers.schedulers import KarrasDiffusionSchedulers
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

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()





def 					__lowerCamelCase	( UpperCamelCase__      ):







							'''simple docstring'''






							snake_case_							=    [tensor.shape for tensor in tensor_list]
							return all(shape == shapes[0] for shape in shapes[1:]      )







class  lowercase				(       lowercase_	,  lowercase_	,  lowercase_	,  unittest.TestCase			):
				__SCREAMING_SNAKE_CASE					: Union[str, Any]       			=			StableDiffusionLatentUpscalePipeline
				__SCREAMING_SNAKE_CASE					: str       			=			TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
				    '''height''',
				    '''width''',
				    '''cross_attention_kwargs''',
				    '''negative_prompt_embeds''',
				    '''prompt_embeds''',
				}
				__SCREAMING_SNAKE_CASE					: Dict       			=			PipelineTesterMixin.required_optional_params - {'''num_images_per_prompt'''}
				__SCREAMING_SNAKE_CASE					: Optional[int]       			=			TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
				__SCREAMING_SNAKE_CASE					: Optional[int]       			=			frozenset(
				    []			)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
				__SCREAMING_SNAKE_CASE					: Any       			=			frozenset([]			)

				__SCREAMING_SNAKE_CASE					: Any       			=			True




				@property
				def     a							(					self       ):
											snake_case_							=    1
											snake_case_							=    4
											snake_case_							=    (16, 16)

											snake_case_							=    floats_tensor((batch_size, num_channels) + sizes      ,   rng=random.Random(0       )       ).to(snake_case       )
											return image




				def     a							(					self       ):
											torch.manual_seed(0       )
											snake_case_							=    UNetaDConditionModel(
											    act_fn='gelu'      ,   attention_head_dim=8      ,   norm_num_groups=snake_case      ,   block_out_channels=[32, 32, 64, 64]      ,   time_cond_proj_dim=160      ,   conv_in_kernel=1      ,   conv_out_kernel=1      ,   cross_attention_dim=32      ,   down_block_types=(
											        'KDownBlock2D',
											        'KCrossAttnDownBlock2D',
											        'KCrossAttnDownBlock2D',
											        'KCrossAttnDownBlock2D',
											    )      ,   in_channels=8      ,   mid_block_type=snake_case      ,   only_cross_attention=snake_case      ,   out_channels=5      ,   resnet_time_scale_shift='scale_shift'      ,   time_embedding_type='fourier'      ,   timestep_post_act='gelu'      ,   up_block_types=('KCrossAttnUpBlock2D', 'KCrossAttnUpBlock2D', 'KCrossAttnUpBlock2D', 'KUpBlock2D')      ,   )
											snake_case_							=    AutoencoderKL(
											    block_out_channels=[32, 32, 64, 64]      ,   in_channels=3      ,   out_channels=3      ,   down_block_types=[
											        'DownEncoderBlock2D',
											        'DownEncoderBlock2D',
											        'DownEncoderBlock2D',
											        'DownEncoderBlock2D',
											    ]      ,   up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D', 'UpDecoderBlock2D', 'UpDecoderBlock2D']      ,   latent_channels=4      ,   )
											snake_case_							=    EulerDiscreteScheduler(prediction_type='sample'       )
											snake_case_							=    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=1000      ,   hidden_act='quick_gelu'      ,   projection_dim=512      ,   )
											snake_case_							=    CLIPTextModel(snake_case       )
											snake_case_							=    CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'       )

											snake_case_							=    {
											    'unet': model.eval(),
											    'vae': vae.eval(),
											    'scheduler': scheduler,
											    'text_encoder': text_encoder,
											    'tokenizer': tokenizer,
											}

											return components




				def     a							(					self      ,   snake_case      ,   snake_case=0       ):
											if str(snake_case       ).startswith('mps'       ):
																		snake_case_							=    torch.manual_seed(snake_case       )
											else:
																		snake_case_							=    torch.Generator(device=snake_case       ).manual_seed(snake_case       )
											snake_case_							=    {
											    'prompt': 'A painting of a squirrel eating a burger',
											    'image': self.dummy_image.cpu(),
											    'generator': generator,
											    'num_inference_steps': 2,
											    'output_type': 'numpy',
											}
											return inputs




				def     a							(					self       ):
											snake_case_							=    'cpu'

											snake_case_							=    self.get_dummy_components()
											snake_case_							=    self.pipeline_class(**snake_case       )
											pipe.to(snake_case       )
											pipe.set_progress_bar_config(disable=snake_case       )

											snake_case_							=    self.get_dummy_inputs(snake_case       )
											snake_case_							=    pipe(**snake_case       ).images
											snake_case_							=    image[0, -3:, -3:, -1]

											self.assertEqual(image.shape      ,   (1, 256, 256, 3)       )
											snake_case_							=    np.array(
											    [0.47_22_24_12, 0.41_92_16_33, 0.44_71_74_34, 0.46_87_41_92, 0.42_58_82_58, 0.46_15_07_26, 0.4_67_75_34, 0.45_58_38_32, 0.48_57_90_55]       )
											snake_case_							=    np.abs(image_slice.flatten() - expected_slice       ).max()
											self.assertLessEqual(snake_case      ,   1e-3       )




				def     a							(					self       ):
											super().test_attention_slicing_forward_pass(expected_max_diff=7e-3       )




				def     a							(					self       ):
											super().test_cpu_offload_forward_pass(expected_max_diff=3e-3       )




				def     a							(					self       ):
											super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3       )




				def     a							(					self       ):
											super().test_inference_batch_single_identical(expected_max_diff=7e-3       )




				def     a							(					self       ):
											super().test_pt_np_pil_outputs_equivalent(expected_max_diff=3e-3       )




				def     a							(					self       ):
											super().test_save_load_local(expected_max_difference=3e-3       )




				def     a							(					self       ):
											super().test_save_load_optional_components(expected_max_difference=3e-3       )




				def     a							(					self       ):
											snake_case_							=    [
											    'DDIMScheduler',
											    'DDPMScheduler',
											    'PNDMScheduler',
											    'HeunDiscreteScheduler',
											    'EulerAncestralDiscreteScheduler',
											    'KDPM2DiscreteScheduler',
											    'KDPM2AncestralDiscreteScheduler',
											    'DPMSolverSDEScheduler',
											]
											snake_case_							=    self.get_dummy_components()
											snake_case_							=    self.pipeline_class(**snake_case       )

											# make sure that PNDM does not need warm-up
											pipe.scheduler.register_to_config(skip_prk_steps=snake_case       )

											pipe.to(snake_case       )
											pipe.set_progress_bar_config(disable=snake_case       )
											snake_case_							=    self.get_dummy_inputs(snake_case       )
											snake_case_							=    2

											snake_case_							=    []
											for scheduler_enum in KarrasDiffusionSchedulers:
																		if scheduler_enum.name in skip_schedulers:
																									# no sigma schedulers are not supported
																									# no schedulers
																									continue

																		snake_case_							=    getattr(snake_case      ,   scheduler_enum.name       )
																		snake_case_							=    scheduler_cls.from_config(pipe.scheduler.config       )
																		snake_case_							=    pipe(**snake_case       )[0]
																		outputs.append(snake_case       )

											assert check_same_shape(snake_case       )







@require_torch_gpu
@slow
class  lowercase				(       unittest.TestCase			):




				def     a							(					self       ):
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()




				def     a							(					self       ):
											snake_case_							=    torch.manual_seed(33       )

											snake_case_							=    StableDiffusionPipeline.from_pretrained('CompVis/stable-diffusion-v1-4'      ,   torch_dtype=torch.floataa       )
											pipe.to('cuda'       )

											snake_case_							=    StableDiffusionLatentUpscalePipeline.from_pretrained(
											    'stabilityai/sd-x2-latent-upscaler'      ,   torch_dtype=torch.floataa       )
											upscaler.to('cuda'       )

											snake_case_							=    'a photo of an astronaut high resolution, unreal engine, ultra realistic'

											snake_case_							=    pipe(snake_case      ,   generator=snake_case      ,   output_type='latent'       ).images

											snake_case_							=    upscaler(
											    prompt=snake_case      ,   image=snake_case      ,   num_inference_steps=20      ,   guidance_scale=0      ,   generator=snake_case      ,   output_type='np'      ,   ).images[0]

											snake_case_							=    load_numpy(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/astronaut_1024.npy'       )
											assert np.abs((expected_image - image).mean()       ) < 5e-2




				def     a							(					self       ):
											snake_case_							=    torch.manual_seed(33       )

											snake_case_							=    StableDiffusionLatentUpscalePipeline.from_pretrained(
											    'stabilityai/sd-x2-latent-upscaler'      ,   torch_dtype=torch.floataa       )
											upscaler.to('cuda'       )

											snake_case_							=    'the temple of fire by Ross Tran and Gerardo Dottori, oil on canvas'

											snake_case_							=    load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_512.png'       )

											snake_case_							=    upscaler(
											    prompt=snake_case      ,   image=snake_case      ,   num_inference_steps=20      ,   guidance_scale=0      ,   generator=snake_case      ,   output_type='np'      ,   ).images[0]

											snake_case_							=    load_numpy(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/latent-upscaler/fire_temple_1024.npy'       )
											assert np.abs((expected_image - image).max()       ) < 5e-2







from abc import ABC, abstractmethod
from argparse import ArgumentParser







class  lowercase				(       lowercase_			):




				@staticmethod
				@abstractmethod
				def     a							(					snake_case       ):
											raise NotImplementedError()




				@abstractmethod
				def     a							(					self       ):
											raise NotImplementedError()




