





'''simple docstring'''




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_							)   ->str:
	snake_case__	       =   [tensor.shape for tensor in tensor_list]
	return all(shape == shapes[0] for shape in shapes[1:]							)






class 				__snake_case     (					__magic_name__       ,       __magic_name__       ,       __magic_name__       ,       unittest.TestCase							):
	__lowerCAmelCase       						=  StableDiffusionLatentUpscalePipeline
	__lowerCAmelCase       						=  TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
	    '''height''',
	    '''width''',
	    '''cross_attention_kwargs''',
	    '''negative_prompt_embeds''',
	    '''prompt_embeds''',
	}
	__lowerCAmelCase       						=  PipelineTesterMixin.required_optional_params - {'''num_images_per_prompt'''}
	__lowerCAmelCase       						=  TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
	__lowerCAmelCase       						=  frozenset(
	    []							)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
	__lowerCAmelCase       						=  frozenset([]							)

	__lowerCAmelCase       						=  True





	@property
	def   _snake_case   (		self		)      ->		Optional[int]:
		snake_case__	       =   1
		snake_case__	       =   4
		snake_case__	       =   (16, 16)

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





	def   _snake_case   (		self		)      ->		Optional[int]:
		torch.manual_seed(0		)
		snake_case__	       =   UNetaDConditionModel(
		    act_fn='gelu'      ,       attention_head_dim=8      ,       norm_num_groups=UpperCamelCase_      ,       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=UpperCamelCase_      ,       only_cross_attention=UpperCamelCase_      ,       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(UpperCamelCase_		)
		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   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_=0		)      ->		int:
		if str(UpperCamelCase_		).startswith('mps'		):
			snake_case__	       =   torch.manual_seed(UpperCamelCase_		)
		else:
			snake_case__	       =   torch.Generator(device=UpperCamelCase_		).manual_seed(UpperCamelCase_		)
		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   _snake_case   (		self		)      ->		Union[str, Any]:
		snake_case__	       =   'cpu'

		snake_case__	       =   self.get_dummy_components()
		snake_case__	       =   self.pipeline_class(**UpperCamelCase_		)
		pipe.to(UpperCamelCase_		)
		pipe.set_progress_bar_config(disable=UpperCamelCase_		)

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

		self.assertEqual(image.shape      ,       (1, 256, 256, 3)		)
		snake_case__	       =   np.array(
		    [0.4_7_2_2_2_4_1_2, 0.4_1_9_2_1_6_3_3, 0.4_4_7_1_7_4_3_4, 0.4_6_8_7_4_1_9_2, 0.4_2_5_8_8_2_5_8, 0.4_6_1_5_0_7_2_6, 0.4_6_7_7_5_3_4, 0.4_5_5_8_3_8_3_2, 0.4_8_5_7_9_0_5_5]		)
		snake_case__	       =   np.abs(image_slice.flatten() - expected_slice		).max()
		self.assertLessEqual(UpperCamelCase_      ,       1E-3		)





	def   _snake_case   (		self		)      ->		Optional[Any]:
		super().test_attention_slicing_forward_pass(expected_max_diff=7E-3		)





	def   _snake_case   (		self		)      ->		Union[str, Any]:
		super().test_cpu_offload_forward_pass(expected_max_diff=3E-3		)





	def   _snake_case   (		self		)      ->		Tuple:
		super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3		)





	def   _snake_case   (		self		)      ->		str:
		super().test_inference_batch_single_identical(expected_max_diff=7E-3		)





	def   _snake_case   (		self		)      ->		Optional[Any]:
		super().test_pt_np_pil_outputs_equivalent(expected_max_diff=3E-3		)





	def   _snake_case   (		self		)      ->		Any:
		super().test_save_load_local(expected_max_difference=3E-3		)





	def   _snake_case   (		self		)      ->		List[Any]:
		super().test_save_load_optional_components(expected_max_difference=3E-3		)





	def   _snake_case   (		self		)      ->		str:
		snake_case__	       =   [
		    'DDIMScheduler',
		    'DDPMScheduler',
		    'PNDMScheduler',
		    'HeunDiscreteScheduler',
		    'EulerAncestralDiscreteScheduler',
		    'KDPM2DiscreteScheduler',
		    'KDPM2AncestralDiscreteScheduler',
		    'DPMSolverSDEScheduler',
		]
		snake_case__	       =   self.get_dummy_components()
		snake_case__	       =   self.pipeline_class(**UpperCamelCase_		)

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

		pipe.to(UpperCamelCase_		)
		pipe.set_progress_bar_config(disable=UpperCamelCase_		)
		snake_case__	       =   self.get_dummy_inputs(UpperCamelCase_		)
		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(UpperCamelCase_      ,       scheduler_enum.name		)
			snake_case__	       =   scheduler_cls.from_config(pipe.scheduler.config		)
			snake_case__	       =   pipe(**UpperCamelCase_		)[0]
			outputs.append(UpperCamelCase_		)

		assert check_same_shape(UpperCamelCase_		)






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





	def   _snake_case   (		self		)      ->		Dict:
		super().tearDown()
		gc.collect()
		torch.cuda.empty_cache()





	def   _snake_case   (		self		)      ->		Union[str, Any]:
		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(UpperCamelCase_      ,       generator=UpperCamelCase_      ,       output_type='latent'		).images

		snake_case__	       =   upscaler(
		    prompt=UpperCamelCase_      ,       image=UpperCamelCase_      ,       num_inference_steps=20      ,       guidance_scale=0      ,       generator=UpperCamelCase_      ,       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   _snake_case   (		self		)      ->		List[str]:
		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=UpperCamelCase_      ,       image=UpperCamelCase_      ,       num_inference_steps=20      ,       guidance_scale=0      ,       generator=UpperCamelCase_      ,       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










'''simple docstring'''




import enum
import shutil
import sys


a__      ,       a__					:			Any          =     shutil.get_terminal_size()

a__					:			Optional[int]          =     {'''UP''': '''A''', '''DOWN''': '''B''', '''RIGHT''': '''C''', '''LEFT''': '''D'''}






class 				__snake_case     (					enum.Enum							):
	__lowerCAmelCase       						=  0
	__lowerCAmelCase       						=  1





def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_=""							)   ->Optional[Any]:
	sys.stdout.write(str(UpperCAmelCase_							) + end							)
	sys.stdout.flush()




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_=""							)   ->List[str]:
	forceWrite(f'''\u001b[{color}m{content}\u001b[0m'''					,				UpperCAmelCase_							)




def 						__lowerCamelCase			(	)   ->Optional[Any]:
	forceWrite('\r'							)




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_							)   ->Any:
	forceWrite(f'''\033[{num_lines}{CURSOR_TO_CHAR[direction.upper()]}'''							)




def 						__lowerCamelCase			(	)   ->str:
	forceWrite(' ' * TERMINAL_WIDTH							)
	reset_cursor()







def 						__lowerCamelCase			(	)   ->Tuple:
	reset_cursor()
	forceWrite('-' * TERMINAL_WIDTH							)



