

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        UpperCamelCase_(							_snake_case   :  Tuple	):




			"""simple docstring"""






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






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

			SCREAMING_SNAKE_CASE							   =						True





			@property
			def 		__magic_name__    (		self					)   -> List[Any]:




						'''simple docstring'''
						__a        =1
						__a        =4
						__a        =(16, 16)

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





			def 		__magic_name__    (		self					)   -> str:




						'''simple docstring'''
						torch.manual_seed(0					)
						__a        =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')					,  )
						__a        =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					,  )
						__a        =EulerDiscreteScheduler(prediction_type='sample'					)
						__a        =CLIPTextConfig(
						    bos_token_id=0					,  eos_token_id=2					,  hidden_size=32					,  intermediate_size=37					,  layer_norm_eps=1e-05					,  num_attention_heads=4					,  num_hidden_layers=5					,  pad_token_id=1					,  vocab_size=1000					,  hidden_act='quick_gelu'					,  projection_dim=512					,  )
						__a        =CLIPTextModel(__snake_case					)
						__a        =CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'					)

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

						return components





			def 		__magic_name__    (		self					,  __snake_case					,  __snake_case=0					)   -> Union[str, Any]:




						'''simple docstring'''
						if str(__snake_case					).startswith('mps'					):
									__a        =torch.manual_seed(__snake_case					)
						else:
									__a        =torch.Generator(device=__snake_case					).manual_seed(__snake_case					)
						__a        ={
						    '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 		__magic_name__    (		self					)   -> List[Any]:




						'''simple docstring'''
						__a        ='cpu'

						__a        =self.get_dummy_components()
						__a        =self.pipeline_class(**__snake_case					)
						pipe.to(__snake_case					)
						pipe.set_progress_bar_config(disable=__snake_case					)

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

						self.assertEqual(image.shape					,  (1, 256, 256, 3)					)
						__a        =np.array(
						    [0.4722_2412, 0.4192_1633, 0.4471_7434, 0.4687_4192, 0.4258_8258, 0.4615_0726, 0.467_7534, 0.4558_3832, 0.4857_9055]					)
						__a        =np.abs(image_slice.flatten() - expected_slice					).max()
						self.assertLessEqual(__snake_case					,  1e-3					)





			def 		__magic_name__    (		self					)   -> int:




						'''simple docstring'''
						super().test_attention_slicing_forward_pass(expected_max_diff=7e-3					)





			def 		__magic_name__    (		self					)   -> int:




						'''simple docstring'''
						super().test_cpu_offload_forward_pass(expected_max_diff=3e-3					)





			def 		__magic_name__    (		self					)   -> str:




						'''simple docstring'''
						super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3					)





			def 		__magic_name__    (		self					)   -> Any:




						'''simple docstring'''
						super().test_inference_batch_single_identical(expected_max_diff=7e-3					)





			def 		__magic_name__    (		self					)   -> Tuple:




						'''simple docstring'''
						super().test_pt_np_pil_outputs_equivalent(expected_max_diff=3e-3					)





			def 		__magic_name__    (		self					)   -> List[Any]:




						'''simple docstring'''
						super().test_save_load_local(expected_max_difference=3e-3					)





			def 		__magic_name__    (		self					)   -> Dict:




						'''simple docstring'''
						super().test_save_load_optional_components(expected_max_difference=3e-3					)





			def 		__magic_name__    (		self					)   -> Tuple:




						'''simple docstring'''
						__a        =[
						    'DDIMScheduler',
						    'DDPMScheduler',
						    'PNDMScheduler',
						    'HeunDiscreteScheduler',
						    'EulerAncestralDiscreteScheduler',
						    'KDPM2DiscreteScheduler',
						    'KDPM2AncestralDiscreteScheduler',
						    'DPMSolverSDEScheduler',
						]
						__a        =self.get_dummy_components()
						__a        =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					)
						__a        =self.get_dummy_inputs(__snake_case					)
						__a        =2

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

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

						assert check_same_shape(__snake_case					)






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





			def 		__magic_name__    (		self					)   -> List[str]:




						'''simple docstring'''
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()





			def 		__magic_name__    (		self					)   -> Optional[int]:




						'''simple docstring'''
						__a        =torch.manual_seed(33					)

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

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

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

						__a        =pipe(__snake_case					,  generator=__snake_case					,  output_type='latent'					).images

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

						__a        =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 		__magic_name__    (		self					)   -> str:




						'''simple docstring'''
						__a        =torch.manual_seed(33					)

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

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

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

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

						__a        =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 ... import PretrainedConfig


_lowerCAmelCase					:			Any  				=			{
    "sijunhe/nezha-cn-base": "https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json",
}






class 	__magic_name__    (			lowerCAmelCase_      ):
			SCREAMING_SNAKE_CASE							   =						NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
			SCREAMING_SNAKE_CASE							   =						'nezha'





			def __init__(		self					,  __snake_case=2_1128					,  __snake_case=768					,  __snake_case=12					,  __snake_case=12					,  __snake_case=3072					,  __snake_case="gelu"					,  __snake_case=0.1					,  __snake_case=0.1					,  __snake_case=512					,  __snake_case=64					,  __snake_case=2					,  __snake_case=0.02					,  __snake_case=1e-12					,  __snake_case=0.1					,  __snake_case=0					,  __snake_case=2					,  __snake_case=3					,  __snake_case=True					,  **__snake_case					,  )   -> str:




						'''simple docstring'''
						super().__init__(pad_token_id=__snake_case					,  bos_token_id=__snake_case					,  eos_token_id=__snake_case					,  **__snake_case					)

						__a        =vocab_size
						__a        =hidden_size
						__a        =num_hidden_layers
						__a        =num_attention_heads
						__a        =hidden_act
						__a        =intermediate_size
						__a        =hidden_dropout_prob
						__a        =attention_probs_dropout_prob
						__a        =max_position_embeddings
						__a        =max_relative_position
						__a        =type_vocab_size
						__a        =initializer_range
						__a        =layer_norm_eps
						__a        =classifier_dropout
						__a        =use_cache






