
"""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__		(			__snake_case	)							->		Dict:


					"""simple docstring"""





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






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

		lowercase__						=      True



		@property
		def 						UpperCAmelCase							(					self)       ->       Dict:






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

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



		def 						UpperCAmelCase							(					self)       ->       Optional[Any]:






							'''simple docstring'''
							torch.manual_seed(0)
							_UpperCamelCase							 =  UNetaDConditionModel(
							    act_fn='''gelu'''				,		attention_head_dim=8				,		norm_num_groups=__a				,		block_out_channels=[32, 32, 64, 64]				,		time_cond_proj_dim=1_60				,		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=__a				,		only_cross_attention=__a				,		out_channels=5				,		resnet_time_scale_shift='''scale_shift'''				,		time_embedding_type='''fourier'''				,		timestep_post_act='''gelu'''				,		up_block_types=('''KCrossAttnUpBlock2D''', '''KCrossAttnUpBlock2D''', '''KCrossAttnUpBlock2D''', '''KUpBlock2D''')				,		)
							_UpperCamelCase							 =  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				,		)
							_UpperCamelCase							 =  EulerDiscreteScheduler(prediction_type='''sample''')
							_UpperCamelCase							 =  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=10_00				,		hidden_act='''quick_gelu'''				,		projection_dim=5_12				,		)
							_UpperCamelCase							 =  CLIPTextModel(__a)
							_UpperCamelCase							 =  CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')

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

							return components



		def 						UpperCAmelCase							(					self				,		__a				,		__a=0)       ->       Dict:






							'''simple docstring'''
							if str(__a).startswith('''mps'''):
												_UpperCamelCase							 =  torch.manual_seed(__a)
							else:
												_UpperCamelCase							 =  torch.Generator(device=__a).manual_seed(__a)
							_UpperCamelCase							 =  {
							    '''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 						UpperCAmelCase							(					self)       ->       int:






							'''simple docstring'''
							_UpperCamelCase							 =  '''cpu'''

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

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

							self.assertEqual(image.shape				,		(1, 2_56, 2_56, 3))
							_UpperCamelCase							 =  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])
							_UpperCamelCase							 =  np.abs(image_slice.flatten() - expected_slice).max()
							self.assertLessEqual(__a				,		1e-3)



		def 						UpperCAmelCase							(					self)       ->       int:






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



		def 						UpperCAmelCase							(					self)       ->       Tuple:






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



		def 						UpperCAmelCase							(					self)       ->       Optional[Any]:






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



		def 						UpperCAmelCase							(					self)       ->       Dict:






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



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






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



		def 						UpperCAmelCase							(					self)       ->       Dict:






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



		def 						UpperCAmelCase							(					self)       ->       Any:






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




		def 						UpperCAmelCase							(					self)       ->       Dict:






							'''simple docstring'''
							_UpperCamelCase							 =  [
							    '''DDIMScheduler''',
							    '''DDPMScheduler''',
							    '''PNDMScheduler''',
							    '''HeunDiscreteScheduler''',
							    '''EulerAncestralDiscreteScheduler''',
							    '''KDPM2DiscreteScheduler''',
							    '''KDPM2AncestralDiscreteScheduler''',
							    '''DPMSolverSDEScheduler''',
							]
							_UpperCamelCase							 =  self.get_dummy_components()
							_UpperCamelCase							 =  self.pipeline_class(**__a)

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

							pipe.to(__a)
							pipe.set_progress_bar_config(disable=__a)
							_UpperCamelCase							 =  self.get_dummy_inputs(__a)
							_UpperCamelCase							 =  2

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

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

							assert check_same_shape(__a)






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



		def 						UpperCAmelCase							(					self)       ->       Dict:






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



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






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

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

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

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

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

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

							_UpperCamelCase							 =  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 						UpperCAmelCase							(					self)       ->       str:






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

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

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

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

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

							_UpperCamelCase							 =  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 __future__ import annotations




def 		lowerCAmelCase_    (  __UpperCAmelCase:			list[int]		,   __UpperCAmelCase:			int      )      ->	list[int]:
    UpperCamelCase__							:				Optional[int]          =      0
    UpperCamelCase__							:				Tuple          =      len(__UpperCAmelCase      ) - 1

    while i < j:
        if nums[i] + nums[j] == target:
            return [i, j]
        elif nums[i] + nums[j] < target:
            UpperCamelCase__							:				Tuple          =      i + 1
        else:
            UpperCamelCase__							:				str          =      j - 1

    return []


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  print(F'''{two_pointer([2, 7, 11, 15], 9) = }''')

