






'''simple docstring'''







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 	SCREAMING_SNAKE_CASE					(					__SCREAMING_SNAKE_CASE	,   __SCREAMING_SNAKE_CASE	,   unittest.TestCase       ):
			snake_case__  =				CycleDiffusionPipeline
			snake_case__  =				TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
			    "negative_prompt",
			    "height",
			    "width",
			    "negative_prompt_embeds",
			}
			snake_case__  =				PipelineTesterMixin.required_optional_params - {"latents"}
			snake_case__  =				TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"source_prompt"}       )
			snake_case__  =				IMAGE_TO_IMAGE_IMAGE_PARAMS
			snake_case__  =				IMAGE_TO_IMAGE_IMAGE_PARAMS






			def 			SCREAMING_SNAKE_CASE      ( self      :			Union[str, Any]					)			->			str:
							torch.manual_seed(0					)
							a_		:  Any											=      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  ,						)
							a_		:  str											=      DDIMScheduler(
							    beta_start=0.0_0085  ,						beta_end=0.012  ,						beta_schedule='''scaled_linear'''  ,						num_train_timesteps=1000  ,						clip_sample=__snake_case  ,						set_alpha_to_one=__snake_case  ,						)
							torch.manual_seed(0					)
							a_		:  Tuple											=      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					)
							a_		:  List[str]											=      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  ,						)
							a_		:  Optional[int]											=      CLIPTextModel(__snake_case					)
							a_		:  Optional[int]											=      CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''					)

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






			def 			SCREAMING_SNAKE_CASE      ( self      :			Tuple  ,						__SCREAMING_SNAKE_CASE      :			Tuple  ,						__SCREAMING_SNAKE_CASE      :			Any=0					)			->			List[Any]:
							a_		:  Any											=      floats_tensor((1, 3, 32, 32)  ,						rng=random.Random(__snake_case					)					).to(__snake_case					)
							a_		:  int											=      image / 2 + 0.5
							if str(__snake_case					).startswith('''mps'''					):
											a_		:  Union[str, Any]											=      torch.manual_seed(__snake_case					)
							else:
											a_		:  List[str]											=      torch.Generator(device=__snake_case					).manual_seed(__snake_case					)
							a_		:  str											=      {
							    '''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 			SCREAMING_SNAKE_CASE      ( self      :			Union[str, Any]					)			->			Union[str, Any]:
							a_		:  Tuple											=      '''cpu'''  # ensure determinism for the device-dependent torch.Generator

							a_		:  int											=      self.get_dummy_components()
							a_		:  Optional[int]											=      CycleDiffusionPipeline(**__snake_case					)
							a_		:  Tuple											=      pipe.to(__snake_case					)
							pipe.set_progress_bar_config(disable=__snake_case					)

							a_		:  int											=      self.get_dummy_inputs(__snake_case					)
							a_		:  Optional[Any]											=      pipe(**__snake_case					)
							a_		:  int											=      output.images

							a_		:  Tuple											=      images[0, -3:, -3:, -1]

							assert images.shape == (1, 32, 32, 3)
							a_		:  Any											=      np.array([0.4459, 0.4943, 0.4544, 0.6643, 0.5474, 0.4327, 0.5701, 0.5959, 0.5179]					)

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






			@unittest.skipIf(torch_device != '''cuda'''  ,						'''This test requires a GPU'''					)
			def 			SCREAMING_SNAKE_CASE      ( self      :			List[Any]					)			->			List[str]:
							a_		:  Union[str, Any]											=      self.get_dummy_components()
							for name, module in components.items():
											if hasattr(__snake_case  ,						'''half'''					):
															a_		:  Tuple											=      module.half()
							a_		:  List[str]											=      CycleDiffusionPipeline(**__snake_case					)
							a_		:  Any											=      pipe.to(__snake_case					)
							pipe.set_progress_bar_config(disable=__snake_case					)

							a_		:  List[Any]											=      self.get_dummy_inputs(__snake_case					)
							a_		:  Any											=      pipe(**__snake_case					)
							a_		:  Dict											=      output.images

							a_		:  List[str]											=      images[0, -3:, -3:, -1]

							assert images.shape == (1, 32, 32, 3)
							a_		:  Optional[int]											=      np.array([0.3506, 0.4543, 0.446, 0.4575, 0.5195, 0.4155, 0.5273, 0.518, 0.4116]					)

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






			@skip_mps
			def 			SCREAMING_SNAKE_CASE      ( self      :			Optional[Any]					)			->			List[Any]:
							return super().test_save_load_local()






			@unittest.skip('''non-deterministic pipeline'''					)
			def 			SCREAMING_SNAKE_CASE      ( self      :			Optional[Any]					)			->			Union[str, Any]:
							return super().test_inference_batch_single_identical()






			@skip_mps
			def 			SCREAMING_SNAKE_CASE      ( self      :			Union[str, Any]					)			->			str:
							return super().test_dict_tuple_outputs_equivalent()






			@skip_mps
			def 			SCREAMING_SNAKE_CASE      ( self      :			str					)			->			int:
							return super().test_save_load_optional_components()







			@skip_mps
			def 			SCREAMING_SNAKE_CASE      ( self      :			Any					)			->			Optional[int]:
							return super().test_attention_slicing_forward_pass()





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






			def 			SCREAMING_SNAKE_CASE      ( self      :			Union[str, Any]					)			->			int:
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()






			def 			SCREAMING_SNAKE_CASE      ( self      :			Dict					)			->			Any:
							a_		:  Union[str, Any]											=      load_image(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
							    '''/cycle-diffusion/black_colored_car.png'''					)
							a_		:  Union[str, Any]											=      load_numpy(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy'''					)
							a_		:  Optional[Any]											=      init_image.resize((512, 512)					)

							a_		:  List[Any]											=      '''CompVis/stable-diffusion-v1-4'''
							a_		:  List[Any]											=      DDIMScheduler.from_pretrained(__snake_case  ,						subfolder='''scheduler'''					)
							a_		:  Dict											=      CycleDiffusionPipeline.from_pretrained(
							    __snake_case  ,						scheduler=__snake_case  ,						safety_checker=__snake_case  ,						torch_dtype=torch.floataa  ,						revision='''fp16'''					)

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

							a_		:  Union[str, Any]											=      '''A black colored car'''
							a_		:  List[Any]											=      '''A blue colored car'''

							a_		:  Optional[Any]											=      torch.manual_seed(0					)
							a_		:  str											=      pipe(
							    prompt=__snake_case  ,						source_prompt=__snake_case  ,						image=__snake_case  ,						num_inference_steps=100  ,						eta=0.1  ,						strength=0.85  ,						guidance_scale=3  ,						source_guidance_scale=1  ,						generator=__snake_case  ,						output_type='''np'''  ,						)
							a_		:  Optional[Any]											=      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 			SCREAMING_SNAKE_CASE      ( self      :			str					)			->			Optional[Any]:
							a_		:  List[Any]											=      load_image(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
							    '''/cycle-diffusion/black_colored_car.png'''					)
							a_		:  Tuple											=      load_numpy(
							    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy'''					)
							a_		:  Tuple											=      init_image.resize((512, 512)					)

							a_		:  List[str]											=      '''CompVis/stable-diffusion-v1-4'''
							a_		:  Any											=      DDIMScheduler.from_pretrained(__snake_case  ,						subfolder='''scheduler'''					)
							a_		:  Optional[Any]											=      CycleDiffusionPipeline.from_pretrained(__snake_case  ,						scheduler=__snake_case  ,						safety_checker=__snake_case					)

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

							a_		:  Optional[int]											=      '''A black colored car'''
							a_		:  Optional[int]											=      '''A blue colored car'''

							a_		:  Optional[Any]											=      torch.manual_seed(0					)
							a_		:  str											=      pipe(
							    prompt=__snake_case  ,						source_prompt=__snake_case  ,						image=__snake_case  ,						num_inference_steps=100  ,						eta=0.1  ,						strength=0.85  ,						guidance_scale=3  ,						source_guidance_scale=1  ,						generator=__snake_case  ,						output_type='''np'''  ,						)
							a_		:  Optional[Any]											=      output.images

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












'''simple docstring'''







from __future__ import annotations





def 		_UpperCAmelCase   (							__A							:   list[int]      ):
				a_		:  int											=      len(__A      ) // 2

				# choose the middle 3 elements
				a_		:  Dict											=      lst[m - 1 : m + 2]

				# if middle element is peak
				if three[1] > three[0] and three[1] > three[2]:
								return three[1]

				# if increasing, recurse on right
				elif three[0] < three[2]:
								if len(lst[:m]      ) == 2:
												m -= 1
								return peak(lst[m:]      )

				# decreasing
				else:
								if len(lst[:m]      ) == 2:
												m += 1
								return peak(lst[:m]      )


if __name__ == "__main__":
		import doctest

		doctest.testmod()




