


import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionPanoramaPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()







@skip_mps
class 	__lowercase    (__SCREAMING_SNAKE_CASE	,       __SCREAMING_SNAKE_CASE	,       unittest.TestCase  ):





							"""simple docstring"""







							_UpperCAmelCase      =						StableDiffusionPanoramaPipeline
							_UpperCAmelCase      =						TEXT_TO_IMAGE_PARAMS
							_UpperCAmelCase      =						TEXT_TO_IMAGE_BATCH_PARAMS
							_UpperCAmelCase      =						TEXT_TO_IMAGE_IMAGE_PARAMS
							_UpperCAmelCase      =						TEXT_TO_IMAGE_IMAGE_PARAMS






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										torch.manual_seed(0       )
										SCREAMING_SNAKE_CASE_		:     str					=					UNetaDConditionModel(
										    block_out_channels=(3_2, 6_4)			,						layers_per_block=1			,						sample_size=3_2			,						in_channels=4			,						out_channels=4			,						down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')			,						up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')			,						cross_attention_dim=3_2			,						)
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					DDIMScheduler()
										torch.manual_seed(0       )
										SCREAMING_SNAKE_CASE_		:     int					=					AutoencoderKL(
										    block_out_channels=[3_2, 6_4]			,						in_channels=3			,						out_channels=3			,						down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']			,						up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']			,						latent_channels=4			,						)
										torch.manual_seed(0       )
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					CLIPTextConfig(
										    bos_token_id=0			,						eos_token_id=2			,						hidden_size=3_2			,						intermediate_size=3_7			,						layer_norm_eps=1E-05			,						num_attention_heads=4			,						num_hidden_layers=5			,						pad_token_id=1			,						vocab_size=1_0_0_0			,						)
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					CLIPTextModel(lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     Tuple					=					CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'       )

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






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__			,						lowerCAmelCase__=0       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     int					=					torch.manual_seed(lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     Tuple					=					{
										    'prompt': 'a photo of the dolomites',
										    'generator': generator,
										    # Setting height and width to None to prevent OOMs on CPU.
										    'height': None,
										    'width': None,
										    'num_inference_steps': 1,
										    'guidance_scale': 6.0,
										    'output_type': 'numpy',
										}
										return inputs






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Dict					=					'cpu'  # ensure determinism for the device-dependent torch.Generator
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					self.get_dummy_components()
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					StableDiffusionPanoramaPipeline(**lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     str					=					sd_pipe.to(lowerCAmelCase__       )
										sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		:     List[Any]					=					self.get_dummy_inputs(lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     Dict					=					sd_pipe(**lowerCAmelCase__       ).images
										SCREAMING_SNAKE_CASE_		:     List[str]					=					image[0, -3:, -3:, -1]
										assert image.shape == (1, 6_4, 6_4, 3)

										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					np.array([0.6_186, 0.5_374, 0.4_915, 0.4_135, 0.4_114, 0.4_563, 0.5_128, 0.4_977, 0.4_757]       )

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






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										super().test_inference_batch_consistent(batch_sizes=[1, 2]       )






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										super().test_inference_batch_single_identical(batch_size=2			,						expected_max_diff=3.25E-3       )






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     str					=					'cpu'  # ensure determinism for the device-dependent torch.Generator
										SCREAMING_SNAKE_CASE_		:     Any					=					self.get_dummy_components()
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					StableDiffusionPanoramaPipeline(**lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     str					=					sd_pipe.to(lowerCAmelCase__       )
										sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					self.get_dummy_inputs(lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     Dict					=					'french fries'
										SCREAMING_SNAKE_CASE_		:     str					=					sd_pipe(**lowerCAmelCase__			,						negative_prompt=lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     Tuple					=					output.images
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					image[0, -3:, -3:, -1]

										assert image.shape == (1, 6_4, 6_4, 3)

										SCREAMING_SNAKE_CASE_		:     str					=					np.array([0.6_187, 0.5_375, 0.4_915, 0.4_136, 0.4_114, 0.4_563, 0.5_128, 0.4_976, 0.4_757]       )

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






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					'cpu'  # ensure determinism for the device-dependent torch.Generator
										SCREAMING_SNAKE_CASE_		:     str					=					self.get_dummy_components()
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					StableDiffusionPanoramaPipeline(**lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     str					=					sd_pipe.to(lowerCAmelCase__       )
										sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		:     Any					=					self.get_dummy_inputs(lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     str					=					sd_pipe(**lowerCAmelCase__			,						view_batch_size=2       )
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					output.images
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					image[0, -3:, -3:, -1]

										assert image.shape == (1, 6_4, 6_4, 3)

										SCREAMING_SNAKE_CASE_		:     List[Any]					=					np.array([0.6_187, 0.5_375, 0.4_915, 0.4_136, 0.4_114, 0.4_563, 0.5_128, 0.4_976, 0.4_757]       )

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






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     List[str]					=					'cpu'  # ensure determinism for the device-dependent torch.Generator
										SCREAMING_SNAKE_CASE_		:     int					=					self.get_dummy_components()
										SCREAMING_SNAKE_CASE_		:     Tuple					=					EulerAncestralDiscreteScheduler(
										    beta_start=0.00_085			,						beta_end=0.012			,						beta_schedule='scaled_linear'       )
										SCREAMING_SNAKE_CASE_		:     Dict					=					StableDiffusionPanoramaPipeline(**lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     Any					=					sd_pipe.to(lowerCAmelCase__       )
										sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		:     List[Any]					=					self.get_dummy_inputs(lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     Any					=					sd_pipe(**lowerCAmelCase__       ).images
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					image[0, -3:, -3:, -1]

										assert image.shape == (1, 6_4, 6_4, 3)

										SCREAMING_SNAKE_CASE_		:     List[Any]					=					np.array([0.4_024, 0.6_510, 0.4_901, 0.5_378, 0.5_813, 0.5_622, 0.4_795, 0.4_467, 0.4_952]       )

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






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					'cpu'  # ensure determinism for the device-dependent torch.Generator
										SCREAMING_SNAKE_CASE_		:     int					=					self.get_dummy_components()
										SCREAMING_SNAKE_CASE_		:     int					=					PNDMScheduler(
										    beta_start=0.00_085			,						beta_end=0.012			,						beta_schedule='scaled_linear'			,						skip_prk_steps=lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					StableDiffusionPanoramaPipeline(**lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     List[str]					=					sd_pipe.to(lowerCAmelCase__       )
										sd_pipe.set_progress_bar_config(disable=lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		:     str					=					self.get_dummy_inputs(lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     str					=					sd_pipe(**lowerCAmelCase__       ).images
										SCREAMING_SNAKE_CASE_		:     str					=					image[0, -3:, -3:, -1]

										assert image.shape == (1, 6_4, 6_4, 3)

										SCREAMING_SNAKE_CASE_		:     Dict					=					np.array([0.6_391, 0.6_291, 0.4_861, 0.5_134, 0.5_552, 0.4_578, 0.5_032, 0.5_023, 0.4_539]       )

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







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





							"""simple docstring"""







							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__=0       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Dict					=					torch.manual_seed(lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					{
										    'prompt': 'a photo of the dolomites',
										    'generator': generator,
										    'num_inference_steps': 3,
										    'guidance_scale': 7.5,
										    'output_type': 'numpy',
										}
										return inputs






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     List[Any]					=					'stabilityai/stable-diffusion-2-base'
										SCREAMING_SNAKE_CASE_		:     int					=					DDIMScheduler.from_pretrained(lowerCAmelCase__			,						subfolder='scheduler'       )
										SCREAMING_SNAKE_CASE_		:     int					=					StableDiffusionPanoramaPipeline.from_pretrained(lowerCAmelCase__			,						scheduler=lowerCAmelCase__			,						safety_checker=lowerCAmelCase__       )
										pipe.to(lowerCAmelCase__       )
										pipe.set_progress_bar_config(disable=lowerCAmelCase__       )
										pipe.enable_attention_slicing()

										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					self.get_inputs()
										SCREAMING_SNAKE_CASE_		:     Tuple					=					pipe(**lowerCAmelCase__       ).images
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					image[0, -3:, -3:, -1].flatten()

										assert image.shape == (1, 5_1_2, 2_0_4_8, 3)

										SCREAMING_SNAKE_CASE_		:     List[Any]					=					np.array(
										    [
										        0.36_968_392,
										        0.27_025_372,
										        0.32_446_766,
										        0.28_379_387,
										        0.36_363_274,
										        0.30_733_347,
										        0.27_100_027,
										        0.27_054_125,
										        0.25_536_096,
										    ]       )

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






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					StableDiffusionPanoramaPipeline.from_pretrained(
										    'stabilityai/stable-diffusion-2-base'			,						safety_checker=lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     List[str]					=					LMSDiscreteScheduler.from_config(pipe.scheduler.config       )
										pipe.to(lowerCAmelCase__       )
										pipe.set_progress_bar_config(disable=lowerCAmelCase__       )
										pipe.enable_attention_slicing()

										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					self.get_inputs()
										SCREAMING_SNAKE_CASE_		:     Any					=					pipe(**lowerCAmelCase__       ).images
										SCREAMING_SNAKE_CASE_		:     List[str]					=					image[0, -3:, -3:, -1].flatten()

										assert image.shape == (1, 5_1_2, 2_0_4_8, 3)

										SCREAMING_SNAKE_CASE_		:     Any					=					np.array(
										    [
										        [
										            0.0,
										            0.0,
										            0.0,
										            0.0,
										            0.0,
										            0.0,
										            0.0,
										            0.0,
										            0.0,
										        ]
										    ]       )

										assert np.abs(expected_slice - image_slice       ).max() < 1E-3






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					0

										def callback_fn(lowerCAmelCase__			,						lowerCAmelCase__			,						lowerCAmelCase__       ) -> None:
													SCREAMING_SNAKE_CASE_		:     Any					=					True
													nonlocal number_of_steps
													number_of_steps += 1
													if step == 1:
																SCREAMING_SNAKE_CASE_		:     Tuple					=					latents.detach().cpu().numpy()
																assert latents.shape == (1, 4, 6_4, 2_5_6)
																SCREAMING_SNAKE_CASE_		:     str					=					latents[0, -3:, -3:, -1]

																SCREAMING_SNAKE_CASE_		:     List[Any]					=					np.array(
																    [
																        0.18_681_869,
																        0.33_907_816,
																        0.5_361_276,
																        0.14_432_865,
																        -0.02_856_611,
																        -0.73_941_123,
																        0.23_397_987,
																        0.47_322_682,
																        -0.37_823_164,
																    ]       )
																assert np.abs(latents_slice.flatten() - expected_slice       ).max() < 5E-2
													elif step == 2:
																SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					latents.detach().cpu().numpy()
																assert latents.shape == (1, 4, 6_4, 2_5_6)
																SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					latents[0, -3:, -3:, -1]

																SCREAMING_SNAKE_CASE_		:     int					=					np.array(
																    [
																        0.18_539_645,
																        0.33_987_248,
																        0.5_378_559,
																        0.14_437_142,
																        -0.02_455_261,
																        -0.7_338_317,
																        0.23_990_755,
																        0.47_356_272,
																        -0.3_786_505,
																    ]       )

																assert np.abs(latents_slice.flatten() - expected_slice       ).max() < 5E-2

										SCREAMING_SNAKE_CASE_		:     Tuple					=					False

										SCREAMING_SNAKE_CASE_		:     Any					=					'stabilityai/stable-diffusion-2-base'
										SCREAMING_SNAKE_CASE_		:     int					=					DDIMScheduler.from_pretrained(lowerCAmelCase__			,						subfolder='scheduler'       )
										SCREAMING_SNAKE_CASE_		:     int					=					StableDiffusionPanoramaPipeline.from_pretrained(lowerCAmelCase__			,						scheduler=lowerCAmelCase__			,						safety_checker=lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					pipe.to(lowerCAmelCase__       )
										pipe.set_progress_bar_config(disable=lowerCAmelCase__       )
										pipe.enable_attention_slicing()

										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					self.get_inputs()
										pipe(**lowerCAmelCase__			,						callback=lowerCAmelCase__			,						callback_steps=1       )
										assert callback_fn.has_been_called
										assert number_of_steps == 3






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										torch.cuda.empty_cache()
										torch.cuda.reset_max_memory_allocated()
										torch.cuda.reset_peak_memory_stats()

										SCREAMING_SNAKE_CASE_		:     int					=					'stabilityai/stable-diffusion-2-base'
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					DDIMScheduler.from_pretrained(lowerCAmelCase__			,						subfolder='scheduler'       )
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					StableDiffusionPanoramaPipeline.from_pretrained(lowerCAmelCase__			,						scheduler=lowerCAmelCase__			,						safety_checker=lowerCAmelCase__       )
										SCREAMING_SNAKE_CASE_		:     Dict					=					pipe.to(lowerCAmelCase__       )
										pipe.set_progress_bar_config(disable=lowerCAmelCase__       )
										pipe.enable_attention_slicing(1       )
										pipe.enable_sequential_cpu_offload()

										SCREAMING_SNAKE_CASE_		:     List[Any]					=					self.get_inputs()
										SCREAMING_SNAKE_CASE_		:     int					=					pipe(**lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		:     List[str]					=					torch.cuda.max_memory_allocated()
										# make sure that less than 5.2 GB is allocated
										assert mem_bytes < 5.5 * 1_0**9









def 						a__			(				A__,      A__				):

			def get_matched_characters(A__,      A__				) -> str:
						SCREAMING_SNAKE_CASE_		:     Dict					=					[]
						SCREAMING_SNAKE_CASE_		:     Any					=					min(len(_stra				),      len(_stra				)				) // 2
						for i, l in enumerate(_stra				):
									SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					int(max(0,      i - limit				)				)
									SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					int(min(i + limit + 1,      len(_stra				)				)				)
									if l in _stra[left:right]:
												matched.append(A__				)
												SCREAMING_SNAKE_CASE_		:     List[str]					=					F'''{_stra[0:_stra.index(A__				)]} {_stra[_stra.index(A__				) + 1:]}'''

						return "".join(A__				)

			# matching characters
			SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					get_matched_characters(A__,      A__				)
			SCREAMING_SNAKE_CASE_		:     int					=					get_matched_characters(A__,      A__				)
			SCREAMING_SNAKE_CASE_		:     Any					=					len(A__				)

			# transposition
			SCREAMING_SNAKE_CASE_		:     Optional[int]					=					(
			    len([(ca, ca) for ca, ca in zip(A__,      A__				) if ca != ca]				) // 2
			)

			if not match_count:
						SCREAMING_SNAKE_CASE_		:     Dict					=					0.0
			else:
						SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					(
						    1
						    / 3
						    * (
						        match_count / len(A__				)
						        + match_count / len(A__				)
						        + (match_count - transpositions) / match_count
						    )
						)

			# common prefix up to 4 characters
			SCREAMING_SNAKE_CASE_		:     List[Any]					=					0
			for ca, ca in zip(stra[:4],      stra[:4]				):
						if ca == ca:
									prefix_len += 1
						else:
									break

			return jaro + 0.1 * prefix_len * (1 - jaro)


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				print(jaro_winkler('hello', 'world'))





