"""simple docstring"""






import random
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    UNetaDConditionModel,
    VideoToVideoSDPipeline,
)
from diffusers.utils import floats_tensor, is_xformers_available, skip_mps
from diffusers.utils.testing_utils import enable_full_determinism, slow, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()
@skip_mps
class        snake_case (				__UpperCAmelCase   ,					unittest.TestCase       ):




					"""simple docstring"""

					snake_case__       					=				VideoToVideoSDPipeline
					snake_case__       					=				TEXT_GUIDED_IMAGE_VARIATION_PARAMS.union({"video"}       ) - {"image", "width", "height"}
					snake_case__       					=				TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"video"}       ) - {"image"}
					snake_case__       					=				PipelineTesterMixin.required_optional_params - {"latents"}
					snake_case__       					=				False

					# No `output_type`.
					snake_case__       					=				frozenset(
					    [
					        "num_inference_steps",
					        "generator",
					        "latents",
					        "return_dict",
					        "callback",
					        "callback_steps",
					    ]       )







					def 	__lowerCAmelCase		(       self     :			List[Any]    ):
									torch.manual_seed(0    )
									UpperCAmelCase__    =							UNetaDConditionModel(
									    block_out_channels=(32, 64, 64, 64)					,layers_per_block=2					,sample_size=32					,in_channels=4					,out_channels=4					,down_block_types=('CrossAttnDownBlock3D', 'CrossAttnDownBlock3D', 'CrossAttnDownBlock3D', 'DownBlock3D')					,up_block_types=('UpBlock3D', 'CrossAttnUpBlock3D', 'CrossAttnUpBlock3D', 'CrossAttnUpBlock3D')					,cross_attention_dim=32					,attention_head_dim=4					,)
									UpperCAmelCase__    =							DDIMScheduler(
									    beta_start=0.0_0_0_8_5					,beta_end=0.0_1_2					,beta_schedule='scaled_linear'					,clip_sample=lowerCamelCase__					,set_alpha_to_one=lowerCamelCase__					,)
									torch.manual_seed(0    )
									UpperCAmelCase__    =							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					,sample_size=128					,)
									torch.manual_seed(0    )
									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=1_000					,hidden_act='gelu'					,projection_dim=512					,)
									UpperCAmelCase__    =							CLIPTextModel(lowerCamelCase__    )
									UpperCAmelCase__    =							CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'    )

									UpperCAmelCase__    =							{
									    'unet': unet,
									    'scheduler': scheduler,
									    'vae': vae,
									    'text_encoder': text_encoder,
									    'tokenizer': tokenizer,
									}
									return components







					def 	__lowerCAmelCase		(       self     :			str					,lowerCamelCase__     :			Any					,lowerCamelCase__     :			Union[str, Any]=0    ):
									# 3 frames
									UpperCAmelCase__    =							floats_tensor((1, 3, 3, 32, 32)					,rng=random.Random(lowerCamelCase__    )    ).to(lowerCamelCase__    )

									if str(lowerCamelCase__    ).startswith('mps'    ):
													UpperCAmelCase__    =							torch.manual_seed(lowerCamelCase__    )
									else:
													UpperCAmelCase__    =							torch.Generator(device=lowerCamelCase__    ).manual_seed(lowerCamelCase__    )
									UpperCAmelCase__    =							{
									    'prompt': 'A painting of a squirrel eating a burger',
									    'video': video,
									    'generator': generator,
									    'num_inference_steps': 2,
									    'guidance_scale': 6.0,
									    'output_type': 'pt',
									}
									return inputs







					def 	__lowerCAmelCase		(       self     :			Dict    ):
									UpperCAmelCase__    =							'cpu'  # ensure determinism for the device-dependent torch.Generator
									UpperCAmelCase__    =							self.get_dummy_components()
									UpperCAmelCase__    =							VideoToVideoSDPipeline(**lowerCamelCase__    )
									UpperCAmelCase__    =							sd_pipe.to(lowerCamelCase__    )
									sd_pipe.set_progress_bar_config(disable=lowerCamelCase__    )

									UpperCAmelCase__    =							self.get_dummy_inputs(lowerCamelCase__    )
									UpperCAmelCase__    =							'np'
									UpperCAmelCase__    =							sd_pipe(**lowerCamelCase__    ).frames
									UpperCAmelCase__    =							frames[0][-3:, -3:, -1]

									assert frames[0].shape == (32, 32, 3)
									UpperCAmelCase__    =							np.array([106, 117, 113, 174, 137, 112, 148, 151, 131]    )

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







					@unittest.skipIf(
					    torch_device != 'cuda' or not is_xformers_available()					,reason='XFormers attention is only available with CUDA and `xformers` installed'					,)
					def 	__lowerCAmelCase		(       self     :			Union[str, Any]    ):
									self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=lowerCamelCase__					,expected_max_diff=5e-3    )







					@unittest.skip(reason='Batching needs to be properly figured out first for this pipeline.'    )
					def 	__lowerCAmelCase		(       self     :			Optional[Any]    ):
									pass







					@unittest.skip(reason='Batching needs to be properly figured out first for this pipeline.'    )
					def 	__lowerCAmelCase		(       self     :			List[Any]    ):
									pass







					@unittest.skip(reason='`num_images_per_prompt` argument is not supported for this pipeline.'    )
					def 	__lowerCAmelCase		(       self     :			int    ):
									pass







					def 	__lowerCAmelCase		(       self     :			int    ):
									return super().test_progress_bar()






@slow
@skip_mps
class        snake_case (				unittest.TestCase       ):




					"""simple docstring"""

					def 	__lowerCAmelCase		(       self     :			List[Any]    ):
									UpperCAmelCase__    =							VideoToVideoSDPipeline.from_pretrained('cerspense/zeroscope_v2_XL'					,torch_dtype=torch.floataa    )
									pipe.enable_model_cpu_offload()

									# 10 frames
									UpperCAmelCase__    =							torch.Generator(device='cpu'    ).manual_seed(0    )
									UpperCAmelCase__    =							torch.randn((1, 10, 3, 1_024, 576)					,generator=lowerCamelCase__    )
									UpperCAmelCase__    =							video.to('cuda'    )

									UpperCAmelCase__    =							'Spiderman is surfing'

									UpperCAmelCase__    =							pipe(lowerCamelCase__					,video=lowerCamelCase__					,generator=lowerCamelCase__					,num_inference_steps=3					,output_type='pt'    ).frames

									UpperCAmelCase__    =							np.array([-1.0_4_5_8_9_8_4, -1.1_2_7_9_2_9_7, -0.9_6_6_3_0_8_6, -0.9_1_5_0_3_9_0_6, -0.7_5_0_9_7_6_5_6]    )
									assert np.abs(video_frames.cpu().numpy()[0, 0, 0, 0, -5:] - expected_array    ).sum() < 1e-2




"""simple docstring"""






from __future__ import annotations

import collections
import pprint
from pathlib import Path



def 						a_		(      lowerCamelCase ):
				return "".join(sorted(lowerCamelCase ) )



def 						a_		(      lowerCamelCase ):
				return word_by_signature[signature(lowerCamelCase )]


lowerCAmelCase__						:						str		  =			Path(__file__).parent.joinpath('words.txt').read_text(encoding='utf-8')
lowerCAmelCase__						:						str		  =			sorted({word.strip().lower() for word in data.splitlines()})

lowerCAmelCase__						:						Optional[Any]		  =			collections.defaultdict(list)
for word in word_list:
						word_by_signature[signature(word)].append(word)

if __name__ == "__main__":
						lowerCAmelCase__						:						int		  =			{word: anagram(word) for word in word_list if len(anagram(word)) > 1}

						with open('anagrams.txt', 'w') as file:
												file.write('all_anagrams = \n ')
												file.write(pprint.pformat(all_anagrams))



