


import gc
import unittest

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

from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
from diffusers.utils import load_numpy, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu


lowerCAmelCase__			:      int								=False







class 	__lowercase    (unittest.TestCase  ):





							"""simple docstring"""







							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




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






							@property
							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										return 1_2






							@property
							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										return 1_2






							@property
							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										return 3_2






							@property
							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										torch.manual_seed(0       )
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					VQModel(
										    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=3			,						num_vq_embeddings=self.num_embed			,						vq_embed_dim=3			,						)
										return model






							@property
							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     List[Any]					=					CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'       )
										return tokenizer






							@property
							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										torch.manual_seed(0       )
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					CLIPTextConfig(
										    bos_token_id=0			,						eos_token_id=2			,						hidden_size=self.text_embedder_hidden_size			,						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			,						)
										return CLIPTextModel(lowerCAmelCase__       )






							@property
							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										torch.manual_seed(0       )

										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					1_2
										SCREAMING_SNAKE_CASE_		:     Any					=					1_2

										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					{
										    'attention_bias': True,
										    'cross_attention_dim': 3_2,
										    'attention_head_dim': height * width,
										    'num_attention_heads': 1,
										    'num_vector_embeds': self.num_embed,
										    'num_embeds_ada_norm': self.num_embeds_ada_norm,
										    'norm_num_groups': 3_2,
										    'sample_size': width,
										    'activation_fn': 'geglu-approximate',
										}

										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					TransformeraDModel(**lowerCAmelCase__       )
										return model






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					'cpu'

										SCREAMING_SNAKE_CASE_		:     Dict					=					self.dummy_vqvae
										SCREAMING_SNAKE_CASE_		:     List[str]					=					self.dummy_text_encoder
										SCREAMING_SNAKE_CASE_		:     str					=					self.dummy_tokenizer
										SCREAMING_SNAKE_CASE_		:     int					=					self.dummy_transformer
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					VQDiffusionScheduler(self.num_embed       )
										SCREAMING_SNAKE_CASE_		:     Tuple					=					LearnedClassifierFreeSamplingEmbeddings(learnable=lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		:     List[str]					=					VQDiffusionPipeline(
										    vqvae=lowerCAmelCase__			,						text_encoder=lowerCAmelCase__			,						tokenizer=lowerCAmelCase__			,						transformer=lowerCAmelCase__			,						scheduler=lowerCAmelCase__			,						learned_classifier_free_sampling_embeddings=lowerCAmelCase__			,						)
										SCREAMING_SNAKE_CASE_		:     List[str]					=					pipe.to(lowerCAmelCase__       )
										pipe.set_progress_bar_config(disable=lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		:     List[str]					=					'teddy bear playing in the pool'

										SCREAMING_SNAKE_CASE_		:     Tuple					=					torch.Generator(device=lowerCAmelCase__       ).manual_seed(0       )
										SCREAMING_SNAKE_CASE_		:     Any					=					pipe([prompt]			,						generator=lowerCAmelCase__			,						num_inference_steps=2			,						output_type='np'       )
										SCREAMING_SNAKE_CASE_		:     Tuple					=					output.images

										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					torch.Generator(device=lowerCAmelCase__       ).manual_seed(0       )
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					pipe(
										    [prompt]			,						generator=lowerCAmelCase__			,						output_type='np'			,						return_dict=lowerCAmelCase__			,						num_inference_steps=2       )[0]

										SCREAMING_SNAKE_CASE_		:     int					=					image[0, -3:, -3:, -1]
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					image_from_tuple[0, -3:, -3:, -1]

										assert image.shape == (1, 2_4, 2_4, 3)

										SCREAMING_SNAKE_CASE_		:     int					=					np.array([0.6_551, 0.6_168, 0.5_008, 0.5_676, 0.5_659, 0.4_295, 0.6_073, 0.5_599, 0.4_992]       )

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






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     List[str]					=					'cpu'

										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					self.dummy_vqvae
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					self.dummy_text_encoder
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					self.dummy_tokenizer
										SCREAMING_SNAKE_CASE_		:     Tuple					=					self.dummy_transformer
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					VQDiffusionScheduler(self.num_embed       )
										SCREAMING_SNAKE_CASE_		:     str					=					LearnedClassifierFreeSamplingEmbeddings(
										    learnable=lowerCAmelCase__			,						hidden_size=self.text_embedder_hidden_size			,						length=tokenizer.model_max_length       )

										SCREAMING_SNAKE_CASE_		:     Dict					=					VQDiffusionPipeline(
										    vqvae=lowerCAmelCase__			,						text_encoder=lowerCAmelCase__			,						tokenizer=lowerCAmelCase__			,						transformer=lowerCAmelCase__			,						scheduler=lowerCAmelCase__			,						learned_classifier_free_sampling_embeddings=lowerCAmelCase__			,						)
										SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					pipe.to(lowerCAmelCase__       )
										pipe.set_progress_bar_config(disable=lowerCAmelCase__       )

										SCREAMING_SNAKE_CASE_		:     List[str]					=					'teddy bear playing in the pool'

										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					torch.Generator(device=lowerCAmelCase__       ).manual_seed(0       )
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					pipe([prompt]			,						generator=lowerCAmelCase__			,						num_inference_steps=2			,						output_type='np'       )
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					output.images

										SCREAMING_SNAKE_CASE_		:     Dict					=					torch.Generator(device=lowerCAmelCase__       ).manual_seed(0       )
										SCREAMING_SNAKE_CASE_		:     Dict					=					pipe(
										    [prompt]			,						generator=lowerCAmelCase__			,						output_type='np'			,						return_dict=lowerCAmelCase__			,						num_inference_steps=2       )[0]

										SCREAMING_SNAKE_CASE_		:     List[str]					=					image[0, -3:, -3:, -1]
										SCREAMING_SNAKE_CASE_		:     Tuple					=					image_from_tuple[0, -3:, -3:, -1]

										assert image.shape == (1, 2_4, 2_4, 3)

										SCREAMING_SNAKE_CASE_		:     List[str]					=					np.array([0.6_693, 0.6_075, 0.4_959, 0.5_701, 0.5_583, 0.4_333, 0.6_171, 0.5_684, 0.4_988]       )

										assert np.abs(image_slice.flatten() - expected_slice       ).max() < 2.0
										assert np.abs(image_from_tuple_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       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     List[str]					=					load_numpy(
										    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
										    '/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy'       )

										SCREAMING_SNAKE_CASE_		:     List[Any]					=					VQDiffusionPipeline.from_pretrained('microsoft/vq-diffusion-ithq'       )
										SCREAMING_SNAKE_CASE_		:     List[Any]					=					pipeline.to(lowerCAmelCase__       )
										pipeline.set_progress_bar_config(disable=lowerCAmelCase__       )

										# requires GPU generator for gumbel softmax
										# don't use GPU generator in tests though
										SCREAMING_SNAKE_CASE_		:     int					=					torch.Generator(device=lowerCAmelCase__       ).manual_seed(0       )
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					pipeline(
										    'teddy bear playing in the pool'			,						num_images_per_prompt=1			,						generator=lowerCAmelCase__			,						output_type='np'			,						)

										SCREAMING_SNAKE_CASE_		:     List[Any]					=					output.images[0]

										assert image.shape == (2_5_6, 2_5_6, 3)
										assert np.abs(expected_image - image       ).max() < 2.0









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'))





