


from __future__ import annotations

import collections
import pprint
from pathlib import Path






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE )->  str:




      """simple docstring"""

      return "".join(sorted(SCREAMING_SNAKE_CASE ) )






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE )->  list[str]:




      """simple docstring"""

      return word_by_signature[signature(SCREAMING_SNAKE_CASE )]


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

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

if __name__ == "__main__":
   UpperCAmelCase      =   {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))



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


UpperCAmelCase      =   False







class lowerCAmelCase_						(			unittest.TestCase	):

 '''simple docstring'''

 def 						UpperCamelCase__					(     self ):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()



 @property
 def 						UpperCamelCase__					(     self ):
       return 12



 @property
 def 						UpperCamelCase__					(     self ):
       return 12



 @property
 def 						UpperCamelCase__					(     self ):
       return 32



 @property
 def 						UpperCamelCase__					(     self ):
       torch.manual_seed(0 )
       snake_case_	=				VQModel(
           block_out_channels=[32, 64]		,				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 ):
       snake_case_	=				CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
       return tokenizer



 @property
 def 						UpperCamelCase__					(     self ):
       torch.manual_seed(0 )
       snake_case_	=				CLIPTextConfig(
           bos_token_id=0		,				eos_token_id=2		,				hidden_size=self.text_embedder_hidden_size		,				intermediate_size=37		,				layer_norm_eps=1E-05		,				num_attention_heads=4		,				num_hidden_layers=5		,				pad_token_id=1		,				vocab_size=10_00		,				)
       return CLIPTextModel(_UpperCAmelCase )



 @property
 def 						UpperCamelCase__					(     self ):
       torch.manual_seed(0 )

       snake_case_	=				12
       snake_case_	=				12

       snake_case_	=				{
           '''attention_bias''': True,
           '''cross_attention_dim''': 32,
           '''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''': 32,
           '''sample_size''': width,
           '''activation_fn''': '''geglu-approximate''',
       }

       snake_case_	=				TransformeraDModel(**_UpperCAmelCase )
       return model



 def 						UpperCamelCase__					(     self ):
       snake_case_	=				'''cpu'''

       snake_case_	=				self.dummy_vqvae
       snake_case_	=				self.dummy_text_encoder
       snake_case_	=				self.dummy_tokenizer
       snake_case_	=				self.dummy_transformer
       snake_case_	=				VQDiffusionScheduler(self.num_embed )
       snake_case_	=				LearnedClassifierFreeSamplingEmbeddings(learnable=_UpperCAmelCase )

       snake_case_	=				VQDiffusionPipeline(
           vqvae=_UpperCAmelCase		,				text_encoder=_UpperCAmelCase		,				tokenizer=_UpperCAmelCase		,				transformer=_UpperCAmelCase		,				scheduler=_UpperCAmelCase		,				learned_classifier_free_sampling_embeddings=_UpperCAmelCase		,				)
       snake_case_	=				pipe.to(_UpperCAmelCase )
       pipe.set_progress_bar_config(disable=_UpperCAmelCase )

       snake_case_	=				'''teddy bear playing in the pool'''

       snake_case_	=				torch.Generator(device=_UpperCAmelCase ).manual_seed(0 )
       snake_case_	=				pipe([prompt]		,				generator=_UpperCAmelCase		,				num_inference_steps=2		,				output_type='''np''' )
       snake_case_	=				output.images

       snake_case_	=				torch.Generator(device=_UpperCAmelCase ).manual_seed(0 )
       snake_case_	=				pipe(
           [prompt]		,				generator=_UpperCAmelCase		,				output_type='''np'''		,				return_dict=_UpperCAmelCase		,				num_inference_steps=2 )[0]

       snake_case_	=				image[0, -3:, -3:, -1]
       snake_case_	=				image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 24, 24, 3)

       snake_case_	=				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 ):
       snake_case_	=				'''cpu'''

       snake_case_	=				self.dummy_vqvae
       snake_case_	=				self.dummy_text_encoder
       snake_case_	=				self.dummy_tokenizer
       snake_case_	=				self.dummy_transformer
       snake_case_	=				VQDiffusionScheduler(self.num_embed )
       snake_case_	=				LearnedClassifierFreeSamplingEmbeddings(
           learnable=_UpperCAmelCase		,				hidden_size=self.text_embedder_hidden_size		,				length=tokenizer.model_max_length )

       snake_case_	=				VQDiffusionPipeline(
           vqvae=_UpperCAmelCase		,				text_encoder=_UpperCAmelCase		,				tokenizer=_UpperCAmelCase		,				transformer=_UpperCAmelCase		,				scheduler=_UpperCAmelCase		,				learned_classifier_free_sampling_embeddings=_UpperCAmelCase		,				)
       snake_case_	=				pipe.to(_UpperCAmelCase )
       pipe.set_progress_bar_config(disable=_UpperCAmelCase )

       snake_case_	=				'''teddy bear playing in the pool'''

       snake_case_	=				torch.Generator(device=_UpperCAmelCase ).manual_seed(0 )
       snake_case_	=				pipe([prompt]		,				generator=_UpperCAmelCase		,				num_inference_steps=2		,				output_type='''np''' )
       snake_case_	=				output.images

       snake_case_	=				torch.Generator(device=_UpperCAmelCase ).manual_seed(0 )
       snake_case_	=				pipe(
           [prompt]		,				generator=_UpperCAmelCase		,				output_type='''np'''		,				return_dict=_UpperCAmelCase		,				num_inference_steps=2 )[0]

       snake_case_	=				image[0, -3:, -3:, -1]
       snake_case_	=				image_from_tuple[0, -3:, -3:, -1]

       assert image.shape == (1, 24, 24, 3)

       snake_case_	=				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 lowerCAmelCase_						(			unittest.TestCase	):

 '''simple docstring'''

 def 						UpperCamelCase__					(     self ):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()





 def 						UpperCamelCase__					(     self ):
       snake_case_	=				load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy''' )

       snake_case_	=				VQDiffusionPipeline.from_pretrained('''microsoft/vq-diffusion-ithq''' )
       snake_case_	=				pipeline.to(_UpperCAmelCase )
       pipeline.set_progress_bar_config(disable=_UpperCAmelCase )

       # requires GPU generator for gumbel softmax
       # don't use GPU generator in tests though
       snake_case_	=				torch.Generator(device=_UpperCAmelCase ).manual_seed(0 )
       snake_case_	=				pipeline(
           '''teddy bear playing in the pool'''		,				num_images_per_prompt=1		,				generator=_UpperCAmelCase		,				output_type='''np'''		,				)

       snake_case_	=				output.images[0]

       assert image.shape == (2_56, 2_56, 3)
       assert np.abs(expected_image - image ).max() < 2.0