




'''simple docstring'''



import unittest

import torch

from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()





class      lowerCAmelCase (			__lowerCAmelCase					, __lowerCAmelCase					, unittest.TestCase):
       __lowercase	:   List[Any]										=      VQModel
       __lowercase	:   str										=      '''sample'''


       @property
       def    lowerCAmelCase   ( self     ,	__SCREAMING_SNAKE_CASE=(32, 32)	)   ->	Dict:



            '''simple docstring'''




            __snake_case            =	4
            __snake_case            =	3

            __snake_case            =	floats_tensor((batch_size, num_channels) + sizes	).to(__SCREAMING_SNAKE_CASE	)

            return {"sample": image}


       @property
       def    lowerCAmelCase   ( self	)   ->	Optional[Any]:



            '''simple docstring'''




            return (3, 32, 32)


       @property
       def    lowerCAmelCase   ( self	)   ->	Tuple:



            '''simple docstring'''




            return (3, 32, 32)


       def    lowerCAmelCase   ( self	)   ->	Optional[int]:



            '''simple docstring'''




            __snake_case            =	{
                '''block_out_channels''': [32, 64],
                '''in_channels''': 3,
                '''out_channels''': 3,
                '''down_block_types''': ['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],
                '''up_block_types''': ['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],
                '''latent_channels''': 3,
            }
            __snake_case            =	self.dummy_input
            return init_dict, inputs_dict


       def    lowerCAmelCase   ( self	)   ->	Tuple:



            '''simple docstring'''




            pass


       def    lowerCAmelCase   ( self	)   ->	Optional[Any]:



            '''simple docstring'''




            pass


       def    lowerCAmelCase   ( self	)   ->	Tuple:



            '''simple docstring'''




            __snake_case       ,     __snake_case            =	VQModel.from_pretrained('''fusing/vqgan-dummy'''     ,	output_loading_info=__SCREAMING_SNAKE_CASE	)
            self.assertIsNotNone(__SCREAMING_SNAKE_CASE	)
            self.assertEqual(len(loading_info['''missing_keys''']	)     ,	0	)

            model.to(__SCREAMING_SNAKE_CASE	)
            __snake_case            =	model(**self.dummy_input	)

            assert image is not None, "Make sure output is not None"




       def    lowerCAmelCase   ( self	)   ->	Any:



            '''simple docstring'''




            __snake_case            =	VQModel.from_pretrained('''fusing/vqgan-dummy'''	)
            model.to(__SCREAMING_SNAKE_CASE	).eval()

            torch.manual_seed(0	)
            if torch.cuda.is_available():
                 torch.cuda.manual_seed_all(0	)

            __snake_case            =	torch.randn(1     ,	model.config.in_channels     ,	model.config.sample_size     ,	model.config.sample_size	)
            __snake_case            =	image.to(__SCREAMING_SNAKE_CASE	)
            with torch.no_grad():
                 __snake_case            =	model(__SCREAMING_SNAKE_CASE	).sample

            __snake_case            =	output[0, -1, -3:, -3:].flatten().cpu()
            # fmt: off
            __snake_case            =	torch.tensor([-0.0_153, -0.4_044, -0.1_880, -0.5_161, -0.2_418, -0.4_072, -0.1_612, -0.0_633, -0.0_143]	)
            # fmt: on
            self.assertTrue(torch.allclose(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	atol=1E-3	)	)












'''simple docstring'''





from __future__ import annotations

import collections
import pprint
from pathlib import Path





def       __a      (  _UpperCamelCase:       str       )		->					str:

     """simple docstring"""




     return "".join(sorted(_UpperCamelCase       )       )





def       __a      (  _UpperCamelCase:       str       )		->					list[str]:

     """simple docstring"""




     return word_by_signature[signature(_UpperCamelCase       )]


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

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

if __name__ == "__main__":
   UpperCamelCase_		:     List[str]          =		{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))


