


"""simple docstring"""


import unittest
from dataclasses import dataclass
from typing import List, Union

import numpy as np
import PIL.Image

from diffusers.utils.outputs import BaseOutput
@dataclass
class snake_case					(     _UpperCamelCase):
		__UpperCamelCase							=	42





class snake_case					(     unittest.TestCase):







		def        a_       (    self					:				Any						)				->  Tuple:





						'''simple docstring'''







						_A         =	CustomOutput(images=np.random.rand(1			,      3			,      4			,      4						)						)

						# check every way of getting the attribute
						assert isinstance(outputs.images			,      np.ndarray						)
						assert outputs.images.shape == (1, 3, 4, 4)
						assert isinstance(outputs["images"]			,      np.ndarray						)
						assert outputs["images"].shape == (1, 3, 4, 4)
						assert isinstance(outputs[0]			,      np.ndarray						)
						assert outputs[0].shape == (1, 3, 4, 4)

						# test with a non-tensor attribute
						_A         =	CustomOutput(images=[PIL.Image.new("RGB"			,      (4, 4)						)]						)

						# check every way of getting the attribute
						assert isinstance(outputs.images			,      a__						)
						assert isinstance(outputs.images[0]			,      PIL.Image.Image						)
						assert isinstance(outputs["images"]			,      a__						)
						assert isinstance(outputs["images"][0]			,      PIL.Image.Image						)
						assert isinstance(outputs[0]			,      a__						)
						assert isinstance(outputs[0][0]			,      PIL.Image.Image						)







		def        a_       (    self					:				str						)				->  Union[str, Any]:





						'''simple docstring'''







						_A         =	CustomOutput({"images": np.random.rand(1			,      3			,      4			,      4						)}						)

						# check every way of getting the attribute
						assert isinstance(outputs.images			,      np.ndarray						)
						assert outputs.images.shape == (1, 3, 4, 4)
						assert isinstance(outputs["images"]			,      np.ndarray						)
						assert outputs["images"].shape == (1, 3, 4, 4)
						assert isinstance(outputs[0]			,      np.ndarray						)
						assert outputs[0].shape == (1, 3, 4, 4)

						# test with a non-tensor attribute
						_A         =	CustomOutput({"images": [PIL.Image.new("RGB"			,      (4, 4)						)]}						)

						# check every way of getting the attribute
						assert isinstance(outputs.images			,      a__						)
						assert isinstance(outputs.images[0]			,      PIL.Image.Image						)
						assert isinstance(outputs["images"]			,      a__						)
						assert isinstance(outputs["images"][0]			,      PIL.Image.Image						)
						assert isinstance(outputs[0]			,      a__						)
						assert isinstance(outputs[0][0]			,      PIL.Image.Image						)







"""simple docstring"""



def      SCREAMING_SNAKE_CASE__    (   snake_case    :      str							)->						str:






     '''simple docstring'''



     UpperCAmelCase__		:  Tuple				=				word.upper()
     UpperCAmelCase__		:  Union[str, Any]				=				0
     UpperCAmelCase__		:  Optional[Any]				=				{}
     UpperCAmelCase__		:  List[Any]				=				[]

     for letter in word:
          if letter not in letter_nums:
               UpperCAmelCase__		:  Tuple				=				str(snake_case							)
               next_num += 1
          word_pattern.append(letter_nums[letter]							)
     return ".".join(snake_case							)


if __name__ == "__main__":
 import pprint
 import time

 _lowerCAmelCase     :     Optional[Any]            =	time.time()
 with open("""dictionary.txt""") as in_file:
  _lowerCAmelCase     :     Optional[int]            =	in_file.read().splitlines()

 _lowerCAmelCase     :     dict            =	{}
 for word in word_list:
  _lowerCAmelCase     :     int            =	get_word_pattern(word)
  if pattern in all_patterns:
   all_patterns[pattern].append(word)
  else:
   _lowerCAmelCase     :     Any            =	[word]

 with open("""word_patterns.txt""", """w""") as out_file:
  out_file.write(pprint.pformat(all_patterns))

 _lowerCAmelCase     :     Union[str, Any]            =	round(time.time() - start_time, 2)
 print(F"""Done!  {len(all_patterns):,} word patterns found in {total_time} seconds.""")
 # Done!  9,581 word patterns found in 0.58 seconds.


