



from __future__ import annotations
class    __UpperCamelCase			:

 """simple docstring"""



 def __init__(	self		,				SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE      ) ->			int:
  a__												= text, pattern
  a__												= len(snake_case_      ), len(snake_case_      )

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE      ) ->			Dict:

  for i in range(self.patLen - 1		,				-1		,				-1      ):
   if char == self.pattern[i]:
    return i
  return -1

 def 						_UpperCAmelCase       (	self		,				SCREAMING_SNAKE_CASE      ) ->			Any:

  for i in range(self.patLen - 1		,				-1		,				-1      ):
   if self.pattern[i] != self.text[current_pos + i]:
    return current_pos + i
  return -1



 def 						_UpperCAmelCase       (	self      ) ->			Tuple:
  # searches pattern in text and returns index positions
  a__												= []
  for i in range(self.textLen - self.patLen + 1      ):
   a__												= self.mismatch_in_text(snake_case_      )
   if mismatch_index == -1:
    positions.append(snake_case_      )
   else:
    a__												= self.match_in_pattern(self.text[mismatch_index]      )
    a__												= (
        mismatch_index - match_index
    )  # shifting index lgtm [py/multiple-definition]
  return positions





a_      :      Any    =     'ABAABA'
a_      :      List[str]    =     'AB'
a_      :      int    =     BoyerMooreSearch(text, pattern)
a_      :      List[str]    =     bms.bad_character_heuristic()

if len(positions) == 0:
     print('No match found')
else:
     print('Pattern found in following positions: ')
     print(positions)










'''simple docstring'''





import unittest

import torch
from torch import nn

from diffusers.models.activations import get_activation







class        UpperCAmelCase_			(			unittest.TestCase      ):
    """simple docstring"""







    def lowerCamelCase			(       self   :	Union[str, Any]		):
          snake_case__    :       int       		= get_activation("""swish"""		)

          self.assertIsInstance(snake_case_					,	nn.SiLU		)

          self.assertEqual(act(torch.tensor(-100					,	dtype=torch.floataa		)		).item()					,	0		)
          self.assertNotEqual(act(torch.tensor(-1					,	dtype=torch.floataa		)		).item()					,	0		)
          self.assertEqual(act(torch.tensor(0					,	dtype=torch.floataa		)		).item()					,	0		)
          self.assertEqual(act(torch.tensor(20					,	dtype=torch.floataa		)		).item()					,	20		)




    def lowerCamelCase			(       self   :	Optional[Any]		):
          snake_case__    :       int       		= get_activation("""silu"""		)

          self.assertIsInstance(snake_case_					,	nn.SiLU		)

          self.assertEqual(act(torch.tensor(-100					,	dtype=torch.floataa		)		).item()					,	0		)
          self.assertNotEqual(act(torch.tensor(-1					,	dtype=torch.floataa		)		).item()					,	0		)
          self.assertEqual(act(torch.tensor(0					,	dtype=torch.floataa		)		).item()					,	0		)
          self.assertEqual(act(torch.tensor(20					,	dtype=torch.floataa		)		).item()					,	20		)




    def lowerCamelCase			(       self   :	Dict		):
          snake_case__    :       Union[str, Any]       		= get_activation("""mish"""		)

          self.assertIsInstance(snake_case_					,	nn.Mish		)

          self.assertEqual(act(torch.tensor(-200					,	dtype=torch.floataa		)		).item()					,	0		)
          self.assertNotEqual(act(torch.tensor(-1					,	dtype=torch.floataa		)		).item()					,	0		)
          self.assertEqual(act(torch.tensor(0					,	dtype=torch.floataa		)		).item()					,	0		)
          self.assertEqual(act(torch.tensor(20					,	dtype=torch.floataa		)		).item()					,	20		)




    def lowerCamelCase			(       self   :	Union[str, Any]		):
          snake_case__    :       int       		= get_activation("""gelu"""		)

          self.assertIsInstance(snake_case_					,	nn.GELU		)

          self.assertEqual(act(torch.tensor(-100					,	dtype=torch.floataa		)		).item()					,	0		)
          self.assertNotEqual(act(torch.tensor(-1					,	dtype=torch.floataa		)		).item()					,	0		)
          self.assertEqual(act(torch.tensor(0					,	dtype=torch.floataa		)		).item()					,	0		)
          self.assertEqual(act(torch.tensor(20					,	dtype=torch.floataa		)		).item()					,	20		)
