






"""simple docstring"""






from __future__ import annotations



class 							lowerCAmelCase__       :






      '''simple docstring'''

      def __init__(		self  :						Union[str, Any]		,			lowercase_  :						Optional[int]		,			lowercase_  :						Tuple):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_   :						int							=	text, pattern
          SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_   :						Any							=	len(lowercase_), len(lowercase_)
      def _SCREAMING_SNAKE_CASE				(		self  :						List[str]		,			lowercase_  :						Dict):




          '''simple docstring'''


          for i in range(self.patLen - 1		,			-1		,			-1):
              if char == self.pattern[i]:
                  return i
          return -1
      def _SCREAMING_SNAKE_CASE				(		self  :						Optional[int]		,			lowercase_  :						Dict):




          '''simple docstring'''


          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 _SCREAMING_SNAKE_CASE				(		self  :						List[str]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	[]
          for i in range(self.textLen - self.patLen + 1):
              SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	self.mismatch_in_text(lowercase_)
              if mismatch_index == -1:
                  positions.append(lowercase_)
              else:
                  SCREAMING_SNAKE_CASE_   :						Dict							=	self.match_in_pattern(self.text[mismatch_index])
                  SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	(
                      mismatch_index - match_index
                  )  # shifting index lgtm [py/multiple-definition]
          return positions



UpperCAmelCase_     :				Tuple         =				"""ABAABA"""
UpperCAmelCase_     :				Optional[int]         =				"""AB"""
UpperCAmelCase_     :				List[str]         =				BoyerMooreSearch(text, pattern)
UpperCAmelCase_     :				List[Any]         =				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 numpy as np

from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
    expand_dims,
    flatten_dict,
    is_flax_available,
    is_tf_available,
    is_torch_available,
    reshape,
    squeeze,
    transpose,
)


if is_flax_available():
  import jax.numpy as jnp

if is_tf_available():
  import tensorflow as tf

if is_torch_available():
  import torch




class 							lowerCAmelCase__       (    unittest.TestCase				):






      '''simple docstring'''

      def _SCREAMING_SNAKE_CASE				(		self  :						str):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						str							=	{
              '''task_specific_params''': {
                  '''summarization''': {'''length_penalty''': 1.0, '''max_length''': 128, '''min_length''': 12, '''num_beams''': 4},
                  '''summarization_cnn''': {'''length_penalty''': 2.0, '''max_length''': 142, '''min_length''': 56, '''num_beams''': 4},
                  '''summarization_xsum''': {'''length_penalty''': 1.0, '''max_length''': 62, '''min_length''': 11, '''num_beams''': 6},
              }
          }
          SCREAMING_SNAKE_CASE_   :						List[str]							=	{
              '''task_specific_params.summarization.length_penalty''': 1.0,
              '''task_specific_params.summarization.max_length''': 128,
              '''task_specific_params.summarization.min_length''': 12,
              '''task_specific_params.summarization.num_beams''': 4,
              '''task_specific_params.summarization_cnn.length_penalty''': 2.0,
              '''task_specific_params.summarization_cnn.max_length''': 142,
              '''task_specific_params.summarization_cnn.min_length''': 56,
              '''task_specific_params.summarization_cnn.num_beams''': 4,
              '''task_specific_params.summarization_xsum.length_penalty''': 1.0,
              '''task_specific_params.summarization_xsum.max_length''': 62,
              '''task_specific_params.summarization_xsum.min_length''': 11,
              '''task_specific_params.summarization_xsum.num_beams''': 6,
          }

          self.assertEqual(flatten_dict(lowercase_)		,			lowercase_)
      def _SCREAMING_SNAKE_CASE				(		self  :						List[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	np.random.randn(3		,			4)
          self.assertTrue(np.allclose(transpose(lowercase_)		,			x.transpose()))

          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	np.random.randn(3		,			4		,			5)
          self.assertTrue(np.allclose(transpose(lowercase_		,			axes=(1, 2, 0))		,			x.transpose((1, 2, 0))))
      @require_torch
      def _SCREAMING_SNAKE_CASE				(		self  :						Dict):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						str							=	np.random.randn(3		,			4)
          SCREAMING_SNAKE_CASE_   :						str							=	torch.tensor(lowercase_)
          self.assertTrue(np.allclose(transpose(lowercase_)		,			transpose(lowercase_).numpy()))

          SCREAMING_SNAKE_CASE_   :						List[Any]							=	np.random.randn(3		,			4		,			5)
          SCREAMING_SNAKE_CASE_   :						Dict							=	torch.tensor(lowercase_)
          self.assertTrue(np.allclose(transpose(lowercase_		,			axes=(1, 2, 0))		,			transpose(lowercase_		,			axes=(1, 2, 0)).numpy()))
      @require_tf
      def _SCREAMING_SNAKE_CASE				(		self  :						Tuple):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	np.random.randn(3		,			4)
          SCREAMING_SNAKE_CASE_   :						str							=	tf.constant(lowercase_)
          self.assertTrue(np.allclose(transpose(lowercase_)		,			transpose(lowercase_).numpy()))

          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	np.random.randn(3		,			4		,			5)
          SCREAMING_SNAKE_CASE_   :						int							=	tf.constant(lowercase_)
          self.assertTrue(np.allclose(transpose(lowercase_		,			axes=(1, 2, 0))		,			transpose(lowercase_		,			axes=(1, 2, 0)).numpy()))
      @require_flax
      def _SCREAMING_SNAKE_CASE				(		self  :						Optional[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Tuple							=	np.random.randn(3		,			4)
          SCREAMING_SNAKE_CASE_   :						Any							=	jnp.array(lowercase_)
          self.assertTrue(np.allclose(transpose(lowercase_)		,			np.asarray(transpose(lowercase_))))

          SCREAMING_SNAKE_CASE_   :						Dict							=	np.random.randn(3		,			4		,			5)
          SCREAMING_SNAKE_CASE_   :						Any							=	jnp.array(lowercase_)
          self.assertTrue(np.allclose(transpose(lowercase_		,			axes=(1, 2, 0))		,			np.asarray(transpose(lowercase_		,			axes=(1, 2, 0)))))
      def _SCREAMING_SNAKE_CASE				(		self  :						Optional[int]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Dict							=	np.random.randn(3		,			4)
          self.assertTrue(np.allclose(reshape(lowercase_		,			(4, 3))		,			np.reshape(lowercase_		,			(4, 3))))

          SCREAMING_SNAKE_CASE_   :						Dict							=	np.random.randn(3		,			4		,			5)
          self.assertTrue(np.allclose(reshape(lowercase_		,			(12, 5))		,			np.reshape(lowercase_		,			(12, 5))))
      @require_torch
      def _SCREAMING_SNAKE_CASE				(		self  :						Union[str, Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	np.random.randn(3		,			4)
          SCREAMING_SNAKE_CASE_   :						List[str]							=	torch.tensor(lowercase_)
          self.assertTrue(np.allclose(reshape(lowercase_		,			(4, 3))		,			reshape(lowercase_		,			(4, 3)).numpy()))

          SCREAMING_SNAKE_CASE_   :						int							=	np.random.randn(3		,			4		,			5)
          SCREAMING_SNAKE_CASE_   :						int							=	torch.tensor(lowercase_)
          self.assertTrue(np.allclose(reshape(lowercase_		,			(12, 5))		,			reshape(lowercase_		,			(12, 5)).numpy()))
      @require_tf
      def _SCREAMING_SNAKE_CASE				(		self  :						Dict):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	np.random.randn(3		,			4)
          SCREAMING_SNAKE_CASE_   :						int							=	tf.constant(lowercase_)
          self.assertTrue(np.allclose(reshape(lowercase_		,			(4, 3))		,			reshape(lowercase_		,			(4, 3)).numpy()))

          SCREAMING_SNAKE_CASE_   :						List[Any]							=	np.random.randn(3		,			4		,			5)
          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	tf.constant(lowercase_)
          self.assertTrue(np.allclose(reshape(lowercase_		,			(12, 5))		,			reshape(lowercase_		,			(12, 5)).numpy()))
      @require_flax
      def _SCREAMING_SNAKE_CASE				(		self  :						Any):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Any							=	np.random.randn(3		,			4)
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	jnp.array(lowercase_)
          self.assertTrue(np.allclose(reshape(lowercase_		,			(4, 3))		,			np.asarray(reshape(lowercase_		,			(4, 3)))))

          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	np.random.randn(3		,			4		,			5)
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	jnp.array(lowercase_)
          self.assertTrue(np.allclose(reshape(lowercase_		,			(12, 5))		,			np.asarray(reshape(lowercase_		,			(12, 5)))))
      def _SCREAMING_SNAKE_CASE				(		self  :						List[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						List[Any]							=	np.random.randn(1		,			3		,			4)
          self.assertTrue(np.allclose(squeeze(lowercase_)		,			np.squeeze(lowercase_)))

          SCREAMING_SNAKE_CASE_   :						str							=	np.random.randn(1		,			4		,			1		,			5)
          self.assertTrue(np.allclose(squeeze(lowercase_		,			axis=2)		,			np.squeeze(lowercase_		,			axis=2)))
      @require_torch
      def _SCREAMING_SNAKE_CASE				(		self  :						Any):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	np.random.randn(1		,			3		,			4)
          SCREAMING_SNAKE_CASE_   :						int							=	torch.tensor(lowercase_)
          self.assertTrue(np.allclose(squeeze(lowercase_)		,			squeeze(lowercase_).numpy()))

          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	np.random.randn(1		,			4		,			1		,			5)
          SCREAMING_SNAKE_CASE_   :						str							=	torch.tensor(lowercase_)
          self.assertTrue(np.allclose(squeeze(lowercase_		,			axis=2)		,			squeeze(lowercase_		,			axis=2).numpy()))
      @require_tf
      def _SCREAMING_SNAKE_CASE				(		self  :						List[str]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						int							=	np.random.randn(1		,			3		,			4)
          SCREAMING_SNAKE_CASE_   :						Dict							=	tf.constant(lowercase_)
          self.assertTrue(np.allclose(squeeze(lowercase_)		,			squeeze(lowercase_).numpy()))

          SCREAMING_SNAKE_CASE_   :						List[Any]							=	np.random.randn(1		,			4		,			1		,			5)
          SCREAMING_SNAKE_CASE_   :						Any							=	tf.constant(lowercase_)
          self.assertTrue(np.allclose(squeeze(lowercase_		,			axis=2)		,			squeeze(lowercase_		,			axis=2).numpy()))
      @require_flax
      def _SCREAMING_SNAKE_CASE				(		self  :						int):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	np.random.randn(1		,			3		,			4)
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	jnp.array(lowercase_)
          self.assertTrue(np.allclose(squeeze(lowercase_)		,			np.asarray(squeeze(lowercase_))))

          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	np.random.randn(1		,			4		,			1		,			5)
          SCREAMING_SNAKE_CASE_   :						str							=	jnp.array(lowercase_)
          self.assertTrue(np.allclose(squeeze(lowercase_		,			axis=2)		,			np.asarray(squeeze(lowercase_		,			axis=2))))
      def _SCREAMING_SNAKE_CASE				(		self  :						Tuple):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						int							=	np.random.randn(3		,			4)
          self.assertTrue(np.allclose(expand_dims(lowercase_		,			axis=1)		,			np.expand_dims(lowercase_		,			axis=1)))
      @require_torch
      def _SCREAMING_SNAKE_CASE				(		self  :						Dict):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						int							=	np.random.randn(3		,			4)
          SCREAMING_SNAKE_CASE_   :						int							=	torch.tensor(lowercase_)
          self.assertTrue(np.allclose(expand_dims(lowercase_		,			axis=1)		,			expand_dims(lowercase_		,			axis=1).numpy()))
      @require_tf
      def _SCREAMING_SNAKE_CASE				(		self  :						str):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						List[str]							=	np.random.randn(3		,			4)
          SCREAMING_SNAKE_CASE_   :						Any							=	tf.constant(lowercase_)
          self.assertTrue(np.allclose(expand_dims(lowercase_		,			axis=1)		,			expand_dims(lowercase_		,			axis=1).numpy()))



      @require_flax
      def _SCREAMING_SNAKE_CASE				(		self  :						Optional[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Tuple							=	np.random.randn(3		,			4)
          SCREAMING_SNAKE_CASE_   :						int							=	jnp.array(lowercase_)
          self.assertTrue(np.allclose(expand_dims(lowercase_		,			axis=1)		,			np.asarray(expand_dims(lowercase_		,			axis=1))))


