


from __future__ import annotations




class 		a__    :





							'''simple docstring'''






							def __init__(		self ,   lowerCamelCase_ ,   lowerCamelCase_       )	->							int:
														lowerCAmelCase__	       =     text, pattern
														lowerCAmelCase__	       =     len(_SCREAMING_SNAKE_CASE       ), len(_SCREAMING_SNAKE_CASE       )







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_       )	->							int:




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







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_       )	->							int:




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

__UpperCAmelCase       =							"ABAABA"
__UpperCAmelCase       =							"AB"
__UpperCAmelCase       =							BoyerMooreSearch(text, pattern)
__UpperCAmelCase       =							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

from transformers import (
    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TextClassificationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow

from .test_pipelines_common import ANY


# These 2 model types require different inputs than those of the usual text models.
__UpperCAmelCase       =							{'''LayoutLMv2Config''', '''LayoutLMv3Config'''}




@is_pipeline_test
class 		a__    (							unittest.TestCase  ):





							'''simple docstring'''






							lowercase__      :       Optional[Any]     						=				MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
							lowercase__      :       List[Any]     						=				TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

							if model_mapping is not None:
														lowercase__      :       Optional[Any]     						=				{config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
							if tf_model_mapping is not None:
														lowercase__      :       Tuple     						=				{
														    config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
														}







							@require_torch
							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Optional[int]:
														lowerCAmelCase__	       =     pipeline(
														    task='''text-classification''' ,   model='''hf-internal-testing/tiny-random-distilbert''' ,   framework='''pt'''       )

														lowerCAmelCase__	       =     text_classifier('''This is great !'''       )
														self.assertEqual(nested_simplify(lowerCamelCase_       ) ,   [{'''label''': '''LABEL_0''', '''score''': 0.504}]       )

														lowerCAmelCase__	       =     text_classifier('''This is great !''' ,   top_k=2       )
														self.assertEqual(
														    nested_simplify(lowerCamelCase_       ) ,   [{'''label''': '''LABEL_0''', '''score''': 0.504}, {'''label''': '''LABEL_1''', '''score''': 0.496}]       )

														lowerCAmelCase__	       =     text_classifier(['''This is great !''', '''This is bad'''] ,   top_k=2       )
														self.assertEqual(
														    nested_simplify(lowerCamelCase_       ) ,   [
														        [{'''label''': '''LABEL_0''', '''score''': 0.504}, {'''label''': '''LABEL_1''', '''score''': 0.496}],
														        [{'''label''': '''LABEL_0''', '''score''': 0.504}, {'''label''': '''LABEL_1''', '''score''': 0.496}],
														    ] ,   )

														lowerCAmelCase__	       =     text_classifier('''This is great !''' ,   top_k=1       )
														self.assertEqual(nested_simplify(lowerCamelCase_       ) ,   [{'''label''': '''LABEL_0''', '''score''': 0.504}]       )

														# Legacy behavior
														lowerCAmelCase__	       =     text_classifier('''This is great !''' ,   return_all_scores=lowerCamelCase_       )
														self.assertEqual(nested_simplify(lowerCamelCase_       ) ,   [{'''label''': '''LABEL_0''', '''score''': 0.504}]       )

														lowerCAmelCase__	       =     text_classifier('''This is great !''' ,   return_all_scores=lowerCamelCase_       )
														self.assertEqual(
														    nested_simplify(lowerCamelCase_       ) ,   [[{'''label''': '''LABEL_0''', '''score''': 0.504}, {'''label''': '''LABEL_1''', '''score''': 0.496}]]       )

														lowerCAmelCase__	       =     text_classifier(['''This is great !''', '''Something else'''] ,   return_all_scores=lowerCamelCase_       )
														self.assertEqual(
														    nested_simplify(lowerCamelCase_       ) ,   [
														        [{'''label''': '''LABEL_0''', '''score''': 0.504}, {'''label''': '''LABEL_1''', '''score''': 0.496}],
														        [{'''label''': '''LABEL_0''', '''score''': 0.504}, {'''label''': '''LABEL_1''', '''score''': 0.496}],
														    ] ,   )

														lowerCAmelCase__	       =     text_classifier(['''This is great !''', '''Something else'''] ,   return_all_scores=lowerCamelCase_       )
														self.assertEqual(
														    nested_simplify(lowerCamelCase_       ) ,   [
														        {'''label''': '''LABEL_0''', '''score''': 0.504},
														        {'''label''': '''LABEL_0''', '''score''': 0.504},
														    ] ,   )







							@require_torch
							def   __SCREAMING_SNAKE_CASE							(		self       )	->							int:
														import torch

														lowerCAmelCase__	       =     pipeline(
														    task='''text-classification''' ,   model='''hf-internal-testing/tiny-random-distilbert''' ,   framework='''pt''' ,   device=torch.device('''cpu'''       ) ,   )

														lowerCAmelCase__	       =     text_classifier('''This is great !'''       )
														self.assertEqual(nested_simplify(lowerCamelCase_       ) ,   [{'''label''': '''LABEL_0''', '''score''': 0.504}]       )







							@require_tf
							def   __SCREAMING_SNAKE_CASE							(		self       )	->							str:
														lowerCAmelCase__	       =     pipeline(
														    task='''text-classification''' ,   model='''hf-internal-testing/tiny-random-distilbert''' ,   framework='''tf'''       )

														lowerCAmelCase__	       =     text_classifier('''This is great !'''       )
														self.assertEqual(nested_simplify(lowerCamelCase_       ) ,   [{'''label''': '''LABEL_0''', '''score''': 0.504}]       )







							@slow
							@require_torch
							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Dict:
														lowerCAmelCase__	       =     pipeline('''text-classification'''       )

														lowerCAmelCase__	       =     text_classifier('''This is great !'''       )
														self.assertEqual(nested_simplify(lowerCamelCase_       ) ,   [{'''label''': '''POSITIVE''', '''score''': 1.0}]       )
														lowerCAmelCase__	       =     text_classifier('''This is bad !'''       )
														self.assertEqual(nested_simplify(lowerCamelCase_       ) ,   [{'''label''': '''NEGATIVE''', '''score''': 1.0}]       )
														lowerCAmelCase__	       =     text_classifier('''Birds are a type of animal'''       )
														self.assertEqual(nested_simplify(lowerCamelCase_       ) ,   [{'''label''': '''POSITIVE''', '''score''': 0.988}]       )







							@slow
							@require_tf
							def   __SCREAMING_SNAKE_CASE							(		self       )	->							Optional[Any]:
														lowerCAmelCase__	       =     pipeline('''text-classification''' ,   framework='''tf'''       )

														lowerCAmelCase__	       =     text_classifier('''This is great !'''       )
														self.assertEqual(nested_simplify(lowerCamelCase_       ) ,   [{'''label''': '''POSITIVE''', '''score''': 1.0}]       )
														lowerCAmelCase__	       =     text_classifier('''This is bad !'''       )
														self.assertEqual(nested_simplify(lowerCamelCase_       ) ,   [{'''label''': '''NEGATIVE''', '''score''': 1.0}]       )
														lowerCAmelCase__	       =     text_classifier('''Birds are a type of animal'''       )
														self.assertEqual(nested_simplify(lowerCamelCase_       ) ,   [{'''label''': '''POSITIVE''', '''score''': 0.988}]       )







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_ ,   lowerCamelCase_       )	->							Tuple:
														lowerCAmelCase__	       =     TextClassificationPipeline(model=lowerCamelCase_ ,   tokenizer=lowerCamelCase_       )
														return text_classifier, ["HuggingFace is in", "This is another test"]







							def   __SCREAMING_SNAKE_CASE							(		self ,   lowerCamelCase_ ,   lowerCamelCase_       )	->							Union[str, Any]:
														lowerCAmelCase__	       =     text_classifier.model
														# Small inputs because BartTokenizer tiny has maximum position embeddings = 22
														lowerCAmelCase__	       =     '''HuggingFace is in'''
														lowerCAmelCase__	       =     text_classifier(lowerCamelCase_       )

														self.assertEqual(nested_simplify(lowerCamelCase_       ) ,   [{'''label''': ANY(lowerCamelCase_       ), '''score''': ANY(lowerCamelCase_       )}]       )
														self.assertTrue(outputs[0]['''label'''] in model.config.idalabel.values()       )

														lowerCAmelCase__	       =     ['''HuggingFace is in ''', '''Paris is in France''']
														lowerCAmelCase__	       =     text_classifier(lowerCamelCase_       )
														self.assertEqual(
														    nested_simplify(lowerCamelCase_       ) ,   [{'''label''': ANY(lowerCamelCase_       ), '''score''': ANY(lowerCamelCase_       )}, {'''label''': ANY(lowerCamelCase_       ), '''score''': ANY(lowerCamelCase_       )}] ,   )
														self.assertTrue(outputs[0]['''label'''] in model.config.idalabel.values()       )
														self.assertTrue(outputs[1]['''label'''] in model.config.idalabel.values()       )

														# Forcing to get all results with `top_k=None`
														# This is NOT the legacy format
														lowerCAmelCase__	       =     text_classifier(lowerCamelCase_ ,   top_k=lowerCamelCase_       )
														lowerCAmelCase__	       =     len(model.config.idalabel.values()       )
														self.assertEqual(
														    nested_simplify(lowerCamelCase_       ) ,   [[{'''label''': ANY(lowerCamelCase_       ), '''score''': ANY(lowerCamelCase_       )}] * N, [{'''label''': ANY(lowerCamelCase_       ), '''score''': ANY(lowerCamelCase_       )}] * N] ,   )

														lowerCAmelCase__	       =     {'''text''': '''HuggingFace is in ''', '''text_pair''': '''Paris is in France'''}
														lowerCAmelCase__	       =     text_classifier(lowerCamelCase_       )
														self.assertEqual(
														    nested_simplify(lowerCamelCase_       ) ,   {'''label''': ANY(lowerCamelCase_       ), '''score''': ANY(lowerCamelCase_       )} ,   )
														self.assertTrue(outputs['''label'''] in model.config.idalabel.values()       )

														# This might be used a text pair, but tokenizer + pipe interaction
														# makes it hard to understand that it's not using the pair properly
														# https://github.com/huggingface/transformers/issues/17305
														# We disabled this usage instead as it was outputting wrong outputs.
														lowerCAmelCase__	       =     [['''HuggingFace is in ''', '''Paris is in France''']]
														with self.assertRaises(lowerCamelCase_       ):
																					text_classifier(lowerCamelCase_       )

														# This used to be valid for doing text pairs
														# We're keeping it working because of backward compatibility
														lowerCAmelCase__	       =     text_classifier([[['''HuggingFace is in ''', '''Paris is in France''']]]       )
														self.assertEqual(
														    nested_simplify(lowerCamelCase_       ) ,   [{'''label''': ANY(lowerCamelCase_       ), '''score''': ANY(lowerCamelCase_       )}] ,   )
														self.assertTrue(outputs[0]['''label'''] in model.config.idalabel.values()       )