


'''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.
a_    :							int         =					{"""LayoutLMv2Config""", """LayoutLMv3Config"""}





@is_pipeline_test
class 	snake_case       (       unittest.TestCase     ):





  """simple docstring"""




  _lowerCamelCase						       =    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
  _lowerCamelCase						       =    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

  if model_mapping is not None:
    _lowerCamelCase						       =    {config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
  if tf_model_mapping is not None:
    _lowerCamelCase						       =    {
        config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
    }






  @require_torch
  def     snake_case						(       self	):







   """simple docstring"""
   lowerCamelCase_						=    pipeline(
       task="text-classification"     ,    model="hf-internal-testing/tiny-random-distilbert"     ,    framework="pt"	)

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

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

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

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

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






  @require_torch
  def     snake_case						(       self	):







   """simple docstring"""
   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(_UpperCamelCase	)     ,    [{"label": "LABEL_0", "score": 0.504}]	)






  @require_tf
  def     snake_case						(       self	):







   """simple docstring"""
   lowerCamelCase_						=    pipeline(
       task="text-classification"     ,    model="hf-internal-testing/tiny-random-distilbert"     ,    framework="tf"	)

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






  @slow
  @require_torch
  def     snake_case						(       self	):







   """simple docstring"""
   lowerCamelCase_						=    pipeline("text-classification"	)

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






  @slow
  @require_tf
  def     snake_case						(       self	):







   """simple docstring"""
   lowerCamelCase_						=    pipeline("text-classification"     ,    framework="tf"	)

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






  def     snake_case						(       self     ,    UpperCamelCase     ,    UpperCamelCase     ,    UpperCamelCase	):







   """simple docstring"""
   lowerCamelCase_						=    TextClassificationPipeline(model=_UpperCamelCase     ,    tokenizer=_UpperCamelCase	)
   return text_classifier, ["HuggingFace is in", "This is another test"]







  def     snake_case						(       self     ,    UpperCamelCase     ,    UpperCamelCase	):







   """simple docstring"""
   lowerCamelCase_						=    text_classifier.model
   # Small inputs because BartTokenizer tiny has maximum position embeddings = 22
   lowerCamelCase_						=    "HuggingFace is in"
   lowerCamelCase_						=    text_classifier(_UpperCamelCase	)

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

   lowerCamelCase_						=    ["HuggingFace is in ", "Paris is in France"]
   lowerCamelCase_						=    text_classifier(_UpperCamelCase	)
   self.assertEqual(
       nested_simplify(_UpperCamelCase	)     ,    [{"label": ANY(_UpperCamelCase	), "score": ANY(_UpperCamelCase	)}, {"label": ANY(_UpperCamelCase	), "score": ANY(_UpperCamelCase	)}]     ,    )
   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(_UpperCamelCase     ,    top_k=_UpperCamelCase	)
   lowerCamelCase_						=    len(model.config.idalabel.values()	)
   self.assertEqual(
       nested_simplify(_UpperCamelCase	)     ,    [[{"label": ANY(_UpperCamelCase	), "score": ANY(_UpperCamelCase	)}] * N, [{"label": ANY(_UpperCamelCase	), "score": ANY(_UpperCamelCase	)}] * N]     ,    )

   lowerCamelCase_						=    {"text": "HuggingFace is in ", "text_pair": "Paris is in France"}
   lowerCamelCase_						=    text_classifier(_UpperCamelCase	)
   self.assertEqual(
       nested_simplify(_UpperCamelCase	)     ,    {"label": ANY(_UpperCamelCase	), "score": ANY(_UpperCamelCase	)}     ,    )
   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(_UpperCamelCase	):
    text_classifier(_UpperCamelCase	)

   # 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(_UpperCamelCase	)     ,    [{"label": ANY(_UpperCamelCase	), "score": ANY(_UpperCamelCase	)}]     ,    )
   self.assertTrue(outputs[0]["label"] in model.config.idalabel.values()	)






from __future__ import annotations

class     __snake_case    :
       """simple docstring"""







       def __init__(		self      , _UpperCamelCase      , _UpperCamelCase   )      ->							Tuple:






              """simple docstring"""


              __snake_case	,    __snake_case       	=							text, pattern
              __snake_case	,    __snake_case       	=							len(_UpperCamelCase   ), len(_UpperCamelCase   )


       def 			a		(		self      , _UpperCamelCase   )      ->							int:






              """simple docstring"""


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


       def 			a		(		self      , _UpperCamelCase   )      ->							int:






              """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 			a		(		self   )      ->							list[int]:






              """simple docstring"""


              __snake_case       	=							[]
              for i in range(self.textLen - self.patLen + 1   ):
                     __snake_case       	=							self.mismatch_in_text(_UpperCamelCase   )
                     if mismatch_index == -1:
                            positions.append(_UpperCamelCase   )
                     else:
                            __snake_case       	=							self.match_in_pattern(self.text[mismatch_index]   )
                            __snake_case       	=							(
                                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)

