





'''simple docstring'''

from __future__ import annotations







class __a			:





   def __init__(       self					:      Union[str, Any]					,lowerCamelCase					:      Tuple					,lowerCamelCase					:      str     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							text, pattern
      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							len(lowerCamelCase     ), len(lowerCamelCase     )





   def   UpperCAmelCase__  (       self					:      Any					,lowerCamelCase					:      Union[str, Any]     ):

      '''simple docstring'''

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





   def   UpperCAmelCase__  (       self					:      int					,lowerCamelCase					:      Optional[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   UpperCAmelCase__  (       self					:      Optional[int]     ):

      '''simple docstring'''

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





a   	=    "ABAABA"
a   	=    "AB"
a   	=    BoyerMooreSearch(text, pattern)
a   	=    bms.bad_character_heuristic()

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








# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from packaging import version

from .. import __version__
from .constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_STANDARD_MEAN, IMAGENET_STANDARD_STD
from .doc import (
    add_code_sample_docstrings,
    add_end_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    copy_func,
    replace_return_docstrings,
)
from .generic import (
    ContextManagers,
    ExplicitEnum,
    ModelOutput,
    PaddingStrategy,
    TensorType,
    add_model_info_to_auto_map,
    cached_property,
    can_return_loss,
    expand_dims,
    find_labels,
    flatten_dict,
    infer_framework,
    is_jax_tensor,
    is_numpy_array,
    is_tensor,
    is_tf_symbolic_tensor,
    is_tf_tensor,
    is_torch_device,
    is_torch_dtype,
    is_torch_tensor,
    reshape,
    squeeze,
    strtobool,
    tensor_size,
    to_numpy,
    to_py_obj,
    transpose,
    working_or_temp_dir,
)
from .hub import (
    CLOUDFRONT_DISTRIB_PREFIX,
    DISABLE_TELEMETRY,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_PREFIX,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    PYTORCH_PRETRAINED_BERT_CACHE,
    PYTORCH_TRANSFORMERS_CACHE,
    S3_BUCKET_PREFIX,
    TRANSFORMERS_CACHE,
    TRANSFORMERS_DYNAMIC_MODULE_NAME,
    EntryNotFoundError,
    PushToHubMixin,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    cached_file,
    default_cache_path,
    define_sagemaker_information,
    download_url,
    extract_commit_hash,
    get_cached_models,
    get_file_from_repo,
    get_full_repo_name,
    has_file,
    http_user_agent,
    is_offline_mode,
    is_remote_url,
    move_cache,
    send_example_telemetry,
    try_to_load_from_cache,
)
from .import_utils import (
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    TORCH_FX_REQUIRED_VERSION,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    _LazyModule,
    ccl_version,
    direct_transformers_import,
    get_torch_version,
    is_accelerate_available,
    is_apex_available,
    is_bitsandbytes_available,
    is_bsa_available,
    is_coloredlogs_available,
    is_cython_available,
    is_datasets_available,
    is_decord_available,
    is_detectrona_available,
    is_faiss_available,
    is_flax_available,
    is_ftfy_available,
    is_in_notebook,
    is_ipex_available,
    is_jieba_available,
    is_jumanpp_available,
    is_kenlm_available,
    is_keras_nlp_available,
    is_librosa_available,
    is_natten_available,
    is_ninja_available,
    is_onnx_available,
    is_openai_available,
    is_optimum_available,
    is_pandas_available,
    is_peft_available,
    is_phonemizer_available,
    is_protobuf_available,
    is_psutil_available,
    is_pyanvml_available,
    is_pyctcdecode_available,
    is_pytesseract_available,
    is_pytest_available,
    is_pytorch_quantization_available,
    is_rjieba_available,
    is_sacremoses_available,
    is_safetensors_available,
    is_sagemaker_dp_enabled,
    is_sagemaker_mp_enabled,
    is_scipy_available,
    is_sentencepiece_available,
    is_seqio_available,
    is_sklearn_available,
    is_soundfile_availble,
    is_spacy_available,
    is_speech_available,
    is_sudachi_available,
    is_tensorflow_probability_available,
    is_tensorflow_text_available,
    is_tfaonnx_available,
    is_tf_available,
    is_timm_available,
    is_tokenizers_available,
    is_torch_available,
    is_torch_bfaa_available,
    is_torch_bfaa_cpu_available,
    is_torch_bfaa_gpu_available,
    is_torch_compile_available,
    is_torch_cuda_available,
    is_torch_fx_available,
    is_torch_fx_proxy,
    is_torch_mps_available,
    is_torch_neuroncore_available,
    is_torch_tensorrt_fx_available,
    is_torch_tfaa_available,
    is_torch_tpu_available,
    is_torchaudio_available,
    is_torchdistx_available,
    is_torchdynamo_available,
    is_torchvision_available,
    is_training_run_on_sagemaker,
    is_vision_available,
    requires_backends,
    torch_only_method,
)


lowercase_   							=  'pytorch_model.bin'
lowercase_   							=  'pytorch_model.bin.index.json'
lowercase_   							=  'adapter_config.json'
lowercase_   							=  'adapter_model.bin'
lowercase_   							=  'adapter_model.safetensors'
lowercase_   							=  'tf_model.h5'
lowercase_   							=  'tf_model.h5.index.json'
lowercase_   							=  'model.ckpt'
lowercase_   							=  'flax_model.msgpack'
lowercase_   							=  'flax_model.msgpack.index.json'
lowercase_   							=  'model.safetensors'
lowercase_   							=  'model.safetensors.index.json'
lowercase_   							=  'config.json'
lowercase_   							=  'preprocessor_config.json'
lowercase_   							=  FEATURE_EXTRACTOR_NAME
lowercase_   							=  'generation_config.json'
lowercase_   							=  'modelcard.json'

lowercase_   							=  '▁'
lowercase_   							=  SENTENCEPIECE_UNDERLINE  # Kept for backward compatibility

lowercase_   							=  [
    [[0, 1, 0, 1], [1, 0, 0, 1]]
] * 2  # Needs to have 0s and 1s only since XLM uses it for langs too.
lowercase_   							=  [[7, 6, 0, 0, 1], [1, 2, 3, 0, 0], [0, 0, 0, 4, 5]]
lowercase_   							=  [[1, 1, 1, 1, 1], [1, 1, 1, 0, 0], [0, 0, 0, 1, 1]]
def 	a (     A__      :  List[str]	)       -> str:





      """simple docstring"""



      if version.parse(A__	) < version.parse(A__	):
            if "dev" in min_version:
                  _lowercase      			=(
                      'This example requires a source install from HuggingFace Transformers (see '
                      '`https://huggingface.co/docs/transformers/installation#install-from-source`),'
                  )
            else:
                  _lowercase      			=F'''This example requires a minimum version of {min_version},'''
            error_message += F''' but the version found is {__version__}.\n'''
            raise ImportError(
                error_message
                + 'Check out https://github.com/huggingface/transformers/tree/main/examples#important-note for the examples corresponding to other '
                'versions of HuggingFace Transformers.'	)


