


"""simple docstring"""







def _A						(						lowercase       ,    lowercase		):







    """simple docstring"""
    a 		=len(lowercase		)
    a 		=[]
    for i in range(len(lowercase		) - pat_len + 1		):
        a 		=True
        for j in range(lowercase		):
            if s[i + j] != pattern[j]:
                a 		=False
                break
        if match_found:
            position.append(lowercase		)
    return position


if __name__ == "__main__":
       assert naive_pattern_search("""ABCDEFG""", """DE""") == [3]
       print(naive_pattern_search("""ABAAABCDBBABCDDEBCABC""", """ABC"""))
"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_torch_available,
)


__A			   = {
    "configuration_speecht5": [
        "SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP",
        "SpeechT5Config",
        "SpeechT5HifiGanConfig",
    ],
    "feature_extraction_speecht5": ["SpeechT5FeatureExtractor"],
    "processing_speecht5": ["SpeechT5Processor"],
}

try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __A			   = ["SpeechT5Tokenizer"]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __A			   = [
           "SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST",
           "SpeechT5ForSpeechToText",
           "SpeechT5ForSpeechToSpeech",
           "SpeechT5ForTextToSpeech",
           "SpeechT5Model",
           "SpeechT5PreTrainedModel",
           "SpeechT5HifiGan",
       ]

if TYPE_CHECKING:
       from .configuration_speechta import (
           SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
           SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
           SpeechTaConfig,
           SpeechTaHifiGanConfig,
       )
       from .feature_extraction_speechta import SpeechTaFeatureExtractor
       from .processing_speechta import SpeechTaProcessor

       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_speechta import SpeechTaTokenizer

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_speechta import (
                  SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
                  SpeechTaForSpeechToSpeech,
                  SpeechTaForSpeechToText,
                  SpeechTaForTextToSpeech,
                  SpeechTaHifiGan,
                  SpeechTaModel,
                  SpeechTaPreTrainedModel,
              )

else:
       import sys

       __A			   = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
