




'''simple docstring'''





# Copyright 2023 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 ..models.whisper import WhisperForConditionalGeneration, WhisperProcessor
from .base import PipelineTool

class        _a    (						__lowerCAmelCase							):
     SCREAMING_SNAKE_CASE_							:   List[str]       =						"""openai/whisper-base"""
     SCREAMING_SNAKE_CASE_							:   Tuple       =						(
         """This is a tool that transcribes an audio into text. It takes an input named `audio` and returns the """
         """transcribed text."""
     )
     SCREAMING_SNAKE_CASE_							:   Optional[Any]       =						"""transcriber"""
     SCREAMING_SNAKE_CASE_							:   List[Any]       =						WhisperProcessor
     SCREAMING_SNAKE_CASE_							:   List[str]       =						WhisperForConditionalGeneration

     SCREAMING_SNAKE_CASE_							:   Union[str, Any]       =						["""audio"""]
     SCREAMING_SNAKE_CASE_							:   Dict       =						["""text"""]






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE   )							->      Union[str, Any]:
          return self.pre_processor(_SCREAMING_SNAKE_CASE ,return_tensors="pt"   ).input_features






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE   )							->      Optional[Any]:
          return self.model.generate(inputs=_SCREAMING_SNAKE_CASE   )






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE   )							->      int:
          return self.pre_processor.batch_decode(_SCREAMING_SNAKE_CASE ,skip_special_tokens=_SCREAMING_SNAKE_CASE   )[0]








'''simple docstring'''





from typing import TYPE_CHECKING

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


UpperCamelCase_						:      Union[str, Any]       =						{'''configuration_plbart''': ['''PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PLBartConfig''']}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCamelCase_						:      Optional[int]       =						['''PLBartTokenizer''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCamelCase_						:      int       =						[
       '''PLBART_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''PLBartForCausalLM''',
       '''PLBartForConditionalGeneration''',
       '''PLBartForSequenceClassification''',
       '''PLBartModel''',
       '''PLBartPreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_plbart import PLBartTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_plbart import (
          PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
          PLBartForCausalLM,
          PLBartForConditionalGeneration,
          PLBartForSequenceClassification,
          PLBartModel,
          PLBartPreTrainedModel,
      )


else:
   import sys

   UpperCamelCase_						:      str       =						_LazyModule(__name__, globals()['''__file__'''], _import_structure)


