


'''simple docstring'''






import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin






class  __magic_name__							(		lowerCAmelCase    ):
       UpperCAmelCase										="Speech2TextFeatureExtractor"
       UpperCAmelCase										="Speech2TextTokenizer"






       def __init__(      self  ,							snake_case  ,							snake_case)						->  List[str]:




          '''simple docstring'''
          super().__init__(snake_case  ,							snake_case)
          _UpperCAmelCase       :       List[Any]							   =self.feature_extractor
          _UpperCAmelCase       :       str							   =False






       def __call__(      self  ,							*snake_case  ,							**snake_case)						->  List[Any]:




          '''simple docstring'''
          # For backward compatibility
          if self._in_target_context_manager:
             return self.current_processor(*snake_case  ,							**snake_case)

          if "raw_speech" in kwargs:
             warnings.warn('Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.')
             _UpperCAmelCase       :       str							   =kwargs.pop('raw_speech')
          else:
             _UpperCAmelCase       :       int							   =kwargs.pop('audio'  ,							snake_case)
          _UpperCAmelCase       :       str							   =kwargs.pop('sampling_rate'  ,							snake_case)
          _UpperCAmelCase       :       str							   =kwargs.pop('text'  ,							snake_case)
          if len(snake_case) > 0:
             _UpperCAmelCase       :       str							   =args[0]
             _UpperCAmelCase       :       str							   =args[1:]

          if audio is None and text is None:
             raise ValueError('You need to specify either an `audio` or `text` input to process.')

          if audio is not None:
             _UpperCAmelCase       :       int							   =self.feature_extractor(snake_case  ,							*snake_case  ,							sampling_rate=snake_case  ,							**snake_case)
          if text is not None:
             _UpperCAmelCase       :       List[Any]							   =self.tokenizer(snake_case  ,							**snake_case)

          if text is None:
             return inputs
          elif audio is None:
             return encodings
          else:
             _UpperCAmelCase       :       Any							   =encodings['input_ids']
             return inputs






       def       lowerCAmelCase		(      self  ,							*snake_case  ,							**snake_case)						->  Dict:




          '''simple docstring'''
          return self.tokenizer.batch_decode(*snake_case  ,							**snake_case)






       def       lowerCAmelCase		(      self  ,							*snake_case  ,							**snake_case)						->  Optional[Any]:




          '''simple docstring'''
          return self.tokenizer.decode(*snake_case  ,							**snake_case)






       @contextmanager
       def       lowerCAmelCase		(      self)						->  Dict:




          '''simple docstring'''
          warnings.warn(
              '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
              'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
              'your audio inputs, or in a separate call.')
          _UpperCAmelCase       :       Optional[Any]							   =True
          _UpperCAmelCase       :       Dict							   =self.tokenizer
          yield
          _UpperCAmelCase       :       int							   =self.feature_extractor
          _UpperCAmelCase       :       Dict							   =False





'''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.
import re

from ..utils import cached_file


# docstyle-ignore
lowercase							='\nHuman: <<task>>\n\nAssistant: '


lowercase							='huggingface-tools/default-prompts'
lowercase							={'chat': 'chat_prompt_template.txt', 'run': 'run_prompt_template.txt'}





def lowerCamelCase__      (						__lowerCamelCase	:							Union[str, Any]	,   __lowerCamelCase	:							Union[str, Any]	,   __lowerCamelCase	:							int="run"							):







   '''simple docstring'''




   if prompt_or_repo_id is None:
      _UpperCAmelCase       :       List[str]							   =DEFAULT_PROMPTS_REPO

   # prompt is considered a repo ID when it does not contain any kind of space
   if re.search('\\s'	,   __lowerCamelCase							) is not None:
      return prompt_or_repo_id

   _UpperCAmelCase       :       Dict							   =cached_file(
       __lowerCamelCase	,   PROMPT_FILES[mode]	,   repo_type='dataset'	,   user_agent={'agent': agent_name}							)
   with open(__lowerCamelCase	,   'r'	,   encoding='utf-8'							) as f:
      return f.read()

