


"""simple docstring"""



from typing import List, Optional

import numpy as np

from ...processing_utils import ProcessorMixin
from ...utils import to_numpy
class 					__magic_name__		(			__UpperCAmelCase     ):
       __A			:      Optional[Any]        =   "EncodecFeatureExtractor"
       __A			:      Optional[int]        =   ("T5Tokenizer", "T5TokenizerFast")






       def __init__(   self  :    List[Any]  ,     snake_case__  :    Any  ,     snake_case__  :    Tuple				):

              '''simple docstring'''






              super().__init__(snake_case__  ,     snake_case__				)
              lowercase		:Any       							=				self.feature_extractor
              lowercase		:List[Any]       							=				False






       def  __snake_case						(   self  :    Any  ,     snake_case__  :    List[str]=None  ,     snake_case__  :    Any=None  ,     snake_case__  :    List[str]=True				):

              '''simple docstring'''






              return self.tokenizer.get_decoder_prompt_ids(task=snake_case__  ,     language=snake_case__  ,     no_timestamps=snake_case__				)






       def __call__(   self  :    str  ,     *snake_case__  :    List[Any]  ,     **snake_case__  :    List[Any]				):

              '''simple docstring'''






              if self._in_target_context_manager:
                     return self.current_processor(*snake_case__  ,     **snake_case__				)

              lowercase		:Optional[int]       							=				kwargs.pop('''audio'''  ,     snake_case__				)
              lowercase		:Dict       							=				kwargs.pop('''sampling_rate'''  ,     snake_case__				)
              lowercase		:Dict       							=				kwargs.pop('''text'''  ,     snake_case__				)
              if len(snake_case__				) > 0:
                     lowercase		:int       							=				args[0]
                     lowercase		:List[Any]       							=				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 text is not None:
                     lowercase		:Optional[Any]       							=				self.tokenizer(snake_case__  ,     **snake_case__				)

              if audio is not None:
                     lowercase		:Dict       							=				self.feature_extractor(snake_case__  ,     *snake_case__  ,     sampling_rate=snake_case__  ,     **snake_case__				)

              if audio is None:
                     return inputs

              elif text is None:
                     return audio_inputs

              else:
                     lowercase		:List[str]       							=				audio_inputs['''input_values''']
                     if "padding_mask" in audio_inputs:
                            lowercase		:List[Any]       							=				audio_inputs['''padding_mask''']
                     return inputs






       def  __snake_case						(   self  :    Any  ,     *snake_case__  :    Tuple  ,     **snake_case__  :    Union[str, Any]				):

              '''simple docstring'''






              lowercase		:Tuple       							=				kwargs.pop('''audio'''  ,     snake_case__				)
              lowercase		:Tuple       							=				kwargs.pop('''padding_mask'''  ,     snake_case__				)

              if len(snake_case__				) > 0:
                     lowercase		:Optional[Any]       							=				args[0]
                     lowercase		:int       							=				args[1:]

              if audio_values is not None:
                     return self._decode_audio(snake_case__  ,     padding_mask=snake_case__				)
              else:
                     return self.tokenizer.batch_decode(*snake_case__  ,     **snake_case__				)






       def  __snake_case						(   self  :    Dict  ,     *snake_case__  :    List[str]  ,     **snake_case__  :    Optional[int]				):

              '''simple docstring'''






              return self.tokenizer.decode(*snake_case__  ,     **snake_case__				)







       def  __snake_case						(   self  :    Union[str, Any]  ,     snake_case__  :    Union[str, Any]  ,     snake_case__  :    Optional = None				):

              '''simple docstring'''






              lowercase		:Tuple       							=				to_numpy(snake_case__				)
              lowercase							,  lowercase							,  lowercase		:int       							=				audio_values.shape

              if padding_mask is None:
                     return list(snake_case__				)

              lowercase		:List[str]       							=				to_numpy(snake_case__				)

              # match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
              # token (so that the generated audio values are **not** treated as padded tokens)
              lowercase		:List[str]       							=				seq_len - padding_mask.shape[-1]
              lowercase		:Dict       							=				1 - self.feature_extractor.padding_value
              lowercase		:Union[str, Any]       							=				np.pad(snake_case__  ,     ((0, 0), (0, difference))  ,     '''constant'''  ,     constant_values=snake_case__				)

              lowercase		:int       							=				audio_values.tolist()
              for i in range(snake_case__				):
                     lowercase		:List[str]       							=				np.asarray(audio_values[i]				)[
                         padding_mask[i][None, :] != self.feature_extractor.padding_value
                     ]
                     lowercase		:Optional[Any]       							=				sliced_audio.reshape(snake_case__  ,     -1				)

              return audio_values





"""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
UpperCAmelCase      		=			'''
Human: <<task>>

Assistant: '''


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






def       lowerCamelCase					(a_						:int				,				a_						:str				,				a_						:Dict="run")     ->							Optional[Any]:
       if prompt_or_repo_id is None:
              lowercase		:Tuple       							=				DEFAULT_PROMPTS_REPO

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

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

