





# flake8: noqa
# Lint as: python3

from typing import Dict, List, Optional, Type

from .. import config
from ..utils import logging
from .formatting import (
    ArrowFormatter,
    CustomFormatter,
    Formatter,
    PandasFormatter,
    PythonFormatter,
    TensorFormatter,
    format_table,
    query_table,
)
from .np_formatter import NumpyFormatter


lowercase_					     =	logging.get_logger(__name__)

lowercase_					     =	{}
lowercase_					     =	{}
lowercase_					     =	{}




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE = None	,		)       ->     Dict:
  lowercase__			    =						aliases if aliases is not None else []
  if format_type in _FORMAT_TYPES:
    logger.warning(
        F"""Overwriting format type '{format_type}' ({_FORMAT_TYPES[format_type].__name__} -> {formatter_cls.__name__})"""	)
  lowercase__			    =						formatter_cls
  for alias in set(aliases + [format_type]	):
    if alias in _FORMAT_TYPES_ALIASES:
      logger.warning(
          F"""Overwriting format type alias '{alias}' ({_FORMAT_TYPES_ALIASES[alias]} -> {format_type})"""	)
    lowercase__			    =						format_type




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE = None	)       ->     Tuple:
  lowercase__			    =						aliases if aliases is not None else []
  for alias in set(aliases + [format_type]	):
    lowercase__			    =						unavailable_error


# Here we define all the available formatting functions that can be used by `Dataset.set_format`
_register_formatter(PythonFormatter, None, aliases=["""python"""])
_register_formatter(ArrowFormatter, """arrow""", aliases=["""pa""", """pyarrow"""])
_register_formatter(NumpyFormatter, """numpy""", aliases=["""np"""])
_register_formatter(PandasFormatter, """pandas""", aliases=["""pd"""])
_register_formatter(CustomFormatter, """custom""")

if config.TORCH_AVAILABLE:
      from .torch_formatter import TorchFormatter

      _register_formatter(TorchFormatter, """torch""", aliases=["""pt""", """pytorch"""])
else:
      lowercase_					     =	ValueError("""PyTorch needs to be installed to be able to return PyTorch tensors.""")
      _register_unavailable_formatter(_torch_error, """torch""", aliases=["""pt""", """pytorch"""])

if config.TF_AVAILABLE:
      from .tf_formatter import TFFormatter

      _register_formatter(TFFormatter, """tensorflow""", aliases=["""tf"""])
else:
      lowercase_					     =	ValueError("""Tensorflow needs to be installed to be able to return Tensorflow tensors.""")
      _register_unavailable_formatter(_tf_error, """tensorflow""", aliases=["""tf"""])

if config.JAX_AVAILABLE:
      from .jax_formatter import JaxFormatter

      _register_formatter(JaxFormatter, """jax""", aliases=[])
else:
      lowercase_					     =	ValueError("""JAX needs to be installed to be able to return JAX arrays.""")
      _register_unavailable_formatter(_jax_error, """jax""", aliases=[])




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	)       ->     Optional[str]:
  if format_type in _FORMAT_TYPES_ALIASES:
    return _FORMAT_TYPES_ALIASES[format_type]
  else:
    return format_type




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	,		**_SCREAMING_SNAKE_CASE	)       ->     Formatter:
  lowercase__			    =						get_format_type_from_alias(_SCREAMING_SNAKE_CASE	)
  if format_type in _FORMAT_TYPES:
    return _FORMAT_TYPES[format_type](**_SCREAMING_SNAKE_CASE	)
  if format_type in _FORMAT_TYPES_ALIASES_UNAVAILABLE:
    raise _FORMAT_TYPES_ALIASES_UNAVAILABLE[format_type]
  else:
    raise ValueError(
        F"""Return type should be None or selected in {list(type for type in _FORMAT_TYPES.keys() if type != None	)}, but got '{format_type}'"""	)









from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class        SCREAMING_SNAKE_CASE     (UpperCAmelCase   ):
   _UpperCamelCase					:	Tuple				      = 'ClapFeatureExtractor'
   _UpperCamelCase					:	Union[str, Any]				      = ('RobertaTokenizer', 'RobertaTokenizerFast')


   def __init__(					self    :       List[Any]    ,  a    :       int    ,  a    :       str )->     Any:





     """simple docstring"""







     super().__init__(a    ,  a )


   def __call__(					self    :       Any    ,  a    :       Tuple=None    ,  a    :       Optional[int]=None    ,  a    :       int=None    ,  **a    :       Optional[int] )->     Union[str, Any]:





     """simple docstring"""







     lowercase__			    =						kwargs.pop('sampling_rate'    ,  a )

     if text is None and audios is None:
       raise ValueError('You have to specify either text or audios. Both cannot be none.' )

     if text is not None:
       lowercase__			    =						self.tokenizer(a    ,  return_tensors=a    ,  **a )

     if audios is not None:
       lowercase__			    =						self.feature_extractor(
           a    ,  sampling_rate=a    ,  return_tensors=a    ,  **a )

     if text is not None and audios is not None:
       lowercase__			    =						audio_features.input_features
       return encoding
     elif text is not None:
       return encoding
     else:
       return BatchEncoding(data=dict(**a )    ,  tensor_type=a )


   def  SCREAMING_SNAKE_CASE_							(					self    :       str    ,  *a    :       Dict    ,  **a    :       int )->     Optional[int]:





     """simple docstring"""







     return self.tokenizer.batch_decode(*a    ,  **a )


   def  SCREAMING_SNAKE_CASE_							(					self    :       List[Any]    ,  *a    :       int    ,  **a    :       Dict )->     Dict:





     """simple docstring"""







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


   @property
   def  SCREAMING_SNAKE_CASE_							(					self    :       Union[str, Any] )->     Optional[int]:





     """simple docstring"""







     lowercase__			    =						self.tokenizer.model_input_names
     lowercase__			    =						self.feature_extractor.model_input_names
     return list(dict.fromkeys(tokenizer_input_names + feature_extractor_input_names ) )


