



"""simple docstring"""


from typing import List, Optional

import numpy as np

from ...processing_utils import ProcessorMixin
from ...utils import to_numpy
class 			_a (					lowerCAmelCase):
   """simple docstring"""
   UpperCamelCase__			      =	"""EncodecFeatureExtractor"""
   UpperCamelCase__			      =	("""T5Tokenizer""", """T5TokenizerFast""")



   def __init__(		self     :    List[str]				,   __UpperCamelCase     :    Tuple				,   __UpperCamelCase     :    Tuple   )->Dict:
       super().__init__(__UpperCamelCase				,   __UpperCamelCase   )
       _UpperCAmelCase					=    self.feature_extractor
       _UpperCAmelCase					=    False



   def  lowercase__						(		self     :    Optional[Any]				,   __UpperCamelCase     :    Optional[int]=None				,   __UpperCamelCase     :    Optional[int]=None				,   __UpperCamelCase     :    List[Any]=True   )->int:
       return self.tokenizer.get_decoder_prompt_ids(task=__UpperCamelCase				,   language=__UpperCamelCase				,   no_timestamps=__UpperCamelCase   )



   def __call__(		self     :    Any				,   *__UpperCamelCase     :    Dict				,   **__UpperCamelCase     :    Tuple   )->str:
       # For backward compatibility
       if self._in_target_context_manager:
           return self.current_processor(*__UpperCamelCase				,   **__UpperCamelCase   )

       _UpperCAmelCase					=    kwargs.pop('''audio'''				,   __UpperCamelCase   )
       _UpperCAmelCase					=    kwargs.pop('''sampling_rate'''				,   __UpperCamelCase   )
       _UpperCAmelCase					=    kwargs.pop('''text'''				,   __UpperCamelCase   )
       if len(__UpperCamelCase   ) > 0:
           _UpperCAmelCase					=    args[0]
           _UpperCAmelCase					=    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:
           _UpperCAmelCase					=    self.tokenizer(__UpperCamelCase				,   **__UpperCamelCase   )

       if audio is not None:
           _UpperCAmelCase					=    self.feature_extractor(__UpperCamelCase				,   *__UpperCamelCase				,   sampling_rate=__UpperCamelCase				,   **__UpperCamelCase   )

       if audio is None:
           return inputs

       elif text is None:
           return audio_inputs

       else:
           _UpperCAmelCase					=    audio_inputs['''input_values''']
           if "padding_mask" in audio_inputs:
               _UpperCAmelCase					=    audio_inputs['''padding_mask''']
           return inputs



   def  lowercase__						(		self     :    Any				,   *__UpperCamelCase     :    Tuple				,   **__UpperCamelCase     :    Dict   )->List[Any]:
       _UpperCAmelCase					=    kwargs.pop('''audio'''				,   __UpperCamelCase   )
       _UpperCAmelCase					=    kwargs.pop('''padding_mask'''				,   __UpperCamelCase   )

       if len(__UpperCamelCase   ) > 0:
           _UpperCAmelCase					=    args[0]
           _UpperCAmelCase					=    args[1:]

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



   def  lowercase__						(		self     :    str				,   *__UpperCamelCase     :    Optional[Any]				,   **__UpperCamelCase     :    Union[str, Any]   )->Dict:
       return self.tokenizer.decode(*__UpperCamelCase				,   **__UpperCamelCase   )



   def  lowercase__						(		self     :    Optional[Any]				,   __UpperCamelCase     :    Optional[Any]				,   __UpperCamelCase     :    Optional = None   )->List[np.ndarray]:
       _UpperCAmelCase					=    to_numpy(__UpperCamelCase   )
       _UpperCAmelCase		,						_UpperCAmelCase		,						_UpperCAmelCase					=    audio_values.shape

       if padding_mask is None:
           return list(__UpperCamelCase   )

       _UpperCAmelCase					=    to_numpy(__UpperCamelCase   )

       # 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)
       _UpperCAmelCase					=    seq_len - padding_mask.shape[-1]
       _UpperCAmelCase					=    1 - self.feature_extractor.padding_value
       _UpperCAmelCase					=    np.pad(__UpperCamelCase				,   ((0, 0), (0, difference))				,   '''constant'''				,   constant_values=__UpperCamelCase   )

       _UpperCAmelCase					=    audio_values.tolist()
       for i in range(__UpperCamelCase   ):
           _UpperCAmelCase					=    np.asarray(audio_values[i]   )[
               padding_mask[i][None, :] != self.feature_extractor.padding_value
           ]
           _UpperCAmelCase					=    sliced_audio.reshape(__UpperCamelCase				,   -1   )

       return audio_values








"""simple docstring"""


import functools







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						list[int]					,     _SCREAMING_SNAKE_CASE   :						list[int]				):






				'''simple docstring'''



				if not isinstance(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				) or not all(isinstance(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				) for day in days				):
								raise ValueError('''The parameter days should be a list of integers'''				)

				if len(_SCREAMING_SNAKE_CASE				) != 3 or not all(isinstance(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				) for cost in costs				):
								raise ValueError('''The parameter costs should be a list of three integers'''				)

				if len(_SCREAMING_SNAKE_CASE				) == 0:
								return 0

				if min(_SCREAMING_SNAKE_CASE				) <= 0:
								raise ValueError('''All days elements should be greater than 0'''				)

				if max(_SCREAMING_SNAKE_CASE				) >= 366:
								raise ValueError('''All days elements should be less than 366'''				)

				_UpperCAmelCase					=    set(_SCREAMING_SNAKE_CASE				)

				@functools.cache
				def dynamic_programming(_SCREAMING_SNAKE_CASE   :						int				) -> int:
								if index > 365:
												return 0

								if index not in days_set:
												return dynamic_programming(index + 1				)

								return min(
								    costs[0] + dynamic_programming(index + 1				)					,     costs[1] + dynamic_programming(index + 7				)					,     costs[2] + dynamic_programming(index + 30				)					,     )

				return dynamic_programming(1				)


if __name__ == "__main__":
					import doctest

					doctest.testmod()



