from typing import List, Optional

import numpy as np

from ...processing_utils import ProcessorMixin
from ...utils import to_numpy
class  A_			(							a_			):
  _SCREAMING_SNAKE_CASE     					=      """EncodecFeatureExtractor"""
  _SCREAMING_SNAKE_CASE     					=      ("""T5Tokenizer""", """T5TokenizerFast""")

  def __init__(		self			:   Optional[Any]  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]  ,				__SCREAMING_SNAKE_CASE			:   Tuple							):
   super().__init__(__SCREAMING_SNAKE_CASE  ,				__SCREAMING_SNAKE_CASE							)
   __a							=	self.feature_extractor
   __a							=	False

  def 		_UpperCAmelCase (		self			:   Union[str, Any]  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]=None  ,				__SCREAMING_SNAKE_CASE			:   str=None  ,				__SCREAMING_SNAKE_CASE			:   List[Any]=True							):
   return self.tokenizer.get_decoder_prompt_ids(task=__SCREAMING_SNAKE_CASE  ,				language=__SCREAMING_SNAKE_CASE  ,				no_timestamps=__SCREAMING_SNAKE_CASE							)

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

   __a							=	kwargs.pop("audio"  ,				__SCREAMING_SNAKE_CASE							)
   __a							=	kwargs.pop("sampling_rate"  ,				__SCREAMING_SNAKE_CASE							)
   __a							=	kwargs.pop("text"  ,				__SCREAMING_SNAKE_CASE							)
   if len(__SCREAMING_SNAKE_CASE							) > 0:
    __a							=	args[0]
    __a							=	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:
    __a							=	self.tokenizer(__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)

   if audio is not None:
    __a							=	self.feature_extractor(__SCREAMING_SNAKE_CASE  ,				*__SCREAMING_SNAKE_CASE  ,				sampling_rate=__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)

   if audio is None:
    return inputs

   elif text is None:
    return audio_inputs

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

  def 		_UpperCAmelCase (		self			:   Optional[Any]  ,				*__SCREAMING_SNAKE_CASE			:   Optional[int]  ,				**__SCREAMING_SNAKE_CASE			:   str							):
   __a							=	kwargs.pop("audio"  ,				__SCREAMING_SNAKE_CASE							)
   __a							=	kwargs.pop("padding_mask"  ,				__SCREAMING_SNAKE_CASE							)

   if len(__SCREAMING_SNAKE_CASE							) > 0:
    __a							=	args[0]
    __a							=	args[1:]

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

  def 		_UpperCAmelCase (		self			:   Dict  ,				*__SCREAMING_SNAKE_CASE			:   int  ,				**__SCREAMING_SNAKE_CASE			:   Optional[int]							):
   return self.tokenizer.decode(*__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)

  def 		_UpperCAmelCase (		self			:   List[Any]  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]  ,				__SCREAMING_SNAKE_CASE			:   Optional = None							):
   __a							=	to_numpy(__SCREAMING_SNAKE_CASE							)
   __a							,  __a							,  __a							=	audio_values.shape

   if padding_mask is None:
    return list(__SCREAMING_SNAKE_CASE							)

   __a							=	to_numpy(__SCREAMING_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)
   __a							=	seq_len - padding_mask.shape[-1]
   __a							=	1 - self.feature_extractor.padding_value
   __a							=	np.pad(__SCREAMING_SNAKE_CASE  ,				((0, 0), (0, difference))  ,				"constant"  ,				constant_values=__SCREAMING_SNAKE_CASE							)

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

   return audio_values



from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE			:						int				  =		logging.get_logger(__name__)

SCREAMING_SNAKE_CASE			:						Dict				  =		{
    """google/canine-s""": """https://huggingface.co/google/canine-s/resolve/main/config.json""",
    # See all CANINE models at https://huggingface.co/models?filter=canine
}
class  A_			(							a_			):
  _SCREAMING_SNAKE_CASE     					=      """canine"""

  def __init__(		self			:   int  ,				__SCREAMING_SNAKE_CASE			:   List[str]=7_68  ,				__SCREAMING_SNAKE_CASE			:   Any=12  ,				__SCREAMING_SNAKE_CASE			:   str=12  ,				__SCREAMING_SNAKE_CASE			:   int=30_72  ,				__SCREAMING_SNAKE_CASE			:   int="gelu"  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]=0.1  ,				__SCREAMING_SNAKE_CASE			:   List[str]=0.1  ,				__SCREAMING_SNAKE_CASE			:   List[Any]=1_63_84  ,				__SCREAMING_SNAKE_CASE			:   List[Any]=16  ,				__SCREAMING_SNAKE_CASE			:   int=0.02  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=1E-12  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]=0  ,				__SCREAMING_SNAKE_CASE			:   List[str]=0xE000  ,				__SCREAMING_SNAKE_CASE			:   int=0xE001  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]=4  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=4  ,				__SCREAMING_SNAKE_CASE			:   List[Any]=8  ,				__SCREAMING_SNAKE_CASE			:   List[str]=1_63_84  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=1_28  ,				**__SCREAMING_SNAKE_CASE			:   List[str]  ,				):
   super().__init__(pad_token_id=__SCREAMING_SNAKE_CASE  ,				bos_token_id=__SCREAMING_SNAKE_CASE  ,				eos_token_id=__SCREAMING_SNAKE_CASE  ,				**__SCREAMING_SNAKE_CASE							)

   __a							=	max_position_embeddings
   __a							=	hidden_size
   __a							=	num_hidden_layers
   __a							=	num_attention_heads
   __a							=	intermediate_size
   __a							=	hidden_act
   __a							=	hidden_dropout_prob
   __a							=	attention_probs_dropout_prob
   __a							=	initializer_range
   __a							=	type_vocab_size
   __a							=	layer_norm_eps

   # Character config:
   __a							=	downsampling_rate
   __a							=	upsampling_kernel_size
   __a							=	num_hash_functions
   __a							=	num_hash_buckets
   __a							=	local_transformer_stride


