





'''simple docstring'''
from typing import List, Optional

import numpy as np

from ...processing_utils import ProcessorMixin
from ...utils import to_numpy

class      snake_case     (    __lowerCamelCase       ):




 """simple docstring"""



 SCREAMING_SNAKE_CASE_	:					Optional[int]					="EncodecFeatureExtractor"
 SCREAMING_SNAKE_CASE_	:					List[Any]					=("T5Tokenizer", "T5TokenizerFast")






 def __init__(       self       :							Optional[int]							,	__A       :							Optional[Any]							,	__A       :							List[Any]	):
        super().__init__(__A							,	__A	)
        __UpperCamelCase       =  self.feature_extractor
        __UpperCamelCase       =  False






 def        _lowerCamelCase					(       self       :							Dict							,	__A       :							Dict=None							,	__A       :							Dict=None							,	__A       :							Union[str, Any]=True	):
        return self.tokenizer.get_decoder_prompt_ids(task=__A							,	language=__A							,	no_timestamps=__A	)






 def __call__(       self       :							Union[str, Any]							,	*__A       :							List[Any]							,	**__A       :							List[Any]	):
        # For backward compatibility
        if self._in_target_context_manager:
               return self.current_processor(*__A							,	**__A	)

        __UpperCamelCase       =  kwargs.pop('audio'							,	__A	)
        __UpperCamelCase       =  kwargs.pop('sampling_rate'							,	__A	)
        __UpperCamelCase       =  kwargs.pop('text'							,	__A	)
        if len(__A	) > 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(__A							,	**__A	)

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

        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        _lowerCamelCase					(       self       :							Optional[Any]							,	*__A       :							str							,	**__A       :							Dict	):
        __UpperCamelCase       =  kwargs.pop('audio'							,	__A	)
        __UpperCamelCase       =  kwargs.pop('padding_mask'							,	__A	)

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

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






 def        _lowerCamelCase					(       self       :							str							,	*__A       :							List[str]							,	**__A       :							List[Any]	):
        return self.tokenizer.decode(*__A							,	**__A	)






 def        _lowerCamelCase					(       self       :							Dict							,	__A       :							List[Any]							,	__A       :							Optional = None	):
        __UpperCamelCase       =  to_numpy(__A	)
        __UpperCamelCase       ,						__UpperCamelCase       ,						__UpperCamelCase       =  audio_values.shape

        if padding_mask is None:
               return list(__A	)

        __UpperCamelCase       =  to_numpy(__A	)

        # 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(__A							,	((0, 0), (0, difference))							,	'constant'							,	constant_values=__A	)

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

        return audio_values






from typing import Dict, List, Optional

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


_A     =			logging.get_logger(__name__)


_A     =			{
    '''nielsr/canine-s''': 2_048,
}

# Unicode defines 1,114,112 total “codepoints”
_A     =			1_114_112

# Below: Constants defining canonical codepoints for special, pseudo-characters.
# Copied from https://github.com/google-research/language/blob/master/language/canine/special_codepoints.py
_A     =			0

_A     =			0xe0_00
_A     =			0xe0_01
_A     =			0xe0_02
_A     =			0xe0_03
_A     =			0xe0_04

# Maps special codepoints to human-readable names.
_A     =			{
    # Special symbols are represented using codepoints values that are valid,
    # but designated as "Private Use", meaning that they will never be assigned
    # characters by the Unicode Consortium, and are thus safe for use here.
    #
    # NOTE: Do *NOT* add any sort of [UNK_CHAR] here. They are explicitly
    # excluded and should fail with a hard error.
    CLS: "[CLS]",
    SEP: "[SEP]",
    BOS: "[BOS]",
    MASK: "[MASK]",
    PAD: "[PAD]",
    RESERVED: "[RESERVED]",
}

# Maps special codepoint human-readable names to their codepoint values.
_A     =			{name: codepoint for codepoint, name in SPECIAL_CODEPOINTS.items()}


class  A		(  __UpperCAmelCase     ):
		__snake_case									=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES



		def __init__(     self,	UpperCamelCase__=chr(UpperCamelCase__   ),	UpperCamelCase__=chr(UpperCamelCase__   ),	UpperCamelCase__=chr(UpperCamelCase__   ),	UpperCamelCase__=chr(UpperCamelCase__   ),	UpperCamelCase__=chr(UpperCamelCase__   ),	UpperCamelCase__=chr(UpperCamelCase__   ),	UpperCamelCase__=False,	UpperCamelCase__=2048,	**UpperCamelCase__,	):







								"""simple docstring"""



								lowerCAmelCase_      			=  AddedToken(UpperCamelCase__,	lstrip=UpperCamelCase__,	rstrip=UpperCamelCase__   ) if isinstance(UpperCamelCase__,	UpperCamelCase__   ) else bos_token
								lowerCAmelCase_      			=  AddedToken(UpperCamelCase__,	lstrip=UpperCamelCase__,	rstrip=UpperCamelCase__   ) if isinstance(UpperCamelCase__,	UpperCamelCase__   ) else eos_token
								lowerCAmelCase_      			=  AddedToken(UpperCamelCase__,	lstrip=UpperCamelCase__,	rstrip=UpperCamelCase__   ) if isinstance(UpperCamelCase__,	UpperCamelCase__   ) else sep_token
								lowerCAmelCase_      			=  AddedToken(UpperCamelCase__,	lstrip=UpperCamelCase__,	rstrip=UpperCamelCase__   ) if isinstance(UpperCamelCase__,	UpperCamelCase__   ) else cls_token
								lowerCAmelCase_      			=  AddedToken(UpperCamelCase__,	lstrip=UpperCamelCase__,	rstrip=UpperCamelCase__   ) if isinstance(UpperCamelCase__,	UpperCamelCase__   ) else pad_token

								# Mask token behave like a normal word, i.e. include the space before it
								lowerCAmelCase_      			=  AddedToken(UpperCamelCase__,	lstrip=UpperCamelCase__,	rstrip=UpperCamelCase__   ) if isinstance(UpperCamelCase__,	UpperCamelCase__   ) else mask_token

								super().__init__(
								    bos_token=UpperCamelCase__,	eos_token=UpperCamelCase__,	sep_token=UpperCamelCase__,	cls_token=UpperCamelCase__,	pad_token=UpperCamelCase__,	mask_token=UpperCamelCase__,	add_prefix_space=UpperCamelCase__,	model_max_length=UpperCamelCase__,	**UpperCamelCase__,	)

								# Creates a mapping for looking up the IDs of special symbols.
								lowerCAmelCase_      			=  {}
								for codepoint, name in SPECIAL_CODEPOINTS.items():
														lowerCAmelCase_      			=  codepoint

								# Creates a mapping for looking up the string forms of special symbol IDs.
								lowerCAmelCase_      			=  {
								    codepoint: name for name, codepoint in self._special_codepoints.items()
								}

								lowerCAmelCase_      			=  UNICODE_VOCAB_SIZE
								lowerCAmelCase_      			=  len(self._special_codepoints   )



		@property
		def        SCREAMING_SNAKE_CASE__			(     self   ):







								"""simple docstring"""



								return self._unicode_vocab_size



		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__   ):







								"""simple docstring"""



								return list(UpperCamelCase__   )



		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__   ):







								"""simple docstring"""



								try:
														return ord(UpperCamelCase__   )
								except TypeError:
														raise ValueError(f"invalid token: '{token}'"   )



		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__   ):







								"""simple docstring"""



								try:
														if index in SPECIAL_CODEPOINTS:
																				return SPECIAL_CODEPOINTS[index]
														return chr(UpperCamelCase__   )
								except TypeError:
														raise ValueError(f"invalid id: {index}"   )



		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__   ):







								"""simple docstring"""



								return "".join(UpperCamelCase__   )



		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__,	UpperCamelCase__ = None   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  [self.sep_token_id]
								lowerCAmelCase_      			=  [self.cls_token_id]

								lowerCAmelCase_      			=  cls + token_ids_a + sep
								if token_ids_a is not None:
														result += token_ids_a + sep
								return result



		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__,	UpperCamelCase__ = None,	UpperCamelCase__ = False   ):







								"""simple docstring"""



								if already_has_special_tokens:
														return super().get_special_tokens_mask(
														    token_ids_a=UpperCamelCase__,	token_ids_a=UpperCamelCase__,	already_has_special_tokens=UpperCamelCase__   )

								lowerCAmelCase_      			=  [1] + ([0] * len(UpperCamelCase__   )) + [1]
								if token_ids_a is not None:
														result += ([0] * len(UpperCamelCase__   )) + [1]
								return result



		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__,	UpperCamelCase__ = None   ):







								"""simple docstring"""



								lowerCAmelCase_      			=  [self.sep_token_id]
								lowerCAmelCase_      			=  [self.cls_token_id]

								lowerCAmelCase_      			=  len(cls + token_ids_a + sep   ) * [0]
								if token_ids_a is not None:
														result += len(token_ids_a + sep   ) * [1]
								return result







		def        SCREAMING_SNAKE_CASE__			(     self,	UpperCamelCase__,	UpperCamelCase__ = None   ):







								"""simple docstring"""



								return ()




