





from typing import List, Optional

import numpy as np

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






class    __A       (       __A					):

			'''simple docstring'''







			lowerCAmelCase_					=		"""EncodecFeatureExtractor"""
			lowerCAmelCase_					=		("""T5Tokenizer""", """T5TokenizerFast""")





			def __init__(       self			,    __lowerCAmelCase			,    __lowerCAmelCase				):



				'''simple docstring'''







				super().__init__(lowerCAmelCase_			,    lowerCAmelCase_				)
				lowerCamelCase__														=						self.feature_extractor
				lowerCamelCase__														=						False





			def 						__lowerCamelCase							(       self			,    __lowerCAmelCase=None			,    __lowerCAmelCase=None			,    __lowerCAmelCase=True				):



				'''simple docstring'''







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





			def __call__(       self			,    *__lowerCAmelCase			,    **__lowerCAmelCase				):



				'''simple docstring'''







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

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

				if audio is not None:
					lowerCamelCase__														=						self.feature_extractor(lowerCAmelCase_			,    *lowerCAmelCase_			,    sampling_rate=lowerCAmelCase_			,    **lowerCAmelCase_				)

				if audio is None:
					return inputs

				elif text is None:
					return audio_inputs

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





			def 						__lowerCamelCase							(       self			,    *__lowerCAmelCase			,    **__lowerCAmelCase				):



				'''simple docstring'''







				lowerCamelCase__														=						kwargs.pop('''audio'''			,    lowerCAmelCase_				)
				lowerCamelCase__														=						kwargs.pop('''padding_mask'''			,    lowerCAmelCase_				)

				if len(lowerCAmelCase_				) > 0:
					lowerCamelCase__														=						args[0]
					lowerCamelCase__														=						args[1:]

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





			def 						__lowerCamelCase							(       self			,    *__lowerCAmelCase			,    **__lowerCAmelCase				):



				'''simple docstring'''







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





			def 						__lowerCamelCase							(       self			,    __lowerCAmelCase			,    __lowerCAmelCase = None				):



				'''simple docstring'''







				lowerCamelCase__														=						to_numpy(lowerCAmelCase_				)
				lowerCamelCase__														=						audio_values.shape

				if padding_mask is None:
					return list(lowerCAmelCase_				)

				lowerCamelCase__														=						to_numpy(lowerCAmelCase_				)

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

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

				return audio_values











"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
			from .tokenization_camembert import CamembertTokenizer
else:
			lowerCamelCase_							      = None


lowerCamelCase_							      = logging.get_logger(__name__)

lowerCamelCase_							      = {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

lowerCamelCase_							      = {
    '''vocab_file''': {
        '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model''',
    },
    '''tokenizer_file''': {
        '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/tokenizer.json''',
    },
}

lowerCamelCase_							      = {
    '''camembert-base''': 512,
}

lowerCamelCase_							      = '''▁'''







class      UpperCamelCase_			(__A	):
	__magic_name__       =							VOCAB_FILES_NAMES
	__magic_name__       =							PRETRAINED_VOCAB_FILES_MAP
	__magic_name__       =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	__magic_name__       =							['''input_ids''', '''attention_mask''']
	__magic_name__       =							CamembertTokenizer





	def __init__( self   :					Tuple      ,      lowerCAmelCase_   :					List[str]=None      ,      lowerCAmelCase_   :					Optional[int]=None      ,      lowerCAmelCase_   :					Optional[int]="<s>"      ,      lowerCAmelCase_   :					Dict="</s>"      ,      lowerCAmelCase_   :					Any="</s>"      ,      lowerCAmelCase_   :					Union[str, Any]="<s>"      ,      lowerCAmelCase_   :					int="<unk>"      ,      lowerCAmelCase_   :					Union[str, Any]="<pad>"      ,      lowerCAmelCase_   :					Union[str, Any]="<mask>"      ,      lowerCAmelCase_   :					Any=["<s>NOTUSED", "</s>NOTUSED"]      ,      **lowerCAmelCase_   :					Any      ,      )		->     Union[str, Any]:
				# Mask token behave like a normal word, i.e. include the space before it
				UpperCAmelCase_	:					Dict     	=   AddedToken(lowerCAmelCase_      ,      lstrip=lowerCAmelCase_      ,      rstrip=lowerCAmelCase_		) if isinstance(lowerCAmelCase_      ,      lowerCAmelCase_		) else mask_token

				super().__init__(
				    lowerCAmelCase_      ,      tokenizer_file=lowerCAmelCase_      ,      bos_token=lowerCAmelCase_      ,      eos_token=lowerCAmelCase_      ,      sep_token=lowerCAmelCase_      ,      cls_token=lowerCAmelCase_      ,      unk_token=lowerCAmelCase_      ,      pad_token=lowerCAmelCase_      ,      mask_token=lowerCAmelCase_      ,      additional_special_tokens=lowerCAmelCase_      ,      **lowerCAmelCase_      ,      )

				UpperCAmelCase_	:					str     	=   vocab_file
				UpperCAmelCase_	:					Optional[Any]     	=   False if not self.vocab_file else True





	def 	_SCREAMING_SNAKE_CASE ( self   :					Union[str, Any]      ,      lowerCAmelCase_   :					List[int]      ,      lowerCAmelCase_   :					Optional[List[int]] = None		)		->     List[int]:

				if token_ids_a is None:
							return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
				UpperCAmelCase_	:					Tuple     	=   [self.cls_token_id]
				UpperCAmelCase_	:					Any     	=   [self.sep_token_id]
				return cls + token_ids_a + sep + sep + token_ids_a + sep





	def 	_SCREAMING_SNAKE_CASE ( self   :					Optional[Any]      ,      lowerCAmelCase_   :					List[int]      ,      lowerCAmelCase_   :					Optional[List[int]] = None		)		->     List[int]:
				UpperCAmelCase_	:					List[Any]     	=   [self.sep_token_id]
				UpperCAmelCase_	:					Optional[int]     	=   [self.cls_token_id]

				if token_ids_a is None:
							return len(cls + token_ids_a + sep		) * [0]
				return len(cls + token_ids_a + sep + sep + token_ids_a + sep		) * [0]





	def 	_SCREAMING_SNAKE_CASE ( self   :					Tuple      ,      lowerCAmelCase_   :					str      ,      lowerCAmelCase_   :					Optional[str] = None		)		->     Tuple[str]:
				if not self.can_save_slow_tokenizer:
							raise ValueError(
							    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
							    "tokenizer."		)

				if not os.path.isdir(lowerCAmelCase_		):
							logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""		)
							return
				UpperCAmelCase_	:					Dict     	=   os.path.join(
				    lowerCAmelCase_      ,      (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)

				if os.path.abspath(self.vocab_file		) != os.path.abspath(lowerCAmelCase_		):
							copyfile(self.vocab_file      ,      lowerCAmelCase_		)

				return (out_vocab_file,)



