


import os
import re
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

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


UpperCAmelCase   :    Union[str, Any]     =		logging.get_logger(__name__)

UpperCAmelCase   :    Union[str, Any]     =		{"vocab_file": "spiece.model"}

UpperCAmelCase   :    List[Any]     =		{
    "vocab_file": {
        "google/bigbird-roberta-base": "https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model",
        "google/bigbird-roberta-large": (
            "https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model"
        ),
        "google/bigbird-base-trivia-itc": (
            "https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model"
        ),
    }
}

UpperCAmelCase   :    Any     =		{
    "google/bigbird-roberta-base": 40_96,
    "google/bigbird-roberta-large": 40_96,
    "google/bigbird-base-trivia-itc": 40_96,
}







class        __lowercase       (							a_  ):



		"""simple docstring"""

		UpperCamelCase     :					Tuple			       =		VOCAB_FILES_NAMES
		UpperCamelCase     :					Any			       =		PRETRAINED_VOCAB_FILES_MAP
		UpperCamelCase     :					Optional[Any]			       =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		UpperCamelCase     :					Tuple			       =		["input_ids", "attention_mask"]
		UpperCamelCase     :					List[int]			       =		[]







		def __init__( self		,      A		,      A="<unk>"		,      A="<s>"		,      A="</s>"		,      A="<pad>"		,      A="[SEP]"		,      A="[MASK]"		,      A="[CLS]"		,      A = None		,      **A		,      )  -> None:







					'''simple docstring'''







					lowerCamelCase    		=  AddedToken(A		,      lstrip=A		,      rstrip=A	) if isinstance(A		,      A	) else bos_token
					lowerCamelCase    		=  AddedToken(A		,      lstrip=A		,      rstrip=A	) if isinstance(A		,      A	) else eos_token
					lowerCamelCase    		=  AddedToken(A		,      lstrip=A		,      rstrip=A	) if isinstance(A		,      A	) else unk_token
					lowerCamelCase    		=  AddedToken(A		,      lstrip=A		,      rstrip=A	) if isinstance(A		,      A	) else pad_token
					lowerCamelCase    		=  AddedToken(A		,      lstrip=A		,      rstrip=A	) if isinstance(A		,      A	) else cls_token
					lowerCamelCase    		=  AddedToken(A		,      lstrip=A		,      rstrip=A	) if isinstance(A		,      A	) else sep_token

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

					lowerCamelCase    		=  {} if sp_model_kwargs is None else sp_model_kwargs

					super().__init__(
					    bos_token=A		,      eos_token=A		,      unk_token=A		,      pad_token=A		,      sep_token=A		,      mask_token=A		,      cls_token=A		,      sp_model_kwargs=self.sp_model_kwargs		,      **A		,      )

					lowerCamelCase    		=  vocab_file

					lowerCamelCase    		=  spm.SentencePieceProcessor(**self.sp_model_kwargs	)
					self.sp_model.Load(A	)







		@property
		def 					__A   ( self	)  -> Tuple:







					'''simple docstring'''







					return self.sp_model.get_piece_size()







		def 					__A   ( self	)  -> str:







					'''simple docstring'''







					lowerCamelCase    		=  {self.convert_ids_to_tokens(A	): i for i in range(self.vocab_size	)}
					vocab.update(self.added_tokens_encoder	)
					return vocab







		def __getstate__( self	)  -> List[str]:







					'''simple docstring'''







					lowerCamelCase    		=  self.__dict__.copy()
					lowerCamelCase    		=  None
					return state







		def __setstate__( self		,      A	)  -> int:







					'''simple docstring'''







					lowerCamelCase    		=  d

					# for backward compatibility
					if not hasattr(self		,      """sp_model_kwargs"""	):
								lowerCamelCase    		=  {}

					lowerCamelCase    		=  spm.SentencePieceProcessor(**self.sp_model_kwargs	)
					self.sp_model.Load(self.vocab_file	)







		def 					__A   ( self		,      A	)  -> List[str]:







					'''simple docstring'''







					return self.sp_model.encode(A		,      out_type=A	)







		def 					__A   ( self		,      A	)  -> List[Any]:







					'''simple docstring'''







					return self.sp_model.piece_to_id(A	)







		def 					__A   ( self		,      A	)  -> Dict:







					'''simple docstring'''







					lowerCamelCase    		=  self.sp_model.IdToPiece(A	)
					return token







		def 					__A   ( self		,      A	)  -> Union[str, Any]:







					'''simple docstring'''







					lowerCamelCase    		=  []
					lowerCamelCase    		=  """"""
					lowerCamelCase    		=  False
					for token in tokens:
								# make sure that special tokens are not decoded using sentencepiece model
								if token in self.all_special_tokens:
											if not prev_is_special:
														out_string += " "
											out_string += self.sp_model.decode(A	) + token
											lowerCamelCase    		=  True
											lowerCamelCase    		=  []
								else:
											current_sub_tokens.append(A	)
											lowerCamelCase    		=  False
					out_string += self.sp_model.decode(A	)
					return out_string.strip()







		def 					__A   ( self		,      A		,      A = False		,      A = None		,      A = True		,      **A		,      )  -> str:







					'''simple docstring'''







					lowerCamelCase    		=  kwargs.pop("""use_source_tokenizer"""		,      A	)

					lowerCamelCase    		=  self.convert_ids_to_tokens(A		,      skip_special_tokens=A	)

					# To avoid mixing byte-level and unicode for byte-level BPT
					# we need to build string separately for added tokens and byte-level tokens
					# cf. https://github.com/huggingface/transformers/issues/1133
					lowerCamelCase    		=  []
					lowerCamelCase    		=  []
					for token in filtered_tokens:
								if skip_special_tokens and token in self.all_special_ids:
											continue
								if token in self.added_tokens_encoder:
											if current_sub_text:
														sub_texts.append(self.convert_tokens_to_string(A	)	)
														lowerCamelCase    		=  []
											sub_texts.append(A	)
								else:
											current_sub_text.append(A	)
					if current_sub_text:
								sub_texts.append(self.convert_tokens_to_string(A	)	)

					# Mimic the behavior of the Rust tokenizer:
					# No space before [MASK] and [SEP]
					if spaces_between_special_tokens:
								lowerCamelCase    		=  re.sub(r""" (\[(MASK|SEP)\])"""		,      r"""\1"""		,      """ """.join(A	)	)
					else:
								lowerCamelCase    		=  """""".join(A	)

					lowerCamelCase    		=  (
					    clean_up_tokenization_spaces
					    if clean_up_tokenization_spaces is not None
					    else self.clean_up_tokenization_spaces
					)
					if clean_up_tokenization_spaces:
								lowerCamelCase    		=  self.clean_up_tokenization(A	)
								return clean_text
					else:
								return text







		def 					__A   ( self		,      A		,      A = None	)  -> Tuple[str]:







					'''simple docstring'''







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

					if os.path.abspath(self.vocab_file	) != os.path.abspath(A	) and os.path.isfile(self.vocab_file	):
								copyfile(self.vocab_file		,      A	)
					elif not os.path.isfile(self.vocab_file	):
								with open(A		,      """wb"""	) as fi:
											lowerCamelCase    		=  self.sp_model.serialized_model_proto()
											fi.write(A	)

					return (out_vocab_file,)







		def 					__A   ( self		,      A		,      A = None	)  -> List[int]:







					'''simple docstring'''







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







		def 					__A   ( self		,      A		,      A = None		,      A = False	)  -> List[int]:







					'''simple docstring'''







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

					if token_ids_a is None:
								return [1] + ([0] * len(A	)) + [1]
					return [1] + ([0] * len(A	)) + [1] + ([0] * len(A	)) + [1]







		def 					__A   ( self		,      A		,      A = None	)  -> List[int]:







					'''simple docstring'''







					lowerCamelCase    		=  [self.sep_token_id]
					lowerCamelCase    		=  [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	) * [0] + len(token_ids_a + sep	) * [1]





from math import ceil
from typing import List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging


UpperCAmelCase   :    Any     =		logging.get_logger(__name__)







class        __lowercase       (							a_  ):



		"""simple docstring"""

		UpperCamelCase     :					List[Any]			       =		["audio_values", "audio_mask"]







		def __init__( self		,      A=20_48		,      A=1		,      A=[16, 16]		,      A=1_28		,      A=4_41_00		,      A=86		,      A=20_48		,      A=0.0		,      **A		,      )  -> Dict:







					'''simple docstring'''







					super().__init__(
					    feature_size=A		,      sampling_rate=A		,      padding_value=A		,      **A		,      )

					lowerCamelCase    		=  spectrogram_length
					lowerCamelCase    		=  num_channels
					lowerCamelCase    		=  patch_size
					lowerCamelCase    		=  feature_size // self.patch_size[1]
					lowerCamelCase    		=  n_fft
					lowerCamelCase    		=  sampling_rate // hop_length_to_sampling_rate
					lowerCamelCase    		=  sampling_rate
					lowerCamelCase    		=  padding_value
					lowerCamelCase    		=  mel_filter_bank(
					    num_frequency_bins=1 + n_fft // 2		,      num_mel_filters=A		,      min_frequency=0.0		,      max_frequency=22050.0		,      sampling_rate=A		,      norm="""slaney"""		,      mel_scale="""slaney"""		,      ).T







		def 					__A   ( self		,      A	)  -> np.ndarray:







					'''simple docstring'''







					lowerCamelCase    		=  spectrogram(
					    A		,      window_function(self.n_fft		,      """hann"""	)		,      frame_length=self.n_fft		,      hop_length=self.hop_length		,      power=2.0		,      mel_filters=self.mel_filters.T		,      log_mel="""dB"""		,      db_range=80.0		,      )
					lowerCamelCase    		=  log_spec[:, :-1]
					lowerCamelCase    		=  log_spec - 20.0
					lowerCamelCase    		=  np.clip(log_spec / 40.0		,      -2.0		,      0.0	) + 1.0
					return log_spec







		def __call__( self		,      A		,      A = None		,      A = True		,      A = None		,      A = False		,      A = False		,      **A		,      )  -> BatchFeature:







					'''simple docstring'''







					if sampling_rate is not None:
								if sampling_rate != self.sampling_rate:
											raise ValueError(
											    """This feature extractor is set to support sampling rate"""
											    F' of {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled'
											    F' with {self.sampling_rate} and not {sampling_rate}.'	)
					else:
								logger.warning(
								    """It is strongly recommended to pass the `sampling_rate` argument to this function. """
								    """Failing to do so can result in silent errors that might be hard to debug."""	)

					lowerCamelCase    		=  isinstance(A		,      np.ndarray	) and len(raw_speech.shape	) > 1
					if is_batched_numpy and len(raw_speech.shape	) > 2:
								raise ValueError(F'Only mono-channel audio is supported for input to {self}'	)
					lowerCamelCase    		=  is_batched_numpy or (
					    isinstance(A		,      (list, tuple)	) and (isinstance(raw_speech[0]		,      (np.ndarray, tuple, list)	))
					)
					if is_batched:
								lowerCamelCase    		=  [np.asarray([speech]		,      dtype=np.floataa	).T for speech in raw_speech]
					elif not is_batched and not isinstance(A		,      np.ndarray	):
								lowerCamelCase    		=  np.asarray(A		,      dtype=np.floataa	)
					elif isinstance(A		,      np.ndarray	) and raw_speech.dtype is np.dtype(np.floataa	):
								lowerCamelCase    		=  raw_speech.astype(np.floataa	)
					# always return batch
					if not is_batched:
								lowerCamelCase    		=  [np.asarray([raw_speech]	).T]

					# Convert audio signals to log mel spectrograms, truncate by time axis
					lowerCamelCase    		=  [
					    self._np_extract_fbank_features(waveform.squeeze()	).T[: self.spectrogram_length] for waveform in raw_speech
					]
					if isinstance(audio_features[0]		,      A	):
								lowerCamelCase    		=  [np.asarray(A		,      dtype=np.floataa	) for feature in audio_features]

					# Create audio attention mask
					lowerCamelCase    		=  max(
					    [ceil(feature.shape[0] / self.patch_size[0]	) * self.freq_len for feature in audio_features]	)  # The maximum number of audio patches in a batch
					if return_attention_mask:
								lowerCamelCase    		=  [
								    (ceil(feature.shape[0] / self.patch_size[0]	) * self.freq_len) * [1]
								    + (max_patch_len - ceil(feature.shape[0] / self.patch_size[0]	) * self.freq_len) * [0]
								    for feature in audio_features
								]
								lowerCamelCase    		=  np.array(A	).astype(np.floataa	)

					# convert into correct format for padding
					lowerCamelCase    		=  max_patch_len // self.freq_len * self.patch_size[0]  # The maximum audio size in a batch
					lowerCamelCase    		=  np.ones([len(A	), 1, max_time_len, self.feature_size]	).astype(np.floataa	)
					lowerCamelCase    		=  padded_audio_features * self.padding_value
					for i in range(len(A	)	):
								lowerCamelCase    		=  audio_features[i]
								lowerCamelCase    		=  feature

					# return as BatchFeature
					if return_attention_mask:
								lowerCamelCase    		=  {"""audio_values""": padded_audio_features, """audio_mask""": audio_mask}
					else:
								lowerCamelCase    		=  {"""audio_values""": padded_audio_features}

					lowerCamelCase    		=  BatchFeature(data=A		,      tensor_type=A	)
					return encoded_inputs

