
"""simple docstring"""


import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np
import torch

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


_lowercase				 =					logging.get_logger(__name__)







class 					lowerCAmelCase_					(   _lowercase     ):



						'''simple docstring'''


						_lowerCamelCase:     Any   =     ['''input_features''', '''is_longer''']


						def __init__(			self  :   Dict	,A_  :   Optional[Any]=64	,A_  :   str=4_8000	,A_  :   Dict=480	,A_  :   List[Any]=10	,A_  :   List[str]=1024	,A_  :   Any=0.0	,A_  :   Tuple=False	,A_  :   float = 0	,A_  :   float = 1_4000	,A_  :   int = None	,A_  :   str = "fusion"	,A_  :   str = "repeatpad"	,**A_  :   Tuple	,)  -> Optional[Any]:
							super().__init__(
							    feature_size=A_	,sampling_rate=A_	,padding_value=A_	,return_attention_mask=A_	,**A_	,)
							A     = top_db
							A     = truncation
							A     = padding
							A     = fft_window_size
							A     = (fft_window_size >> 1) + 1
							A     = hop_length
							A     = max_length_s
							A     = max_length_s * sampling_rate
							A     = sampling_rate
							A     = frequency_min
							A     = frequency_max
							A     = mel_filter_bank(
							    num_frequency_bins=self.nb_frequency_bins	,num_mel_filters=A_	,min_frequency=A_	,max_frequency=A_	,sampling_rate=A_	,norm=A_	,mel_scale='htk'	,)
							A     = mel_filter_bank(
							    num_frequency_bins=self.nb_frequency_bins	,num_mel_filters=A_	,min_frequency=A_	,max_frequency=A_	,sampling_rate=A_	,norm='slaney'	,mel_scale='slaney'	,)


						def     _SCREAMING_SNAKE_CASE   (			self  :   Any   )  -> Dict[str, Any]:
							A     = copy.deepcopy(self.__dict__   )
							A     = self.__class__.__name__
							if "mel_filters" in output:
								del output["mel_filters"]
							if "mel_filters_slaney" in output:
								del output["mel_filters_slaney"]
							return output


						def     _SCREAMING_SNAKE_CASE   (			self  :   Any	,A_  :   np.array	,A_  :   Optional[np.array] = None   )  -> np.ndarray:
							A     = spectrogram(
							    A_	,window_function(self.fft_window_size	,'hann'   )	,frame_length=self.fft_window_size	,hop_length=self.hop_length	,power=2.0	,mel_filters=A_	,log_mel='dB'	,)
							return log_mel_spectrogram.T


						def     _SCREAMING_SNAKE_CASE   (			self  :   List[Any]	,A_  :   Any	,A_  :   str	,A_  :   str   )  -> List[str]:
							A     = np.array_split(list(range(0	,total_frames - chunk_frames + 1   )   )	,3   )
							if len(ranges[1]   ) == 0:
								# if the audio is too short, we just use the first chunk
								A     = [0]
							if len(ranges[2]   ) == 0:
								# if the audio is too short, we just use the first chunk
								A     = [0]
							# randomly choose index for each part
							A     = np.random.choice(ranges[0]   )
							A     = np.random.choice(ranges[1]   )
							A     = np.random.choice(ranges[2]   )

							A     = mel[idx_front : idx_front + chunk_frames, :]
							A     = mel[idx_middle : idx_middle + chunk_frames, :]
							A     = mel[idx_back : idx_back + chunk_frames, :]

							A     = torch.tensor(mel[None, None, :]   )
							A     = torch.nn.functional.interpolate(
							    A_	,size=[chunk_frames, 64]	,mode='bilinear'	,align_corners=A_   )
							A     = mel_shrink[0][0].numpy()
							A     = np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back]	,axis=0   )
							return mel_fusion


						def     _SCREAMING_SNAKE_CASE   (			self  :   int	,A_  :   np.array	,A_  :   Optional[int]	,A_  :   Optional[Any]	,A_  :   int   )  -> np.array:
							if waveform.shape[0] > max_length:
								if truncation == "rand_trunc":
									A     = True
									# random crop to max_length (for compatibility) -> this should be handled by self.pad
									A     = len(A_   ) - max_length
									A     = np.random.randint(0	,overflow + 1   )
									A     = waveform[idx : idx + max_length]
									A     = self._np_extract_fbank_features(A_	,self.mel_filters_slaney   )[None, :]
								elif truncation == "fusion":
									A     = self._np_extract_fbank_features(A_	,self.mel_filters   )
									A     = max_length // self.hop_length + 1  # the +1 related to how the spectrogram is computed
									A     = mel.shape[0]
									if chunk_frames == total_frames:
										# there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
										# In this case, we just use the whole audio.
										A     = np.stack([mel, mel, mel, mel]	,axis=0   )
										A     = False
									else:
										A     = self._random_mel_fusion(A_	,A_	,A_   )
										A     = True
								else:
									raise NotImplementedError(F'data_truncating {truncation} not implemented'   )

							else:
								A     = False
								# only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
								if waveform.shape[0] < max_length:
									if padding == "repeat":
										A     = int(max_length / len(A_   )   )
										A     = np.stack(np.tile(A_	,n_repeat + 1   )   )[:max_length]
									if padding == "repeatpad":
										A     = int(max_length / len(A_   )   )
										A     = np.stack(np.tile(A_	,A_   )   )
									A     = np.pad(A_	,(0, max_length - waveform.shape[0])	,mode='constant'	,constant_values=0   )

								if truncation == "fusion":
									A     = self._np_extract_fbank_features(A_	,self.mel_filters   )
									A     = np.stack([input_mel, input_mel, input_mel, input_mel]	,axis=0   )
								else:
									A     = self._np_extract_fbank_features(A_	,self.mel_filters_slaney   )[None, :]

							return input_mel, longer




						def __call__(			self  :   str	,A_  :   Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]	,A_  :   str = None	,A_  :   Optional[str] = None	,A_  :   Optional[int] = None	,A_  :   Optional[int] = None	,A_  :   Optional[Union[str, TensorType]] = None	,**A_  :   Tuple	,)  -> BatchFeature:
							A     = truncation if truncation is not None else self.truncation
							A     = padding if padding else self.padding

							if sampling_rate is not None:
								if sampling_rate != self.sampling_rate:
									raise ValueError(
									    F'The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a'
									    F' sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input'
									    F' was sampled 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.'   )

							A     = 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}'   )
							A     = is_batched_numpy or (
							    isinstance(A_	,(list, tuple)   ) and (isinstance(raw_speech[0]	,(np.ndarray, tuple, list)   ))
							)

							if is_batched:
								A     = [np.asarray(A_	,dtype=np.floataa   ) for speech in raw_speech]
							elif not is_batched and not isinstance(A_	,np.ndarray   ):
								A     = np.asarray(A_	,dtype=np.floataa   )
							elif isinstance(A_	,np.ndarray   ) and raw_speech.dtype is np.dtype(np.floataa   ):
								A     = raw_speech.astype(np.floataa   )

							# always return batch
							if not is_batched:
								A     = [np.asarray(A_   )]

							# convert to mel spectrogram, truncate and pad if needed.
							A     = [
							    self._get_input_mel(A_	,max_length if max_length else self.nb_max_samples	,A_	,A_   )
							    for waveform in raw_speech
							]

							A     = []
							A     = []
							for mel, longer in padded_inputs:
								input_mel.append(A_   )
								is_longer.append(A_   )

							if truncation == "fusion" and sum(A_   ) == 0:
								# if no audio is longer than 10s, then randomly select one audio to be longer
								A     = np.random.randint(0	,len(A_   )   )
								A     = True

							if isinstance(input_mel[0]	,A_   ):
								A     = [np.asarray(A_	,dtype=np.floataa   ) for feature in input_mel]

							# is_longer is a list of bool
							A     = [[longer] for longer in is_longer]

							A     = {'input_features': input_mel, 'is_longer': is_longer}
							A     = BatchFeature(A_   )

							if return_tensors is not None:
								A     = input_features.convert_to_tensors(A_   )

							return input_features

"""simple docstring"""


import os
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


_lowercase				 =					logging.get_logger(__name__)

_lowercase				 =					'''▁'''

_lowercase				 =					{'''vocab_file''': '''sentencepiece.bpe.model'''}

_lowercase				 =					{
    '''vocab_file''': {
        '''xlm-roberta-base''': '''https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model''',
        '''xlm-roberta-large''': '''https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model''',
        '''xlm-roberta-large-finetuned-conll02-dutch''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll02-spanish''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll03-english''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll03-german''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model'''
        ),
    }
}

_lowercase				 =					{
    '''xlm-roberta-base''': 5_12,
    '''xlm-roberta-large''': 5_12,
    '''xlm-roberta-large-finetuned-conll02-dutch''': 5_12,
    '''xlm-roberta-large-finetuned-conll02-spanish''': 5_12,
    '''xlm-roberta-large-finetuned-conll03-english''': 5_12,
    '''xlm-roberta-large-finetuned-conll03-german''': 5_12,
}







class 					lowerCAmelCase_					(   _lowercase     ):



						'''simple docstring'''


						_lowerCamelCase:     Optional[Any]   =     VOCAB_FILES_NAMES
						_lowerCamelCase:     List[str]   =     PRETRAINED_VOCAB_FILES_MAP
						_lowerCamelCase:     Union[str, Any]   =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_lowerCamelCase:     Any   =     ['''input_ids''', '''attention_mask''']


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

							A     = {} if sp_model_kwargs is None else sp_model_kwargs

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

							A     = spm.SentencePieceProcessor(**self.sp_model_kwargs   )
							self.sp_model.Load(str(A_   )   )
							A     = vocab_file

							# Original fairseq vocab and spm vocab must be "aligned":
							# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
							# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
							# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
							# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

							# Mimic fairseq token-to-id alignment for the first 4 token
							A     = {'<s>': 0, '<pad>': 1, '</s>': 2, '<unk>': 3}

							# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
							A     = 1

							A     = len(self.sp_model   ) + self.fairseq_offset
							A     = {v: k for k, v in self.fairseq_tokens_to_ids.items()}


						def __getstate__(			self  :   Union[str, Any]   )  -> Any:
							A     = self.__dict__.copy()
							A     = None
							A     = self.sp_model.serialized_model_proto()
							return state


						def __setstate__(			self  :   str	,A_  :   str   )  -> Optional[Any]:
							A     = d

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

							A     = spm.SentencePieceProcessor(**self.sp_model_kwargs   )
							self.sp_model.LoadFromSerializedProto(self.sp_model_proto   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   Tuple	,A_  :   List[int]	,A_  :   Optional[List[int]] = None   )  -> List[int]:

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


						def     _SCREAMING_SNAKE_CASE   (			self  :   List[Any]	,A_  :   List[int]	,A_  :   Optional[List[int]] = None	,A_  :   bool = False   )  -> List[int]:

							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, 1] + ([0] * len(A_   )) + [1]


						def     _SCREAMING_SNAKE_CASE   (			self  :   str	,A_  :   List[int]	,A_  :   Optional[List[int]] = None   )  -> List[int]:
							A     = [self.sep_token_id]
							A     = [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]


						@property
						def     _SCREAMING_SNAKE_CASE   (			self  :   List[str]   )  -> Optional[Any]:
							return len(self.sp_model   ) + self.fairseq_offset + 1  # Add the <mask> token


						def     _SCREAMING_SNAKE_CASE   (			self  :   Dict   )  -> Optional[Any]:
							A     = {self.convert_ids_to_tokens(A_   ): i for i in range(self.vocab_size   )}
							vocab.update(self.added_tokens_encoder   )
							return vocab


						def     _SCREAMING_SNAKE_CASE   (			self  :   str	,A_  :   str   )  -> List[str]:
							return self.sp_model.encode(A_	,out_type=A_   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   Tuple	,A_  :   List[Any]   )  -> Tuple:
							if token in self.fairseq_tokens_to_ids:
								return self.fairseq_tokens_to_ids[token]
							A     = self.sp_model.PieceToId(A_   )

							# Need to return unknown token if the SP model returned 0
							return spm_id + self.fairseq_offset if spm_id else self.unk_token_id


						def     _SCREAMING_SNAKE_CASE   (			self  :   str	,A_  :   str   )  -> int:
							if index in self.fairseq_ids_to_tokens:
								return self.fairseq_ids_to_tokens[index]
							return self.sp_model.IdToPiece(index - self.fairseq_offset   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   List[str]	,A_  :   Optional[Any]   )  -> List[Any]:
							A     = ''.join(A_   ).replace(A_	,' '   ).strip()
							return out_string




						def     _SCREAMING_SNAKE_CASE   (			self  :   Union[str, Any]	,A_  :   str	,A_  :   Optional[str] = None   )  -> Tuple[str]:
							if not os.path.isdir(A_   ):
								logger.error(F'Vocabulary path ({save_directory}) should be a directory'   )
								return
							A     = 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:
									A     = self.sp_model.serialized_model_proto()
									fi.write(A_   )

							return (out_vocab_file,)