"""simple docstring"""







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

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__SCREAMING_SNAKE_CASE								=logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE								="▁"

__SCREAMING_SNAKE_CASE								={"vocab_file": "prophetnet.tokenizer"}

__SCREAMING_SNAKE_CASE								={
    "vocab_file": {
        "microsoft/xprophetnet-large-wiki100-cased": (
            "https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer"
        ),
    }
}

__SCREAMING_SNAKE_CASE								={
    "microsoft/xprophetnet-large-wiki100-cased": {"do_lower_case": False},
}

__SCREAMING_SNAKE_CASE								={
    "microsoft/xprophetnet-large-wiki100-cased": 512,
}



def     lowercase__( __SCREAMING_SNAKE_CASE							:  Dict   ):
     lowercase_			:       str									=			collections.OrderedDict()
     with open(__SCREAMING_SNAKE_CASE  ,    'r'  ,    encoding='utf-8'   ) as reader:
          lowercase_			:       Union[str, Any]									=			reader.readlines()
     for index, token in enumerate(__SCREAMING_SNAKE_CASE   ):
          lowercase_			:       List[str]									=			token.rstrip('\n'   )
          lowercase_			:       List[str]									=			index
     return vocab







class  UpperCamelCase	(    lowercase_						):
   lowercase							=						VOCAB_FILES_NAMES
   lowercase							=						PRETRAINED_VOCAB_FILES_MAP
   lowercase							=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowercase							=						['input_ids', 'attention_mask']
   def __init__(    self  ,__UpperCamelCase  ,__UpperCamelCase="[SEP]"  ,__UpperCamelCase="[SEP]"  ,__UpperCamelCase="[SEP]"  ,__UpperCamelCase="[UNK]"  ,__UpperCamelCase="[PAD]"  ,__UpperCamelCase="[CLS]"  ,__UpperCamelCase="[MASK]"  ,__UpperCamelCase = None  ,**__UpperCamelCase  ,)			->   None:


        '''simple docstring'''


        lowercase_			:       Tuple									=			{} if sp_model_kwargs is None else sp_model_kwargs

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

        try:
             import sentencepiece as spm
        except ImportError:
             logger.warning(
                 'You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'
                 ' pip install sentencepiece'    )
             raise

        lowercase_			:       int									=			spm.SentencePieceProcessor(**self.sp_model_kwargs    )
        self.sp_model.Load(str(__UpperCamelCase    )    )
        lowercase_			:       List[Any]									=			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'

        # put special tokens and [unused] tokens into the vocab
        lowercase_			:       Dict									=			{'[PAD]': 0, '[CLS]': 1, '[SEP]': 2, '[UNK]': 3, '[MASK]': 4}

        for i in range(10    ):
             lowercase_			:       Optional[Any]									=			f'''[unused{i}]'''
             lowercase_			:       Dict									=			5 + i

        # The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
        lowercase_			:       Union[str, Any]									=			12
        lowercase_			:       Any									=			{v: k for k, v in self.fairseq_tokens_to_ids.items()}
        for k in self.fairseq_tokens_to_ids.keys():
             self.unique_no_split_tokens.append(__UpperCamelCase    )
   def __getstate__(    self    )			->   Any:


        '''simple docstring'''


        lowercase_			:       Tuple									=			self.__dict__.copy()
        lowercase_			:       Tuple									=			None
        return state
   def __setstate__(    self  ,__UpperCamelCase    )			->   Dict:


        '''simple docstring'''


        lowercase_			:       List[Any]									=			d
        try:
             import sentencepiece as spm
        except ImportError:
             logger.warning(
                 'You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'
                 ' pip install sentencepiece'    )
             raise

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

        lowercase_			:       Tuple									=			spm.SentencePieceProcessor(**self.sp_model_kwargs    )
        self.sp_model.Load(self.vocab_file    )
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase = None  ,__UpperCamelCase = False    )			->   List[int]:


        '''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    )

        if token_ids_a is None:
             return ([0] * len(__UpperCamelCase    )) + [1]
        return ([0] * len(__UpperCamelCase    )) + [1] + ([0] * len(__UpperCamelCase    )) + [1]
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase = None    )			->   List[int]:


        '''simple docstring'''


        lowercase_			:       Dict									=			[self.sep_token_id]

        if token_ids_a is None:
             return len(token_ids_a + sep    ) * [0]
        return len(token_ids_a + sep + sep + token_ids_a + sep    ) * [0]
   @property
   def     _UpperCAmelCase     (    self    )			->   str:


        '''simple docstring'''


        return len(self.sp_model    ) + self.fairseq_offset
   def     _UpperCAmelCase     (    self    )			->   Optional[Any]:


        '''simple docstring'''


        lowercase_			:       Union[str, Any]									=			{self.convert_ids_to_tokens(__UpperCamelCase    ): i for i in range(self.vocab_size    )}
        vocab.update(self.added_tokens_encoder    )
        return vocab
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase    )			->   str:


        '''simple docstring'''


        return self.sp_model.encode(__UpperCamelCase  ,out_type=__UpperCamelCase    )
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase    )			->   Optional[Any]:


        '''simple docstring'''


        if token in self.fairseq_tokens_to_ids:
             return self.fairseq_tokens_to_ids[token]
        lowercase_			:       str									=			self.sp_model.PieceToId(__UpperCamelCase    )

        # 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     _UpperCAmelCase     (    self  ,__UpperCamelCase    )			->   Optional[Any]:


        '''simple docstring'''


        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     _UpperCAmelCase     (    self  ,__UpperCamelCase    )			->   List[Any]:


        '''simple docstring'''


        lowercase_			:       List[str]									=			''.join(__UpperCamelCase    ).replace(__UpperCamelCase  ,' '    ).strip()
        return out_string
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase = None    )			->   Tuple[str]:


        '''simple docstring'''


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

        if os.path.abspath(self.vocab_file    ) != os.path.abspath(__UpperCamelCase    ) and os.path.isfile(self.vocab_file    ):
             copyfile(self.vocab_file  ,__UpperCamelCase    )
        elif not os.path.isfile(self.vocab_file    ):
             with open(__UpperCamelCase  ,'wb'    ) as fi:
                  lowercase_			:       Any									=			self.sp_model.serialized_model_proto()
                  fi.write(__UpperCamelCase    )

        return (out_vocab_file,)







   def     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase = None    )			->   List[int]:


        '''simple docstring'''


        if token_ids_a is None:
             return token_ids_a + [self.sep_token_id]
        lowercase_			:       Optional[int]									=			[self.sep_token_id]
        return token_ids_a + sep + token_ids_a + sep







"""simple docstring"""







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

import numpy as np

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


__SCREAMING_SNAKE_CASE								=logging.get_logger(__name__)







class  UpperCamelCase	(    lowercase_						):
   lowercase							=						['input_features']
   def __init__(    self  ,__UpperCamelCase=80  ,__UpperCamelCase=1_6000  ,__UpperCamelCase=160  ,__UpperCamelCase=30  ,__UpperCamelCase=400  ,__UpperCamelCase=0.0  ,__UpperCamelCase=False  ,**__UpperCamelCase  ,)			->   Any:


        '''simple docstring'''


        super().__init__(
            feature_size=__UpperCamelCase  ,sampling_rate=__UpperCamelCase  ,padding_value=__UpperCamelCase  ,return_attention_mask=__UpperCamelCase  ,**__UpperCamelCase  ,)
        lowercase_			:       Dict									=			n_fft
        lowercase_			:       Optional[Any]									=			hop_length
        lowercase_			:       Optional[int]									=			chunk_length
        lowercase_			:       Optional[Any]									=			chunk_length * sampling_rate
        lowercase_			:       Tuple									=			self.n_samples // hop_length
        lowercase_			:       int									=			sampling_rate
        lowercase_			:       int									=			mel_filter_bank(
            num_frequency_bins=1 + n_fft // 2  ,num_mel_filters=__UpperCamelCase  ,min_frequency=0.0  ,max_frequency=8000.0  ,sampling_rate=__UpperCamelCase  ,norm='slaney'  ,mel_scale='slaney'  ,)
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase    )			->   np.ndarray:


        '''simple docstring'''


        lowercase_			:       Union[str, Any]									=			spectrogram(
            __UpperCamelCase  ,window_function(self.n_fft  ,'hann'    )  ,frame_length=self.n_fft  ,hop_length=self.hop_length  ,power=2.0  ,mel_filters=self.mel_filters  ,log_mel='log10'  ,)
        lowercase_			:       Optional[Any]									=			log_spec[:, :-1]
        lowercase_			:       int									=			np.maximum(__UpperCamelCase  ,log_spec.max() - 8.0    )
        lowercase_			:       List[Any]									=			(log_spec + 4.0) / 4.0
        return log_spec
   @staticmethod
   # Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
   def     _UpperCAmelCase     (    __UpperCamelCase  ,__UpperCamelCase  ,__UpperCamelCase = 0.0    )			->   List[np.ndarray]:


        '''simple docstring'''


        if attention_mask is not None:
             lowercase_			:       Optional[int]									=			np.array(__UpperCamelCase  ,np.intaa    )
             lowercase_			:       Optional[Any]									=			[]

             for vector, length in zip(__UpperCamelCase  ,attention_mask.sum(-1    )    ):
                  lowercase_			:       Union[str, Any]									=			(vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7    )
                  if length < normed_slice.shape[0]:
                       lowercase_			:       Any									=			padding_value

                  normed_input_values.append(__UpperCamelCase    )
        else:
             lowercase_			:       Optional[int]									=			[(x - x.mean()) / np.sqrt(x.var() + 1e-7    ) for x in input_values]

        return normed_input_values
   def __call__(    self  ,__UpperCamelCase  ,__UpperCamelCase = True  ,__UpperCamelCase = None  ,__UpperCamelCase = None  ,__UpperCamelCase = None  ,__UpperCamelCase = "max_length"  ,__UpperCamelCase = None  ,__UpperCamelCase = None  ,__UpperCamelCase = None  ,**__UpperCamelCase  ,)			->   BatchFeature:


        '''simple docstring'''


        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.'    )

        lowercase_			:       Optional[int]									=			isinstance(__UpperCamelCase  ,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}'''    )
        lowercase_			:       Union[str, Any]									=			is_batched_numpy or (
            isinstance(__UpperCamelCase  ,(list, tuple)    ) and (isinstance(raw_speech[0]  ,(np.ndarray, tuple, list)    ))
        )

        if is_batched:
             lowercase_			:       Union[str, Any]									=			[np.asarray([speech]  ,dtype=np.floataa    ).T for speech in raw_speech]
        elif not is_batched and not isinstance(__UpperCamelCase  ,np.ndarray    ):
             lowercase_			:       Dict									=			np.asarray(__UpperCamelCase  ,dtype=np.floataa    )
        elif isinstance(__UpperCamelCase  ,np.ndarray    ) and raw_speech.dtype is np.dtype(np.floataa    ):
             lowercase_			:       Dict									=			raw_speech.astype(np.floataa    )

        # always return batch
        if not is_batched:
             lowercase_			:       List[Any]									=			[np.asarray([raw_speech]    ).T]

        lowercase_			:       str									=			BatchFeature({'input_features': raw_speech}    )

        # convert into correct format for padding

        lowercase_			:       str									=			self.pad(
            __UpperCamelCase  ,padding=__UpperCamelCase  ,max_length=max_length if max_length else self.n_samples  ,truncation=__UpperCamelCase  ,pad_to_multiple_of=__UpperCamelCase  ,return_attention_mask=return_attention_mask or do_normalize  ,)

        # zero-mean and unit-variance normalization
        if do_normalize:
             lowercase_			:       int									=			self.zero_mean_unit_var_norm(
                 padded_inputs['input_features']  ,attention_mask=padded_inputs['attention_mask']  ,padding_value=self.padding_value  ,)
             lowercase_			:       Tuple									=			np.stack(padded_inputs['input_features']  ,axis=0    )

        # make sure list is in array format
        lowercase_			:       Dict									=			padded_inputs.get('input_features'    ).transpose(2  ,0  ,1    )

        lowercase_			:       List[str]									=			[self._np_extract_fbank_features(__UpperCamelCase    ) for waveform in input_features[0]]

        if isinstance(input_features[0]  ,__UpperCamelCase    ):
             lowercase_			:       Optional[Any]									=			[np.asarray(__UpperCamelCase  ,dtype=np.floataa    ) for feature in input_features]
        else:
             lowercase_			:       str									=			input_features

        if return_attention_mask:
             # rescale from sample (48000) to feature (3000)
             lowercase_			:       str									=			padded_inputs['attention_mask'][:, :: self.hop_length]

        if return_tensors is not None:
             lowercase_			:       Union[str, Any]									=			padded_inputs.convert_to_tensors(__UpperCamelCase    )

        return padded_inputs







   def     _UpperCAmelCase     (    self    )			->   Dict[str, Any]:


        '''simple docstring'''


        lowercase_			:       Dict									=			copy.deepcopy(self.__dict__    )
        lowercase_			:       Union[str, Any]									=			self.__class__.__name__
        if "mel_filters" in output:
             del output["mel_filters"]
        return output

