






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

import sentencepiece as spm

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


_lowerCamelCase      			=						logging.get_logger(__name__)

_lowerCamelCase      			=						'▁'

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

_lowerCamelCase      			=						{
    'vocab_file': {
        'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model',
    }
}

_lowerCamelCase      			=						{
    'facebook/xglm-564M': 20_48,
}



class       a					(       _A      ):

     '''simple docstring'''





     lowerCAmelCase    :	Optional[Any]       		=  VOCAB_FILES_NAMES
     lowerCAmelCase    :	List[Any]       		=  PRETRAINED_VOCAB_FILES_MAP
     lowerCAmelCase    :	List[str]       		=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowerCAmelCase    :	int       		=  ['input_ids', 'attention_mask']
     def __init__(     self       :      List[str] ,   __snake_case       :      int ,   __snake_case       :      Optional[int]="<s>" ,   __snake_case       :      int="</s>" ,   __snake_case       :      List[str]="</s>" ,   __snake_case       :      Union[str, Any]="<s>" ,   __snake_case       :      str="<unk>" ,   __snake_case       :      str="<pad>" ,   __snake_case       :      Optional[Dict[str, Any]] = None ,   **__snake_case       :      Optional[Any] ,   ):
         UpperCAmelCase_										=					{} if sp_model_kwargs is None else sp_model_kwargs

         # Compatibility with the original tokenizer
         UpperCAmelCase_										=					7
         UpperCAmelCase_										=					[F'<madeupword{i}>' for i in range(self.num_madeup_words    )]

         UpperCAmelCase_										=					kwargs.get('''additional_special_tokens''' ,   []    )
         kwargs["additional_special_tokens"] += [
             word for word in madeup_words if word not in kwargs["additional_special_tokens"]
         ]

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

         UpperCAmelCase_										=					spm.SentencePieceProcessor(**self.sp_model_kwargs    )
         self.sp_model.Load(str(__snake_case    )    )
         UpperCAmelCase_										=					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'

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

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

         UpperCAmelCase_										=					len(self.sp_model    )
         UpperCAmelCase_										=					{F'<madeupword{i}>': sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words    )}
         self.fairseq_tokens_to_ids.update(__snake_case    )

         UpperCAmelCase_										=					{v: k for k, v in self.fairseq_tokens_to_ids.items()}
     def __getstate__(     self       :      Dict    ):
         UpperCAmelCase_										=					self.__dict__.copy()
         UpperCAmelCase_										=					None
         UpperCAmelCase_										=					self.sp_model.serialized_model_proto()
         return state
     def __setstate__(     self       :      Optional[int] ,   __snake_case       :      Any    ):
         UpperCAmelCase_										=					d

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

         UpperCAmelCase_										=					spm.SentencePieceProcessor(**self.sp_model_kwargs    )
         self.sp_model.LoadFromSerializedProto(self.sp_model_proto    )
     def   lowerCamelCase_       (     self       :      List[Any] ,   __snake_case       :      List[int] ,   __snake_case       :      Optional[List[int]] = None    ):

         if token_ids_a is None:
             return [self.sep_token_id] + token_ids_a
         UpperCAmelCase_										=					[self.sep_token_id]
         return sep + token_ids_a + sep + sep + token_ids_a
     def   lowerCamelCase_       (     self       :      Optional[Any] ,   __snake_case       :      List[int] ,   __snake_case       :      Optional[List[int]] = None ,   __snake_case       :      bool = False    ):

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

         if token_ids_a is None:
             return [1] + ([0] * len(__snake_case    ))
         return [1] + ([0] * len(__snake_case    )) + [1, 1] + ([0] * len(__snake_case    ))
     def   lowerCamelCase_       (     self       :      int ,   __snake_case       :      List[int] ,   __snake_case       :      Optional[List[int]] = None    ):
         UpperCAmelCase_										=					[self.sep_token_id]

         if token_ids_a is None:
             return len(sep + token_ids_a    ) * [0]
         return len(sep + token_ids_a + sep + sep + token_ids_a    ) * [0]
     @property
     def   lowerCamelCase_       (     self       :      Optional[int]    ):
         return len(self.sp_model    ) + self.fairseq_offset + self.num_madeup_words
     def   lowerCamelCase_       (     self       :      Any    ):
         UpperCAmelCase_										=					{self.convert_ids_to_tokens(__snake_case    ): i for i in range(self.vocab_size    )}
         vocab.update(self.added_tokens_encoder    )
         return vocab
     def   lowerCamelCase_       (     self       :      List[str] ,   __snake_case       :      str    ):
         return self.sp_model.encode(__snake_case ,   out_type=__snake_case    )
     def   lowerCamelCase_       (     self       :      Optional[int] ,   __snake_case       :      List[Any]    ):
         if token in self.fairseq_tokens_to_ids:
             return self.fairseq_tokens_to_ids[token]
         UpperCAmelCase_										=					self.sp_model.PieceToId(__snake_case    )

         # 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   lowerCamelCase_       (     self       :      Tuple ,   __snake_case       :      Dict    ):
         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   lowerCamelCase_       (     self       :      Union[str, Any] ,   __snake_case       :      int    ):
         UpperCAmelCase_										=					''''''.join(__snake_case    ).replace(__snake_case ,   ''' '''    ).strip()
         return out_string




     def   lowerCamelCase_       (     self       :      str ,   __snake_case       :      str ,   __snake_case       :      Optional[str] = None    ):
         if not os.path.isdir(__snake_case    ):
             logger.error(F'Vocabulary path ({save_directory}) should be a directory'    )
             return
         UpperCAmelCase_										=					os.path.join(
             __snake_case ,   (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']    )

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

         return (out_vocab_file,)











from typing import List, Optional, Union

import numpy as np

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


_lowerCamelCase      			=						logging.get_logger(__name__)



class       a					(       _A      ):

     '''simple docstring'''





     lowerCAmelCase    :	str       		=  ['input_values', 'padding_mask']
     def __init__(     self       :      Optional[Any] ,   __snake_case       :      int = 1 ,   __snake_case       :      int = 2_40_00 ,   __snake_case       :      float = 0.0 ,   __snake_case       :      float = None ,   __snake_case       :      float = None ,   **__snake_case       :      Dict ,   ):
         super().__init__(feature_size=__snake_case ,   sampling_rate=__snake_case ,   padding_value=__snake_case ,   **__snake_case    )
         UpperCAmelCase_										=					chunk_length_s
         UpperCAmelCase_										=					overlap
     @property
     def   lowerCamelCase_       (     self       :      List[str]    ):
         if self.chunk_length_s is None:
             return None
         else:
             return int(self.chunk_length_s * self.sampling_rate    )
     @property
     def   lowerCamelCase_       (     self       :      List[str]    ):
         if self.chunk_length_s is None or self.overlap is None:
             return None
         else:
             return max(1 ,   int((1.0 - self.overlap) * self.chunk_length    )    )




     def __call__(     self       :      List[str] ,   __snake_case       :      Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] ,   __snake_case       :      Optional[Union[bool, str, PaddingStrategy]] = None ,   __snake_case       :      Optional[bool] = False ,   __snake_case       :      Optional[int] = None ,   __snake_case       :      Optional[Union[str, TensorType]] = None ,   __snake_case       :      Optional[int] = None ,   ):
         if sampling_rate is not None:
             if sampling_rate != self.sampling_rate:
                 raise ValueError(
                     F'The model corresponding to this feature extractor: {self} was trained using a sampling rate of'
                     F' {self.sampling_rate}. Please make sure that the provided audio input was sampled with'
                     F' {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.'''    )

         if padding and truncation:
             raise ValueError('''Both padding and truncation were set. Make sure you only set one.'''    )
         elif padding is None:
             # by default let's pad the inputs
             UpperCAmelCase_										=					True

         UpperCAmelCase_										=					bool(
             isinstance(__snake_case ,   (list, tuple)    ) and (isinstance(raw_audio[0] ,   (np.ndarray, tuple, list)    ))    )

         if is_batched:
             UpperCAmelCase_										=					[np.asarray(__snake_case ,   dtype=np.floataa    ).T for audio in raw_audio]
         elif not is_batched and not isinstance(__snake_case ,   np.ndarray    ):
             UpperCAmelCase_										=					np.asarray(__snake_case ,   dtype=np.floataa    )
         elif isinstance(__snake_case ,   np.ndarray    ) and raw_audio.dtype is np.dtype(np.floataa    ):
             UpperCAmelCase_										=					raw_audio.astype(np.floataa    )

         # always return batch
         if not is_batched:
             UpperCAmelCase_										=					[np.asarray(__snake_case    ).T]

         # verify inputs are valid
         for idx, example in enumerate(__snake_case    ):
             if example.ndim > 2:
                 raise ValueError(F'Expected input shape (channels, length) but got shape {example.shape}'    )
             if self.feature_size == 1 and example.ndim != 1:
                 raise ValueError(F'Expected mono audio but example has {example.shape[-1]} channels'    )
             if self.feature_size == 2 and example.shape[-1] != 2:
                 raise ValueError(F'Expected stereo audio but example has {example.shape[-1]} channels'    )

         UpperCAmelCase_										=					None
         UpperCAmelCase_										=					BatchFeature({'''input_values''': raw_audio}    )
         if self.chunk_stride is not None and self.chunk_length is not None and max_length is None:
             if truncation:
                 UpperCAmelCase_										=					min(array.shape[0] for array in raw_audio    )
                 UpperCAmelCase_										=					int(np.floor(max_length / self.chunk_stride    )    )
                 UpperCAmelCase_										=					(nb_step - 1) * self.chunk_stride + self.chunk_length
             elif padding:
                 UpperCAmelCase_										=					max(array.shape[0] for array in raw_audio    )
                 UpperCAmelCase_										=					int(np.ceil(max_length / self.chunk_stride    )    )
                 UpperCAmelCase_										=					(nb_step - 1) * self.chunk_stride + self.chunk_length
                 UpperCAmelCase_										=					'''max_length'''
             else:
                 UpperCAmelCase_										=					input_values

        # normal padding on batch
         if padded_inputs is None:
             UpperCAmelCase_										=					self.pad(
                 __snake_case ,   max_length=__snake_case ,   truncation=__snake_case ,   padding=__snake_case ,   return_attention_mask=__snake_case ,   )
             if padding:
                 UpperCAmelCase_										=					padded_inputs.pop('''attention_mask'''    )

         UpperCAmelCase_										=					[]
         for example in padded_inputs.pop('''input_values'''    ):
             if self.feature_size == 1:
                 UpperCAmelCase_										=					example[..., None]
             input_values.append(example.T    )

         UpperCAmelCase_										=					input_values
         if return_tensors is not None:
             UpperCAmelCase_										=					padded_inputs.convert_to_tensors(__snake_case    )

         return padded_inputs





