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			:							List[Any]     =logging.get_logger(__name__)


lowerCAmelCase			:							Optional[int]     ="▁"

lowerCAmelCase			:							Dict     ={"vocab_file": "spiece.model"}

lowerCAmelCase			:							Optional[int]     ={
    "vocab_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"
        )
    }
}

lowerCAmelCase			:							Union[str, Any]     ={
    "google/reformer-crime-and-punishment": 524288,
}




class 	__snake_case	( __lowerCAmelCase		):





      '''simple docstring'''






      _snake_case    		=       VOCAB_FILES_NAMES
      _snake_case    		=       PRETRAINED_VOCAB_FILES_MAP
      _snake_case    		=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _snake_case    		=       ['input_ids', 'attention_mask']



      def __init__(					self     :				Any    ,  _UpperCamelCase     :				Optional[Any]    ,  _UpperCamelCase     :				List[str]="</s>"    ,  _UpperCamelCase     :				int="<unk>"    ,  _UpperCamelCase     :				List[Any]=[]    ,  _UpperCamelCase     :				Optional[Dict[str, Any]] = None    ,  **_UpperCamelCase     :				Any    ,  )				->None:


          """simple docstring"""


          _lowerCamelCase     :		Any 						=						{} if sp_model_kwargs is None else sp_model_kwargs

          super().__init__(
              eos_token=_UpperCamelCase    ,  unk_token=_UpperCamelCase    ,  additional_special_tokens=_UpperCamelCase    ,  sp_model_kwargs=self.sp_model_kwargs    ,  **_UpperCamelCase    ,  )

          _lowerCamelCase     :		Union[str, Any] 						=						vocab_file
          _lowerCamelCase     :		Tuple 						=						spm.SentencePieceProcessor(**self.sp_model_kwargs)
          self.sp_model.Load(_UpperCamelCase)



      @property
      def 	_SCREAMING_SNAKE_CASE    (					self     :				Tuple)				->Any:


          """simple docstring"""


          return self.sp_model.get_piece_size()



      def 	_SCREAMING_SNAKE_CASE    (					self     :				str)				->Dict[str, int]:


          """simple docstring"""


          _lowerCamelCase     :		List[str] 						=						{self.convert_ids_to_tokens(_UpperCamelCase): i for i in range(self.vocab_size)}
          vocab.update(self.added_tokens_encoder)
          return vocab



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


          """simple docstring"""


          _lowerCamelCase     :		Dict 						=						self.__dict__.copy()
          _lowerCamelCase     :		Optional[int] 						=						None
          return state



      def __setstate__(					self     :				List[Any]    ,  _UpperCamelCase     :				Dict)				->List[str]:


          """simple docstring"""


          _lowerCamelCase     :		List[Any] 						=						d

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

          _lowerCamelCase     :		str 						=						spm.SentencePieceProcessor(**self.sp_model_kwargs)
          self.sp_model.Load(self.vocab_file)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[Any]    ,  _UpperCamelCase     :				str)				->List[str]:


          """simple docstring"""


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



      def 	_SCREAMING_SNAKE_CASE    (					self     :				str    ,  _UpperCamelCase     :				Optional[int])				->Optional[int]:


          """simple docstring"""


          return self.sp_model.piece_to_id(_UpperCamelCase)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[Any]    ,  _UpperCamelCase     :				Union[str, Any])				->Union[str, Any]:


          """simple docstring"""


          if index < self.sp_model.get_piece_size():
              _lowerCamelCase     :		Union[str, Any] 						=						self.sp_model.IdToPiece(_UpperCamelCase)
          return token



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Union[str, Any]    ,  _UpperCamelCase     :				Optional[int])				->List[Any]:


          """simple docstring"""


          _lowerCamelCase     :		List[Any] 						=						[]
          _lowerCamelCase     :		Optional[Any] 						=						""""""
          for token in tokens:
              # make sure that special tokens are not decoded using sentencepiece model
              if token in self.all_special_tokens:
                  out_string += self.sp_model.decode(_UpperCamelCase) + token
                  _lowerCamelCase     :		str 						=						[]
              else:
                  current_sub_tokens.append(_UpperCamelCase)
          out_string += self.sp_model.decode(_UpperCamelCase)
          return out_string.strip()




      def 	_SCREAMING_SNAKE_CASE    (					self     :				Union[str, Any]    ,  _UpperCamelCase     :				str    ,  _UpperCamelCase     :				Optional[str] = None)				->Tuple[str]:


          """simple docstring"""


          if not os.path.isdir(_UpperCamelCase):
              logger.error(F"""Vocabulary path ({save_directory}) should be a directory""")
              return
          _lowerCamelCase     :		Optional[int] 						=						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:
                  _lowerCamelCase     :		Optional[Any] 						=						self.sp_model.serialized_model_proto()
                  fi.write(_UpperCamelCase)

          return (out_vocab_file,)





from __future__ import annotations

from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np




class 	__snake_case	( __lowerCAmelCase		):





      '''simple docstring'''






      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[Any]    ,  _UpperCamelCase     :				float)				->float:


          """simple docstring"""


          return 0.0







def 						A__  (					__A   , __A	):







    '''simple docstring'''
    _lowerCamelCase     :		int 						=						min([-20, np.min(fft_results[1 : samplerate // 2 - 1]	)]	)
    _lowerCamelCase     :		Tuple 						=						max([20, np.max(fft_results[1 : samplerate // 2 - 1]	)]	)
    return lowest, highest







def 						A__  (					__A   , __A	):







    '''simple docstring'''
    _lowerCamelCase     :		Tuple 						=						512
    _lowerCamelCase     :		Tuple 						=						[1] + [0] * (size - 1)
    _lowerCamelCase     :		Optional[Any] 						=						[filter_type.process(__A	) for item in inputs]

    _lowerCamelCase     :		Optional[Any] 						=						[0] * (samplerate - size)  # zero-padding
    outputs += filler
    _lowerCamelCase     :		Tuple 						=						np.abs(np.fft.fft(__A	)	)
    _lowerCamelCase     :		List[Any] 						=						20 * np.logaa(__A	)

    # Frequencies on log scale from 24 to nyquist frequency
    plt.xlim(24   , samplerate / 2 - 1	)
    plt.xlabel("""Frequency (Hz)"""	)
    plt.xscale("""log"""	)

    # Display within reasonable bounds
    _lowerCamelCase     :		Any 						=						get_bounds(__A   , __A	)
    plt.ylim(max([-80, bounds[0]]	)   , min([80, bounds[1]]	)	)
    plt.ylabel("""Gain (dB)"""	)

    plt.plot(__A	)
    plt.show()







def 						A__  (					__A   , __A	):







    '''simple docstring'''
    _lowerCamelCase     :		Tuple 						=						512
    _lowerCamelCase     :		Union[str, Any] 						=						[1] + [0] * (size - 1)
    _lowerCamelCase     :		int 						=						[filter_type.process(__A	) for item in inputs]

    _lowerCamelCase     :		Optional[Any] 						=						[0] * (samplerate - size)  # zero-padding
    outputs += filler
    _lowerCamelCase     :		Any 						=						np.angle(np.fft.fft(__A	)	)

    # Frequencies on log scale from 24 to nyquist frequency
    plt.xlim(24   , samplerate / 2 - 1	)
    plt.xlabel("""Frequency (Hz)"""	)
    plt.xscale("""log"""	)

    plt.ylim(-2 * pi   , 2 * pi	)
    plt.ylabel("""Phase shift (Radians)"""	)
    plt.plot(np.unwrap(__A   , -2 * pi	)	)
    plt.show()




