

'''simple docstring'''
import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer


if TYPE_CHECKING:
    from transformers.pipelines.conversational import Conversation


_snake_case     =  logging.get_logger(__name__)

_snake_case     =  {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

_snake_case     =  {
    'vocab_file': {
        'gpt2': 'https://huggingface.co/gpt2/resolve/main/vocab.json',
        'gpt2-medium': 'https://huggingface.co/gpt2-medium/resolve/main/vocab.json',
        'gpt2-large': 'https://huggingface.co/gpt2-large/resolve/main/vocab.json',
        'gpt2-xl': 'https://huggingface.co/gpt2-xl/resolve/main/vocab.json',
        'distilgpt2': 'https://huggingface.co/distilgpt2/resolve/main/vocab.json',
    },
    'merges_file': {
        'gpt2': 'https://huggingface.co/gpt2/resolve/main/merges.txt',
        'gpt2-medium': 'https://huggingface.co/gpt2-medium/resolve/main/merges.txt',
        'gpt2-large': 'https://huggingface.co/gpt2-large/resolve/main/merges.txt',
        'gpt2-xl': 'https://huggingface.co/gpt2-xl/resolve/main/merges.txt',
        'distilgpt2': 'https://huggingface.co/distilgpt2/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'gpt2': 'https://huggingface.co/gpt2/resolve/main/tokenizer.json',
        'gpt2-medium': 'https://huggingface.co/gpt2-medium/resolve/main/tokenizer.json',
        'gpt2-large': 'https://huggingface.co/gpt2-large/resolve/main/tokenizer.json',
        'gpt2-xl': 'https://huggingface.co/gpt2-xl/resolve/main/tokenizer.json',
        'distilgpt2': 'https://huggingface.co/distilgpt2/resolve/main/tokenizer.json',
    },
}

_snake_case     =  {
    'gpt2': 1_024,
    'gpt2-medium': 1_024,
    'gpt2-large': 1_024,
    'gpt2-xl': 1_024,
    'distilgpt2': 1_024,
}


class    a__  (							lowerCamelCase_  ):
   _SCREAMING_SNAKE_CASE    :     Any	       =							VOCAB_FILES_NAMES
   _SCREAMING_SNAKE_CASE    :     int	       =							PRETRAINED_VOCAB_FILES_MAP
   _SCREAMING_SNAKE_CASE    :     Any	       =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _SCREAMING_SNAKE_CASE    :     Union[str, Any]	       =							['input_ids', 'attention_mask']
   _SCREAMING_SNAKE_CASE    :     Optional[int]	       =							GPTaTokenizer


   def __init__( self			,				_UpperCamelCase=None			,				_UpperCamelCase=None			,				_UpperCamelCase=None			,				_UpperCamelCase="<|endoftext|>"			,				_UpperCamelCase="<|endoftext|>"			,				_UpperCamelCase="<|endoftext|>"			,				_UpperCamelCase=False			,				**_UpperCamelCase			,				):





      """simple docstring"""





      super().__init__(
          __a			,				__a			,				tokenizer_file=__a			,				unk_token=__a			,				bos_token=__a			,				eos_token=__a			,				add_prefix_space=__a			,				**__a			,				)

      _lowercase			:      Tuple			     =   kwargs.pop("add_bos_token"			,				__a  )

      _lowercase			:      Any			     =   json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()  )
      if pre_tok_state.get("add_prefix_space"			,				__a  ) != add_prefix_space:
         _lowercase			:      Optional[int]			     =   getattr(__a			,				pre_tok_state.pop("type"  )  )
         _lowercase			:      str			     =   add_prefix_space
         _lowercase			:      Tuple			     =   pre_tok_class(**__a  )

      _lowercase			:      int			     =   add_prefix_space


   def 						_lowerCamelCase   ( self			,				*_UpperCamelCase			,				**_UpperCamelCase  ):





      """simple docstring"""





      _lowercase			:      List[str]			     =   kwargs.get("is_split_into_words"			,				__a  )
      assert self.add_prefix_space or not is_split_into_words, (
          f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
          "to use it with pretokenized inputs."
      )

      return super()._batch_encode_plus(*__a			,				**__a  )


   def 						_lowerCamelCase   ( self			,				*_UpperCamelCase			,				**_UpperCamelCase  ):





      """simple docstring"""





      _lowercase			:      str			     =   kwargs.get("is_split_into_words"			,				__a  )

      assert self.add_prefix_space or not is_split_into_words, (
          f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
          "to use it with pretokenized inputs."
      )

      return super()._encode_plus(*__a			,				**__a  )


   def 						_lowerCamelCase   ( self			,				_UpperCamelCase			,				_UpperCamelCase = None  ):





      """simple docstring"""





      _lowercase			:      int			     =   self._tokenizer.model.save(__a			,				name=__a  )
      return tuple(__a  )






   def 						_lowerCamelCase   ( self			,				_UpperCamelCase  ):





      """simple docstring"""





      _lowercase			:      int			     =   []
      for is_user, text in conversation.iter_texts():
         input_ids.extend(self.encode(__a			,				add_special_tokens=__a  ) + [self.eos_token_id]  )

      if len(__a  ) > self.model_max_length:
         _lowercase			:      Any			     =   input_ids[-self.model_max_length :]
      return input_ids











from __future__ import annotations

from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np



class   UpperCAmelCase_    (       a):







							def    snake_case__      (   self,			__a):

														'''simple docstring'''

														return 0.0






def 				A				(					_lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase     :							Optional[Any]    =    min([-20, np.min(fft_results[1 : samplerate // 2 - 1]  )]  )
							_lowerCAmelCase     :							Optional[int]    =    max([20, np.max(fft_results[1 : samplerate // 2 - 1]  )]  )
							return lowest, highest






def 				A				(					_lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase     :							int    =    512
							_lowerCAmelCase     :							Union[str, Any]    =    [1] + [0] * (size - 1)
							_lowerCAmelCase     :							Optional[Any]    =    [filter_type.process(_lowerCamelCase  ) for item in inputs]

							_lowerCAmelCase     :							int    =    [0] * (samplerate - size)  # zero-padding
							outputs += filler
							_lowerCAmelCase     :							str    =    np.abs(np.fft.fft(_lowerCamelCase  )  )
							_lowerCAmelCase     :							Union[str, Any]    =    20 * np.logaa(_lowerCamelCase  )

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

							plt.plot(_lowerCamelCase  )
							plt.show()






def 				A				(					_lowerCamelCase				, _lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase     :							Union[str, Any]    =    512
							_lowerCAmelCase     :							Optional[Any]    =    [1] + [0] * (size - 1)
							_lowerCAmelCase     :							str    =    [filter_type.process(_lowerCamelCase  ) for item in inputs]

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

							# 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(_lowerCamelCase				, -2 * pi  )  )
							plt.show()
