

'''simple docstring'''
import warnings

from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum

from ...processing_utils import ProcessorMixin


if is_torch_available():
    import torch


class    a__  (							lowerCamelCase_  ):
   _SCREAMING_SNAKE_CASE    :     Optional[int]	       =							'char'
   _SCREAMING_SNAKE_CASE    :     Optional[Any]	       =							'bpe'
   _SCREAMING_SNAKE_CASE    :     Tuple	       =							'wp'




_snake_case     =  (DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)


class    a__  (							lowerCamelCase_  ):
   _SCREAMING_SNAKE_CASE    :     Optional[int]	       =							['image_processor', 'char_tokenizer']
   _SCREAMING_SNAKE_CASE    :     Tuple	       =							'ViTImageProcessor'
   _SCREAMING_SNAKE_CASE    :     List[Any]	       =							'MgpstrTokenizer'


   def __init__( self			,				_UpperCamelCase=None			,				_UpperCamelCase=None			,				**_UpperCamelCase  ):





      """simple docstring"""





      _lowercase			:      Optional[Any]			     =   None
      if "feature_extractor" in kwargs:
         warnings.warn(
             "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
             " instead."			,				__a			,				)
         _lowercase			:      List[Any]			     =   kwargs.pop("feature_extractor"  )

      _lowercase			:      List[Any]			     =   image_processor if image_processor is not None else feature_extractor
      if image_processor is None:
         raise ValueError("You need to specify an `image_processor`."  )
      if tokenizer is None:
         raise ValueError("You need to specify a `tokenizer`."  )

      _lowercase			:      int			     =   tokenizer
      _lowercase			:      Union[str, Any]			     =   AutoTokenizer.from_pretrained("gpt2"  )
      _lowercase			:      Optional[Any]			     =   AutoTokenizer.from_pretrained("bert-base-uncased"  )

      super().__init__(__a			,				__a  )


   def __call__( self			,				_UpperCamelCase=None			,				_UpperCamelCase=None			,				_UpperCamelCase=None			,				**_UpperCamelCase  ):





      """simple docstring"""





      if images is None and text is None:
         raise ValueError("You need to specify either an `images` or `text` input to process."  )

      if images is not None:
         _lowercase			:      int			     =   self.image_processor(__a			,				return_tensors=__a			,				**__a  )
      if text is not None:
         _lowercase			:      List[Any]			     =   self.char_tokenizer(__a			,				return_tensors=__a			,				**__a  )

      if text is None:
         return inputs
      elif images is None:
         return encodings
      else:
         _lowercase			:      Tuple			     =   encodings["input_ids"]
         return inputs


   def 						_lowerCamelCase   ( self			,				_UpperCamelCase  ):





      """simple docstring"""





      _lowercase			:      Optional[int]			     =   sequences
      _lowercase			:      Optional[int]			     =   char_preds.size(0  )

      _lowercase			:      Optional[Any]			     =   self._decode_helper(__a			,				"char"  )
      _lowercase			:      Dict			     =   self._decode_helper(__a			,				"bpe"  )
      _lowercase			:      Tuple			     =   self._decode_helper(__a			,				"wp"  )

      _lowercase			:      Dict			     =   []
      _lowercase			:      int			     =   []
      for i in range(__a  ):
         _lowercase			:      Dict			     =   [char_scores[i], bpe_scores[i], wp_scores[i]]
         _lowercase			:      List[str]			     =   [char_strs[i], bpe_strs[i], wp_strs[i]]
         _lowercase			:      Optional[int]			     =   scores.index(max(__a  )  )
         final_strs.append(strs[max_score_index]  )
         final_scores.append(scores[max_score_index]  )

      _lowercase			:      Optional[int]			     =   {}
      _lowercase			:      str			     =   final_strs
      _lowercase			:      Any			     =   final_scores
      _lowercase			:      List[Any]			     =   char_strs
      _lowercase			:      List[str]			     =   bpe_strs
      _lowercase			:      Dict			     =   wp_strs
      return out


   def 						_lowerCamelCase   ( self			,				_UpperCamelCase			,				_UpperCamelCase  ):





      """simple docstring"""





      if format == DecodeType.CHARACTER:
         _lowercase			:      int			     =   self.char_decode
         _lowercase			:      Union[str, Any]			     =   1
         _lowercase			:      int			     =   "[s]"
      elif format == DecodeType.BPE:
         _lowercase			:      int			     =   self.bpe_decode
         _lowercase			:      Tuple			     =   2
         _lowercase			:      Dict			     =   "#"
      elif format == DecodeType.WORDPIECE:
         _lowercase			:      Any			     =   self.wp_decode
         _lowercase			:      Union[str, Any]			     =   102
         _lowercase			:      Optional[Any]			     =   "[SEP]"
      else:
         raise ValueError(f'''Format {format} is not supported.'''  )

      _lowercase			:      Union[str, Any]			     =   [], []
      _lowercase			:      Dict			     =   pred_logits.size(0  )
      _lowercase			:      int			     =   pred_logits.size(1  )
      _lowercase			:      Dict			     =   pred_logits.topk(1			,				dim=-1			,				largest=__a			,				sorted=__a  )
      _lowercase			:      Tuple			     =   preds_index.view(-1			,				__a  )[:, 1:]
      _lowercase			:      List[Any]			     =   decoder(__a  )
      _lowercase			:      Optional[int]			     =   torch.nn.functional.softmax(__a			,				dim=2  ).max(dim=2  )
      _lowercase			:      str			     =   preds_max_prob[:, 1:]

      for index in range(__a  ):
         _lowercase			:      List[Any]			     =   preds_str[index].find(__a  )
         _lowercase			:      int			     =   preds_str[index][:pred_eos]
         _lowercase			:      Tuple			     =   preds_index[index].cpu().tolist()
         _lowercase			:      List[Any]			     =   pred_index.index(__a  ) if eos_token in pred_index else -1
         _lowercase			:      Dict			     =   preds_max_prob[index][: pred_eos_index + 1]
         _lowercase			:      int			     =   pred_max_prob.cumprod(dim=0  )[-1] if pred_max_prob.nelement() != 0 else 0.0
         dec_strs.append(__a  )
         conf_scores.append(__a  )

      return dec_strs, conf_scores


   def 						_lowerCamelCase   ( self			,				_UpperCamelCase  ):





      """simple docstring"""





      _lowercase			:      List[Any]			     =   [seq.replace(" "			,				""  ) for seq in self.char_tokenizer.batch_decode(__a  )]
      return decode_strs


   def 						_lowerCamelCase   ( self			,				_UpperCamelCase  ):





      """simple docstring"""





      return self.bpe_tokenizer.batch_decode(__a  )






   def 						_lowerCamelCase   ( self			,				_UpperCamelCase  ):





      """simple docstring"""





      _lowercase			:      Optional[int]			     =   [seq.replace(" "			,				""  ) for seq in self.wp_tokenizer.batch_decode(__a  )]
      return decode_strs











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()
