






from __future__ import annotations

from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np






class 						UpperCAmelCase__ (							__snake_case							):







		def  A__      (					self       ,A__		):
						return 0.0



def  a__ (__lowercase							:np.ndarray					,  __lowercase							:int   )		-> tuple[int | float, int | float]:
				_A	:				Tuple   =      min([-20, np.min(fft_results[1 : samplerate // 2 - 1]   )]   )
				_A	:				Tuple   =      max([20, np.max(fft_results[1 : samplerate // 2 - 1]   )]   )
				return lowest, highest



def  a__ (__lowercase							:FilterType					,  __lowercase							:int   )		-> None:
				_A	:				Tuple   =      512
				_A	:				Any   =      [1] + [0] * (size - 1)
				_A	:				List[Any]   =      [filter_type.process(__lowercase   ) for item in inputs]

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

				# 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
				_A	:				Union[str, Any]   =      get_bounds(__lowercase					,  __lowercase   )
				plt.ylim(max([-80, bounds[0]]   )					,  min([80, bounds[1]]   )   )
				plt.ylabel('''Gain (dB)'''   )

				plt.plot(__lowercase   )
				plt.show()






def  a__ (__lowercase							:FilterType					,  __lowercase							:int   )		-> None:
				_A	:				int   =      512
				_A	:				Tuple   =      [1] + [0] * (size - 1)
				_A	:				Optional[int]   =      [filter_type.process(__lowercase   ) for item in inputs]

				_A	:				str   =      [0] * (samplerate - size)  # zero-padding
				outputs += filler
				_A	:				Tuple   =      np.angle(np.fft.fft(__lowercase   )   )

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











from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType






class 						UpperCAmelCase__ (							__snake_case							):
		__snake_case      :       Optional[Any]     =   ["image_processor", "tokenizer"]
		__snake_case      :       Tuple     =   "BridgeTowerImageProcessor"
		__snake_case      :       List[str]     =   ("RobertaTokenizer", "RobertaTokenizerFast")







		def __init__(					self       ,A__       ,A__		):
						super().__init__(A__       ,A__		)







		def __call__(					self       ,A__       ,A__ = None       ,A__ = True       ,A__ = False       ,A__ = None       ,A__ = None       ,A__ = 0       ,A__ = None       ,A__ = None       ,A__ = None       ,A__ = False       ,A__ = False       ,A__ = False       ,A__ = False       ,A__ = True       ,A__ = None       ,**A__       ,):
						_A	:				List[Any]   =      self.tokenizer(
						    text=A__       ,add_special_tokens=A__       ,padding=A__       ,truncation=A__       ,max_length=A__       ,stride=A__       ,pad_to_multiple_of=A__       ,return_token_type_ids=A__       ,return_attention_mask=A__       ,return_overflowing_tokens=A__       ,return_special_tokens_mask=A__       ,return_offsets_mapping=A__       ,return_length=A__       ,verbose=A__       ,return_tensors=A__       ,**A__       ,)
						# add pixel_values + pixel_mask
						_A	:				Optional[Any]   =      self.image_processor(
						    A__       ,return_tensors=A__       ,do_normalize=A__       ,do_center_crop=A__       ,**A__		)
						encoding.update(A__		)

						return encoding







		def  A__      (					self       ,*A__       ,**A__		):
						return self.tokenizer.batch_decode(*A__       ,**A__		)







		def  A__      (					self       ,*A__       ,**A__		):
						return self.tokenizer.decode(*A__       ,**A__		)







		@property
		def  A__      (					self		):
						_A	:				Union[str, Any]   =      self.tokenizer.model_input_names
						_A	:				str   =      self.image_processor.model_input_names
						return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names		)		)



