
'''simple docstring'''







from __future__ import annotations

from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np


class 						__UpperCAmelCase      (					_lowerCamelCase						):


							def  lowerCamelCase				(       self    ,   lowerCAmelCase_  ):





								"""simple docstring"""






								return 0.0







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A	)       ->	tuple[int | float, int | float]:
	_snake_case   						=		min([-20, np.min(fft_results[1 : samplerate // 2 - 1]	)]	)
	_snake_case   						=		max([20, np.max(fft_results[1 : samplerate // 2 - 1]	)]	)
	return lowest, highest







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A	)       ->	None:
	_snake_case   						=		512
	_snake_case   						=		[1] + [0] * (size - 1)
	_snake_case   						=		[filter_type.process(__A	) for item in inputs]

	_snake_case   						=		[0] * (samplerate - size)  # zero-padding
	outputs += filler
	_snake_case   						=		np.abs(np.fft.fft(__A	)	)
	_snake_case   						=		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
	_snake_case   						=		get_bounds(__A			,				__A	)
	plt.ylim(max([-80, bounds[0]]	)			,				min([80, bounds[1]]	)	)
	plt.ylabel('Gain (dB)'	)

	plt.plot(__A	)
	plt.show()







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A	)       ->	None:
	_snake_case   						=		512
	_snake_case   						=		[1] + [0] * (size - 1)
	_snake_case   						=		[filter_type.process(__A	) for item in inputs]

	_snake_case   						=		[0] * (samplerate - size)  # zero-padding
	outputs += filler
	_snake_case   						=		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()









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 __lowerCamelCase (      snake_case__):





	"""simple docstring"""






	UpperCamelCase__								=      ["image_processor", "tokenizer"]
	UpperCamelCase__								=      "Pix2StructImageProcessor"
	UpperCamelCase__								=      ("T5Tokenizer", "T5TokenizerFast")

	def __init__(							self       ,    UpperCAmelCase       ,    UpperCAmelCase    ):
							"""simple docstring"""







							_UpperCAmelCase									=			False
							super().__init__(UpperCAmelCase       ,    UpperCAmelCase    )

	def __call__(							self       ,    UpperCAmelCase=None       ,    UpperCAmelCase = None       ,    UpperCAmelCase = True       ,    UpperCAmelCase = False       ,    UpperCAmelCase = None       ,    UpperCAmelCase = None       ,    UpperCAmelCase = 2048       ,    UpperCAmelCase = 0       ,    UpperCAmelCase = None       ,    UpperCAmelCase = None       ,    UpperCAmelCase = False       ,    UpperCAmelCase = False       ,    UpperCAmelCase = False       ,    UpperCAmelCase = False       ,    UpperCAmelCase = False       ,    UpperCAmelCase = True       ,    UpperCAmelCase = None       ,    **UpperCAmelCase       ,    ):
							"""simple docstring"""







							if images is None and text is None:
													raise ValueError('You have to specify either images or text.'    )

							# Get only text
							if images is None and not self.image_processor.is_vqa:
													_UpperCAmelCase									=			self.tokenizer
													_UpperCAmelCase									=			self.tokenizer(
													    text=UpperCAmelCase       ,    add_special_tokens=UpperCAmelCase       ,    padding=UpperCAmelCase       ,    truncation=UpperCAmelCase       ,    max_length=UpperCAmelCase       ,    stride=UpperCAmelCase       ,    pad_to_multiple_of=UpperCAmelCase       ,    return_attention_mask=UpperCAmelCase       ,    return_overflowing_tokens=UpperCAmelCase       ,    return_special_tokens_mask=UpperCAmelCase       ,    return_offsets_mapping=UpperCAmelCase       ,    return_token_type_ids=UpperCAmelCase       ,    return_length=UpperCAmelCase       ,    verbose=UpperCAmelCase       ,    return_tensors=UpperCAmelCase       ,    **UpperCAmelCase       ,    )
													return text_encoding

							if not self.image_processor.is_vqa:
													# add pixel_values
													_UpperCAmelCase									=			self.image_processor(
													    UpperCAmelCase       ,    return_tensors=UpperCAmelCase       ,    max_patches=UpperCAmelCase       ,    **UpperCAmelCase    )
							else:
													# add pixel_values and bbox
													_UpperCAmelCase									=			self.image_processor(
													    UpperCAmelCase       ,    return_tensors=UpperCAmelCase       ,    max_patches=UpperCAmelCase       ,    header_text=UpperCAmelCase       ,    **UpperCAmelCase    )

							if text is not None and not self.image_processor.is_vqa:
													_UpperCAmelCase									=			self.tokenizer(
													    text=UpperCAmelCase       ,    add_special_tokens=UpperCAmelCase       ,    padding=UpperCAmelCase       ,    truncation=UpperCAmelCase       ,    max_length=UpperCAmelCase       ,    stride=UpperCAmelCase       ,    pad_to_multiple_of=UpperCAmelCase       ,    return_attention_mask=UpperCAmelCase       ,    return_overflowing_tokens=UpperCAmelCase       ,    return_special_tokens_mask=UpperCAmelCase       ,    return_offsets_mapping=UpperCAmelCase       ,    return_token_type_ids=UpperCAmelCase       ,    return_length=UpperCAmelCase       ,    verbose=UpperCAmelCase       ,    return_tensors=UpperCAmelCase       ,    **UpperCAmelCase       ,    )

													if "attention_mask" in text_encoding:
																			_UpperCAmelCase									=			text_encoding.pop('attention_mask'    )
													if "input_ids" in text_encoding:
																			_UpperCAmelCase									=			text_encoding.pop('input_ids'    )
							else:
													_UpperCAmelCase									=			None

							if text_encoding is not None:
													encoding_image_processor.update(UpperCAmelCase    )

							return encoding_image_processor

	def        UpperCamelCase    (							self       ,    *UpperCAmelCase       ,    **UpperCAmelCase    ):
							"""simple docstring"""







							return self.tokenizer.batch_decode(*UpperCAmelCase       ,    **UpperCAmelCase    )

	def        UpperCamelCase    (							self       ,    *UpperCAmelCase       ,    **UpperCAmelCase    ):
							"""simple docstring"""







							return self.tokenizer.decode(*UpperCAmelCase       ,    **UpperCAmelCase    )

	@property
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							_UpperCAmelCase									=			self.tokenizer.model_input_names
							_UpperCAmelCase									=			self.image_processor.model_input_names
							return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names    )    )
