

'''simple docstring'''



import unittest

import numpy as np

from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image

		from transformers import DPTImageProcessor



class lowerCamelCase			(  unittest.TestCase						):



			'''simple docstring'''






			def __init__(				self	:						str		,      lowerCAmelCase_	:						Union[str, Any]		,      lowerCAmelCase_	:						str=7		,      lowerCAmelCase_	:						Any=3		,      lowerCAmelCase_	:						List[Any]=18		,      lowerCAmelCase_	:						Union[str, Any]=30		,      lowerCAmelCase_	:						Union[str, Any]=4_00		,      lowerCAmelCase_	:						str=True		,      lowerCAmelCase_	:						List[Any]=None		,      lowerCAmelCase_	:						int=True		,      lowerCAmelCase_	:						Union[str, Any]=[0.5, 0.5, 0.5]		,      lowerCAmelCase_	:						Optional[int]=[0.5, 0.5, 0.5]		,      )      ->		List[str]:





									'''simple docstring'''
									A__					:       Any     =size if size is not None else {"""height""": 18, """width""": 18}
									A__					:       List[Any]     =parent
									A__					:       Any     =batch_size
									A__					:       str     =num_channels
									A__					:       Dict     =image_size
									A__					:       int     =min_resolution
									A__					:       int     =max_resolution
									A__					:       Optional[Any]     =do_resize
									A__					:       str     =size
									A__					:       Optional[Any]     =do_normalize
									A__					:       Optional[int]     =image_mean
									A__					:       List[str]     =image_std



			def lowercase__						(				self	:						Optional[Any]			)      ->		Optional[Any]:





									'''simple docstring'''
									return {
									    "image_mean": self.image_mean,
									    "image_std": self.image_std,
									    "do_normalize": self.do_normalize,
									    "do_resize": self.do_resize,
									    "size": self.size,
									}



@require_torch
@require_vision
class lowerCamelCase			(  lowercase_	,						unittest.TestCase						):



			'''simple docstring'''






			__snake_case        =	DPTImageProcessor if is_vision_available() else None


			def lowercase__						(				self	:						Optional[Any]			)      ->		List[Any]:





									'''simple docstring'''
									A__					:       Union[str, Any]     =DPTImageProcessingTester(self			)


			@property
			def lowercase__						(				self	:						Optional[int]			)      ->		Optional[Any]:





									'''simple docstring'''
									return self.image_processor_tester.prepare_image_processor_dict()


			def lowercase__						(				self	:						int			)      ->		Optional[Any]:





									'''simple docstring'''
									A__					:       Dict     =self.image_processing_class(**self.image_processor_dict			)
									self.assertTrue(hasattr(lowerCAmelCase_		,      """image_mean"""			)			)
									self.assertTrue(hasattr(lowerCAmelCase_		,      """image_std"""			)			)
									self.assertTrue(hasattr(lowerCAmelCase_		,      """do_normalize"""			)			)
									self.assertTrue(hasattr(lowerCAmelCase_		,      """do_resize"""			)			)
									self.assertTrue(hasattr(lowerCAmelCase_		,      """size"""			)			)


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





									'''simple docstring'''
									A__					:       str     =self.image_processing_class.from_dict(self.image_processor_dict			)
									self.assertEqual(image_processor.size		,      {"""height""": 18, """width""": 18}			)

									A__					:       Optional[int]     =self.image_processing_class.from_dict(self.image_processor_dict		,      size=42			)
									self.assertEqual(image_processor.size		,      {"""height""": 42, """width""": 42}			)


			def lowercase__						(				self	:						List[str]			)      ->		List[Any]:





									'''simple docstring'''
									A__					:       Optional[int]     =self.image_processing_class(**self.image_processor_dict			)
									# create random PIL images
									A__					:       Union[str, Any]     =prepare_image_inputs(self.image_processor_tester		,      equal_resolution=lowerCAmelCase_			)
									for image in image_inputs:
															self.assertIsInstance(lowerCAmelCase_		,      Image.Image			)

									# Test not batched input
									A__					:       int     =image_processing(image_inputs[0]		,      return_tensors="""pt"""			).pixel_values
									self.assertEqual(
									    encoded_images.shape		,      (
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )		,      )

									# Test batched
									A__					:       Dict     =image_processing(lowerCAmelCase_		,      return_tensors="""pt"""			).pixel_values
									self.assertEqual(
									    encoded_images.shape		,      (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )		,      )


			def lowercase__						(				self	:						List[str]			)      ->		Tuple:





									'''simple docstring'''
									A__					:       List[Any]     =self.image_processing_class(**self.image_processor_dict			)
									# create random numpy tensors
									A__					:       Optional[int]     =prepare_image_inputs(self.image_processor_tester		,      equal_resolution=lowerCAmelCase_		,      numpify=lowerCAmelCase_			)
									for image in image_inputs:
															self.assertIsInstance(lowerCAmelCase_		,      np.ndarray			)

									# Test not batched input
									A__					:       Optional[int]     =image_processing(image_inputs[0]		,      return_tensors="""pt"""			).pixel_values
									self.assertEqual(
									    encoded_images.shape		,      (
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )		,      )

									# Test batched
									A__					:       Optional[Any]     =image_processing(lowerCAmelCase_		,      return_tensors="""pt"""			).pixel_values
									self.assertEqual(
									    encoded_images.shape		,      (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )		,      )



			def lowercase__						(				self	:						Tuple			)      ->		Dict:





									'''simple docstring'''
									A__					:       Optional[Any]     =self.image_processing_class(**self.image_processor_dict			)
									# create random PyTorch tensors
									A__					:       Optional[int]     =prepare_image_inputs(self.image_processor_tester		,      equal_resolution=lowerCAmelCase_		,      torchify=lowerCAmelCase_			)
									for image in image_inputs:
															self.assertIsInstance(lowerCAmelCase_		,      torch.Tensor			)

									# Test not batched input
									A__					:       Optional[Any]     =image_processing(image_inputs[0]		,      return_tensors="""pt"""			).pixel_values
									self.assertEqual(
									    encoded_images.shape		,      (
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )		,      )

									# Test batched
									A__					:       Dict     =image_processing(lowerCAmelCase_		,      return_tensors="""pt"""			).pixel_values
									self.assertEqual(
									    encoded_images.shape		,      (
									        self.image_processor_tester.batch_size,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.size["""height"""],
									        self.image_processor_tester.size["""width"""],
									    )		,      )




'''simple docstring'''



from __future__ import annotations

from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np



class lowerCamelCase			(  lowercase_						):



			'''simple docstring'''






			def lowercase__						(				self	:						Any		,      lowerCAmelCase_	:						float			)      ->		float:





									'''simple docstring'''
									return 0.0


def 						__lowerCamelCase	(       __snake_case					:		np.ndarray,		__snake_case					:		int	)	->							tuple[int | float, int | float]:






						"""simple docstring"""





						A__					:       Tuple     =min([-20, np.min(fft_results[1 : samplerate // 2 - 1]	)]	)
						A__					:       str     =max([20, np.max(fft_results[1 : samplerate // 2 - 1]	)]	)
						return lowest, highest
def 						__lowerCamelCase	(       __snake_case					:		FilterType,		__snake_case					:		int	)	->							None:






						"""simple docstring"""





						A__					:       Any     =512
						A__					:       int     =[1] + [0] * (size - 1)
						A__					:       int     =[filter_type.process(__snake_case	) for item in inputs]

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

						# 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(__snake_case,		__snake_case	)
						plt.ylim(max([-80, bounds[0]]	),		min([80, bounds[1]]	)	)
						plt.ylabel("""Gain (dB)"""	)

						plt.plot(__snake_case	)
						plt.show()
def 						__lowerCamelCase	(       __snake_case					:		FilterType,		__snake_case					:		int	)	->							None:






						"""simple docstring"""





						A__					:       List[Any]     =512
						A__					:       List[Any]     =[1] + [0] * (size - 1)
						A__					:       Dict     =[filter_type.process(__snake_case	) for item in inputs]

						A__					:       Union[str, Any]     =[0] * (samplerate - size)  # zero-padding
						outputs += filler
						A__					:       str     =np.angle(np.fft.fft(__snake_case	)	)

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

