
'''simple docstring'''







import unittest

import numpy as np

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

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_video_inputs


if is_torch_available():
					import torch

if is_vision_available():
					from PIL import Image

					from transformers import VivitImageProcessor


class 						__UpperCAmelCase      (					unittest.TestCase						):


							def __init__(       self    ,   lowerCAmelCase_    ,   lowerCAmelCase_=7    ,   lowerCAmelCase_=3    ,   lowerCAmelCase_=10    ,   lowerCAmelCase_=18    ,   lowerCAmelCase_=30    ,   lowerCAmelCase_=4_00    ,   lowerCAmelCase_=True    ,   lowerCAmelCase_=None    ,   lowerCAmelCase_=True    ,   lowerCAmelCase_=[0.5, 0.5, 0.5]    ,   lowerCAmelCase_=[0.5, 0.5, 0.5]    ,   lowerCAmelCase_=None    ,   ):





								"""simple docstring"""






								_snake_case   						=		size if size is not None else {'shortest_edge': 18}
								_snake_case   						=		crop_size if crop_size is not None else {'height': 18, 'width': 18}

								_snake_case   						=		parent
								_snake_case   						=		batch_size
								_snake_case   						=		num_channels
								_snake_case   						=		num_frames
								_snake_case   						=		image_size
								_snake_case   						=		min_resolution
								_snake_case   						=		max_resolution
								_snake_case   						=		do_resize
								_snake_case   						=		size
								_snake_case   						=		do_normalize
								_snake_case   						=		image_mean
								_snake_case   						=		image_std
								_snake_case   						=		crop_size




							def  lowerCamelCase				(       self  ):





								"""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,
								    "crop_size": self.crop_size,
								}


@require_torch
@require_vision
class 						__UpperCAmelCase      (					_lowerCamelCase						,   unittest.TestCase						):
							__lowercase      		=       VivitImageProcessor if is_vision_available() else None


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		VivitImageProcessingTester(self  )


							@property
							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								return self.image_processor_tester.prepare_image_processor_dict()


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		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_    ,   'do_center_crop'  )  )
								self.assertTrue(hasattr(lowerCAmelCase_    ,   'size'  )  )


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		self.image_processing_class.from_dict(self.image_processor_dict  )
								self.assertEqual(image_processor.size    ,   {'shortest_edge': 18}  )
								self.assertEqual(image_processor.crop_size    ,   {'height': 18, 'width': 18}  )

								_snake_case   						=		self.image_processing_class.from_dict(self.image_processor_dict    ,   size=42    ,   crop_size=84  )
								self.assertEqual(image_processor.size    ,   {'shortest_edge': 42}  )
								self.assertEqual(image_processor.crop_size    ,   {'height': 84, 'width': 84}  )


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		self.image_processing_class(**self.image_processor_dict  )
								# create random PIL videos
								_snake_case   						=		prepare_video_inputs(self.image_processor_tester    ,   equal_resolution=lowerCAmelCase_  )
								for video in video_inputs:
									self.assertIsInstance(lowerCAmelCase_    ,   lowerCAmelCase_  )
									self.assertIsInstance(video[0]    ,   Image.Image  )

								# Test not batched input
								_snake_case   						=		image_processing(video_inputs[0]    ,   return_tensors='pt'  ).pixel_values
								self.assertEqual(
								    encoded_videos.shape    ,   (
								        1,
								        self.image_processor_tester.num_frames,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['height'],
								        self.image_processor_tester.crop_size['width'],
								    )    ,   )

								# Test batched
								_snake_case   						=		image_processing(lowerCAmelCase_    ,   return_tensors='pt'  ).pixel_values
								self.assertEqual(
								    encoded_videos.shape    ,   (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_frames,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['height'],
								        self.image_processor_tester.crop_size['width'],
								    )    ,   )


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		self.image_processing_class(**self.image_processor_dict  )
								# create random numpy tensors
								_snake_case   						=		prepare_video_inputs(self.image_processor_tester    ,   equal_resolution=lowerCAmelCase_    ,   numpify=lowerCAmelCase_  )
								for video in video_inputs:
									self.assertIsInstance(lowerCAmelCase_    ,   lowerCAmelCase_  )
									self.assertIsInstance(video[0]    ,   np.ndarray  )

								# Test not batched input
								_snake_case   						=		image_processing(video_inputs[0]    ,   return_tensors='pt'  ).pixel_values
								self.assertEqual(
								    encoded_videos.shape    ,   (
								        1,
								        self.image_processor_tester.num_frames,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['height'],
								        self.image_processor_tester.crop_size['width'],
								    )    ,   )

								# Test batched
								_snake_case   						=		image_processing(lowerCAmelCase_    ,   return_tensors='pt'  ).pixel_values
								self.assertEqual(
								    encoded_videos.shape    ,   (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_frames,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['height'],
								        self.image_processor_tester.crop_size['width'],
								    )    ,   )




							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		self.image_processing_class(**self.image_processor_dict  )
								# create random PyTorch tensors
								_snake_case   						=		prepare_video_inputs(self.image_processor_tester    ,   equal_resolution=lowerCAmelCase_    ,   torchify=lowerCAmelCase_  )
								for video in video_inputs:
									self.assertIsInstance(lowerCAmelCase_    ,   lowerCAmelCase_  )
									self.assertIsInstance(video[0]    ,   torch.Tensor  )

								# Test not batched input
								_snake_case   						=		image_processing(video_inputs[0]    ,   return_tensors='pt'  ).pixel_values
								self.assertEqual(
								    encoded_videos.shape    ,   (
								        1,
								        self.image_processor_tester.num_frames,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['height'],
								        self.image_processor_tester.crop_size['width'],
								    )    ,   )

								# Test batched
								_snake_case   						=		image_processing(lowerCAmelCase_    ,   return_tensors='pt'  ).pixel_values
								self.assertEqual(
								    encoded_videos.shape    ,   (
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_frames,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['height'],
								        self.image_processor_tester.crop_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 						__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()

