






"""simple docstring"""






import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch


if is_vision_available():
					from transformers import TvltImageProcessor

if is_speech_available():
					from transformers import TvltFeatureExtractor

from transformers import TvltProcessor





@require_torch
class       snake_case_       (    unittest.TestCase       ):



	"""simple docstring"""

	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					A__							 =       'ZinengTang/tvlt-base'
					A__							 =       tempfile.mkdtemp()


	def       _UpperCAmelCase						(      self  ,					**__a						):


					"""simple docstring"""

					return TvltImageProcessor.from_pretrained(self.checkpoint  ,					**__a						)


	def       _UpperCAmelCase						(      self  ,					**__a						):


					"""simple docstring"""

					return TvltFeatureExtractor.from_pretrained(self.checkpoint  ,					**__a						)


	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					shutil.rmtree(self.tmpdirname						)


	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					A__							 =       self.get_image_processor()
					A__							 =       self.get_feature_extractor()

					A__							 =       TvltProcessor(image_processor=__a  ,					feature_extractor=__a						)
					processor.save_pretrained(self.tmpdirname						)
					A__							 =       TvltProcessor.from_pretrained(self.tmpdirname						)

					self.assertIsInstance(processor.feature_extractor  ,					__a						)
					self.assertIsInstance(processor.image_processor  ,					__a						)


	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					A__							 =       self.get_image_processor()
					A__							 =       self.get_feature_extractor()

					A__							 =       TvltProcessor(image_processor=__a  ,					feature_extractor=__a						)

					A__							 =       np.ones([1_2000]						)

					A__							 =       feature_extractor(__a  ,					return_tensors='np'						)
					A__							 =       processor(audio=__a  ,					return_tensors='np'						)

					for key in audio_dict.keys():
									self.assertAlmostEqual(audio_dict[key].sum()  ,					input_processor[key].sum()  ,					delta=1E-2						)


	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					A__							 =       self.get_image_processor()
					A__							 =       self.get_feature_extractor()

					A__							 =       TvltProcessor(image_processor=__a  ,					feature_extractor=__a						)

					A__							 =       np.ones([3, 224, 224]						)

					A__							 =       image_processor(__a  ,					return_tensors='np'						)
					A__							 =       processor(images=__a  ,					return_tensors='np'						)

					for key in image_dict.keys():
									self.assertAlmostEqual(image_dict[key].sum()  ,					input_processor[key].sum()  ,					delta=1E-2						)


	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					A__							 =       self.get_image_processor()
					A__							 =       self.get_feature_extractor()

					A__							 =       TvltProcessor(image_processor=__a  ,					feature_extractor=__a						)

					A__							 =       np.ones([1_2000]						)
					A__							 =       np.ones([3, 224, 224]						)

					A__							 =       processor(audio=__a  ,					images=__a						)

					self.assertListEqual(list(inputs.keys()						)  ,					['audio_values', 'audio_mask', 'pixel_values', 'pixel_mask']						)

					# test if it raises when no input is passed
					with pytest.raises(__a						):
									processor()






	def       _UpperCAmelCase						(      self						):


					"""simple docstring"""

					A__							 =       self.get_image_processor()
					A__							 =       self.get_feature_extractor()

					A__							 =       TvltProcessor(image_processor=__a  ,					feature_extractor=__a						)

					self.assertListEqual(
					    processor.model_input_names  ,					image_processor.model_input_names + feature_extractor.model_input_names  ,					msg='`processor` and `image_processor`+`feature_extractor` model input names do not match'  ,					)










"""simple docstring"""






from math import factorial, pi



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__ = 30				):

				if not isinstance(lowerCAmelCase__       ,(int, float)				):
								raise ValueError('maclaurin_sin() requires either an int or float for theta'				)

				if not isinstance(lowerCAmelCase__       ,lowerCAmelCase__				) or accuracy <= 0:
								raise ValueError('maclaurin_sin() requires a positive int for accuracy'				)

				A__							 =       float(lowerCAmelCase__				)
				A__							 =       theta // (2 * pi)
				theta -= 2 * div * pi
				return sum(
				    (-1) ** r * theta ** (2 * r + 1) / factorial(2 * r + 1				) for r in range(lowerCAmelCase__				)				)



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__ = 30				):

				if not isinstance(lowerCAmelCase__       ,(int, float)				):
								raise ValueError('maclaurin_cos() requires either an int or float for theta'				)

				if not isinstance(lowerCAmelCase__       ,lowerCAmelCase__				) or accuracy <= 0:
								raise ValueError('maclaurin_cos() requires a positive int for accuracy'				)

				A__							 =       float(lowerCAmelCase__				)
				A__							 =       theta // (2 * pi)
				theta -= 2 * div * pi
				return sum((-1) ** r * theta ** (2 * r) / factorial(2 * r				) for r in range(lowerCAmelCase__				)				)


if __name__ == "__main__":
					import doctest

					doctest.testmod()

					print(maclaurin_sin(10))
					print(maclaurin_sin(-10))
					print(maclaurin_sin(10, 15))
					print(maclaurin_sin(-10, 15))

					print(maclaurin_cos(5))
					print(maclaurin_cos(-5))
					print(maclaurin_cos(10, 15))
					print(maclaurin_cos(-10, 15))


