






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				):
	def       __magic_name__    (    self			)							->				int:
							__magic_name__      :       str       						= """ZinengTang/tvlt-base"""
							__magic_name__      :       Optional[int]       						= tempfile.mkdtemp()
	def       __magic_name__    (    self      ,       **lowerCAmelCase__			)							->				Dict:
							return TvltImageProcessor.from_pretrained(self.checkpoint      ,       **lowerCAmelCase__			)
	def       __magic_name__    (    self      ,       **lowerCAmelCase__			)							->				Dict:
							return TvltFeatureExtractor.from_pretrained(self.checkpoint      ,       **lowerCAmelCase__			)
	def       __magic_name__    (    self			)							->				Dict:
							shutil.rmtree(self.tmpdirname			)
	def       __magic_name__    (    self			)							->				int:
							__magic_name__      :       Tuple       						= self.get_image_processor()
							__magic_name__      :       Dict       						= self.get_feature_extractor()

							__magic_name__      :       List[str]       						= TvltProcessor(image_processor=lowerCAmelCase__      ,       feature_extractor=lowerCAmelCase__			)
							processor.save_pretrained(self.tmpdirname			)
							__magic_name__      :       Optional[Any]       						= TvltProcessor.from_pretrained(self.tmpdirname			)

							self.assertIsInstance(processor.feature_extractor      ,       lowerCAmelCase__			)
							self.assertIsInstance(processor.image_processor      ,       lowerCAmelCase__			)
	def       __magic_name__    (    self			)							->				List[Any]:
							__magic_name__      :       Dict       						= self.get_image_processor()
							__magic_name__      :       Union[str, Any]       						= self.get_feature_extractor()

							__magic_name__      :       Optional[int]       						= TvltProcessor(image_processor=lowerCAmelCase__      ,       feature_extractor=lowerCAmelCase__			)

							__magic_name__      :       Optional[Any]       						= np.ones([1_20_00]			)

							__magic_name__      :       List[str]       						= feature_extractor(lowerCAmelCase__      ,       return_tensors="""np"""			)
							__magic_name__      :       int       						= processor(audio=lowerCAmelCase__      ,       return_tensors="""np"""			)

							for key in audio_dict.keys():
													self.assertAlmostEqual(audio_dict[key].sum()      ,       input_processor[key].sum()      ,       delta=1e-2			)
	def       __magic_name__    (    self			)							->				Tuple:
							__magic_name__      :       List[str]       						= self.get_image_processor()
							__magic_name__      :       Union[str, Any]       						= self.get_feature_extractor()

							__magic_name__      :       Dict       						= TvltProcessor(image_processor=lowerCAmelCase__      ,       feature_extractor=lowerCAmelCase__			)

							__magic_name__      :       Dict       						= np.ones([3, 2_24, 2_24]			)

							__magic_name__      :       Optional[int]       						= image_processor(lowerCAmelCase__      ,       return_tensors="""np"""			)
							__magic_name__      :       Any       						= processor(images=lowerCAmelCase__      ,       return_tensors="""np"""			)

							for key in image_dict.keys():
													self.assertAlmostEqual(image_dict[key].sum()      ,       input_processor[key].sum()      ,       delta=1e-2			)
	def       __magic_name__    (    self			)							->				List[str]:
							__magic_name__      :       List[str]       						= self.get_image_processor()
							__magic_name__      :       List[str]       						= self.get_feature_extractor()

							__magic_name__      :       int       						= TvltProcessor(image_processor=lowerCAmelCase__      ,       feature_extractor=lowerCAmelCase__			)

							__magic_name__      :       Optional[Any]       						= np.ones([1_20_00]			)
							__magic_name__      :       Tuple       						= np.ones([3, 2_24, 2_24]			)

							__magic_name__      :       str       						= processor(audio=lowerCAmelCase__      ,       images=lowerCAmelCase__			)

							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(lowerCAmelCase__			):
													processor()







	def       __magic_name__    (    self			)							->				int:
							__magic_name__      :       Optional[Any]       						= self.get_image_processor()
							__magic_name__      :       str       						= self.get_feature_extractor()

							__magic_name__      :       Optional[int]       						= TvltProcessor(image_processor=lowerCAmelCase__      ,       feature_extractor=lowerCAmelCase__			)

							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"""      ,       )








def    UpperCamelCase						(	_A = 1,   _A = 1000			):






						"""simple docstring"""







						__magic_name__      :       Optional[int]       						= 1
						__magic_name__      :       Dict       						= 0

						for divide_by_number in range(_A,   digit + 1			):
												__magic_name__      :       list[int]       						= []
												__magic_name__      :       Any       						= numerator
												for _ in range(1,   digit + 1			):
																		if now_divide in has_been_divided:
																								if longest_list_length < len(_A			):
																														__magic_name__      :       int       						= len(_A			)
																														__magic_name__      :       Dict       						= divide_by_number
																		else:
																								has_been_divided.append(_A			)
																								__magic_name__      :       Optional[int]       						= now_divide * 10 % divide_by_number

						return the_digit


# Tests
if __name__ == "__main__":
		import doctest

		doctest.testmod()
