


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  _a							(    unittest.TestCase):




	def 							UpperCAmelCase__( self						:		Union[str, Any]		)->	Union[str, Any]:
			lowerCAmelCase__       :      Any   					= '''ZinengTang/tvlt-base'''
			lowerCAmelCase__       :      List[Any]   					= tempfile.mkdtemp()




	def 							UpperCAmelCase__( self						:		Optional[Any]		, **_SCREAMING_SNAKE_CASE						:		Optional[int]		)->	Dict:
			return TvltImageProcessor.from_pretrained(self.checkpoint		, **UpperCamelCase__		)




	def 							UpperCAmelCase__( self						:		Any		, **_SCREAMING_SNAKE_CASE						:		Optional[int]		)->	Any:
			return TvltFeatureExtractor.from_pretrained(self.checkpoint		, **UpperCamelCase__		)




	def 							UpperCAmelCase__( self						:		List[Any]		)->	Dict:
			shutil.rmtree(self.tmpdirname		)




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

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

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




	def 							UpperCAmelCase__( self						:		Dict		)->	Union[str, Any]:
			lowerCAmelCase__       :      Tuple   					= self.get_image_processor()
			lowerCAmelCase__       :      Optional[int]   					= self.get_feature_extractor()

			lowerCAmelCase__       :      Optional[Any]   					= TvltProcessor(image_processor=UpperCamelCase__		, feature_extractor=UpperCamelCase__		)

			lowerCAmelCase__       :      str   					= np.ones([1_2000]		)

			lowerCAmelCase__       :      Any   					= feature_extractor(UpperCamelCase__		, return_tensors='''np'''		)
			lowerCAmelCase__       :      str   					= processor(audio=UpperCamelCase__		, 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						:		List[str]		)->	List[str]:
			lowerCAmelCase__       :      List[Any]   					= self.get_image_processor()
			lowerCAmelCase__       :      Any   					= self.get_feature_extractor()

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

			lowerCAmelCase__       :      List[str]   					= np.ones([3, 224, 224]		)

			lowerCAmelCase__       :      Tuple   					= image_processor(UpperCamelCase__		, return_tensors='''np'''		)
			lowerCAmelCase__       :      Optional[int]   					= processor(images=UpperCamelCase__		, 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						:		Dict		)->	List[str]:
			lowerCAmelCase__       :      Optional[Any]   					= self.get_image_processor()
			lowerCAmelCase__       :      Any   					= self.get_feature_extractor()

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

			lowerCAmelCase__       :      int   					= np.ones([1_2000]		)
			lowerCAmelCase__       :      Optional[int]   					= np.ones([3, 224, 224]		)

			lowerCAmelCase__       :      Any   					= processor(audio=UpperCamelCase__		, images=UpperCamelCase__		)

			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(UpperCamelCase__		):
					processor()




	def 							UpperCAmelCase__( self						:		Dict		)->	Optional[Any]:
			lowerCAmelCase__       :      Optional[Any]   					= self.get_image_processor()
			lowerCAmelCase__       :      List[Any]   					= self.get_feature_extractor()

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

			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'''		, )



# using dfs for finding eulerian path traversal






def 					lowerCamelCase_   (	_a     ,       _a     ,       _a     ,       _a=None	):




		"""simple docstring"""






		lowerCAmelCase__       :      Optional[Any]   					= (path or []) + [u]
		for v in graph[u]:
				if visited_edge[u][v] is False:
						lowerCAmelCase__					,     lowerCAmelCase__       :      List[Any]   					= True, True
						lowerCAmelCase__       :      Any   					= dfs(_a     ,       _a     ,       _a     ,       _a	)
		return path






def 					lowerCamelCase_   (	_a     ,       _a	):




		"""simple docstring"""






		lowerCAmelCase__       :      Union[str, Any]   					= 0
		lowerCAmelCase__       :      str   					= -1
		for i in range(_a	):
				if i not in graph.keys():
						continue
				if len(graph[i]	) % 2 == 1:
						odd_degree_nodes += 1
						lowerCAmelCase__       :      Tuple   					= i
		if odd_degree_nodes == 0:
				return 1, odd_node
		if odd_degree_nodes == 2:
				return 2, odd_node
		return 3, odd_node






def 					lowerCamelCase_   (	_a     ,       _a	):




		"""simple docstring"""






		lowerCAmelCase__       :      Optional[Any]   					= [[False for _ in range(max_node + 1	)] for _ in range(max_node + 1	)]
		lowerCAmelCase__					,     lowerCAmelCase__       :      Optional[int]   					= check_circuit_or_path(_a     ,       _a	)
		if check == 3:
				print('''graph is not Eulerian'''	)
				print('''no path'''	)
				return
		lowerCAmelCase__       :      Optional[int]   					= 1
		if check == 2:
				lowerCAmelCase__       :      Any   					= odd_node
				print('''graph has a Euler path'''	)
		if check == 1:
				print('''graph has a Euler cycle'''	)
		lowerCAmelCase__       :      Optional[int]   					= dfs(_a     ,       _a     ,       _a	)
		print(_a	)






def 					lowerCamelCase_   (	):




		"""simple docstring"""






		lowerCAmelCase__       :      List[str]   					= {1: [2, 3, 4], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [4]}
		lowerCAmelCase__       :      Tuple   					= {1: [2, 3, 4, 5], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [1, 4]}
		lowerCAmelCase__       :      str   					= {1: [2, 3, 4], 2: [1, 3, 4], 3: [1, 2], 4: [1, 2, 5], 5: [4]}
		lowerCAmelCase__       :      List[str]   					= {1: [2, 3], 2: [1, 3], 3: [1, 2]}
		lowerCAmelCase__       :      List[Any]   					= {
		    1: [],
		    2: []
		    # all degree is zero
		}
		lowerCAmelCase__       :      Optional[Any]   					= 10
		check_euler(_a     ,       _a	)
		check_euler(_a     ,       _a	)
		check_euler(_a     ,       _a	)
		check_euler(_a     ,       _a	)
		check_euler(_a     ,       _a	)


if __name__ == "__main__":
		main()




