
"""simple docstring"""




import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
			from PIL import Image

			from transformers import AutoProcessor, BertTokenizer, CLIPImageProcessor, GitProcessor, PreTrainedTokenizerFast

@require_vision
class        UpperCAmelCase_  (	unittest.TestCase						):
		def 					_lowerCamelCase    (		self   )		->   List[str]:
					__lowercase	:       List[str]				=   tempfile.mkdtemp()

					__lowercase	:       Optional[Any]				=   CLIPImageProcessor()
					__lowercase	:       List[Any]				=   BertTokenizer.from_pretrained(
					    '''hf-internal-testing/tiny-random-BertModel'''							,		model_input_names=['''input_ids''', '''attention_mask''']   )

					__lowercase	:       Optional[int]				=   GitProcessor(__SCREAMING_SNAKE_CASE							,		__SCREAMING_SNAKE_CASE   )

					processor.save_pretrained(self.tmpdirname   )
		def 					_lowerCamelCase    (		self							,		**UpperCamelCase_   )		->   List[Any]:
					return AutoProcessor.from_pretrained(self.tmpdirname							,		**__SCREAMING_SNAKE_CASE   ).tokenizer
		def 					_lowerCamelCase    (		self							,		**UpperCamelCase_   )		->   int:
					return AutoProcessor.from_pretrained(self.tmpdirname							,		**__SCREAMING_SNAKE_CASE   ).image_processor
		def 					_lowerCamelCase    (		self   )		->   List[str]:
					shutil.rmtree(self.tmpdirname   )
		def 					_lowerCamelCase    (		self   )		->   Optional[Any]:
					__lowercase	:       Optional[Any]				=   [np.random.randint(2_55							,		size=(3, 30, 4_00)							,		dtype=np.uinta   )]

					__lowercase	:       List[str]				=   [Image.fromarray(np.moveaxis(__SCREAMING_SNAKE_CASE							,		0							,		-1   )   ) for x in image_inputs]

					return image_inputs
		def 					_lowerCamelCase    (		self   )		->   List[str]:
					__lowercase	:       Dict				=   GitProcessor(tokenizer=self.get_tokenizer()							,		image_processor=self.get_image_processor()   )
					processor.save_pretrained(self.tmpdirname   )

					__lowercase	:       int				=   self.get_tokenizer(bos_token='''(BOS)'''							,		eos_token='''(EOS)'''   )
					__lowercase	:       Optional[Any]				=   self.get_image_processor(do_normalize=__SCREAMING_SNAKE_CASE							,		padding_value=1.0   )

					__lowercase	:       Union[str, Any]				=   GitProcessor.from_pretrained(
					    self.tmpdirname							,		bos_token='''(BOS)'''							,		eos_token='''(EOS)'''							,		do_normalize=__SCREAMING_SNAKE_CASE							,		padding_value=1.0   )

					self.assertEqual(processor.tokenizer.get_vocab()							,		tokenizer_add_kwargs.get_vocab()   )
					self.assertIsInstance(processor.tokenizer							,		__SCREAMING_SNAKE_CASE   )

					self.assertEqual(processor.image_processor.to_json_string()							,		image_processor_add_kwargs.to_json_string()   )
					self.assertIsInstance(processor.image_processor							,		__SCREAMING_SNAKE_CASE   )
		def 					_lowerCamelCase    (		self   )		->   Optional[Any]:
					__lowercase	:       int				=   self.get_image_processor()
					__lowercase	:       int				=   self.get_tokenizer()

					__lowercase	:       Any				=   GitProcessor(tokenizer=__SCREAMING_SNAKE_CASE							,		image_processor=__SCREAMING_SNAKE_CASE   )

					__lowercase	:       Optional[Any]				=   self.prepare_image_inputs()

					__lowercase	:       List[Any]				=   image_processor(__SCREAMING_SNAKE_CASE							,		return_tensors='''np'''   )
					__lowercase	:       Tuple				=   processor(images=__SCREAMING_SNAKE_CASE							,		return_tensors='''np'''   )

					for key in input_feat_extract.keys():
								self.assertAlmostEqual(input_feat_extract[key].sum()							,		input_processor[key].sum()							,		delta=1E-2   )
		def 					_lowerCamelCase    (		self   )		->   str:
					__lowercase	:       List[str]				=   self.get_image_processor()
					__lowercase	:       Union[str, Any]				=   self.get_tokenizer()

					__lowercase	:       Tuple				=   GitProcessor(tokenizer=__SCREAMING_SNAKE_CASE							,		image_processor=__SCREAMING_SNAKE_CASE   )

					__lowercase	:       Any				=   '''lower newer'''

					__lowercase	:       Any				=   processor(text=__SCREAMING_SNAKE_CASE   )

					__lowercase	:       Optional[Any]				=   tokenizer(__SCREAMING_SNAKE_CASE							,		return_token_type_ids=__SCREAMING_SNAKE_CASE   )

					for key in encoded_tok.keys():
								self.assertListEqual(encoded_tok[key]							,		encoded_processor[key]   )
		def 					_lowerCamelCase    (		self   )		->   List[Any]:
					__lowercase	:       List[Any]				=   self.get_image_processor()
					__lowercase	:       Dict				=   self.get_tokenizer()

					__lowercase	:       str				=   GitProcessor(tokenizer=__SCREAMING_SNAKE_CASE							,		image_processor=__SCREAMING_SNAKE_CASE   )

					__lowercase	:       List[Any]				=   '''lower newer'''
					__lowercase	:       Optional[Any]				=   self.prepare_image_inputs()

					__lowercase	:       Any				=   processor(text=__SCREAMING_SNAKE_CASE							,		images=__SCREAMING_SNAKE_CASE   )

					self.assertListEqual(list(inputs.keys()   )							,		['''input_ids''', '''attention_mask''', '''pixel_values''']   )

					# test if it raises when no input is passed
					with pytest.raises(__SCREAMING_SNAKE_CASE   ):
								processor()
		def 					_lowerCamelCase    (		self   )		->   Any:
					__lowercase	:       int				=   self.get_image_processor()
					__lowercase	:       str				=   self.get_tokenizer()

					__lowercase	:       Tuple				=   GitProcessor(tokenizer=__SCREAMING_SNAKE_CASE							,		image_processor=__SCREAMING_SNAKE_CASE   )

					__lowercase	:       Optional[Any]				=   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

					__lowercase	:       Optional[int]				=   processor.batch_decode(__SCREAMING_SNAKE_CASE   )
					__lowercase	:       Optional[int]				=   tokenizer.batch_decode(__SCREAMING_SNAKE_CASE   )

					self.assertListEqual(__SCREAMING_SNAKE_CASE							,		__SCREAMING_SNAKE_CASE   )
		def 					_lowerCamelCase    (		self   )		->   int:
					__lowercase	:       Optional[int]				=   self.get_image_processor()
					__lowercase	:       Optional[int]				=   self.get_tokenizer()

					__lowercase	:       Any				=   GitProcessor(tokenizer=__SCREAMING_SNAKE_CASE							,		image_processor=__SCREAMING_SNAKE_CASE   )

					__lowercase	:       int				=   '''lower newer'''
					__lowercase	:       int				=   self.prepare_image_inputs()

					__lowercase	:       str				=   processor(text=__SCREAMING_SNAKE_CASE							,		images=__SCREAMING_SNAKE_CASE   )

					# For now the processor supports only ['input_ids', 'attention_mask', 'pixel_values']
					self.assertListEqual(list(inputs.keys()   )							,		['''input_ids''', '''attention_mask''', '''pixel_values''']   )









"""simple docstring"""




# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.
from queue import Queue







def       __UpperCAmelCase      (				__UpperCamelCase					):
			__lowercase	:       int				=   Queue()
			__lowercase	:       str				=   [False] * len(__UpperCamelCase					)
			__lowercase	:       str				=   [-1] * len(__UpperCamelCase					)

			def bfs():
						while not queue.empty():
									__lowercase	:       int				=   queue.get()
									__lowercase	:       List[str]				=   True

									for neighbour in graph[u]:
												if neighbour == u:
															return False

												if color[neighbour] == -1:
															__lowercase	:       str				=   1 - color[u]
															queue.put(__UpperCamelCase					)

												elif color[neighbour] == color[u]:
															return False

						return True

			for i in range(len(__UpperCamelCase					)					):
						if not visited[i]:
									queue.put(__UpperCamelCase					)
									__lowercase	:       int				=   0
									if bfs() is False:
												return False

			return True


if __name__ == "__main__":
			# Adjacency List of graph
			print(check_bipartite({0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2]}))



