




"""simple docstring"""






import unittest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TextaTextGenerationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available

from .test_pipelines_common import ANY


if is_torch_available():
			import torch






@is_pipeline_test
class lowerCAmelCase__	(     unittest.TestCase						):






			'''simple docstring'''



			lowerCamelCase__      =			MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
			lowerCamelCase__      =			TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
			def 					A_    (  self  ,      lowercase  ,      lowercase  ,      lowercase  ):
							_lowerCamelCase     :			Optional[int]    =    TextaTextGenerationPipeline(model=lowercase  ,      tokenizer=lowercase  )
							return generator, ["Something to write", "Something else"]
			def 					A_    (  self  ,      lowercase  ,      lowercase  ):
							_lowerCamelCase     :			int    =    generator('Something there'  )
							self.assertEqual(lowercase  ,      [{'generated_text': ANY(lowercase  )}]  )
							# These are encoder decoder, they don't just append to incoming string
							self.assertFalse(outputs[0]['generated_text'].startswith('Something there'  )  )

							_lowerCamelCase     :			Optional[Any]    =    generator(['This is great !', 'Something else']  ,      num_return_sequences=2  ,      do_sample=lowercase  )
							self.assertEqual(
							    lowercase  ,      [
							        [{'generated_text': ANY(lowercase  )}, {'generated_text': ANY(lowercase  )}],
							        [{'generated_text': ANY(lowercase  )}, {'generated_text': ANY(lowercase  )}],
							    ]  ,      )

							_lowerCamelCase     :			Union[str, Any]    =    generator(
							    ['This is great !', 'Something else']  ,      num_return_sequences=2  ,      batch_size=2  ,      do_sample=lowercase  )
							self.assertEqual(
							    lowercase  ,      [
							        [{'generated_text': ANY(lowercase  )}, {'generated_text': ANY(lowercase  )}],
							        [{'generated_text': ANY(lowercase  )}, {'generated_text': ANY(lowercase  )}],
							    ]  ,      )

							with self.assertRaises(lowercase  ):
											generator(4  )
			@require_torch
			def 					A_    (  self  ):
							_lowerCamelCase     :			int    =    pipeline('text2text-generation'  ,      model='patrickvonplaten/t5-tiny-random'  ,      framework='pt'  )
							# do_sample=False necessary for reproducibility
							_lowerCamelCase     :			Dict    =    generator('Something there'  ,      do_sample=lowercase  )
							self.assertEqual(lowercase  ,      [{'generated_text': ''}]  )

							_lowerCamelCase     :			str    =    3
							_lowerCamelCase     :			str    =    generator(
							    'Something there'  ,      num_return_sequences=lowercase  ,      num_beams=lowercase  ,      )
							_lowerCamelCase     :			int    =    [
							    {'generated_text': 'Beide Beide Beide Beide Beide Beide Beide Beide Beide'},
							    {'generated_text': 'Beide Beide Beide Beide Beide Beide Beide Beide'},
							    {'generated_text': ''},
							]
							self.assertEqual(lowercase  ,      lowercase  )

							_lowerCamelCase     :			int    =    generator('This is a test'  ,      do_sample=lowercase  ,      num_return_sequences=2  ,      return_tensors=lowercase  )
							self.assertEqual(
							    lowercase  ,      [
							        {'generated_token_ids': ANY(torch.Tensor  )},
							        {'generated_token_ids': ANY(torch.Tensor  )},
							    ]  ,      )
							_lowerCamelCase     :			Optional[int]    =    generator.model.config.eos_token_id
							_lowerCamelCase     :			Union[str, Any]    =    '<pad>'
							_lowerCamelCase     :			List[str]    =    generator(
							    ['This is a test', 'This is a second test']  ,      do_sample=lowercase  ,      num_return_sequences=2  ,      batch_size=2  ,      return_tensors=lowercase  ,      )
							self.assertEqual(
							    lowercase  ,      [
							        [
							            {'generated_token_ids': ANY(torch.Tensor  )},
							            {'generated_token_ids': ANY(torch.Tensor  )},
							        ],
							        [
							            {'generated_token_ids': ANY(torch.Tensor  )},
							            {'generated_token_ids': ANY(torch.Tensor  )},
							        ],
							    ]  ,      )






			@require_tf
			def 					A_    (  self  ):
							_lowerCamelCase     :			List[str]    =    pipeline('text2text-generation'  ,      model='patrickvonplaten/t5-tiny-random'  ,      framework='tf'  )
							# do_sample=False necessary for reproducibility
							_lowerCamelCase     :			List[str]    =    generator('Something there'  ,      do_sample=lowercase  )
							self.assertEqual(lowercase  ,      [{'generated_text': ''}]  )

"""simple docstring"""





from graphs.minimum_spanning_tree_kruskal import kruskal



def       lowerCamelCase__		(			)							->		List[Any]:


					"""simple docstring"""





					_UpperCamelCase							 =  9
					_UpperCamelCase							 =  [
					    [0, 1, 4],
					    [0, 7, 8],
					    [1, 2, 8],
					    [7, 8, 7],
					    [7, 6, 1],
					    [2, 8, 2],
					    [8, 6, 6],
					    [2, 3, 7],
					    [2, 5, 4],
					    [6, 5, 2],
					    [3, 5, 14],
					    [3, 4, 9],
					    [5, 4, 10],
					    [1, 7, 11],
					]

					_UpperCamelCase							 =  kruskal(__snake_case,     __snake_case	)

					_UpperCamelCase							 =  [
					    [7, 6, 1],
					    [2, 8, 2],
					    [6, 5, 2],
					    [0, 1, 4],
					    [2, 5, 4],
					    [2, 3, 7],
					    [0, 7, 8],
					    [3, 4, 9],
					]

					assert sorted(__snake_case	) == sorted(__snake_case	)






