"""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 				snake_case       ( unittest.TestCase				):




					"""simple docstring"""

					snake_case__				    = MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
					snake_case__				    = TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING







					def 		__lowerCAmelCase						(				self   :	Dict    ,lowerCamelCase__   :	Optional[Any]    ,lowerCamelCase__   :	Any    ,lowerCamelCase__   :	Any			):
									UpperCAmelCase__             =							TextaTextGenerationPipeline(model=lowerCamelCase__    ,tokenizer=lowerCamelCase__			)
									return generator, ["Something to write", "Something else"]







					def 		__lowerCAmelCase						(				self   :	Dict    ,lowerCamelCase__   :	Optional[Any]    ,lowerCamelCase__   :	Optional[Any]			):
									UpperCAmelCase__             =							generator('Something there'			)
									self.assertEqual(lowerCamelCase__    ,[{'generated_text': ANY(lowerCamelCase__			)}]			)
									# These are encoder decoder, they don't just append to incoming string
									self.assertFalse(outputs[0]['generated_text'].startswith('Something there'			)			)

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

									UpperCAmelCase__             =							generator(
									    ['This is great !', 'Something else']    ,num_return_sequences=2    ,batch_size=2    ,do_sample=lowerCamelCase__			)
									self.assertEqual(
									    lowerCamelCase__    ,[
									        [{'generated_text': ANY(lowerCamelCase__			)}, {'generated_text': ANY(lowerCamelCase__			)}],
									        [{'generated_text': ANY(lowerCamelCase__			)}, {'generated_text': ANY(lowerCamelCase__			)}],
									    ]    ,)

									with self.assertRaises(lowerCamelCase__			):
													generator(4			)







					@require_torch
					def 		__lowerCAmelCase						(				self   :	Optional[Any]			):
									UpperCAmelCase__             =							pipeline('text2text-generation'    ,model='patrickvonplaten/t5-tiny-random'    ,framework='pt'			)
									# do_sample=False necessary for reproducibility
									UpperCAmelCase__             =							generator('Something there'    ,do_sample=lowerCamelCase__			)
									self.assertEqual(lowerCamelCase__    ,[{'generated_text': ''}]			)

									UpperCAmelCase__             =							3
									UpperCAmelCase__             =							generator(
									    'Something there'    ,num_return_sequences=lowerCamelCase__    ,num_beams=lowerCamelCase__    ,)
									UpperCAmelCase__             =							[
									    {'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(lowerCamelCase__    ,lowerCamelCase__			)

									UpperCAmelCase__             =							generator('This is a test'    ,do_sample=lowerCamelCase__    ,num_return_sequences=2    ,return_tensors=lowerCamelCase__			)
									self.assertEqual(
									    lowerCamelCase__    ,[
									        {'generated_token_ids': ANY(torch.Tensor			)},
									        {'generated_token_ids': ANY(torch.Tensor			)},
									    ]    ,)
									UpperCAmelCase__             =							generator.model.config.eos_token_id
									UpperCAmelCase__             =							'<pad>'
									UpperCAmelCase__             =							generator(
									    ['This is a test', 'This is a second test']    ,do_sample=lowerCamelCase__    ,num_return_sequences=2    ,batch_size=2    ,return_tensors=lowerCamelCase__    ,)
									self.assertEqual(
									    lowerCamelCase__    ,[
									        [
									            {'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 		__lowerCAmelCase						(				self   :	Any			):
									UpperCAmelCase__             =							pipeline('text2text-generation'    ,model='patrickvonplaten/t5-tiny-random'    ,framework='tf'			)
									# do_sample=False necessary for reproducibility
									UpperCAmelCase__             =							generator('Something there'    ,do_sample=lowerCamelCase__			)
									self.assertEqual(lowerCamelCase__    ,[{'generated_text': ''}]			)





"""simple docstring"""




def lowercase_  (   _snake_case   ,_snake_case						):

							if not (isinstance(_snake_case   ,_snake_case						) and isinstance(_snake_case   ,_snake_case						)):
														raise ValueError("""longest_common_substring() takes two strings for inputs"""						)

							SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   len(_snake_case						)
							SCREAMING_SNAKE_CASE__						:       int           =   len(_snake_case						)

							SCREAMING_SNAKE_CASE__						:       Dict           =   [[0] * (texta_length + 1) for _ in range(texta_length + 1						)]
							SCREAMING_SNAKE_CASE__						:       List[Any]           =   0
							SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   0

							for i in range(1   ,texta_length + 1						):
														for j in range(1   ,texta_length + 1						):
																					if texta[i - 1] == texta[j - 1]:
																												SCREAMING_SNAKE_CASE__						:       int           =   1 + dp[i - 1][j - 1]
																												if dp[i][j] > ans_length:
																																			SCREAMING_SNAKE_CASE__						:       List[Any]           =   i
																																			SCREAMING_SNAKE_CASE__						:       List[str]           =   dp[i][j]

							return texta[ans_index - ans_length : ans_index]


if __name__ == "__main__":
					import doctest

					doctest.testmod()




