




"""simple docstring"""




from __future__ import annotations



def     a__ (				snake_case__				)						->					int:

			# preprocessing the first row
			for i in range(1       , len(matrix[0]				)				):
						matrix[0][i] += matrix[0][i - 1]

			# preprocessing the first column
			for i in range(1       , len(snake_case__				)				):
						matrix[i][0] += matrix[i - 1][0]

			# updating the path cost for current position
			for i in range(1       , len(snake_case__				)				):
						for j in range(1       , len(matrix[0]				)				):
									matrix[i][j] += min(matrix[i - 1][j]       , matrix[i][j - 1]				)

			return matrix[-1][-1]


if __name__ == "__main__":
					import doctest

					doctest.testmod()









"""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     __magic_name__							(       unittest.TestCase				):






				'''simple docstring'''
				__UpperCamelCase			  =						MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
				__UpperCamelCase			  =						TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING





				def    _lowerCAmelCase				(       self		, _a		, _a		, _a ):





							"""simple docstring"""
							lowerCamelCase			      =					TextaTextGenerationPipeline(model=_a		, tokenizer=_a )
							return generator, ["Something to write", "Something else"]





				def    _lowerCAmelCase				(       self		, _a		, _a ):





							"""simple docstring"""
							lowerCamelCase			      =					generator("""Something there""" )
							self.assertEqual(_a		, [{"""generated_text""": ANY(_a )}] )
							# These are encoder decoder, they don't just append to incoming string
							self.assertFalse(outputs[0]["""generated_text"""].startswith("""Something there""" ) )

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

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

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





				@require_torch
				def    _lowerCAmelCase				(       self ):





							"""simple docstring"""
							lowerCamelCase			      =					pipeline("""text2text-generation"""		, model="""patrickvonplaten/t5-tiny-random"""		, framework="""pt""" )
							# do_sample=False necessary for reproducibility
							lowerCamelCase			      =					generator("""Something there"""		, do_sample=_a )
							self.assertEqual(_a		, [{"""generated_text""": """"""}] )

							lowerCamelCase			      =					3
							lowerCamelCase			      =					generator(
							    """Something there"""		, num_return_sequences=_a		, num_beams=_a		, )
							lowerCamelCase			      =					[
							    {"""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(_a		, _a )

							lowerCamelCase			      =					generator("""This is a test"""		, do_sample=_a		, num_return_sequences=2		, return_tensors=_a )
							self.assertEqual(
							    _a		, [
							        {"""generated_token_ids""": ANY(torch.Tensor )},
							        {"""generated_token_ids""": ANY(torch.Tensor )},
							    ]		, )
							lowerCamelCase			      =					generator.model.config.eos_token_id
							lowerCamelCase			      =					"""<pad>"""
							lowerCamelCase			      =					generator(
							    ["""This is a test""", """This is a second test"""]		, do_sample=_a		, num_return_sequences=2		, batch_size=2		, return_tensors=_a		, )
							self.assertEqual(
							    _a		, [
							        [
							            {"""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 ):





							"""simple docstring"""
							lowerCamelCase			      =					pipeline("""text2text-generation"""		, model="""patrickvonplaten/t5-tiny-random"""		, framework="""tf""" )
							# do_sample=False necessary for reproducibility
							lowerCamelCase			      =					generator("""Something there"""		, do_sample=_a )
							self.assertEqual(_a		, [{"""generated_text""": """"""}] )



