


"""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    __a						( unittest.TestCase							):
				lowerCamelCase     :       str			       =MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
				lowerCamelCase     :       Union[str, Any]			       =TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING







				def      lowerCamelCase_     ( self      ,	UpperCAmelCase      ,	UpperCAmelCase      ,	UpperCAmelCase   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			TextaTextGenerationPipeline(model=__SCREAMING_SNAKE_CASE      ,	tokenizer=__SCREAMING_SNAKE_CASE   )
					return generator, ["Something to write", "Something else"]







				def      lowerCamelCase_     ( self      ,	UpperCAmelCase      ,	UpperCAmelCase   ):

					'''simple docstring'''






					lowerCAmelCase_      							=			generator('''Something there'''   )
					self.assertEqual(__SCREAMING_SNAKE_CASE      ,	[{'''generated_text''': ANY(__SCREAMING_SNAKE_CASE   )}]   )
					# 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=__SCREAMING_SNAKE_CASE   )
					self.assertEqual(
					    __SCREAMING_SNAKE_CASE      ,	[
					        [{'''generated_text''': ANY(__SCREAMING_SNAKE_CASE   )}, {'''generated_text''': ANY(__SCREAMING_SNAKE_CASE   )}],
					        [{'''generated_text''': ANY(__SCREAMING_SNAKE_CASE   )}, {'''generated_text''': ANY(__SCREAMING_SNAKE_CASE   )}],
					    ]      ,	)

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

					with self.assertRaises(__SCREAMING_SNAKE_CASE   ):
						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=__SCREAMING_SNAKE_CASE   )
					self.assertEqual(__SCREAMING_SNAKE_CASE      ,	[{'''generated_text''': ''''''}]   )

					lowerCAmelCase_      							=			3
					lowerCAmelCase_      							=			generator(
					    '''Something there'''      ,	num_return_sequences=__SCREAMING_SNAKE_CASE      ,	num_beams=__SCREAMING_SNAKE_CASE      ,	)
					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(__SCREAMING_SNAKE_CASE      ,	__SCREAMING_SNAKE_CASE   )

					lowerCAmelCase_      							=			generator('''This is a test'''      ,	do_sample=__SCREAMING_SNAKE_CASE      ,	num_return_sequences=2      ,	return_tensors=__SCREAMING_SNAKE_CASE   )
					self.assertEqual(
					    __SCREAMING_SNAKE_CASE      ,	[
					        {'''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=__SCREAMING_SNAKE_CASE      ,	num_return_sequences=2      ,	batch_size=2      ,	return_tensors=__SCREAMING_SNAKE_CASE      ,	)
					self.assertEqual(
					    __SCREAMING_SNAKE_CASE      ,	[
					        [
					            {'''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=__SCREAMING_SNAKE_CASE   )
					self.assertEqual(__SCREAMING_SNAKE_CASE      ,	[{'''generated_text''': ''''''}]   )





'''simple docstring'''



from collections import deque







def       _UpperCamelCase			(_lowerCamelCase				:  Union[str, Any]						)-> Optional[int]:




     '''simple docstring'''





     __snake_case            =	len(_lowerCamelCase						)
     __snake_case            =	deque()
     __snake_case            =	[False for _ in range(_lowerCamelCase						)]
     __snake_case            =	[-1 for _ in range(_lowerCamelCase						)]
     __snake_case            =	index_of[:]

     def strong_connect(_lowerCamelCase				:  Any ,						_lowerCamelCase				:  Dict ,						_lowerCamelCase				:  List[str]						):
          __snake_case            =	index  # the number when this node is seen
          __snake_case            =	index  # lowest rank node reachable from here
          index += 1
          stack.append(_lowerCamelCase						)
          __snake_case            =	True

          for w in g[v]:
               if index_of[w] == -1:
                    __snake_case            =	strong_connect(_lowerCamelCase ,						_lowerCamelCase ,						_lowerCamelCase						)
                    __snake_case            =	(
                        lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
                    )
               elif on_stack[w]:
                    __snake_case            =	(
                        lowlink_of[w] if lowlink_of[w] < lowlink_of[v] else lowlink_of[v]
                    )

          if lowlink_of[v] == index_of[v]:
               __snake_case            =	[]
               __snake_case            =	stack.pop()
               __snake_case            =	False
               component.append(_lowerCamelCase						)
               while w != v:
                    __snake_case            =	stack.pop()
                    __snake_case            =	False
                    component.append(_lowerCamelCase						)
               components.append(_lowerCamelCase						)
          return index

     __snake_case            =	[]
     for v in range(_lowerCamelCase						):
          if index_of[v] == -1:
               strong_connect(_lowerCamelCase ,						0 ,						_lowerCamelCase						)

     return components







def       _UpperCamelCase			(_lowerCamelCase				:  Tuple ,						_lowerCamelCase				:  Optional[Any]						)-> Dict:




     '''simple docstring'''





     __snake_case            =	[[] for _ in range(_lowerCamelCase						)]
     for u, v in edges:
          g[u].append(_lowerCamelCase						)
     return g


if __name__ == "__main__":
     # Test
     UpperCAmelCase_							:	List[str]	     =		7
     UpperCAmelCase_							:	int	     =		[0, 0, 1, 2, 3, 3, 4, 4, 6]
     UpperCAmelCase_							:	Dict	     =		[1, 3, 2, 0, 1, 4, 5, 6, 5]
     UpperCAmelCase_							:	List[str]	     =		[(u, v) for u, v in zip(source, target)]
     UpperCAmelCase_							:	Tuple	     =		create_graph(n_vertices, edges)

     assert [[5], [6], [4], [3, 2, 1, 0]] == tarjan(g)






