





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						):
  A_      							=   MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
  A_      							=   TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING






  def 				UpperCAmelCase__						(							self			:					Optional[Any]       ,  _snake_case			:					Optional[Any]       ,  _snake_case			:					Union[str, Any]       ,  _snake_case			:					Optional[int]      )->int:






   '''simple docstring'''

   __lowerCAmelCase					:	str      =       TextaTextGenerationPipeline(model=__UpperCamelCase       ,  tokenizer=__UpperCamelCase      )
   return generator, ["Something to write", "Something else"]






  def 				UpperCAmelCase__						(							self			:					Tuple       ,  _snake_case			:					str       ,  _snake_case			:					List[Any]      )->Union[str, Any]:






   '''simple docstring'''

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

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

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

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






  @require_torch
  def 				UpperCAmelCase__						(							self			:					List[str]      )->List[str]:






   '''simple docstring'''

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

   __lowerCAmelCase					:	int      =       3
   __lowerCAmelCase					:	int      =       generator(
       """Something there"""       ,  num_return_sequences=__UpperCamelCase       ,  num_beams=__UpperCamelCase       ,  )
   __lowerCAmelCase					:	List[str]      =       [
       {"""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(__UpperCamelCase       ,  __UpperCamelCase      )

   __lowerCAmelCase					:	List[str]      =       generator("""This is a test"""       ,  do_sample=__UpperCamelCase       ,  num_return_sequences=2       ,  return_tensors=__UpperCamelCase      )
   self.assertEqual(
       __UpperCamelCase       ,  [
           {"""generated_token_ids""": ANY(torch.Tensor      )},
           {"""generated_token_ids""": ANY(torch.Tensor      )},
       ]       ,  )
   __lowerCAmelCase					:	str      =       generator.model.config.eos_token_id
   __lowerCAmelCase					:	Dict      =       """<pad>"""
   __lowerCAmelCase					:	int      =       generator(
       ["""This is a test""", """This is a second test"""]       ,  do_sample=__UpperCamelCase       ,  num_return_sequences=2       ,  batch_size=2       ,  return_tensors=__UpperCamelCase       ,  )
   self.assertEqual(
       __UpperCamelCase       ,  [
           [
               {"""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 				UpperCAmelCase__						(							self			:					Optional[Any]      )->List[Any]:






   '''simple docstring'''

   __lowerCAmelCase					:	Optional[int]      =       pipeline("""text2text-generation"""       ,  model="""patrickvonplaten/t5-tiny-random"""       ,  framework="""tf"""      )
   # do_sample=False necessary for reproducibility
   __lowerCAmelCase					:	Optional[Any]      =       generator("""Something there"""       ,  do_sample=__UpperCamelCase      )
   self.assertEqual(__UpperCamelCase       ,  [{"""generated_text""": """"""}]      )






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:int   ,   SCREAMING_SNAKE_CASE		:list   )      ->   Dict:
 _enforce_args(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )
 if n == 0:
  return 0
 __lowerCAmelCase					:	Union[str, Any]      =       float("""-inf"""   )
 for i in range(1   ,   n + 1   ):
  __lowerCAmelCase					:	Union[str, Any]      =       max(
      SCREAMING_SNAKE_CASE   ,   prices[i - 1] + naive_cut_rod_recursive(n - i   ,   SCREAMING_SNAKE_CASE   )   )

 return max_revue






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:int   ,   SCREAMING_SNAKE_CASE		:list   )      ->   List[str]:
 _enforce_args(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )
 __lowerCAmelCase					:	List[str]      =       [float("""-inf"""   ) for _ in range(n + 1   )]
 return _top_down_cut_rod_recursive(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:int   ,   SCREAMING_SNAKE_CASE		:list   ,   SCREAMING_SNAKE_CASE		:list   )      ->   List[str]:
 if max_rev[n] >= 0:
  return max_rev[n]
 elif n == 0:
  return 0
 else:
  __lowerCAmelCase					:	str      =       float("""-inf"""   )
  for i in range(1   ,   n + 1   ):
   __lowerCAmelCase					:	List[Any]      =       max(
       SCREAMING_SNAKE_CASE   ,   prices[i - 1] + _top_down_cut_rod_recursive(n - i   ,   SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )   ,   )

  __lowerCAmelCase					:	List[str]      =       max_revenue

 return max_rev[n]






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:int   ,   SCREAMING_SNAKE_CASE		:list   )      ->   Union[str, Any]:
 _enforce_args(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )

 # length(max_rev) = n + 1, to accommodate for the revenue obtainable from a rod of
 # length 0.
 __lowerCAmelCase					:	Optional[int]      =       [float("""-inf"""   ) for _ in range(n + 1   )]
 __lowerCAmelCase					:	List[str]      =       0

 for i in range(1   ,   n + 1   ):
  __lowerCAmelCase					:	Optional[Any]      =       max_rev[i]
  for j in range(1   ,   i + 1   ):
   __lowerCAmelCase					:	Optional[int]      =       max(SCREAMING_SNAKE_CASE   ,   prices[j - 1] + max_rev[i - j]   )

  __lowerCAmelCase					:	Optional[int]      =       max_revenue_i

 return max_rev[n]






def 	_SCREAMING_SNAKE_CASE		(      SCREAMING_SNAKE_CASE		:int   ,   SCREAMING_SNAKE_CASE		:list   )      ->   List[Any]:
 if n < 0:
  __lowerCAmelCase					:	Any      =       F'''n must be greater than or equal to 0. Got n = {n}'''
  raise ValueError(SCREAMING_SNAKE_CASE   )

 if n > len(SCREAMING_SNAKE_CASE   ):
  __lowerCAmelCase					:	Any      =       (
      """Each integral piece of rod must have a corresponding price. """
      F'''Got n = {n} but length of prices = {len(SCREAMING_SNAKE_CASE   )}'''
  )
  raise ValueError(SCREAMING_SNAKE_CASE   )






def 	_SCREAMING_SNAKE_CASE		(      )      ->   Optional[int]:
 __lowerCAmelCase					:	Tuple      =       [6, 10, 12, 15, 20, 23]
 __lowerCAmelCase					:	List[str]      =       len(SCREAMING_SNAKE_CASE   )

 # the best revenue comes from cutting the rod into 6 pieces, each
 # of length 1 resulting in a revenue of 6 * 6 = 36.
 __lowerCAmelCase					:	Union[str, Any]      =       36

 __lowerCAmelCase					:	Optional[int]      =       top_down_cut_rod(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )
 __lowerCAmelCase					:	List[str]      =       bottom_up_cut_rod(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )
 __lowerCAmelCase					:	int      =       naive_cut_rod_recursive(SCREAMING_SNAKE_CASE   ,   SCREAMING_SNAKE_CASE   )

 assert expected_max_revenue == max_rev_top_down
 assert max_rev_top_down == max_rev_bottom_up
 assert max_rev_bottom_up == max_rev_naive


if __name__ == "__main__":
      main()