'''simple docstring'''





import numpy as np

SCREAMING_SNAKE_CASE_:     Dict  =[
    ['a', 'b', 'c', 'd', 'e'],
    ['f', 'g', 'h', 'i', 'k'],
    ['l', 'm', 'n', 'o', 'p'],
    ['q', 'r', 's', 't', 'u'],
    ['v', 'w', 'x', 'y', 'z'],
]




class        __A      :



    def __init__(self    :    Tuple ):
         UpperCAmelCase_           =   np.array(__a )



    def 					_lowercase						(self    :    Union[str, Any]   ,			__a    :    str ):
         UpperCAmelCase_    , UpperCAmelCase_           =   np.where(letter == self.SQUARE )
         UpperCAmelCase_           =   np.concatenate([indexa + 1, indexa + 1] )
         return indexes



    def 					_lowercase						(self    :    str   ,			__a    :    int   ,			__a    :    int ):
         UpperCAmelCase_           =   self.SQUARE[indexa - 1, indexa - 1]
         return letter



    def 					_lowercase						(self    :    Optional[Any]   ,			__a    :    str ):
         UpperCAmelCase_           =   message.lower()
         UpperCAmelCase_           =   message.replace(" "   ,			"" )
         UpperCAmelCase_           =   message.replace("j"   ,			"i" )

         UpperCAmelCase_           =   np.empty((2, len(__a )) )
         for letter_index in range(len(__a ) ):
              UpperCAmelCase_           =   self.letter_to_numbers(message[letter_index] )

              UpperCAmelCase_           =   numbers[0]
              UpperCAmelCase_           =   numbers[1]

         UpperCAmelCase_           =   first_step.reshape(2 * len(__a ) )
         UpperCAmelCase_           =   ""
         for numbers_index in range(len(__a ) ):
              UpperCAmelCase_           =   int(second_step[numbers_index * 2] )
              UpperCAmelCase_           =   int(second_step[(numbers_index * 2) + 1] )
              UpperCAmelCase_           =   self.numbers_to_letter(__a   ,			__a )
              UpperCAmelCase_           =   encoded_message + letter

         return encoded_message






    def 					_lowercase						(self    :    Dict   ,			__a    :    str ):
         UpperCAmelCase_           =   message.lower()
         message.replace(" "   ,			"" )
         UpperCAmelCase_           =   np.empty(2 * len(__a ) )
         for letter_index in range(len(__a ) ):
              UpperCAmelCase_           =   self.letter_to_numbers(message[letter_index] )
              UpperCAmelCase_           =   numbers[0]
              UpperCAmelCase_           =   numbers[1]

         UpperCAmelCase_           =   first_step.reshape((2, len(__a )) )
         UpperCAmelCase_           =   ""
         for numbers_index in range(len(__a ) ):
              UpperCAmelCase_           =   int(second_step[0, numbers_index] )
              UpperCAmelCase_           =   int(second_step[1, numbers_index] )
              UpperCAmelCase_           =   self.numbers_to_letter(__a   ,			__a )
              UpperCAmelCase_           =   decoded_message + letter

         return decoded_message






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        UpperCAmelCase_			( unittest.TestCase     ):


  '''simple docstring'''







  a__           =		MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
  a__           =		TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
  def 							_lowercase							( self			: str			,							UpperCamelCase__			: Optional[int]			,							UpperCamelCase__			: int			,							UpperCamelCase__			: int    )			->	Tuple:







    """simple docstring"""






    __magic_name__				     =       TextaTextGenerationPipeline(model=UpperCamelCase__			,							tokenizer=UpperCamelCase__    )
    return generator, ["Something to write", "Something else"]
  def 							_lowercase							( self			: Any			,							UpperCamelCase__			: Tuple			,							UpperCamelCase__			: List[str]    )			->	Optional[Any]:







    """simple docstring"""






    __magic_name__				     =       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"""    )    )

    __magic_name__				     =       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__    )}],
        ]			,							)

    __magic_name__				     =       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 							_lowercase							( self			: Optional[int]    )			->	Union[str, Any]:







    """simple docstring"""






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

    __magic_name__				     =       3
    __magic_name__				     =       generator(
        """Something there"""			,							num_return_sequences=UpperCamelCase__			,							num_beams=UpperCamelCase__			,							)
    __magic_name__				     =       [
        {"""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__    )

    __magic_name__				     =       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    )},
        ]			,							)
    __magic_name__				     =       generator.model.config.eos_token_id
    __magic_name__				     =       """<pad>"""
    __magic_name__				     =       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 							_lowercase							( self			: int    )			->	str:







    """simple docstring"""






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

