'''simple docstring'''




import unittest

import pytest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    MBartaaTokenizerFast,
    MBartConfig,
    MBartForConditionalGeneration,
    TranslationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch, slow

from .test_pipelines_common import ANY




@is_pipeline_test
class 	__UpperCAmelCase				(				unittest.TestCase     ):





       '''simple docstring'''



       __lowercase					:					Optional[Any]    =				MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
       __lowercase					:					Any    =				TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

       def 					__A       (      self    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE )		->     int:
           if isinstance(model.config    ,     _SCREAMING_SNAKE_CASE ):
               A_   ,A_     =   list(tokenizer.lang_code_to_id.keys() )[:2]
               A_     =   TranslationPipeline(model=_SCREAMING_SNAKE_CASE    ,     tokenizer=_SCREAMING_SNAKE_CASE    ,     src_lang=_SCREAMING_SNAKE_CASE    ,     tgt_lang=_SCREAMING_SNAKE_CASE )
           else:
               A_     =   TranslationPipeline(model=_SCREAMING_SNAKE_CASE    ,     tokenizer=_SCREAMING_SNAKE_CASE )
           return translator, ["Some string", "Some other text"]

       def 					__A       (      self    ,     _SCREAMING_SNAKE_CASE    ,     _SCREAMING_SNAKE_CASE )		->     str:
           A_     =   translator('''Some string''' )
           self.assertEqual(_SCREAMING_SNAKE_CASE    ,     [{'''translation_text''': ANY(_SCREAMING_SNAKE_CASE )}] )

           A_     =   translator(['''Some string'''] )
           self.assertEqual(_SCREAMING_SNAKE_CASE    ,     [{'''translation_text''': ANY(_SCREAMING_SNAKE_CASE )}] )

           A_     =   translator(['''Some string''', '''other string'''] )
           self.assertEqual(_SCREAMING_SNAKE_CASE    ,     [{'''translation_text''': ANY(_SCREAMING_SNAKE_CASE )}, {'''translation_text''': ANY(_SCREAMING_SNAKE_CASE )}] )

       @require_torch
       def 					__A       (      self )		->     Dict:
           A_     =   pipeline('''translation_en_to_ro'''    ,     model='''patrickvonplaten/t5-tiny-random'''    ,     framework='''pt''' )
           A_     =   translator('''This is a test string'''    ,     max_length=20 )
           self.assertEqual(
               _SCREAMING_SNAKE_CASE    ,     [
                   {
                       '''translation_text''': (
                           '''Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide'''
                           ''' Beide Beide'''
                       )
                   }
               ]    ,     )

       @require_tf
       def 					__A       (      self )		->     Any:
           A_     =   pipeline('''translation_en_to_ro'''    ,     model='''patrickvonplaten/t5-tiny-random'''    ,     framework='''tf''' )
           A_     =   translator('''This is a test string'''    ,     max_length=20 )
           self.assertEqual(
               _SCREAMING_SNAKE_CASE    ,     [
                   {
                       '''translation_text''': (
                           '''Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide'''
                           ''' Beide Beide'''
                       )
                   }
               ]    ,     )

       @require_torch
       def 					__A       (      self )		->     List[str]:
           A_     =   pipeline('''translation_en_to_de'''    ,     model='''patrickvonplaten/t5-tiny-random'''    ,     framework='''pt''' )
           A_     =   translator('''This is a test string'''    ,     max_length=20 )
           self.assertEqual(
               _SCREAMING_SNAKE_CASE    ,     [
                   {
                       '''translation_text''': (
                           '''monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine'''
                           ''' urine urine urine urine urine urine urine'''
                       )
                   }
               ]    ,     )

       @require_tf
       def 					__A       (      self )		->     List[str]:
           A_     =   pipeline('''translation_en_to_de'''    ,     model='''patrickvonplaten/t5-tiny-random'''    ,     framework='''tf''' )
           A_     =   translator('''This is a test string'''    ,     max_length=20 )
           self.assertEqual(
               _SCREAMING_SNAKE_CASE    ,     [
                   {
                       '''translation_text''': (
                           '''monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine'''
                           ''' urine urine urine urine urine urine urine'''
                       )
                   }
               ]    ,     )




class 	__UpperCAmelCase				(				unittest.TestCase     ):





       '''simple docstring'''



       @require_torch
       @slow
       def 					__A       (      self )		->     int:
           # We don't provide a default for this pair
           with self.assertRaises(_SCREAMING_SNAKE_CASE ):
               pipeline(task='''translation_cn_to_ar''' )

           # but we do for this one
           A_     =   pipeline(task='''translation_en_to_de''' )
           self.assertEqual(translator._preprocess_params['''src_lang''']    ,     '''en''' )
           self.assertEqual(translator._preprocess_params['''tgt_lang''']    ,     '''de''' )

       @require_torch
       @slow
       def 					__A       (      self )		->     Optional[int]:
           A_     =   MBartForConditionalGeneration.from_pretrained('''facebook/mbart-large-50-many-to-many-mmt''' )
           A_     =   MBartaaTokenizerFast.from_pretrained('''facebook/mbart-large-50-many-to-many-mmt''' )

           A_     =   pipeline(task='''translation'''    ,     model=_SCREAMING_SNAKE_CASE    ,     tokenizer=_SCREAMING_SNAKE_CASE )
           # Missing src_lang, tgt_lang
           with self.assertRaises(_SCREAMING_SNAKE_CASE ):
               translator('''This is a test''' )

           A_     =   translator('''This is a test'''    ,     src_lang='''en_XX'''    ,     tgt_lang='''ar_AR''' )
           self.assertEqual(_SCREAMING_SNAKE_CASE    ,     [{'''translation_text''': '''هذا إختبار'''}] )

           A_     =   translator('''This is a test'''    ,     src_lang='''en_XX'''    ,     tgt_lang='''hi_IN''' )
           self.assertEqual(_SCREAMING_SNAKE_CASE    ,     [{'''translation_text''': '''यह एक परीक्षण है'''}] )

           # src_lang, tgt_lang can be defined at pipeline call time
           A_     =   pipeline(task='''translation'''    ,     model=_SCREAMING_SNAKE_CASE    ,     tokenizer=_SCREAMING_SNAKE_CASE    ,     src_lang='''en_XX'''    ,     tgt_lang='''ar_AR''' )
           A_     =   translator('''This is a test''' )
           self.assertEqual(_SCREAMING_SNAKE_CASE    ,     [{'''translation_text''': '''هذا إختبار'''}] )

       @require_torch
       def 					__A       (      self )		->     Optional[int]:
           A_     =   '''patrickvonplaten/t5-tiny-random'''
           A_     =   pipeline(task='''translation_cn_to_ar'''    ,     model=_SCREAMING_SNAKE_CASE )
           self.assertEqual(translator._preprocess_params['''src_lang''']    ,     '''cn''' )
           self.assertEqual(translator._preprocess_params['''tgt_lang''']    ,     '''ar''' )

       @require_torch
       def 					__A       (      self )		->     Any:
           A_     =   '''patrickvonplaten/t5-tiny-random'''
           with pytest.warns(_SCREAMING_SNAKE_CASE    ,     match=R'''.*translation_en_to_de.*''' ):
               A_     =   pipeline(task='''translation'''    ,     model=_SCREAMING_SNAKE_CASE )
           self.assertEqual(translator.task    ,     '''translation_en_to_de''' )
           self.assertEqual(translator._preprocess_params['''src_lang''']    ,     '''en''' )
           self.assertEqual(translator._preprocess_params['''tgt_lang''']    ,     '''de''' )

       @require_torch
       def 					__A       (      self )		->     Union[str, Any]:
           with self.assertRaises(_SCREAMING_SNAKE_CASE ):
               pipeline(task='''translation''' )







'''simple docstring'''




__snake_case   :      List[Any]						    =  [chr(i) for i in range(32, 126)]
__snake_case   :      Dict						    =  list(range(len(alphabets)))
__snake_case   :      Dict						    =  list(range(len(alphabets)))
__snake_case   :      Dict						    =  list(range(len(alphabets)))
__snake_case   :      List[Any]						    =  list(reversed(range(len(alphabets))))
__snake_case   :      Optional[int]						    =  []
__snake_case   :      List[Any]						    =  0





def      _UpperCAmelCase    (							)	->      Any:
    global gear_one_pos
    global gear_two_pos
    global gear_three_pos
    A_     =   gear_one[0]
    gear_one.append(_UpperCamelCase   )
    del gear_one[0]
    gear_one_pos += 1
    if gear_one_pos % int(len(_UpperCamelCase   )   ) == 0:
        A_     =   gear_two[0]
        gear_two.append(_UpperCamelCase   )
        del gear_two[0]
        gear_two_pos += 1
        if gear_two_pos % int(len(_UpperCamelCase   )   ) == 0:
            A_     =   gear_three[0]
            gear_three.append(_UpperCamelCase   )
            del gear_three[0]
            gear_three_pos += 1





def      _UpperCAmelCase    (							_UpperCamelCase    :	Tuple   )	->      Any:
    A_     =   alphabets.index(_UpperCamelCase   )
    A_     =   gear_one[target]
    A_     =   gear_two[target]
    A_     =   gear_three[target]
    A_     =   reflector[target]
    A_     =   gear_three.index(_UpperCamelCase   )
    A_     =   gear_two.index(_UpperCamelCase   )
    A_     =   gear_one.index(_UpperCamelCase   )
    code.append(alphabets[target]   )
    rotator()


if __name__ == "__main__":
  __snake_case   :      List[str]						    =  list(input('Type your message:\n'))
  while True:
    try:
      __snake_case   :      Any						    =  int(input('Please set token:(must be only digits)\n'))
      break
    except Exception as error:
      print(error)
  for _ in range(token):
    rotator()
  for j in decode:
    engine(j)
  print('\n' + ''.join(code))
  print(
      F"""\nYour Token is {token} please write it down.\nIf you want to decode """
      'this message again you should input same digits as token!'
  )






