code: 
  """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    _lowerCAmelCase						(						unittest.TestCase						):
         lowercase_     :     int       =							MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
         lowercase_     :     Dict       =							TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
  
  
  
         def       _a   (      self ,						a_ ,						a_ ,						a_   )							->							Any:
          if isinstance(model.config ,						a_   ):
           _UpperCAmelCase ,      _UpperCAmelCase =						list(tokenizer.lang_code_to_id.keys()   )[:2]
           _UpperCAmelCase =						TranslationPipeline(model=a_ ,						tokenizer=a_ ,						src_lang=a_ ,						tgt_lang=a_   )
          else:
           _UpperCAmelCase =						TranslationPipeline(model=a_ ,						tokenizer=a_   )
          return translator, ["Some string", "Some other text"]
  
  
  
         def       _a   (      self ,						a_ ,						a_   )							->							Optional[int]:
          _UpperCAmelCase =						translator("Some string"   )
          self.assertEqual(a_ ,						[{"translation_text": ANY(a_   )}]   )
  
          _UpperCAmelCase =						translator(["Some string"]   )
          self.assertEqual(a_ ,						[{"translation_text": ANY(a_   )}]   )
  
          _UpperCAmelCase =						translator(["Some string", "other string"]   )
          self.assertEqual(a_ ,						[{"translation_text": ANY(a_   )}, {"translation_text": ANY(a_   )}]   )
  
  
  
         @require_torch
         def       _a   (      self   )							->							Optional[Any]:
          _UpperCAmelCase =						pipeline("translation_en_to_ro" ,						model="patrickvonplaten/t5-tiny-random" ,						framework="pt"   )
          _UpperCAmelCase =						translator("This is a test string" ,						max_length=20   )
          self.assertEqual(
              a_ ,						[
                  {
                      "translation_text": (
                          "Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide"
                          " Beide Beide"
                      )
                  }
              ] ,						)
  
  
  
         @require_tf
         def       _a   (      self   )							->							str:
          _UpperCAmelCase =						pipeline("translation_en_to_ro" ,						model="patrickvonplaten/t5-tiny-random" ,						framework="tf"   )
          _UpperCAmelCase =						translator("This is a test string" ,						max_length=20   )
          self.assertEqual(
              a_ ,						[
                  {
                      "translation_text": (
                          "Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide"
                          " Beide Beide"
                      )
                  }
              ] ,						)
  
  
  
         @require_torch
         def       _a   (      self   )							->							int:
          _UpperCAmelCase =						pipeline("translation_en_to_de" ,						model="patrickvonplaten/t5-tiny-random" ,						framework="pt"   )
          _UpperCAmelCase =						translator("This is a test string" ,						max_length=20   )
          self.assertEqual(
              a_ ,						[
                  {
                      "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   )							->							Union[str, Any]:
          _UpperCAmelCase =						pipeline("translation_en_to_de" ,						model="patrickvonplaten/t5-tiny-random" ,						framework="tf"   )
          _UpperCAmelCase =						translator("This is a test string" ,						max_length=20   )
          self.assertEqual(
              a_ ,						[
                  {
                      "translation_text": (
                          "monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine"
                          " urine urine urine urine urine urine urine"
                      )
                  }
              ] ,						)
  
  
  
  
  
  class    _lowerCAmelCase						(						unittest.TestCase						):
  
  
  
         @require_torch
         @slow
         def       _a   (      self   )							->							List[Any]:
          # We don't provide a default for this pair
          with self.assertRaises(a_   ):
           pipeline(task="translation_cn_to_ar"   )
  
          # but we do for this one
          _UpperCAmelCase =						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   )							->							int:
          _UpperCAmelCase =						MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-50-many-to-many-mmt"   )
          _UpperCAmelCase =						MBartaaTokenizerFast.from_pretrained("facebook/mbart-large-50-many-to-many-mmt"   )
  
          _UpperCAmelCase =						pipeline(task="translation" ,						model=a_ ,						tokenizer=a_   )
          # Missing src_lang, tgt_lang
          with self.assertRaises(a_   ):
           translator("This is a test"   )
  
          _UpperCAmelCase =						translator("This is a test" ,						src_lang="en_XX" ,						tgt_lang="ar_AR"   )
          self.assertEqual(a_ ,						[{"translation_text": "هذا إختبار"}]   )
  
          _UpperCAmelCase =						translator("This is a test" ,						src_lang="en_XX" ,						tgt_lang="hi_IN"   )
          self.assertEqual(a_ ,						[{"translation_text": "यह एक परीक्षण है"}]   )
  
          # src_lang, tgt_lang can be defined at pipeline call time
          _UpperCAmelCase =						pipeline(task="translation" ,						model=a_ ,						tokenizer=a_ ,						src_lang="en_XX" ,						tgt_lang="ar_AR"   )
          _UpperCAmelCase =						translator("This is a test"   )
          self.assertEqual(a_ ,						[{"translation_text": "هذا إختبار"}]   )
  
  
  
         @require_torch
         def       _a   (      self   )							->							Dict:
          _UpperCAmelCase =						"patrickvonplaten/t5-tiny-random"
          _UpperCAmelCase =						pipeline(task="translation_cn_to_ar" ,						model=a_   )
          self.assertEqual(translator._preprocess_params["src_lang"] ,						"cn"   )
          self.assertEqual(translator._preprocess_params["tgt_lang"] ,						"ar"   )
  
  
  
         @require_torch
         def       _a   (      self   )							->							Tuple:
          _UpperCAmelCase =						"patrickvonplaten/t5-tiny-random"
          with pytest.warns(a_ ,						match=r".*translation_en_to_de.*"   ):
           _UpperCAmelCase =						pipeline(task="translation" ,						model=a_   )
          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   )							->							int:
          with self.assertRaises(a_   ):
           pipeline(task="translation"   )
  
  
  
  
  
style_context: 
  """simple docstring"""
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_tokenizers_available
  
  
  __magic_name__		       =				{}
  
  try:
  			if not is_sentencepiece_available():
  						raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  			pass
  else:
  			__magic_name__		       =				['''CpmTokenizer''']
  
  try:
  			if not is_tokenizers_available():
  						raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  			pass
  else:
  			__magic_name__		       =				['''CpmTokenizerFast''']
  
  
  if TYPE_CHECKING:
  			try:
  						if not is_sentencepiece_available():
  									raise OptionalDependencyNotAvailable()
  			except OptionalDependencyNotAvailable:
  						pass
  			else:
  						from .tokenization_cpm import CpmTokenizer
  
  			try:
  						if not is_tokenizers_available():
  									raise OptionalDependencyNotAvailable()
  			except OptionalDependencyNotAvailable:
  						pass
  			else:
  						from .tokenization_cpm_fast import CpmTokenizerFast
  
  else:
  			import sys
  
  			__magic_name__		       =				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
  
  
  
  
  