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   lowercase__							(				unittest.TestCase							):
     __UpperCAmelCase       					=				MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
     __UpperCAmelCase       					=				TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
  
  
  
  
  
     def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE)     ->     Tuple:
        if isinstance(model.config			,      SCREAMING_SNAKE_CASE):
           _lowerCamelCase      ,						_lowerCamelCase  :				Tuple      =		list(tokenizer.lang_code_to_id.keys())[:2]
           _lowerCamelCase  :				List[Any]      =		TranslationPipeline(model=SCREAMING_SNAKE_CASE			,      tokenizer=SCREAMING_SNAKE_CASE			,      src_lang=SCREAMING_SNAKE_CASE			,      tgt_lang=SCREAMING_SNAKE_CASE)
        else:
           _lowerCamelCase  :				List[str]      =		TranslationPipeline(model=SCREAMING_SNAKE_CASE			,      tokenizer=SCREAMING_SNAKE_CASE)
        return translator, ["Some string", "Some other text"]
  
  
  
  
  
     def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE)     ->     Tuple:
        _lowerCamelCase  :				Any      =		translator("""Some string""")
        self.assertEqual(SCREAMING_SNAKE_CASE			,      [{"""translation_text""": ANY(SCREAMING_SNAKE_CASE)}])
  
        _lowerCamelCase  :				Optional[Any]      =		translator(["""Some string"""])
        self.assertEqual(SCREAMING_SNAKE_CASE			,      [{"""translation_text""": ANY(SCREAMING_SNAKE_CASE)}])
  
        _lowerCamelCase  :				List[Any]      =		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 		UpperCamelCase_     (       self)     ->     Union[str, Any]:
        _lowerCamelCase  :				Optional[Any]      =		pipeline("""translation_en_to_ro"""			,      model="""patrickvonplaten/t5-tiny-random"""			,      framework="""pt""")
        _lowerCamelCase  :				Any      =		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 		UpperCamelCase_     (       self)     ->     List[Any]:
        _lowerCamelCase  :				List[Any]      =		pipeline("""translation_en_to_ro"""			,      model="""patrickvonplaten/t5-tiny-random"""			,      framework="""tf""")
        _lowerCamelCase  :				Optional[int]      =		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 		UpperCamelCase_     (       self)     ->     Any:
        _lowerCamelCase  :				List[str]      =		pipeline("""translation_en_to_de"""			,      model="""patrickvonplaten/t5-tiny-random"""			,      framework="""pt""")
        _lowerCamelCase  :				List[str]      =		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 		UpperCamelCase_     (       self)     ->     Tuple:
        _lowerCamelCase  :				Optional[int]      =		pipeline("""translation_en_to_de"""			,      model="""patrickvonplaten/t5-tiny-random"""			,      framework="""tf""")
        _lowerCamelCase  :				Optional[int]      =		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   lowercase__							(				unittest.TestCase							):
  
  
  
  
  
     @require_torch
     @slow
     def 		UpperCamelCase_     (       self)     ->     Any:
        # 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
        _lowerCamelCase  :				int      =		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 		UpperCamelCase_     (       self)     ->     Union[str, Any]:
        _lowerCamelCase  :				int      =		MBartForConditionalGeneration.from_pretrained("""facebook/mbart-large-50-many-to-many-mmt""")
        _lowerCamelCase  :				Optional[Any]      =		MBartaaTokenizerFast.from_pretrained("""facebook/mbart-large-50-many-to-many-mmt""")
  
        _lowerCamelCase  :				str      =		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""")
  
        _lowerCamelCase  :				List[Any]      =		translator("""This is a test"""			,      src_lang="""en_XX"""			,      tgt_lang="""ar_AR""")
        self.assertEqual(SCREAMING_SNAKE_CASE			,      [{"""translation_text""": """هذا إختبار"""}])
  
        _lowerCamelCase  :				Any      =		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
        _lowerCamelCase  :				List[Any]      =		pipeline(task="""translation"""			,      model=SCREAMING_SNAKE_CASE			,      tokenizer=SCREAMING_SNAKE_CASE			,      src_lang="""en_XX"""			,      tgt_lang="""ar_AR""")
        _lowerCamelCase  :				Union[str, Any]      =		translator("""This is a test""")
        self.assertEqual(SCREAMING_SNAKE_CASE			,      [{"""translation_text""": """هذا إختبار"""}])
  
  
  
  
  
     @require_torch
     def 		UpperCamelCase_     (       self)     ->     Any:
        _lowerCamelCase  :				List[str]      =		"""patrickvonplaten/t5-tiny-random"""
        _lowerCamelCase  :				Optional[Any]      =		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 		UpperCamelCase_     (       self)     ->     Optional[Any]:
        _lowerCamelCase  :				Optional[Any]      =		"""patrickvonplaten/t5-tiny-random"""
        with pytest.warns(SCREAMING_SNAKE_CASE			,      match=r""".*translation_en_to_de.*"""):
           _lowerCamelCase  :				List[Any]      =		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 		UpperCamelCase_     (       self)     ->     Optional[int]:
        with self.assertRaises(SCREAMING_SNAKE_CASE):
           pipeline(task="""translation""")
  
  
style_context: 
  
  
  """simple docstring"""
  from typing import List, Optional, Tuple
  
  from tokenizers import pre_tokenizers
  
  from ...tokenization_utils_fast import PreTrainedTokenizerFast
  from ...utils import logging
  from .tokenization_clip import CLIPTokenizer
  
  
  UpperCAmelCase   =	logging.get_logger(__name__)
  
  UpperCAmelCase   =	{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}
  
  UpperCAmelCase   =	{
      """vocab_file""": {
          """openai/clip-vit-base-patch32""": """https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/vocab.json""",
      },
      """merges_file""": {
          """openai/clip-vit-base-patch32""": """https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/merges.txt""",
      },
      """tokenizer_file""": {
          """openai/clip-vit-base-patch32""": (
              """https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/tokenizer.json"""
          ),
      },
  }
  
  UpperCAmelCase   =	{
      """openai/clip-vit-base-patch32""": 77,
  }
  
  
  
  
  class   lowercase__							(				A_							):
     __UpperCAmelCase       					=				VOCAB_FILES_NAMES
     __UpperCAmelCase       					=				PRETRAINED_VOCAB_FILES_MAP
     __UpperCAmelCase       					=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __UpperCAmelCase       					=				['''input_ids''', '''attention_mask''']
     __UpperCAmelCase       					=				CLIPTokenizer
  
  
  
  
  
     def __init__(       self			,      SCREAMING_SNAKE_CASE=None			,      SCREAMING_SNAKE_CASE=None			,      SCREAMING_SNAKE_CASE=None			,      SCREAMING_SNAKE_CASE="<|endoftext|>"			,      SCREAMING_SNAKE_CASE="<|startoftext|>"			,      SCREAMING_SNAKE_CASE="<|endoftext|>"			,      SCREAMING_SNAKE_CASE="<|endoftext|>"			,      **SCREAMING_SNAKE_CASE			,      )     ->     List[Any]:
        super().__init__(
            SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE			,      tokenizer_file=SCREAMING_SNAKE_CASE			,      unk_token=SCREAMING_SNAKE_CASE			,      bos_token=SCREAMING_SNAKE_CASE			,      eos_token=SCREAMING_SNAKE_CASE			,      pad_token=SCREAMING_SNAKE_CASE			,      **SCREAMING_SNAKE_CASE			,      )
  
        if not isinstance(self.backend_tokenizer.pre_tokenizer			,      pre_tokenizers.Sequence):
           raise ValueError(
               """The `backend_tokenizer` provided does not match the expected format. The CLIP tokenizer has been"""
               """ heavily modified from transformers version 4.17.0. You need to convert the tokenizer you are using"""
               """ to be compatible with this version.The easiest way to do so is"""
               """ `CLIPTokenizerFast.from_pretrained(\"path_to_local_folder_or_hub_repo, from_slow=True)`. If you want"""
               """ to use your existing tokenizer, you will have to revert to a version prior to 4.17.0 of"""
               """ transformers.""")
  
        self._wrap_decode_method_backend_tokenizer()
  
  
  
  
  
     def 		UpperCamelCase_     (       self)     ->     Union[str, Any]:
        _lowerCamelCase  :				str      =		self.backend_tokenizer.decode
  
        def new_decode_method(*SCREAMING_SNAKE_CASE			,      **SCREAMING_SNAKE_CASE):
           _lowerCamelCase  :				Any      =		orig_decode_method(*SCREAMING_SNAKE_CASE			,      **SCREAMING_SNAKE_CASE)
           _lowerCamelCase  :				Optional[int]      =		text.replace(self.backend_tokenizer.model.end_of_word_suffix			,      """ """).strip()
           return text
  
        _lowerCamelCase  :				List[str]      =		new_decode_method
  
  
  
  
  
     def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE = None)     ->     List[int]:
        _lowerCamelCase  :				str      =		[self.bos_token_id]
        _lowerCamelCase  :				Union[str, Any]      =		[self.eos_token_id]
  
        if token_ids_a is None:
           return bos_token + token_ids_a + eos_token
        return bos_token + token_ids_a + eos_token + eos_token + token_ids_a + eos_token
  
  
  
  
  
     def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE = None)     ->     List[int]:
        _lowerCamelCase  :				Any      =		[self.bos_token_id]
        _lowerCamelCase  :				int      =		[self.eos_token_id]
  
        if token_ids_a is None:
           return len(bos_token + token_ids_a + eos_token) * [0]
        return len(bos_token + token_ids_a + eos_token + eos_token + token_ids_a + eos_token) * [0]
  
  
  
  
  
     def 		UpperCamelCase_     (       self			,      SCREAMING_SNAKE_CASE			,      SCREAMING_SNAKE_CASE = None)     ->     Tuple[str]:
        _lowerCamelCase  :				Dict      =		self._tokenizer.model.save(SCREAMING_SNAKE_CASE			,      name=SCREAMING_SNAKE_CASE)
        return tuple(SCREAMING_SNAKE_CASE)
  
  