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  :				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"""
  import torch
  import trajectory.utils as utils
  
  from transformers import TrajectoryTransformerModel
  
  
  
  
  class   lowercase__							(				utils.Parser							):
     __UpperCAmelCase       					=				"halfcheetah-medium-expert-v2"
     __UpperCAmelCase       					=				"config.offline"
  
  
  
  
  
  
  
  def 							_snake_case			(      __snake_case       :			Any	,   __snake_case       :			Optional[Any]	,   __snake_case       :			Optional[int]	,   __snake_case       :			List[str]	,   __snake_case       :			Union[str, Any]      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _lowerCamelCase      ,						_lowerCamelCase  :				int      =		utils.load_model(__snake_case	,   __snake_case	,   __snake_case	,   epoch=__snake_case	,   device=__snake_case      )
     _lowerCamelCase  :				List[str]      =		TrajectoryTransformerModel(gpt.config      )
  
     trajectory_transformer.tok_emb.load_state_dict(gpt.tok_emb.state_dict()      )
     _lowerCamelCase  :				str      =		gpt.pos_emb
     trajectory_transformer.drop.load_state_dict(gpt.drop.state_dict()      )
     trajectory_transformer.ln_f.load_state_dict(gpt.ln_f.state_dict()      )
     trajectory_transformer.head.load_state_dict(gpt.head.state_dict()      )
  
     for i, block in enumerate(gpt.blocks      ):
        trajectory_transformer.blocks[i].lna.load_state_dict(gpt.blocks[i].lna.state_dict()      )
        trajectory_transformer.blocks[i].lna.load_state_dict(gpt.blocks[i].lna.state_dict()      )
        trajectory_transformer.blocks[i].attn.load_state_dict(gpt.blocks[i].attn.state_dict()      )
  
        trajectory_transformer.blocks[i].la.load_state_dict(gpt.blocks[i].mlp[0].state_dict()      )
        trajectory_transformer.blocks[i].act.load_state_dict(gpt.blocks[i].mlp[1].state_dict()      )
        trajectory_transformer.blocks[i].la.load_state_dict(gpt.blocks[i].mlp[2].state_dict()      )
        trajectory_transformer.blocks[i].drop.load_state_dict(gpt.blocks[i].mlp[3].state_dict()      )
  
     torch.save(trajectory_transformer.state_dict()	,   """pytorch_model.bin"""      )
  
  
  if __name__ == "__main__":
   UpperCAmelCase   =	Parser().parse_args("""plan""")
   convert_trajectory_transformer_original_pytorch_checkpoint_to_pytorch(
       args.logbase, args.dataset, args.gpt_loadpath, args.gpt_epoch, args.device
   )
  
  