


"""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""")





"""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
 )

