

'''simple docstring'''



import unittest

import numpy as np

from transformers import is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
  import tensorflow as tf

  from transformers.activations_tf import get_tf_activation







@require_tf
class 		lowerCAmelCase__     (   unittest.TestCase      ):



 """simple docstring"""



 def    UpperCAmelCase__	(	self      : str    )						->				Any:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE						     =   tf.constant([-100, -1.0, -0.1, 0, 0.1, 1.0, 100.0]    )
      __SCREAMING_SNAKE_CASE						     =   get_tf_activation("""gelu"""    )
      __SCREAMING_SNAKE_CASE						     =   get_tf_activation("""gelu_10"""    )

      __SCREAMING_SNAKE_CASE						     =   gelu(__snake_case    )
      __SCREAMING_SNAKE_CASE						     =   geluaa(__snake_case    )

      __SCREAMING_SNAKE_CASE						     =   tf.where(y_gelu_aa < 10.0      ,							1.0      ,							0.0    )

      self.assertEqual(tf.math.reduce_max(__snake_case    ).numpy().item()      ,							10.0    )
      self.assertTrue(np.allclose(y_gelu * clipped_mask      ,							y_gelu_aa * clipped_mask    )    )


 def    UpperCAmelCase__	(	self      : Optional[int]    )						->				Union[str, Any]:


      """simple docstring"""




      get_tf_activation("""gelu"""    )
      get_tf_activation("""gelu_10"""    )
      get_tf_activation("""gelu_fast"""    )
      get_tf_activation("""gelu_new"""    )
      get_tf_activation("""glu"""    )
      get_tf_activation("""mish"""    )
      get_tf_activation("""quick_gelu"""    )
      get_tf_activation("""relu"""    )
      get_tf_activation("""sigmoid"""    )
      get_tf_activation("""silu"""    )
      get_tf_activation("""swish"""    )
      get_tf_activation("""tanh"""    )
      with self.assertRaises(__snake_case    ):
           get_tf_activation("""bogus"""    )
      with self.assertRaises(__snake_case    ):
           get_tf_activation(__snake_case    )









'''simple docstring'''



import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel







class 		lowerCAmelCase__     (   utils.Parser      ):



 """simple docstring"""



 lowerCAmelCase__						      =   "halfcheetah-medium-expert-v2"
 lowerCAmelCase__						      =   "config.offline"







def      a__ (						a__   ,					a__   ,					a__   ,					a__   ,					a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     ,    __SCREAMING_SNAKE_CASE						     =   utils.load_model(a__   ,					a__   ,					a__   ,					epoch=a__   ,					device=a__		)
     __SCREAMING_SNAKE_CASE						     =   TrajectoryTransformerModel(gpt.config		)

     trajectory_transformer.tok_emb.load_state_dict(gpt.tok_emb.state_dict()		)
     __SCREAMING_SNAKE_CASE						     =   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				: Dict         =  Parser().parse_args('plan')
  convert_trajectory_transformer_original_pytorch_checkpoint_to_pytorch(
      args.logbase, args.dataset, args.gpt_loadpath, args.gpt_epoch, args.device
  )






