




from ....configuration_utils import PretrainedConfig
from ....utils import logging


snake_case_ : Dict    		=       logging.get_logger(__name__)

snake_case_ : List[Any]    		=       {
    "CarlCochet/trajectory-transformer-halfcheetah-medium-v2": (
        "https://huggingface.co/CarlCochet/trajectory-transformer-halfcheetah-medium-v2/resolve/main/config.json"
    ),
    # See all TrajectoryTransformer models at https://huggingface.co/models?filter=trajectory_transformer
}







class __snake_case			(			lowerCamelCase_		):
   UpperCAmelCase__  :    str          =  """trajectory_transformer"""
   UpperCAmelCase__  :    Tuple          =  ["""past_key_values"""]
   UpperCAmelCase__  :    Optional[Any]          =  {
       """hidden_size""": """n_embd""",
       """num_attention_heads""": """n_head""",
       """num_hidden_layers""": """n_layer""",
   }







   def __init__(      self		:						Optional[int]					,						_snake_case		:						Any=100					,						_snake_case		:						Union[str, Any]=5					,						_snake_case		:						str=1					,						_snake_case		:						Dict=1					,						_snake_case		:						Optional[int]=249					,						_snake_case		:						Optional[Any]=6					,						_snake_case		:						Union[str, Any]=17					,						_snake_case		:						Union[str, Any]=25					,						_snake_case		:						Any=4					,						_snake_case		:						Dict=4					,						_snake_case		:						int=128					,						_snake_case		:						Any=0.1					,						_snake_case		:						Union[str, Any]=0.1					,						_snake_case		:						Any=0.1					,						_snake_case		:						List[Any]=0.0_0_0_6					,						_snake_case		:						int=512					,						_snake_case		:						Any=0.0_2					,						_snake_case		:						Union[str, Any]=1e-12					,						_snake_case		:						int=1					,						_snake_case		:						Optional[Any]=True					,						_snake_case		:						Tuple=1					,						_snake_case		:						Union[str, Any]=50256					,						_snake_case		:						Any=50256					,						**_snake_case		:						Tuple					,						):

        """simple docstring"""


        UpperCAmelCase_          =							vocab_size
        UpperCAmelCase_          =							action_weight
        UpperCAmelCase_          =							reward_weight
        UpperCAmelCase_          =							value_weight
        UpperCAmelCase_          =							max_position_embeddings
        UpperCAmelCase_          =							block_size
        UpperCAmelCase_          =							action_dim
        UpperCAmelCase_          =							observation_dim
        UpperCAmelCase_          =							transition_dim
        UpperCAmelCase_          =							learning_rate
        UpperCAmelCase_          =							n_layer
        UpperCAmelCase_          =							n_head
        UpperCAmelCase_          =							n_embd
        UpperCAmelCase_          =							embd_pdrop
        UpperCAmelCase_          =							attn_pdrop
        UpperCAmelCase_          =							resid_pdrop
        UpperCAmelCase_          =							initializer_range
        UpperCAmelCase_          =							layer_norm_eps
        UpperCAmelCase_          =							kaiming_initializer_range
        UpperCAmelCase_          =							use_cache
        super().__init__(pad_token_id=lowerCAmelCase__					,						bos_token_id=lowerCAmelCase__					,						eos_token_id=lowerCAmelCase__					,						**lowerCAmelCase__)










"""simple docstring"""




import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel


def     lowercase			(SCREAMING_SNAKE_CASE_		:				BertModel ,			SCREAMING_SNAKE_CASE_		:				str ,			SCREAMING_SNAKE_CASE_		:				str  )   ->						Dict:
						SCREAMING_SNAKE_CASE													=					('dense.weight', 'attention.self.query', 'attention.self.key', 'attention.self.value')

						SCREAMING_SNAKE_CASE													=					(
						    ('layer.', 'layer_'),
						    ('word_embeddings.weight', 'word_embeddings'),
						    ('position_embeddings.weight', 'position_embeddings'),
						    ('token_type_embeddings.weight', 'token_type_embeddings'),
						    ('.', '/'),
						    ('LayerNorm/weight', 'LayerNorm/gamma'),
						    ('LayerNorm/bias', 'LayerNorm/beta'),
						    ('weight', 'kernel'),
						)

						if not os.path.isdir(SCREAMING_SNAKE_CASE_  ):
												os.makedirs(SCREAMING_SNAKE_CASE_  )

						SCREAMING_SNAKE_CASE													=					model.state_dict()

						def to_tf_var_name(SCREAMING_SNAKE_CASE_		:				str  ):
												for patt, repl in iter(SCREAMING_SNAKE_CASE_  ):
																		SCREAMING_SNAKE_CASE													=					name.replace(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )
												return F'bert/{name}'

						def create_tf_var(SCREAMING_SNAKE_CASE_		:				np.ndarray ,			SCREAMING_SNAKE_CASE_		:				str ,			SCREAMING_SNAKE_CASE_		:				tf.Session  ):
												SCREAMING_SNAKE_CASE													=					tf.dtypes.as_dtype(tensor.dtype  )
												SCREAMING_SNAKE_CASE													=					tf.get_variable(dtype=SCREAMING_SNAKE_CASE_ ,			shape=tensor.shape ,			name=SCREAMING_SNAKE_CASE_ ,			initializer=tf.zeros_initializer()  )
												session.run(tf.variables_initializer([tf_var]  )  )
												session.run(SCREAMING_SNAKE_CASE_  )
												return tf_var

						tf.reset_default_graph()
						with tf.Session() as session:
												for var_name in state_dict:
																		SCREAMING_SNAKE_CASE													=					to_tf_var_name(SCREAMING_SNAKE_CASE_  )
																		SCREAMING_SNAKE_CASE													=					state_dict[var_name].numpy()
																		if any(x in var_name for x in tensors_to_transpose  ):
																								SCREAMING_SNAKE_CASE													=					torch_tensor.T
																		SCREAMING_SNAKE_CASE													=					create_tf_var(tensor=SCREAMING_SNAKE_CASE_ ,			name=SCREAMING_SNAKE_CASE_ ,			session=SCREAMING_SNAKE_CASE_  )
																		tf.keras.backend.set_value(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )
																		SCREAMING_SNAKE_CASE													=					session.run(SCREAMING_SNAKE_CASE_  )
																		print(F'Successfully created {tf_name}: {np.allclose(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )}'  )

												SCREAMING_SNAKE_CASE													=					tf.train.Saver(tf.trainable_variables()  )
												saver.save(SCREAMING_SNAKE_CASE_ ,			os.path.join(SCREAMING_SNAKE_CASE_ ,			model_name.replace('-' ,			'_'  ) + '.ckpt'  )  )


def     lowercase			(SCREAMING_SNAKE_CASE_		:				Union[str, Any]=None  )   ->						Any:
						SCREAMING_SNAKE_CASE													=					argparse.ArgumentParser()
						parser.add_argument('--model_name' ,			type=SCREAMING_SNAKE_CASE_ ,			required=SCREAMING_SNAKE_CASE_ ,			help='model name e.g. bert-base-uncased'  )
						parser.add_argument(
						    '--cache_dir' ,			type=SCREAMING_SNAKE_CASE_ ,			default=SCREAMING_SNAKE_CASE_ ,			required=SCREAMING_SNAKE_CASE_ ,			help='Directory containing pytorch model'  )
						parser.add_argument('--pytorch_model_path' ,			type=SCREAMING_SNAKE_CASE_ ,			required=SCREAMING_SNAKE_CASE_ ,			help='/path/to/<pytorch-model-name>.bin'  )
						parser.add_argument('--tf_cache_dir' ,			type=SCREAMING_SNAKE_CASE_ ,			required=SCREAMING_SNAKE_CASE_ ,			help='Directory in which to save tensorflow model'  )
						SCREAMING_SNAKE_CASE													=					parser.parse_args(SCREAMING_SNAKE_CASE_  )

						SCREAMING_SNAKE_CASE													=					BertModel.from_pretrained(
						    pretrained_model_name_or_path=args.model_name ,			state_dict=torch.load(args.pytorch_model_path  ) ,			cache_dir=args.cache_dir ,			)

						convert_pytorch_checkpoint_to_tf(model=SCREAMING_SNAKE_CASE_ ,			ckpt_dir=args.tf_cache_dir ,			model_name=args.model_name  )


if __name__ == "__main__":
	main()


