


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


__UpperCAmelCase     							=						logging.get_logger(__name__)

__UpperCAmelCase     							=						{
    """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   lowerCamelCase				(SCREAMING_SNAKE_CASE_      ):







		'''simple docstring'''



		_snake_case   :   Any							=  '''trajectory_transformer'''
		_snake_case   :   Optional[Any]							=  ['''past_key_values''']
		_snake_case   :   List[Any]							=  {
		    '''hidden_size''': '''n_embd''',
		    '''num_attention_heads''': '''n_head''',
		    '''num_hidden_layers''': '''n_layer''',
		}



		def __init__(							self    ,		_UpperCamelCase=1_0_0    ,		_UpperCamelCase=5    ,		_UpperCamelCase=1    ,		_UpperCamelCase=1    ,		_UpperCamelCase=2_4_9    ,		_UpperCamelCase=6    ,		_UpperCamelCase=1_7    ,		_UpperCamelCase=2_5    ,		_UpperCamelCase=4    ,		_UpperCamelCase=4    ,		_UpperCamelCase=1_2_8    ,		_UpperCamelCase=0.1    ,		_UpperCamelCase=0.1    ,		_UpperCamelCase=0.1    ,		_UpperCamelCase=0.00_06    ,		_UpperCamelCase=5_1_2    ,		_UpperCamelCase=0.02    ,		_UpperCamelCase=1E-12    ,		_UpperCamelCase=1    ,		_UpperCamelCase=True    ,		_UpperCamelCase=1    ,		_UpperCamelCase=5_0_2_5_6    ,		_UpperCamelCase=5_0_2_5_6    ,		**_UpperCamelCase    ,		)     ->							str:
							UpperCAmelCase_   :				List[Any]						=						vocab_size
							UpperCAmelCase_   :				Any						=						action_weight
							UpperCAmelCase_   :				Optional[int]						=						reward_weight
							UpperCAmelCase_   :				Union[str, Any]						=						value_weight
							UpperCAmelCase_   :				List[str]						=						max_position_embeddings
							UpperCAmelCase_   :				Tuple						=						block_size
							UpperCAmelCase_   :				List[Any]						=						action_dim
							UpperCAmelCase_   :				List[Any]						=						observation_dim
							UpperCAmelCase_   :				Union[str, Any]						=						transition_dim
							UpperCAmelCase_   :				Tuple						=						learning_rate
							UpperCAmelCase_   :				int						=						n_layer
							UpperCAmelCase_   :				Any						=						n_head
							UpperCAmelCase_   :				Tuple						=						n_embd
							UpperCAmelCase_   :				Optional[Any]						=						embd_pdrop
							UpperCAmelCase_   :				Union[str, Any]						=						attn_pdrop
							UpperCAmelCase_   :				Any						=						resid_pdrop
							UpperCAmelCase_   :				Optional[Any]						=						initializer_range
							UpperCAmelCase_   :				List[Any]						=						layer_norm_eps
							UpperCAmelCase_   :				Union[str, Any]						=						kaiming_initializer_range
							UpperCAmelCase_   :				List[Any]						=						use_cache
							super().__init__(pad_token_id=snake_case__    ,		bos_token_id=snake_case__    ,		eos_token_id=snake_case__    ,		**snake_case__       )














import argparse

import torch

from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging


logging.set_verbosity_info()




def 					lowercase__				(					__snake_case   :							Optional[Any]					,	__snake_case   :							Any					,	__snake_case   :							int  ):


					'''simple docstring'''


					UpperCAmelCase_   :				Union[str, Any]						=						RemBertConfig.from_json_file(__snake_case  )
					print('Building PyTorch model from configuration: {}'.format(str(__snake_case  )  )  )
					UpperCAmelCase_   :				Dict						=						RemBertModel(__snake_case  )

					# Load weights from tf checkpoint
					load_tf_weights_in_rembert(__snake_case					,	__snake_case					,	__snake_case  )

					# Save pytorch-model
					print('Save PyTorch model to {}'.format(__snake_case  )  )
					torch.save(model.state_dict()					,	__snake_case  )


if __name__ == "__main__":
		__UpperCAmelCase     							=						argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
		)
		parser.add_argument(
		    '--rembert_config_file',
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        'The config json file corresponding to the pre-trained RemBERT model. \n'
		        'This specifies the model architecture.'
		    ),
		)
		parser.add_argument(
		    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
		)
		__UpperCAmelCase     							=						parser.parse_args()
		convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)






