






'''simple docstring'''





import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()


def 					UpperCamelCase_(     snake_case							:	int							,    snake_case							:	Optional[int]							,    snake_case							:	str					):

							'''simple docstring'''






							snake_case_      =				MobileBertConfig.from_json_file(snake_case					)
							print(f'Building PyTorch model from configuration: {config}'					)
							snake_case_      =				MobileBertForPreTraining(snake_case					)
							# Load weights from tf checkpoint
							snake_case_      =				load_tf_weights_in_mobilebert(snake_case							,    snake_case							,    snake_case					)
							# Save pytorch-model
							print(f'Save PyTorch model to {pytorch_dump_path}'					)
							torch.save(model.state_dict()							,    snake_case					)


if __name__ == "__main__":
		_SCREAMING_SNAKE_CASE							:       int						=			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(
		    "--mobilebert_config_file",
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        "The config json file corresponding to the pre-trained MobileBERT 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."
		)
		_SCREAMING_SNAKE_CASE							:       str						=			parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)










'''simple docstring'''





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


_SCREAMING_SNAKE_CASE							:       List[Any]						=			logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE							:       Optional[int]						=			{
    "edbeeching/decision-transformer-gym-hopper-medium": (
        "https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/main/config.json"
    ),
    # See all DecisionTransformer models at https://huggingface.co/models?filter=decision_transformer
}




class 			_snake_case		(     lowercase_							):
			lowerCAmelCase_					:				Dict							=   "decision_transformer"
			lowerCAmelCase_					:				List[Any]							=   ["past_key_values"]
			lowerCAmelCase_					:				Tuple							=   {
			    "max_position_embeddings": "n_positions",
			    "num_attention_heads": "n_head",
			    "num_hidden_layers": "n_layer",
			}




			def __init__( self    ,							a__=17    ,							a__=4    ,							a__=128    ,							a__=4_096    ,							a__=True    ,							a__=1    ,							a__=1_024    ,							a__=3    ,							a__=1    ,							a__=None    ,							a__="relu"    ,							a__=0.1    ,							a__=0.1    ,							a__=0.1    ,							a__=1e-5    ,							a__=0.0_2    ,							a__=True    ,							a__=True    ,							a__=50_256    ,							a__=50_256    ,							a__=False    ,							a__=False    ,							**a__    ,							)    ->    Optional[int]:

										'''simple docstring'''




										snake_case_      =				state_dim
										snake_case_      =				act_dim
										snake_case_      =				hidden_size
										snake_case_      =				max_ep_len
										snake_case_      =				action_tanh
										snake_case_      =				vocab_size
										snake_case_      =				n_positions
										snake_case_      =				n_layer
										snake_case_      =				n_head
										snake_case_      =				n_inner
										snake_case_      =				activation_function
										snake_case_      =				resid_pdrop
										snake_case_      =				embd_pdrop
										snake_case_      =				attn_pdrop
										snake_case_      =				layer_norm_epsilon
										snake_case_      =				initializer_range
										snake_case_      =				scale_attn_weights
										snake_case_      =				use_cache
										snake_case_      =				scale_attn_by_inverse_layer_idx
										snake_case_      =				reorder_and_upcast_attn

										snake_case_      =				bos_token_id
										snake_case_      =				eos_token_id

										super().__init__(bos_token_id=a__    ,							eos_token_id=a__    ,							**a__							)




