




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


UpperCamelCase__    			=			logging.get_logger(__name__)

UpperCamelCase__    			=			{
    """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 			a__    (					snake_case__      ):
						_a							:    Optional[int]					=    """decision_transformer"""
						_a							:    Optional[int]					=    ["""past_key_values"""]
						_a							:    Dict					=    {
						    """max_position_embeddings""": """n_positions""",
						    """num_attention_heads""": """n_head""",
						    """num_hidden_layers""": """n_layer""",
						}






						def __init__(   self		,			_A=1_7		,			_A=4		,			_A=1_2_8		,			_A=4_0_9_6		,			_A=True		,			_A=1		,			_A=1_0_2_4		,			_A=3		,			_A=1		,			_A=None		,			_A="relu"		,			_A=0.1		,			_A=0.1		,			_A=0.1		,			_A=1E-5		,			_A=0.02		,			_A=True		,			_A=True		,			_A=5_0_2_5_6		,			_A=5_0_2_5_6		,			_A=False		,			_A=False		,			**_A		,			):




											"""simple docstring"""



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

											__lowerCAmelCase				   =       bos_token_id
											__lowerCAmelCase				   =       eos_token_id

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








'''simple docstring'''

import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration

def _SCREAMING_SNAKE_CASE      (A       )       ->       Optional[Any]:






       """simple docstring"""







       lowercase__      			= [
           '''encoder.version''',
           '''decoder.version''',
           '''model.encoder.version''',
           '''model.decoder.version''',
           '''_float_tensor''',
           '''decoder.output_projection.weight''',
       ]
       for k in ignore_keys:
              state_dict.pop(A  , A       )

def _SCREAMING_SNAKE_CASE      (A       )       ->       List[str]:






       """simple docstring"""







       lowercase__			,lowercase__      			= emb.weight.shape
       lowercase__      			= nn.Linear(A  , A  , bias=A       )
       lowercase__      			= emb.weight.data
       return lin_layer

def _SCREAMING_SNAKE_CASE      (A  , A="facebook/mbart-large-en-ro"  , A=False  , A=False       )       ->       Union[str, Any]:






       """simple docstring"""







       lowercase__      			= torch.load(A  , map_location='''cpu'''       )['''model''']
       remove_ignore_keys_(A       )
       lowercase__      			= state_dict['''encoder.embed_tokens.weight'''].shape[0]

       lowercase__      			= MBartConfig.from_pretrained(A  , vocab_size=A       )
       if mbart_aa and finetuned:
              lowercase__      			= '''relu'''

       lowercase__      			= state_dict['''decoder.embed_tokens.weight''']
       lowercase__      			= MBartForConditionalGeneration(A       )
       model.model.load_state_dict(A       )

       if finetuned:
              lowercase__      			= make_linear_from_emb(model.model.shared       )

       return model


if __name__ == "__main__":
 lowerCamelCase							:   Any  						=	argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     'fairseq_path', type=str, help='bart.large, bart.large.cnn or a path to a model.pt on local filesystem.'
 )
 parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
 parser.add_argument(
     '--hf_config',
     default='facebook/mbart-large-cc25',
     type=str,
     help='Which huggingface architecture to use: mbart-large',
 )
 parser.add_argument('--mbart_50', action='store_true', help='whether the model is mMART-50 checkpoint')
 parser.add_argument('--finetuned', action='store_true', help='whether the model is a fine-tuned checkpoint')
 lowerCamelCase							:   Any  						=	parser.parse_args()
 lowerCamelCase							:   List[str]  						=	convert_fairseq_mbart_checkpoint_from_disk(
     args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
 )
 model.save_pretrained(args.pytorch_dump_folder_path)




