

import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM
def     UpperCAmelCase_       (						_A    ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   [
         '''decoder.version''',
         '''decoder.output_projection.weight''',
         '''_float_tensor''',
         '''decoder.embed_positions._float_tensor''',
     ]
     for k in ignore_keys:
          state_dict.pop(_A				,		_A    )
def     UpperCAmelCase_       (						_A    ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__   =   emb.weight.shape
     SCREAMING_SNAKE_CASE__   =   nn.Linear(_A				,		_A				,		bias=_A    )
     SCREAMING_SNAKE_CASE__   =   emb.weight.data
     return lin_layer
def     UpperCAmelCase_       (						_A    ):





     '''simple docstring'''




     SCREAMING_SNAKE_CASE__   =   torch.load(_A				,		map_location='''cpu'''    )
     SCREAMING_SNAKE_CASE__   =   Namespace(**checkpoint['''cfg''']['''model''']    )
     SCREAMING_SNAKE_CASE__   =   checkpoint['''model''']
     remove_ignore_keys_(_A    )
     SCREAMING_SNAKE_CASE__   =   state_dict['''decoder.embed_tokens.weight'''].shape[0]

     SCREAMING_SNAKE_CASE__   =   {key.replace('''decoder'''				,		'''model'''    ): val for key, val in state_dict.items()}

     SCREAMING_SNAKE_CASE__   =   XGLMConfig(
         vocab_size=_A				,		max_position_embeddings=args.max_target_positions				,		num_layers=args.decoder_layers				,		attention_heads=args.decoder_attention_heads				,		ffn_dim=args.decoder_ffn_embed_dim				,		d_model=args.decoder_embed_dim				,		layerdrop=args.decoder_layerdrop				,		dropout=args.dropout				,		attention_dropout=args.attention_dropout				,		activation_dropout=args.activation_dropout				,		activation_function='''gelu'''				,		scale_embedding=not args.no_scale_embedding				,		tie_word_embeddings=args.share_decoder_input_output_embed				,		)

     SCREAMING_SNAKE_CASE__   =   XGLMForCausalLM(_A    )
     SCREAMING_SNAKE_CASE__   =   model.load_state_dict(_A				,		strict=_A    )
     print(_A    )
     SCREAMING_SNAKE_CASE__   =   make_linear_from_emb(model.model.embed_tokens    )

     return model


if __name__ == "__main__":
       _SCREAMING_SNAKE_CASE					:  Optional[Any]             =      argparse.ArgumentParser()
       # Required parameters
       parser.add_argument('''fairseq_path''', type=str, help='''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.''')
       _SCREAMING_SNAKE_CASE					:  int             =      parser.parse_args()
       _SCREAMING_SNAKE_CASE					:  str             =      convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
       model.save_pretrained(args.pytorch_dump_folder_path)





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


_SCREAMING_SNAKE_CASE					:  Any             =      logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE					:  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 	UpperCAmelCase__		(	A__	):






  """simple docstring"""


  a									= "decision_transformer"
  a									= ["past_key_values"]
  a									= {
      "max_position_embeddings": "n_positions",
      "num_attention_heads": "n_head",
      "num_hidden_layers": "n_layer",
  }






  def __init__(							self						:		Tuple     ,       __lowerCamelCase						:		Any=17     ,       __lowerCamelCase						:		Any=4     ,       __lowerCamelCase						:		List[Any]=128     ,       __lowerCamelCase						:		Union[str, Any]=4096     ,       __lowerCamelCase						:		Optional[Any]=True     ,       __lowerCamelCase						:		Any=1     ,       __lowerCamelCase						:		List[Any]=1024     ,       __lowerCamelCase						:		Any=3     ,       __lowerCamelCase						:		Dict=1     ,       __lowerCamelCase						:		List[Any]=None     ,       __lowerCamelCase						:		str="relu"     ,       __lowerCamelCase						:		Dict=0.1     ,       __lowerCamelCase						:		int=0.1     ,       __lowerCamelCase						:		List[str]=0.1     ,       __lowerCamelCase						:		str=1e-5     ,       __lowerCamelCase						:		Optional[Any]=0.02     ,       __lowerCamelCase						:		int=True     ,       __lowerCamelCase						:		Tuple=True     ,       __lowerCamelCase						:		Optional[int]=5_0256     ,       __lowerCamelCase						:		Tuple=5_0256     ,       __lowerCamelCase						:		int=False     ,       __lowerCamelCase						:		Optional[int]=False     ,       **__lowerCamelCase						:		Tuple     ,       )		->							Optional[Any]:
       SCREAMING_SNAKE_CASE__   =   state_dim
       SCREAMING_SNAKE_CASE__   =   act_dim
       SCREAMING_SNAKE_CASE__   =   hidden_size
       SCREAMING_SNAKE_CASE__   =   max_ep_len
       SCREAMING_SNAKE_CASE__   =   action_tanh
       SCREAMING_SNAKE_CASE__   =   vocab_size
       SCREAMING_SNAKE_CASE__   =   n_positions
       SCREAMING_SNAKE_CASE__   =   n_layer
       SCREAMING_SNAKE_CASE__   =   n_head
       SCREAMING_SNAKE_CASE__   =   n_inner
       SCREAMING_SNAKE_CASE__   =   activation_function
       SCREAMING_SNAKE_CASE__   =   resid_pdrop
       SCREAMING_SNAKE_CASE__   =   embd_pdrop
       SCREAMING_SNAKE_CASE__   =   attn_pdrop
       SCREAMING_SNAKE_CASE__   =   layer_norm_epsilon
       SCREAMING_SNAKE_CASE__   =   initializer_range
       SCREAMING_SNAKE_CASE__   =   scale_attn_weights
       SCREAMING_SNAKE_CASE__   =   use_cache
       SCREAMING_SNAKE_CASE__   =   scale_attn_by_inverse_layer_idx
       SCREAMING_SNAKE_CASE__   =   reorder_and_upcast_attn

       SCREAMING_SNAKE_CASE__   =   bos_token_id
       SCREAMING_SNAKE_CASE__   =   eos_token_id

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


