





import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM





def 					lowerCAmelCase_			(	__a		)					->		Optional[int]:







  """simple docstring"""





  lowerCamelCase__:				Union[str, Any]        =[
      "decoder.version",
      "decoder.output_projection.weight",
      "_float_tensor",
      "decoder.embed_positions._float_tensor",
  ]
  for k in ignore_keys:
    state_dict.pop(__a     ,   __a		)





def 					lowerCAmelCase_			(	__a		)					->		Any:







  """simple docstring"""





  lowerCamelCase__  , lowerCamelCase__:				Optional[Any]        =emb.weight.shape
  lowerCamelCase__:				Dict        =nn.Linear(__a     ,   __a     ,   bias=__a		)
  lowerCamelCase__:				Optional[int]        =emb.weight.data
  return lin_layer





def 					lowerCAmelCase_			(	__a		)					->		Any:







  """simple docstring"""





  lowerCamelCase__:				int        =torch.load(__a     ,   map_location="cpu"		)
  lowerCamelCase__:				str        =Namespace(**checkpoint["cfg"]["model"]		)
  lowerCamelCase__:				Any        =checkpoint["model"]
  remove_ignore_keys_(__a		)
  lowerCamelCase__:				Dict        =state_dict["decoder.embed_tokens.weight"].shape[0]

  lowerCamelCase__:				Union[str, Any]        ={key.replace("decoder"     ,   "model"		): val for key, val in state_dict.items()}

  lowerCamelCase__:				List[str]        =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     ,   )

  lowerCamelCase__:				Optional[Any]        =XGLMForCausalLM(__a		)
  lowerCamelCase__:				str        =model.load_state_dict(__a     ,   strict=__a		)
  print(__a		)
  lowerCamelCase__:				int        =make_linear_from_emb(model.model.embed_tokens		)

  return model


if __name__ == "__main__":
     __A     =    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.")
     __A     =    parser.parse_args()
     __A     =    convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
     model.save_pretrained(args.pytorch_dump_folder_path)








from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__A     =    logging.get_logger(__name__)

__A     =    {
    "distilbert-base-uncased": "https://huggingface.co/distilbert-base-uncased/resolve/main/config.json",
    "distilbert-base-uncased-distilled-squad": (
        "https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/config.json"
    ),
    "distilbert-base-cased": "https://huggingface.co/distilbert-base-cased/resolve/main/config.json",
    "distilbert-base-cased-distilled-squad": (
        "https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/config.json"
    ),
    "distilbert-base-german-cased": "https://huggingface.co/distilbert-base-german-cased/resolve/main/config.json",
    "distilbert-base-multilingual-cased": (
        "https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/config.json"
    ),
    "distilbert-base-uncased-finetuned-sst-2-english": (
        "https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english/resolve/main/config.json"
    ),
}



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''
 lowercase_		=      "distilbert"
 lowercase_		=      {
     "hidden_size": "dim",
     "num_attention_heads": "n_heads",
     "num_hidden_layers": "n_layers",
 }

 def __init__(self    :       Any	,			UpperCAmelCase_    :       str=30_522	,			UpperCAmelCase_    :       Union[str, Any]=512	,			UpperCAmelCase_    :       int=False	,			UpperCAmelCase_    :       Optional[Any]=6	,			UpperCAmelCase_    :       Optional[Any]=12	,			UpperCAmelCase_    :       Any=768	,			UpperCAmelCase_    :       List[Any]=4 * 768	,			UpperCAmelCase_    :       Tuple=0.1	,			UpperCAmelCase_    :       List[Any]=0.1	,			UpperCAmelCase_    :       Any="gelu"	,			UpperCAmelCase_    :       int=0.02	,			UpperCAmelCase_    :       Optional[Any]=0.1	,			UpperCAmelCase_    :       Optional[int]=0.2	,			UpperCAmelCase_    :       int=0	,			**UpperCAmelCase_    :       List[Any]	,			)	->Any:



   '''simple docstring'''



   lowerCamelCase__:				int        =vocab_size
   lowerCamelCase__:				Any        =max_position_embeddings
   lowerCamelCase__:				Optional[int]        =sinusoidal_pos_embds
   lowerCamelCase__:				str        =n_layers
   lowerCamelCase__:				str        =n_heads
   lowerCamelCase__:				str        =dim
   lowerCamelCase__:				Optional[Any]        =hidden_dim
   lowerCamelCase__:				Dict        =dropout
   lowerCamelCase__:				Optional[Any]        =attention_dropout
   lowerCamelCase__:				int        =activation
   lowerCamelCase__:				Dict        =initializer_range
   lowerCamelCase__:				Optional[Any]        =qa_dropout
   lowerCamelCase__:				int        =seq_classif_dropout
   super().__init__(**UpperCAmelCase_	,			pad_token_id=UpperCAmelCase_)



class        _SCREAMING_SNAKE_CASE						(     __SCREAMING_SNAKE_CASE   ):







 '''simple docstring'''

 @property
 def 							SCREAMING_SNAKE_CASE_ (self    :       List[str])	->Mapping[str, Mapping[int, str]]:



   '''simple docstring'''



   if self.task == "multiple-choice":
     lowerCamelCase__:				Dict        ={0: "batch", 1: "choice", 2: "sequence"}
   else:
     lowerCamelCase__:				Optional[int]        ={0: "batch", 1: "sequence"}
   return OrderedDict(
       [
           ("input_ids", dynamic_axis),
           ("attention_mask", dynamic_axis),
       ])

