



'''simple docstring'''






import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM




def     snake_case_					(     lowerCAmelCase_		)->		Tuple:





       '''simple docstring'''


       _UpperCAmelCase  :						Any			     = [
           """decoder.version""",
           """decoder.output_projection.weight""",
           """_float_tensor""",
           """decoder.embed_positions._float_tensor""",
       ]
       for k in ignore_keys:
              state_dict.pop(lowerCAmelCase_       ,   lowerCAmelCase_		)




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





       '''simple docstring'''


       _UpperCAmelCase     ,_UpperCAmelCase  :						Union[str, Any]			     = emb.weight.shape
       _UpperCAmelCase  :						Optional[int]			     = nn.Linear(lowerCAmelCase_       ,   lowerCAmelCase_       ,   bias=lowerCAmelCase_		)
       _UpperCAmelCase  :						int			     = emb.weight.data
       return lin_layer




def     snake_case_					(     lowerCAmelCase_		)->		Tuple:





       '''simple docstring'''


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

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

       _UpperCAmelCase  :						Union[str, Any]			     = XGLMConfig(
           vocab_size=lowerCAmelCase_       ,   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       ,   )

       _UpperCAmelCase  :						Dict			     = XGLMForCausalLM(lowerCAmelCase_		)
       _UpperCAmelCase  :						List[str]			     = model.load_state_dict(lowerCAmelCase_       ,   strict=lowerCAmelCase_		)
       print(lowerCAmelCase_		)
       _UpperCAmelCase  :						List[Any]			     = make_linear_from_emb(model.model.embed_tokens		)

       return model


if __name__ == "__main__":
   A_							: int						 =	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_							: List[str]						 =	parser.parse_args()
   A_							: Dict						 =	convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
   model.save_pretrained(args.pytorch_dump_folder_path)








'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A_							: Optional[int]						 =	{"""configuration_ibert""": ["""IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """IBertConfig""", """IBertOnnxConfig"""]}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A_							: str						 =	[
       """IBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """IBertForMaskedLM""",
       """IBertForMultipleChoice""",
       """IBertForQuestionAnswering""",
       """IBertForSequenceClassification""",
       """IBertForTokenClassification""",
       """IBertModel""",
       """IBertPreTrainedModel""",
   ]

if TYPE_CHECKING:
   from .configuration_ibert import IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, IBertConfig, IBertOnnxConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_ibert import (
          IBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          IBertForMaskedLM,
          IBertForMultipleChoice,
          IBertForQuestionAnswering,
          IBertForSequenceClassification,
          IBertForTokenClassification,
          IBertModel,
          IBertPreTrainedModel,
      )

else:
   import sys

   A_							: str						 =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



