



import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM

def     __lowercase(	UpperCAmelCase__ ):


      """simple docstring"""



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

def     __lowercase(	UpperCAmelCase__ ):


      """simple docstring"""



      lowerCamelCase			,							lowerCamelCase								=		emb.weight.shape
      lowerCamelCase								=		nn.Linear(UpperCAmelCase__   ,   UpperCAmelCase__   ,   bias=UpperCAmelCase__ )
      lowerCamelCase								=		emb.weight.data
      return lin_layer

def     __lowercase(	UpperCAmelCase__ ):


      """simple docstring"""



      lowerCamelCase								=		torch.load(UpperCAmelCase__   ,   map_location="cpu" )
      lowerCamelCase								=		Namespace(**checkpoint["cfg"]["model"] )
      lowerCamelCase								=		checkpoint["model"]
      remove_ignore_keys_(UpperCAmelCase__ )
      lowerCamelCase								=		state_dict["decoder.embed_tokens.weight"].shape[0]

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

      lowerCamelCase								=		XGLMConfig(
          vocab_size=UpperCAmelCase__   ,   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								=		XGLMForCausalLM(UpperCAmelCase__ )
      lowerCamelCase								=		model.load_state_dict(UpperCAmelCase__   ,   strict=UpperCAmelCase__ )
      print(UpperCAmelCase__ )
      lowerCamelCase								=		make_linear_from_emb(model.model.embed_tokens )

      return model


if __name__ == "__main__":
     a_						:     str	     =		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_						:     str	     =		parser.parse_args()
     a_						:     Any	     =		convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
     model.save_pretrained(args.pytorch_dump_folder_path)




from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


a_						:     int	     =		{'configuration_van': ['VAN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'VanConfig']}


try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     a_						:     Dict	     =		[
         'VAN_PRETRAINED_MODEL_ARCHIVE_LIST',
         'VanForImageClassification',
         'VanModel',
         'VanPreTrainedModel',
     ]

if TYPE_CHECKING:
     from .configuration_van import VAN_PRETRAINED_CONFIG_ARCHIVE_MAP, VanConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_van import (
              VAN_PRETRAINED_MODEL_ARCHIVE_LIST,
              VanForImageClassification,
              VanModel,
              VanPreTrainedModel,
          )

else:
     import sys

     a_						:     Union[str, Any]	     =		_LazyModule(__name__, globals()['__file__'], _import_structure)