





import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM


def     lowerCamelCase_	(      lowerCAmelCase:	Dict				)->		List[str]:
  _snake_case   :				str														=	[
      'decoder.version',
      'decoder.output_projection.weight',
      '_float_tensor',
      'decoder.embed_positions._float_tensor',
  ]
  for k in ignore_keys:
    state_dict.pop(lowerCAmelCase    ,							lowerCAmelCase				)


def     lowerCamelCase_	(      lowerCAmelCase:	Any				)->		Optional[Any]:
  _snake_case							,					_snake_case   :				Any														=	emb.weight.shape
  _snake_case   :				Union[str, Any]														=	nn.Linear(lowerCAmelCase    ,							lowerCAmelCase    ,							bias=lowerCAmelCase				)
  _snake_case   :				Tuple														=	emb.weight.data
  return lin_layer


def     lowerCamelCase_	(      lowerCAmelCase:	Tuple				)->		Any:
  _snake_case   :				Optional[Any]														=	torch.load(lowerCAmelCase    ,							map_location='cpu'				)
  _snake_case   :				Optional[int]														=	Namespace(**checkpoint['cfg']['model']				)
  _snake_case   :				Optional[Any]														=	checkpoint['model']
  remove_ignore_keys_(lowerCAmelCase				)
  _snake_case   :				str														=	state_dict['decoder.embed_tokens.weight'].shape[0]

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

  _snake_case   :				List[str]														=	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    ,							)

  _snake_case   :				List[Any]														=	XGLMForCausalLM(lowerCAmelCase				)
  _snake_case   :				Optional[int]														=	model.load_state_dict(lowerCAmelCase    ,							strict=lowerCAmelCase				)
  print(lowerCAmelCase				)
  _snake_case   :				Tuple														=	make_linear_from_emb(model.model.embed_tokens				)

  return model


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











import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer


lowerCAmelCase_						   =    {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

lowerCAmelCase_						   =    {
    """vocab_file""": {
        """unc-nlp/lxmert-base-uncased""": """https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/vocab.txt""",
    },
    """tokenizer_file""": {
        """unc-nlp/lxmert-base-uncased""": (
            """https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/tokenizer.json"""
        ),
    },
}

lowerCAmelCase_						   =    {
    """unc-nlp/lxmert-base-uncased""": 512,
}

lowerCAmelCase_						   =    {
    """unc-nlp/lxmert-base-uncased""": {"""do_lower_case""": True},
}



class        _lowerCAmelCase    (			UpperCAmelCase_							):


  '''simple docstring'''
  a_							:       List[Any]      					=VOCAB_FILES_NAMES
  a_							:       Tuple      					=PRETRAINED_VOCAB_FILES_MAP
  a_							:       Optional[Any]      					=PRETRAINED_INIT_CONFIGURATION
  a_							:       Any      					=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  a_							:       Any      					=LxmertTokenizer







  def __init__(      self      :					Any      ,				UpperCamelCase      :					Union[str, Any]=None      ,				UpperCamelCase      :					Dict=None      ,				UpperCamelCase      :					List[str]=True      ,				UpperCamelCase      :					List[str]="[UNK]"      ,				UpperCamelCase      :					List[Any]="[SEP]"      ,				UpperCamelCase      :					List[Any]="[PAD]"      ,				UpperCamelCase      :					Optional[Any]="[CLS]"      ,				UpperCamelCase      :					Optional[int]="[MASK]"      ,				UpperCamelCase      :					Optional[int]=True      ,				UpperCamelCase      :					str=None      ,				**UpperCamelCase      :					List[str]      ,				):


    '''simple docstring'''





    super().__init__(
        UpperCamelCase      ,				tokenizer_file=UpperCamelCase      ,				do_lower_case=UpperCamelCase      ,				unk_token=UpperCamelCase      ,				sep_token=UpperCamelCase      ,				pad_token=UpperCamelCase      ,				cls_token=UpperCamelCase      ,				mask_token=UpperCamelCase      ,				tokenize_chinese_chars=UpperCamelCase      ,				strip_accents=UpperCamelCase      ,				**UpperCamelCase      ,				)

    _snake_case   :				Tuple														=	json.loads(self.backend_tokenizer.normalizer.__getstate__()							)
    if (
        normalizer_state.get('lowercase'      ,				UpperCamelCase							) != do_lower_case
        or normalizer_state.get('strip_accents'      ,				UpperCamelCase							) != strip_accents
        or normalizer_state.get('handle_chinese_chars'      ,				UpperCamelCase							) != tokenize_chinese_chars
    ):
      _snake_case   :				List[Any]														=	getattr(UpperCamelCase      ,				normalizer_state.pop('type'							)							)
      _snake_case   :				Optional[int]														=	do_lower_case
      _snake_case   :				Dict														=	strip_accents
      _snake_case   :				Optional[int]														=	tokenize_chinese_chars
      _snake_case   :				Optional[Any]														=	normalizer_class(**UpperCamelCase							)

    _snake_case   :				int														=	do_lower_case







  def UpperCamelCase_ (      self      :					int      ,				UpperCamelCase      :					List[str]      ,				UpperCamelCase      :					str=None							):


    '''simple docstring'''





    _snake_case   :				List[Any]														=	[self.cls_token_id] + token_ids_a + [self.sep_token_id]

    if token_ids_a:
      output += token_ids_a + [self.sep_token_id]

    return output







  def UpperCamelCase_ (      self      :					List[str]      ,				UpperCamelCase      :					List[int]      ,				UpperCamelCase      :					Optional[List[int]] = None							):


    '''simple docstring'''





    _snake_case   :				Tuple														=	[self.sep_token_id]
    _snake_case   :				List[str]														=	[self.cls_token_id]
    if token_ids_a is None:
      return len(cls + token_ids_a + sep							) * [0]
    return len(cls + token_ids_a + sep							) * [0] + len(token_ids_a + sep							) * [1]







  def UpperCamelCase_ (      self      :					int      ,				UpperCamelCase      :					str      ,				UpperCamelCase      :					Optional[str] = None							):


    '''simple docstring'''





    _snake_case   :				int														=	self._tokenizer.model.save(UpperCamelCase      ,				name=UpperCamelCase							)
    return tuple(UpperCamelCase							)




