

'''simple docstring'''




import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : Optional[Any]    ):
  UpperCAmelCase    				=						[
      'decoder.version',
      'decoder.output_projection.weight',
      '_float_tensor',
      'decoder.embed_positions._float_tensor',
  ]
  for k in ignore_keys:
    state_dict.pop(SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE    )


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : Union[str, Any]    ):
  UpperCAmelCase				,   UpperCAmelCase    				=						emb.weight.shape
  UpperCAmelCase    				=						nn.Linear(SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE							, bias=SCREAMING_SNAKE_CASE    )
  UpperCAmelCase    				=						emb.weight.data
  return lin_layer


def    lowerCamelCase__		(    SCREAMING_SNAKE_CASE   : Tuple    ):
  UpperCAmelCase    				=						torch.load(SCREAMING_SNAKE_CASE							, map_location='cpu'    )
  UpperCAmelCase    				=						Namespace(**checkpoint['cfg']['model']    )
  UpperCAmelCase    				=						checkpoint['model']
  remove_ignore_keys_(SCREAMING_SNAKE_CASE    )
  UpperCAmelCase    				=						state_dict['decoder.embed_tokens.weight'].shape[0]

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

  UpperCAmelCase    				=						XGLMConfig(
      vocab_size=SCREAMING_SNAKE_CASE							, 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    				=						XGLMForCausalLM(SCREAMING_SNAKE_CASE    )
  UpperCAmelCase    				=						model.load_state_dict(SCREAMING_SNAKE_CASE							, strict=SCREAMING_SNAKE_CASE    )
  print(SCREAMING_SNAKE_CASE    )
  UpperCAmelCase    				=						make_linear_from_emb(model.model.embed_tokens    )

  return model


if __name__ == "__main__":
  _a					:			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.')
  _a					:			Optional[int]							=    parser.parse_args()
  _a					:			List[Any]							=    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_tf_available, is_torch_available


_a					:			Dict							=    {
    'configuration_xlm': ['XLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'XLMConfig', 'XLMOnnxConfig'],
    'tokenization_xlm': ['XLMTokenizer'],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _a					:			Optional[Any]							=    [
      'XLM_PRETRAINED_MODEL_ARCHIVE_LIST',
      'XLMForMultipleChoice',
      'XLMForQuestionAnswering',
      'XLMForQuestionAnsweringSimple',
      'XLMForSequenceClassification',
      'XLMForTokenClassification',
      'XLMModel',
      'XLMPreTrainedModel',
      'XLMWithLMHeadModel',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _a					:			int							=    [
      'TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TFXLMForMultipleChoice',
      'TFXLMForQuestionAnsweringSimple',
      'TFXLMForSequenceClassification',
      'TFXLMForTokenClassification',
      'TFXLMMainLayer',
      'TFXLMModel',
      'TFXLMPreTrainedModel',
      'TFXLMWithLMHeadModel',
  ]


if TYPE_CHECKING:
  from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
  from .tokenization_xlm import XLMTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_xlm import (
        XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        XLMForMultipleChoice,
        XLMForQuestionAnswering,
        XLMForQuestionAnsweringSimple,
        XLMForSequenceClassification,
        XLMForTokenClassification,
        XLMModel,
        XLMPreTrainedModel,
        XLMWithLMHeadModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_xlm import (
        TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFXLMForMultipleChoice,
        TFXLMForQuestionAnsweringSimple,
        TFXLMForSequenceClassification,
        TFXLMForTokenClassification,
        TFXLMMainLayer,
        TFXLMModel,
        TFXLMPreTrainedModel,
        TFXLMWithLMHeadModel,
    )

else:
  import sys

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


