





"""simple docstring"""


from collections import OrderedDict
from typing import Mapping

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


lowerCamelCase__						= logging.get_logger(__name__)

lowerCamelCase__						= {
    "facebook/xmod-base": "https://huggingface.co/facebook/xmod-base/resolve/main/config.json",
    "facebook/xmod-large-prenorm": "https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json",
    "facebook/xmod-base-13-125k": "https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json",
    "facebook/xmod-base-30-125k": "https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json",
    "facebook/xmod-base-30-195k": "https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json",
    "facebook/xmod-base-60-125k": "https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json",
    "facebook/xmod-base-60-265k": "https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json",
    "facebook/xmod-base-75-125k": "https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json",
    "facebook/xmod-base-75-269k": "https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json",
}



class   __SCREAMING_SNAKE_CASE     (    _UpperCamelCase ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__				:Optional[Any]						  =       "xmod"

  def __init__(		self :							Dict       , __a :							str=3_0522       , __a :							Optional[int]=768       , __a :							List[Any]=12       , __a :							Any=12       , __a :							List[str]=3072       , __a :							Union[str, Any]="gelu"       , __a :							Dict=0.1       , __a :							Tuple=0.1       , __a :							List[str]=512       , __a :							Tuple=2       , __a :							List[str]=0.02       , __a :							Optional[Any]=1e-1_2       , __a :							Tuple=1       , __a :							str=0       , __a :							List[str]=2       , __a :							Any="absolute"       , __a :							List[str]=True       , __a :							List[str]=None       , __a :							Optional[Any]=False       , __a :							Optional[int]=2       , __a :							Dict=False       , __a :							Tuple=True       , __a :							Optional[int]=True       , __a :							Optional[int]=("en_XX",)       , __a :							Any=None       , **__a :							Tuple       , )		->					Union[str, Any]:
        super().__init__(pad_token_id=__a       , bos_token_id=__a       , eos_token_id=__a       , **__a						)

        _UpperCamelCase   :						Any    =							vocab_size
        _UpperCamelCase   :						Tuple    =							hidden_size
        _UpperCamelCase   :						Optional[Any]    =							num_hidden_layers
        _UpperCamelCase   :						Any    =							num_attention_heads
        _UpperCamelCase   :						Optional[int]    =							hidden_act
        _UpperCamelCase   :						Any    =							intermediate_size
        _UpperCamelCase   :						Optional[Any]    =							hidden_dropout_prob
        _UpperCamelCase   :						int    =							attention_probs_dropout_prob
        _UpperCamelCase   :						Union[str, Any]    =							max_position_embeddings
        _UpperCamelCase   :						Any    =							type_vocab_size
        _UpperCamelCase   :						Optional[int]    =							initializer_range
        _UpperCamelCase   :						Any    =							layer_norm_eps
        _UpperCamelCase   :						Tuple    =							position_embedding_type
        _UpperCamelCase   :						Any    =							use_cache
        _UpperCamelCase   :						Union[str, Any]    =							classifier_dropout
        _UpperCamelCase   :						Dict    =							pre_norm
        _UpperCamelCase   :						Dict    =							adapter_reduction_factor
        _UpperCamelCase   :						Optional[Any]    =							adapter_layer_norm
        _UpperCamelCase   :						List[Any]    =							adapter_reuse_layer_norm
        _UpperCamelCase   :						List[Any]    =							ln_before_adapter
        _UpperCamelCase   :						Dict    =							list(__a						)
        _UpperCamelCase   :						Union[str, Any]    =							default_language





class   __SCREAMING_SNAKE_CASE     (    _UpperCamelCase ):







  '''simple docstring'''


  @property
  def  __SCREAMING_SNAKE_CASE					(		self :							Optional[Any]						)		->					Mapping[str, Mapping[int, str]]:
        if self.task == "multiple-choice":
              _UpperCamelCase   :						List[str]    =							{0: "batch", 1: "choice", 2: "sequence"}
        else:
              _UpperCamelCase   :						List[Any]    =							{0: "batch", 1: "sequence"}
        return OrderedDict(
            [
                ("input_ids", dynamic_axis),
                ("attention_mask", dynamic_axis),
            ]						)







"""simple docstring"""


import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration




def        lowercase__     (       lowercase_      )       ->		Tuple:




      """simple docstring"""

      _UpperCamelCase   :						Optional[int]    =							[
          "encoder.version",
          "decoder.version",
          "model.encoder.version",
          "model.decoder.version",
          "_float_tensor",
          "decoder.output_projection.weight",
      ]
      for k in ignore_keys:
            state_dict.pop(lowercase_		,lowercase_      )




def        lowercase__     (       lowercase_      )       ->		List[Any]:




      """simple docstring"""

      _UpperCamelCase,	_UpperCamelCase   :						Union[str, Any]    =							emb.weight.shape
      _UpperCamelCase   :						str    =							nn.Linear(lowercase_		,lowercase_		,bias=lowercase_      )
      _UpperCamelCase   :						List[Any]    =							emb.weight.data
      return lin_layer




def        lowercase__     (       lowercase_		,lowercase_="facebook/mbart-large-en-ro"		,lowercase_=False		,lowercase_=False      )       ->		List[str]:




      """simple docstring"""

      _UpperCamelCase   :						int    =							torch.load(lowercase_		,map_location="cpu"      )["model"]
      remove_ignore_keys_(lowercase_      )
      _UpperCamelCase   :						str    =							state_dict["encoder.embed_tokens.weight"].shape[0]

      _UpperCamelCase   :						int    =							MBartConfig.from_pretrained(lowercase_		,vocab_size=lowercase_      )
      if mbart_aa and finetuned:
            _UpperCamelCase   :						Union[str, Any]    =							"relu"

      _UpperCamelCase   :						Optional[int]    =							state_dict["decoder.embed_tokens.weight"]
      _UpperCamelCase   :						List[str]    =							MBartForConditionalGeneration(lowercase_      )
      model.model.load_state_dict(lowercase_      )

      if finetuned:
            _UpperCamelCase   :						Optional[Any]    =							make_linear_from_emb(model.model.shared      )

      return model


if __name__ == "__main__":
  lowerCamelCase__						= argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "fairseq_path", type=str, help="bart.large, bart.large.cnn or a 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.")
  parser.add_argument(
      "--hf_config",
      default="facebook/mbart-large-cc25",
      type=str,
      help="Which huggingface architecture to use: mbart-large",
  )
  parser.add_argument("--mbart_50", action="store_true", help="whether the model is mMART-50 checkpoint")
  parser.add_argument("--finetuned", action="store_true", help="whether the model is a fine-tuned checkpoint")
  lowerCamelCase__						= parser.parse_args()
  lowerCamelCase__						= convert_fairseq_mbart_checkpoint_from_disk(
      args.fairseq_path, hf_config_path=args.hf_config, finetuned=args.finetuned, mbart_aa=args.mbart_aa
  )
  model.save_pretrained(args.pytorch_dump_folder_path)
