





import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration






def 				_UpperCAmelCase			(    A     ):




   '''simple docstring'''

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






def 				_UpperCAmelCase			(    A     ):




   '''simple docstring'''

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






def 				_UpperCAmelCase			(    A				,							A="facebook/mbart-large-en-ro"				,							A=False				,							A=False     ):




   '''simple docstring'''

   UpperCAmelCase__						=torch.load(A				,							map_location="cpu"     )["model"]
   remove_ignore_keys_(A     )
   UpperCAmelCase__						=state_dict["encoder.embed_tokens.weight"].shape[0]

   UpperCAmelCase__						=MBartConfig.from_pretrained(A				,							vocab_size=A     )
   if mbart_aa and finetuned:
      UpperCAmelCase__						="relu"

   UpperCAmelCase__						=state_dict["decoder.embed_tokens.weight"]
   UpperCAmelCase__						=MBartForConditionalGeneration(A     )
   model.model.load_state_dict(A     )

   if finetuned:
      UpperCAmelCase__						=make_linear_from_emb(model.model.shared     )

   return model


if __name__ == "__main__":
    UpperCamelCase_												=	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')
    UpperCamelCase_												=	parser.parse_args()
    UpperCamelCase_												=	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)










from collections import OrderedDict
from typing import Mapping

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


UpperCamelCase_												=	logging.get_logger(__name__)

UpperCamelCase_												=	{
    'junnyu/roformer_chinese_small': 'https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json',
    'junnyu/roformer_chinese_base': 'https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json',
    'junnyu/roformer_chinese_char_small': (
        'https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json'
    ),
    'junnyu/roformer_chinese_char_base': (
        'https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json'
    ),
    'junnyu/roformer_small_discriminator': (
        'https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json'
    ),
    'junnyu/roformer_small_generator': (
        'https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json'
    ),
    # See all RoFormer models at https://huggingface.co/models?filter=roformer
}

class     snake_case_		(   a							):






 '''simple docstring'''





 __UpperCamelCase       =      'roformer'

 def __init__(  self,		A_=5_0000,		A_=None,		A_=768,		A_=12,		A_=12,		A_=3072,		A_="gelu",		A_=0.1,		A_=0.1,		A_=1536,		A_=2,		A_=0.02,		A_=1E-12,		A_=0,		A_=False,		A_=True,		**A_,		)	->       Dict:
    super().__init__(pad_token_id=A_,		**A_		)

    UpperCAmelCase__						=vocab_size
    UpperCAmelCase__						=hidden_size if embedding_size is None else embedding_size
    UpperCAmelCase__						=hidden_size
    UpperCAmelCase__						=num_hidden_layers
    UpperCAmelCase__						=num_attention_heads
    UpperCAmelCase__						=hidden_act
    UpperCAmelCase__						=intermediate_size
    UpperCAmelCase__						=hidden_dropout_prob
    UpperCAmelCase__						=attention_probs_dropout_prob
    UpperCAmelCase__						=max_position_embeddings
    UpperCAmelCase__						=type_vocab_size
    UpperCAmelCase__						=initializer_range
    UpperCAmelCase__						=layer_norm_eps
    UpperCAmelCase__						=rotary_value
    UpperCAmelCase__						=use_cache

class     snake_case_		(   a							):






 '''simple docstring'''





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



