

'''simple docstring'''


import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase  )    ->    Any:
	UpperCAmelCase_		:      List[str]       	=					[
	    'encoder.version',
	    'decoder.version',
	    'model.encoder.version',
	    'model.decoder.version',
	    '_float_tensor',
	    'decoder.output_projection.weight',
	]
	for k in ignore_keys:
		state_dict.pop(UpperCamelCase   ,UpperCamelCase  )







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase  )    ->    Tuple:
	UpperCAmelCase_	,    UpperCAmelCase_		:      int       	=					emb.weight.shape
	UpperCAmelCase_		:      List[str]       	=					nn.Linear(UpperCamelCase   ,UpperCamelCase   ,bias=UpperCamelCase  )
	UpperCAmelCase_		:      Optional[Any]       	=					emb.weight.data
	return lin_layer







def 						SCREAMING_SNAKE_CASE(  UpperCamelCase   ,UpperCamelCase="facebook/mbart-large-en-ro"   ,UpperCamelCase=False   ,UpperCamelCase=False  )    ->    List[str]:
	UpperCAmelCase_		:      Tuple       	=					torch.load(UpperCamelCase   ,map_location='cpu'  )['model']
	remove_ignore_keys_(UpperCamelCase  )
	UpperCAmelCase_		:      List[Any]       	=					state_dict['encoder.embed_tokens.weight'].shape[0]

	UpperCAmelCase_		:      str       	=					MBartConfig.from_pretrained(UpperCamelCase   ,vocab_size=UpperCamelCase  )
	if mbart_aa and finetuned:
		UpperCAmelCase_		:      str       	=					'relu'

	UpperCAmelCase_		:      List[str]       	=					state_dict['decoder.embed_tokens.weight']
	UpperCAmelCase_		:      Dict       	=					MBartForConditionalGeneration(UpperCamelCase  )
	model.model.load_state_dict(UpperCamelCase  )

	if finetuned:
		UpperCAmelCase_		:      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)









'''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/xlm-roberta-xl": "https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json",
    "facebook/xlm-roberta-xxl": "https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json",
    # See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}



class 					lowercase						(  a_ ):
							_lowerCamelCase		:      Any=   "xlm-roberta-xl"


							def __init__(			self    ,			_snake_case=25_0880    ,			_snake_case=2560    ,			_snake_case=36    ,			_snake_case=32    ,			_snake_case=1_0240    ,			_snake_case="gelu"    ,			_snake_case=0.1    ,			_snake_case=0.1    ,			_snake_case=514    ,			_snake_case=1    ,			_snake_case=0.02    ,			_snake_case=1e-05    ,			_snake_case=1    ,			_snake_case=0    ,			_snake_case=2    ,			_snake_case="absolute"    ,			_snake_case=True    ,			_snake_case=None    ,			**_snake_case    ,			)							->				Optional[Any]:
								super().__init__(pad_token_id=_snake_case    ,			bos_token_id=_snake_case    ,			eos_token_id=_snake_case    ,			**_snake_case)
								UpperCAmelCase_		:      List[str]       	=					vocab_size
								UpperCAmelCase_		:      int       	=					hidden_size
								UpperCAmelCase_		:      List[Any]       	=					num_hidden_layers
								UpperCAmelCase_		:      Optional[int]       	=					num_attention_heads
								UpperCAmelCase_		:      Union[str, Any]       	=					hidden_act
								UpperCAmelCase_		:      int       	=					intermediate_size
								UpperCAmelCase_		:      Tuple       	=					hidden_dropout_prob
								UpperCAmelCase_		:      List[str]       	=					attention_probs_dropout_prob
								UpperCAmelCase_		:      str       	=					max_position_embeddings
								UpperCAmelCase_		:      Union[str, Any]       	=					type_vocab_size
								UpperCAmelCase_		:      str       	=					initializer_range
								UpperCAmelCase_		:      List[str]       	=					layer_norm_eps
								UpperCAmelCase_		:      Dict       	=					position_embedding_type
								UpperCAmelCase_		:      int       	=					use_cache
								UpperCAmelCase_		:      List[Any]       	=					classifier_dropout



class 					lowercase						(  a_ ):


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






