


'''simple docstring'''






import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration


def    _snake_case  (	A			)		->       Any:
							lowerCAmelCase__			  =      [
							    '''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    _snake_case  (	A			)		->       Optional[int]:
							lowerCAmelCase__  ,   lowerCAmelCase__			  =      emb.weight.shape
							lowerCAmelCase__			  =      nn.Linear(A					,       A					,       bias=A			)
							lowerCAmelCase__			  =      emb.weight.data
							return lin_layer


def    _snake_case  (	A					,       A="facebook/mbart-large-en-ro"					,       A=False					,       A=False			)		->       int:
							lowerCAmelCase__			  =      torch.load(A					,       map_location='''cpu'''			)['''model''']
							remove_ignore_keys_(A			)
							lowerCAmelCase__			  =      state_dict['''encoder.embed_tokens.weight'''].shape[0]

							lowerCAmelCase__			  =      MBartConfig.from_pretrained(A					,       vocab_size=A			)
							if mbart_aa and finetuned:
														lowerCAmelCase__			  =      '''relu'''

							lowerCAmelCase__			  =      state_dict['''decoder.embed_tokens.weight''']
							lowerCAmelCase__			  =      MBartForConditionalGeneration(A			)
							model.model.load_state_dict(A			)

							if finetuned:
														lowerCAmelCase__			  =      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)



'''simple docstring'''






import logging
from dataclasses import dataclass, field
from pathlib import Path
from typing import Optional, Union

from .generation.configuration_utils import GenerationConfig
from .training_args import TrainingArguments
from .utils import add_start_docstrings


__UpperCAmelCase       							=				logging.getLogger(__name__)




@dataclass
@add_start_docstrings(TrainingArguments.__doc__    )
class 						a__							(				a__    ):





							'''simple docstring'''






							lowercase__     :  bool      		= field(default=a__							,							metadata={"help": "Whether to use SortishSampler or not."}    )
							lowercase__     :  bool      		= field(
							    default=a__							,							metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}    )
							lowercase__     :  Optional[int]      		= field(
							    default=a__							,							metadata={
							        "help": (
							            "The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default "
							            "to the `max_length` value of the model configuration."
							        )
							    }							,							)
							lowercase__     :  Optional[int]      		= field(
							    default=a__							,							metadata={
							        "help": (
							            "The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default "
							            "to the `num_beams` value of the model configuration."
							        )
							    }							,							)
							lowercase__     :  Optional[Union[str, Path, GenerationConfig]]      		= field(
							    default=a__							,							metadata={
							        "help": "Model id, file path or url pointing to a GenerationConfig json file, to use during prediction."
							    }							,							)







							def     __SCREAMING_SNAKE_CASE				(						self  )			->				Dict:
														lowerCAmelCase__			  =      super().to_dict()
														for k, v in d.items():
																					if isinstance(lowerCamelCase_   ,       lowerCamelCase_  ):
																												lowerCAmelCase__			  =      v.to_dict()
														return d