



import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration

def        a						(      _UpperCAmelCase							:					List[Any]						):







		'''simple docstring'''





		__UpperCAmelCase    :     Any													=	[
		    '''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        a						(      _UpperCAmelCase							:					List[Any]						):







		'''simple docstring'''





		__UpperCAmelCase							,					__UpperCAmelCase    :     int													=	emb.weight.shape
		__UpperCAmelCase    :     str													=	nn.Linear(_UpperCAmelCase ,   _UpperCAmelCase ,   bias=_UpperCAmelCase						)
		__UpperCAmelCase    :     Any													=	emb.weight.data
		return lin_layer

def        a						(      _UpperCAmelCase							:					Optional[int] ,   _UpperCAmelCase							:					Any="facebook/mbart-large-en-ro" ,   _UpperCAmelCase							:					str=False ,   _UpperCAmelCase							:					Optional[int]=False						):







		'''simple docstring'''





		__UpperCAmelCase    :     int													=	torch.load(_UpperCAmelCase ,   map_location='''cpu'''						)['''model''']
		remove_ignore_keys_(_UpperCAmelCase						)
		__UpperCAmelCase    :     Union[str, Any]													=	state_dict['''encoder.embed_tokens.weight'''].shape[0]

		__UpperCAmelCase    :     List[Any]													=	MBartConfig.from_pretrained(_UpperCAmelCase ,   vocab_size=_UpperCAmelCase						)
		if mbart_aa and finetuned:
				__UpperCAmelCase    :     Optional[int]													=	'''relu'''

		__UpperCAmelCase    :     Optional[int]													=	state_dict['''decoder.embed_tokens.weight''']
		__UpperCAmelCase    :     int													=	MBartForConditionalGeneration(_UpperCAmelCase						)
		model.model.load_state_dict(_UpperCAmelCase						)

		if finetuned:
				__UpperCAmelCase    :     Dict													=	make_linear_from_emb(model.model.shared						)

		return model


if __name__ == "__main__":
				__A   			=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")
				__A   			=parser.parse_args()
				__A   			=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)










import json
import os
import tempfile

from transformers.testing_utils import check_json_file_has_correct_format

class 		UpperCAmelCase__			:




			'''simple docstring'''






			UpperCamelCase									=       None




			def  snake_case__     (   self							: List[str] ):
					'''simple docstring'''





					__UpperCAmelCase    :     List[Any]													=	self.feature_extraction_class(**self.feat_extract_dict )
					__UpperCAmelCase    :     Optional[int]													=	json.loads(feat_extract.to_json_string() )
					for key, value in self.feat_extract_dict.items():
							self.assertEqual(obj[key]       ,       a_ )




			def  snake_case__     (   self							: Any ):
					'''simple docstring'''





					__UpperCAmelCase    :     str													=	self.feature_extraction_class(**self.feat_extract_dict )

					with tempfile.TemporaryDirectory() as tmpdirname:
							__UpperCAmelCase    :     Union[str, Any]													=	os.path.join(a_       ,       '''feat_extract.json''' )
							feat_extract_first.to_json_file(a_ )
							__UpperCAmelCase    :     Any													=	self.feature_extraction_class.from_json_file(a_ )

					self.assertEqual(feat_extract_second.to_dict()       ,       feat_extract_first.to_dict() )




			def  snake_case__     (   self							: Optional[int] ):
					'''simple docstring'''





					__UpperCAmelCase    :     Tuple													=	self.feature_extraction_class(**self.feat_extract_dict )

					with tempfile.TemporaryDirectory() as tmpdirname:
							__UpperCAmelCase    :     List[str]													=	feat_extract_first.save_pretrained(a_ )[0]
							check_json_file_has_correct_format(a_ )
							__UpperCAmelCase    :     Optional[Any]													=	self.feature_extraction_class.from_pretrained(a_ )

					self.assertEqual(feat_extract_second.to_dict()       ,       feat_extract_first.to_dict() )




			def  snake_case__     (   self							: Dict ):
					'''simple docstring'''





					__UpperCAmelCase    :     int													=	self.feature_extraction_class()
					self.assertIsNotNone(a_ )





