



import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration






def 			lowerCAmelCase__	(					lowerCamelCase_	:	int):





				'''simple docstring'''

				lowerCAmelCase__  :       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(lowerCamelCase_ ,lowerCamelCase_)






def 			lowerCAmelCase__	(					lowerCamelCase_	:	Optional[Any]):





				'''simple docstring'''

				lowerCAmelCase__  :       Dict				=  emb.weight.shape
				lowerCAmelCase__  :       Tuple				=  nn.Linear(lowerCamelCase_ ,lowerCamelCase_ ,bias=lowerCamelCase_)
				lowerCAmelCase__  :       Tuple				=  emb.weight.data
				return lin_layer






def 			lowerCAmelCase__	(					lowerCamelCase_	:	List[Any] ,lowerCamelCase_	:	List[Any]="facebook/mbart-large-en-ro" ,lowerCamelCase_	:	str=False ,lowerCamelCase_	:	int=False):





				'''simple docstring'''

				lowerCAmelCase__  :       int				=  torch.load(lowerCamelCase_ ,map_location='''cpu''')['''model''']
				remove_ignore_keys_(lowerCamelCase_)
				lowerCAmelCase__  :       int				=  state_dict['''encoder.embed_tokens.weight'''].shape[0]

				lowerCAmelCase__  :       str				=  MBartConfig.from_pretrained(lowerCamelCase_ ,vocab_size=lowerCamelCase_)
				if mbart_aa and finetuned:
								lowerCAmelCase__  :       List[Any]				=  '''relu'''

				lowerCAmelCase__  :       Dict				=  state_dict['''decoder.embed_tokens.weight''']
				lowerCAmelCase__  :       Any				=  MBartForConditionalGeneration(lowerCamelCase_)
				model.model.load_state_dict(lowerCamelCase_)

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

				return model


if __name__ == "__main__":
			__snake_case       :	Optional[Any]            =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')
			__snake_case       :	Tuple            =parser.parse_args()
			__snake_case       :	Union[str, Any]            =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 io
import json
import unittest

from parameterized import parameterized

from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu


__snake_case       :	Optional[int]            =get_tests_dir() + '/test_data/fsmt/fsmt_val_data.json'
with io.open(filename, 'r', encoding='utf-8') as f:
							__snake_case       :	Optional[Any]            =json.load(f)



@require_torch
class  lowerCamelCase__    (							unittest.TestCase):




		'''simple docstring'''



		def 	lowerCAmelCase__   (self	,__lowerCamelCase				)  ->    Dict:

						"""simple docstring"""






						return FSMTTokenizer.from_pretrained(__lowerCamelCase				)



		def 	lowerCAmelCase__   (self	,__lowerCamelCase				)  ->    Optional[int]:

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[Any]				=  FSMTForConditionalGeneration.from_pretrained(__lowerCamelCase				).to(__lowerCamelCase				)
						if torch_device == "cuda":
										model.half()
						return model



		@parameterized.expand(
		    [
		        ['''en-ru''', 26.0],
		        ['''ru-en''', 22.0],
		        ['''en-de''', 22.0],
		        ['''de-en''', 29.0],
		    ]				)
		@slow
		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase				)  ->    Any:

						"""simple docstring"""






						lowerCAmelCase__  :       List[str]				=  f"""facebook/wmt19-{pair}"""
						lowerCAmelCase__  :       int				=  self.get_tokenizer(__lowerCamelCase				)
						lowerCAmelCase__  :       Optional[int]				=  self.get_model(__lowerCamelCase				)

						lowerCAmelCase__  :       List[Any]				=  bleu_data[pair]['''src''']
						lowerCAmelCase__  :       List[Any]				=  bleu_data[pair]['''tgt''']

						lowerCAmelCase__  :       List[str]				=  tokenizer(__lowerCamelCase	,return_tensors='''pt'''	,truncation=__lowerCamelCase	,padding='''longest'''				).to(__lowerCamelCase				)
						lowerCAmelCase__  :       List[str]				=  model.generate(
						    input_ids=batch.input_ids	,num_beams=8	,)
						lowerCAmelCase__  :       Tuple				=  tokenizer.batch_decode(
						    __lowerCamelCase	,skip_special_tokens=__lowerCamelCase	,clean_up_tokenization_spaces=__lowerCamelCase				)
						lowerCAmelCase__  :       Union[str, Any]				=  calculate_bleu(__lowerCamelCase	,__lowerCamelCase				)
						print(__lowerCamelCase				)
						self.assertGreaterEqual(scores['''bleu''']	,__lowerCamelCase				)

