
"""simple docstring"""


import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration






def 					_snake_case (						snake_case__      :   Dict   ):
	A        =      [
	    'encoder.version',
	    'decoder.version',
	    'model.encoder.version',
	    'model.decoder.version',
	    '_float_tensor',
	    'decoder.output_projection.weight',
	]
	for k in ignore_keys:
		state_dict.pop(snake_case__		,					snake_case__   )






def 					_snake_case (						snake_case__      :   int   ):
	A      ,					A        =      emb.weight.shape
	A        =      nn.Linear(snake_case__		,					snake_case__		,					bias=snake_case__   )
	A        =      emb.weight.data
	return lin_layer






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

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

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

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

	return model


if __name__ == "__main__":
		_lowercase							      = 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''')
		_lowercase							      = parser.parse_args()
		_lowercase							      = 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 pytest
import requests

from datasets.utils.file_utils import http_head

from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline



@pytest.mark.integration
def 		A_				(     )						-> List[Any]:
 with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT     ):
  with pytest.raises(_lowerCAmelCase     ):
   requests.request("GET"							,   "https://huggingface.co"     )
  with pytest.raises(requests.exceptions.ConnectTimeout     ):
   requests.request("GET"							,   "https://huggingface.co"							,   timeout=1.0     )



@pytest.mark.integration
def 		A_				(     )						-> Tuple:
 with offline(OfflineSimulationMode.CONNECTION_FAILS     ):
  with pytest.raises(requests.exceptions.ConnectionError     ):
   requests.request("GET"							,   "https://huggingface.co"     )



def 		A_				(     )						-> Optional[int]:
 with offline(OfflineSimulationMode.HF_DATASETS_OFFLINE_SET_TO_1     ):
  with pytest.raises(_lowerCAmelCase     ):
   http_head("https://huggingface.co"     )
