






'''simple docstring'''
import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration





def 	_a(					UpperCamelCase__			:  Optional[int]							):




						'''simple docstring'''






						SCREAMING_SNAKE_CASE__       :			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 	_a(					UpperCamelCase__			:  Any							):




						'''simple docstring'''






						SCREAMING_SNAKE_CASE__       :			Tuple													=emb.weight.shape
						SCREAMING_SNAKE_CASE__       :			Tuple													=nn.Linear(_UpperCAmelCase,		_UpperCAmelCase,		bias=_UpperCAmelCase							)
						SCREAMING_SNAKE_CASE__       :			List[str]													=emb.weight.data
						return lin_layer





def 	_a(					UpperCamelCase__			:  Dict,		UpperCamelCase__			:  Dict="facebook/mbart-large-en-ro",		UpperCamelCase__			:  List[Any]=False,		UpperCamelCase__			:  List[str]=False							):




						'''simple docstring'''






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

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

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

						if finetuned:
												SCREAMING_SNAKE_CASE__       :			Optional[int]													=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)







'''simple docstring'''
from datetime import datetime as dt
import os

from github import Github


a_					=	[
    'good first issue',
    'good second issue',
    'good difficult issue',
    'feature request',
    'new model',
    'wip',
]





def 	_a(					):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__       :			List[str]													=Github(os.environ['''GITHUB_TOKEN''']							)
      SCREAMING_SNAKE_CASE__       :			List[Any]													=g.get_repo('''huggingface/transformers'''							)
      SCREAMING_SNAKE_CASE__       :			List[Any]													=repo.get_issues(state='''open'''							)

      for issue in open_issues:
            SCREAMING_SNAKE_CASE__       :			List[Any]													=sorted([comment for comment in issue.get_comments()],		key=lambda UpperCamelCase__							: i.created_at,		reverse=UpperCamelCase__							)
            SCREAMING_SNAKE_CASE__       :			Dict													=comments[0] if len(UpperCamelCase__							) > 0 else None
            if (
                last_comment is not None
                and last_comment.user.login == "github-actions[bot]"
                and (dt.utcnow() - issue.updated_at).days > 7
                and (dt.utcnow() - issue.created_at).days >= 3_0
                and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()							)
            ):
                  # print(f"Would close issue {issue.number} since it has been 7 days of inactivity since bot mention.")
                  issue.edit(state='''closed'''							)
            elif (
                (dt.utcnow() - issue.updated_at).days > 2_3
                and (dt.utcnow() - issue.created_at).days >= 3_0
                and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()							)
            ):
                  # print(f"Would add stale comment to {issue.number}")
                  issue.create_comment(
                      '''This issue has been automatically marked as stale because it has not had '''
                      '''recent activity. If you think this still needs to be addressed '''
                      '''please comment on this thread.\n\nPlease note that issues that do not follow the '''
                      '''[contributing guidelines](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md) '''
                      '''are likely to be ignored.'''							)


if __name__ == "__main__":
  main()