
"""simple docstring"""


import argparse

import torch
from torch import nn

from transformers import MBartConfig, MBartForConditionalGeneration






def       _snake_case				(		snake_case__   :     int     ):
	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__   :     Any     ):
	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__   :     Optional[Any]	,      snake_case__   :     int="facebook/mbart-large-en-ro"	,      snake_case__   :     Any=False	,      snake_case__   :     int=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)

"""simple docstring"""


# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
#   python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the output of this script is fed into Makefile commands it doesn't print a newline after the results

import re
import subprocess
import sys


_lowercase							   =				subprocess.check_output('''git merge-base main HEAD'''.split()).decode('''utf-8''')
_lowercase							   =				subprocess.check_output(F"""git diff --name-only {fork_point_sha}""".split()).decode('''utf-8''').split()

_lowercase							   =				'''|'''.join(sys.argv[1:])
_lowercase							   =				re.compile(rF"""^({joined_dirs}).*?\.py$""")

_lowercase							   =				[x for x in modified_files if regex.match(x)]
print(''' '''.join(relevant_modified_files), end='''''')