
'''simple docstring'''
import argparse
import json

import numpy
import torch

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()






def      UpperCamelCase_				(      A__  ,      A__						):
	# Load checkpoint
	a_      				=						torch.load(A__  ,      map_location="""cpu"""						)

	a_      				=						chkpt["""model"""]

	# We have the base model one level deeper than the original XLM repository
	a_      				=						{}
	for k, v in state_dict.items():
		if "pred_layer" in k:
			a_      				=						v
		else:
			a_      				=						v

	a_      				=						chkpt["""params"""]
	a_      				=						{n: v for n, v in config.items() if not isinstance(A__  ,      (torch.FloatTensor, numpy.ndarray)						)}

	a_      				=						chkpt["""dico_word2id"""]
	a_      				=						{s + """</w>""" if s.find("""@@"""						) == -1 and i > 13 else s.replace("""@@"""  ,      """"""						): i for s, i in vocab.items()}

	# Save pytorch-model
	a_      				=						pytorch_dump_folder_path + """/""" + WEIGHTS_NAME
	a_      				=						pytorch_dump_folder_path + """/""" + CONFIG_NAME
	a_      				=						pytorch_dump_folder_path + """/""" + VOCAB_FILES_NAMES["""vocab_file"""]

	print(F'''Save PyTorch model to {pytorch_weights_dump_path}'''						)
	torch.save(A__  ,      A__						)

	print(F'''Save configuration file to {pytorch_config_dump_path}'''						)
	with open(A__  ,      """w"""  ,      encoding="""utf-8"""						) as f:
		f.write(json.dumps(A__  ,      indent=2						) + """\n"""						)

	print(F'''Save vocab file to {pytorch_config_dump_path}'''						)
	with open(A__  ,      """w"""  ,      encoding="""utf-8"""						) as f:
		f.write(json.dumps(A__  ,      indent=2						) + """\n"""						)


if __name__ == "__main__":
		lowercase__											=argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--xlm_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
		)
		parser.add_argument(
		    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
		)
		lowercase__											=parser.parse_args()
		convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, 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 --diff-filter=d --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='')
