

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    SCREAMING_SNAKE_CASE__       (  lowerCamelCase__	, lowerCamelCase__			)  ->     Any:
	# Load checkpoint
	__lowerCamelCase				:				Union[str, Any]					       = torch.load(lowerCamelCase__	, map_location='cpu'			)

	__lowerCamelCase				:				Dict					       = chkpt['model']

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

	__lowerCamelCase				:				Any					       = chkpt['params']
	__lowerCamelCase				:				List[Any]					       = {n: v for n, v in config.items() if not isinstance(lowerCamelCase__	, (torch.FloatTensor, numpy.ndarray)			)}

	__lowerCamelCase				:				Union[str, Any]					       = chkpt['dico_word2id']
	__lowerCamelCase				:				Union[str, Any]					       = {s + '</w>' if s.find('@@'			) == -1 and i > 1_3 else s.replace('@@'	, ''			): i for s, i in vocab.items()}

	# Save pytorch-model
	__lowerCamelCase				:				Tuple					       = pytorch_dump_folder_path + '/' + WEIGHTS_NAME
	__lowerCamelCase				:				List[Any]					       = pytorch_dump_folder_path + '/' + CONFIG_NAME
	__lowerCamelCase				:				List[str]					       = pytorch_dump_folder_path + '/' + VOCAB_FILES_NAMES['vocab_file']

	print(F"Save PyTorch model to {pytorch_weights_dump_path}"			)
	torch.save(lowerCamelCase__	, lowerCamelCase__			)

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

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


if __name__ == "__main__":
					a		=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."""
					)
					a		=parser.parse_args()
					convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)








'''simple docstring'''






from statistics import mean, stdev







def 				UpperCamelCase_			(    A__					:   list							,    A__					:   int = 3					):
  '''simple docstring'''





  lowerCAmelCase_			:      List[str]			=   min(A__					)
  lowerCAmelCase_			:      Optional[int]			=   max(A__					)
  # normalize data
  return [round((x - x_min) / (x_max - x_min)							,    A__					) for x in data]







def 				UpperCamelCase_			(    A__					:   list							,    A__					:   int = 3					):
  '''simple docstring'''





  lowerCAmelCase_			:      str			=   mean(A__					)
  lowerCAmelCase_			:      List[Any]			=   stdev(A__					)
  # standardize data
  return [round((x - mu) / (sigma)							,    A__					) for x in data]



