"""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 	lowercase						(		a__      :						Any					,    a__      :						Optional[Any]	)		->  Union[str, Any]:
					# Load checkpoint
					_UpperCamelCase 		=      torch.load(a__					,    map_location='''cpu'''	)

					_UpperCamelCase 		=      chkpt["""model"""]

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

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

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

					# Save pytorch-model
					_UpperCamelCase 		=      pytorch_dump_folder_path + """/""" + WEIGHTS_NAME
					_UpperCamelCase 		=      pytorch_dump_folder_path + """/""" + CONFIG_NAME
					_UpperCamelCase 		=      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__":
		UpperCAmelCase    	=    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."""
		)
		UpperCAmelCase    	=    parser.parse_args()
		convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)



import fire

from utils import calculate_rouge, save_json
def 							__SCREAMING_SNAKE_CASE     (   a__    :   Any			,a__    :   Tuple			,a__    :   Any=None			,**a__    :   Dict       )					->				Optional[Any]:
	__A :			int		    =			[x.strip() for x in open(a__       ).readlines()]
	__A :			List[str]		    =			[x.strip() for x in open(a__       ).readlines()][: len(a__       )]
	__A :			List[Any]		    =			calculate_rouge(a__			,a__			,**a__       )
	if save_path is not None:
		save_json(a__			,a__			,indent=a__       )
	return metrics  # these print nicely


if __name__ == "__main__":
			fire.Fire(calculate_rouge_path)





