



"""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 			_snake_case		(		UpperCamelCase		:	str    ,	UpperCamelCase		:	Tuple		):
 # Load checkpoint
 UpperCAmelCase			:      Union[str, Any]			=			torch.load(_lowerCamelCase    ,	map_location="""cpu"""		)

 UpperCAmelCase			:      Dict			=			chkpt["""model"""]

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

 UpperCAmelCase			:      Tuple			=			chkpt["""params"""]
 UpperCAmelCase			:      List[str]			=			{n: v for n, v in config.items() if not isinstance(_lowerCamelCase    ,	(torch.FloatTensor, numpy.ndarray)		)}

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

 # Save pytorch-model
 UpperCAmelCase			:      Tuple			=			pytorch_dump_folder_path + """/""" + WEIGHTS_NAME
 UpperCAmelCase			:      List[Any]			=			pytorch_dump_folder_path + """/""" + CONFIG_NAME
 UpperCAmelCase			:      Optional[int]			=			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:     List[str]										=	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:     List[str]										=	parser.parse_args()
      convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)





"""simple docstring"""
import json
import os
import unittest

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin



class    SCREAMING_SNAKE_CASE__						(      UpperCAmelCase__   ,    unittest.TestCase				):
   __lowerCAmelCase						:     Any		=		XLMTokenizer
   __lowerCAmelCase						:     List[Any]		=		False

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Optional[int]:





    '''simple docstring'''




    super().setUp()

    # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
    UpperCAmelCase			:      Optional[int]			=			[
        """l""",
        """o""",
        """w""",
        """e""",
        """r""",
        """s""",
        """t""",
        """i""",
        """d""",
        """n""",
        """w</w>""",
        """r</w>""",
        """t</w>""",
        """lo""",
        """low""",
        """er</w>""",
        """low</w>""",
        """lowest</w>""",
        """newer</w>""",
        """wider</w>""",
        """<unk>""",
    ]
    UpperCAmelCase			:      str			=			dict(zip(_SCREAMING_SNAKE_CASE   ,  range(len(_SCREAMING_SNAKE_CASE		)		)		)		)
    UpperCAmelCase			:      List[Any]			=			["""l o 123""", """lo w 1456""", """e r</w> 1789""", """"""]

    UpperCAmelCase			:      Optional[Any]			=			os.path.join(self.tmpdirname   ,  VOCAB_FILES_NAMES["""vocab_file"""]		)
    UpperCAmelCase			:      Optional[Any]			=			os.path.join(self.tmpdirname   ,  VOCAB_FILES_NAMES["""merges_file"""]		)
    with open(self.vocab_file   ,  """w"""		) as fp:
     fp.write(json.dumps(_SCREAMING_SNAKE_CASE		)		)
    with open(self.merges_file   ,  """w"""		) as fp:
     fp.write("""\n""".join(_SCREAMING_SNAKE_CASE		)		)

   def 			SCREAMING_SNAKE_CASE  (     self   ,  _SCREAMING_SNAKE_CASE		)						->   Any:





    '''simple docstring'''




    UpperCAmelCase			:      Optional[int]			=			"""lower newer"""
    UpperCAmelCase			:      Dict			=			"""lower newer"""
    return input_text, output_text

   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Optional[int]:





    '''simple docstring'''




    UpperCAmelCase			:      Union[str, Any]			=			XLMTokenizer(self.vocab_file   ,  self.merges_file		)

    UpperCAmelCase			:      Optional[int]			=			"""lower"""
    UpperCAmelCase			:      Optional[int]			=			["""low""", """er</w>"""]
    UpperCAmelCase			:      List[Any]			=			tokenizer.tokenize(_SCREAMING_SNAKE_CASE		)
    self.assertListEqual(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)

    UpperCAmelCase			:      Optional[int]			=			tokens + ["""<unk>"""]
    UpperCAmelCase			:      List[Any]			=			[14, 15, 20]
    self.assertListEqual(tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE		)   ,  _SCREAMING_SNAKE_CASE		)




   @slow
   def 			SCREAMING_SNAKE_CASE  (     self		)						->   Any:





    '''simple docstring'''




    UpperCAmelCase			:      List[Any]			=			XLMTokenizer.from_pretrained("""xlm-mlm-en-2048"""		)

    UpperCAmelCase			:      int			=			tokenizer.encode("""sequence builders"""   ,  add_special_tokens=_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      List[Any]			=			tokenizer.encode("""multi-sequence build"""   ,  add_special_tokens=_SCREAMING_SNAKE_CASE		)

    UpperCAmelCase			:      Tuple			=			tokenizer.build_inputs_with_special_tokens(_SCREAMING_SNAKE_CASE		)
    UpperCAmelCase			:      str			=			tokenizer.build_inputs_with_special_tokens(_SCREAMING_SNAKE_CASE   ,  _SCREAMING_SNAKE_CASE		)

    assert encoded_sentence == [0] + text + [1]
    assert encoded_pair == [0] + text + [1] + text_a + [1]
