



"""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 			snake_case_						(						a_     ,unittest.TestCase				):
							__lowerCAmelCase    					=					XLMTokenizer
							__lowerCAmelCase    					=					False






							def snake_case_  (   self      ):
									super().setUp()

									# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
									a_     :      Dict 						=     [
									    "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>",
									]
									a_     :      Optional[Any] 						=     dict(zip(a_			,     range(len(a_      )      )      )      )
									a_     :      Optional[Any] 						=     ["l o 123", "lo w 1456", "e r</w> 1789", ""]

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






							def snake_case_  (   self			,     a_      ):
									a_     :      Tuple 						=     "lower newer"
									a_     :      Any 						=     "lower newer"
									return input_text, output_text






							def snake_case_  (   self      ):
									a_     :      Optional[int] 						=     XLMTokenizer(self.vocab_file			,     self.merges_file      )

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

									a_     :      List[str] 						=     tokens + ["<unk>"]
									a_     :      List[Any] 						=     [1_4, 1_5, 2_0]
									self.assertListEqual(tokenizer.convert_tokens_to_ids(a_      )			,     a_      )






							@slow
							def snake_case_  (   self      ):
									a_     :      Any 						=     XLMTokenizer.from_pretrained("xlm-mlm-en-2048"      )

									a_     :      Tuple 						=     tokenizer.encode("sequence builders"			,     add_special_tokens=a_      )
									a_     :      List[str] 						=     tokenizer.encode("multi-sequence build"			,     add_special_tokens=a_      )

									a_     :      int 						=     tokenizer.build_inputs_with_special_tokens(a_      )
									a_     :      Optional[int] 						=     tokenizer.build_inputs_with_special_tokens(a_			,     a_      )

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




"""simple docstring"""
import argparse
import os

import torch

from transformers import (
    XLNetConfig,
    XLNetForQuestionAnswering,
    XLNetForSequenceClassification,
    XLNetLMHeadModel,
    load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


SCREAMING_SNAKE_CASE_				= {
    """cola""": 2,
    """mnli""": 3,
    """mrpc""": 2,
    """sst-2""": 2,
    """sts-b""": 1,
    """qqp""": 2,
    """qnli""": 2,
    """rte""": 2,
    """wnli""": 2,
}


logging.set_verbosity_info()



def        lowerCAmelCase_						(							SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__=None	)			->      int:
		# Initialise PyTorch model
		a_     :      Optional[int] 						=     XLNetConfig.from_json_file(SCREAMING_SNAKE_CASE__	)

		a_     :      List[Any] 						=     finetuning_task.lower() if finetuning_task is not None else ""
		if finetuning_task in GLUE_TASKS_NUM_LABELS:
				print(F"""Building PyTorch XLNetForSequenceClassification model from configuration: {config}"""	)
				a_     :      Tuple 						=     finetuning_task
				a_     :      Dict 						=     GLUE_TASKS_NUM_LABELS[finetuning_task]
				a_     :      Tuple 						=     XLNetForSequenceClassification(SCREAMING_SNAKE_CASE__	)
		elif "squad" in finetuning_task:
				a_     :      Tuple 						=     finetuning_task
				a_     :      List[Any] 						=     XLNetForQuestionAnswering(SCREAMING_SNAKE_CASE__	)
		else:
				a_     :      Any 						=     XLNetLMHeadModel(SCREAMING_SNAKE_CASE__	)

		# Load weights from tf checkpoint
		load_tf_weights_in_xlnet(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)

		# Save pytorch-model
		a_     :      Tuple 						=     os.path.join(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)
		a_     :      str 						=     os.path.join(SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__	)
		print(F"""Save PyTorch model to {os.path.abspath(SCREAMING_SNAKE_CASE__	)}"""	)
		torch.save(model.state_dict(), SCREAMING_SNAKE_CASE__	)
		print(F"""Save configuration file to {os.path.abspath(SCREAMING_SNAKE_CASE__	)}"""	)
		with open(SCREAMING_SNAKE_CASE__, "w", encoding="utf-8"	) as f:
				f.write(config.to_json_string()	)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE_				= argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
					)
					parser.add_argument(
					    """--xlnet_config_file""",
					    default=None,
					    type=str,
					    required=True,
					    help=(
					        """The config json file corresponding to the pre-trained XLNet model. \n"""
					        """This specifies the model architecture."""
					    ),
					)
					parser.add_argument(
					    """--pytorch_dump_folder_path""",
					    default=None,
					    type=str,
					    required=True,
					    help="""Path to the folder to store the PyTorch model or dataset/vocab.""",
					)
					parser.add_argument(
					    """--finetuning_task""",
					    default=None,
					    type=str,
					    help="""Name of a task on which the XLNet TensorFlow model was fine-tuned""",
					)
					SCREAMING_SNAKE_CASE_				= parser.parse_args()
					print(args)

					convert_xlnet_checkpoint_to_pytorch(
					    args.tf_checkpoint_path, args.xlnet_config_file, args.pytorch_dump_folder_path, args.finetuning_task
					)