






from pathlib import PurePosixPath
from typing import Optional

import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo

from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url




class  UpperCamelCase_      (			snake_case_				):



	'''simple docstring'''




	lowerCAmelCase													= ''''''
	lowerCAmelCase													= '''hf-legacy'''  # "hf://"" is reserved for hffs




	def __init__(       self       ,			a = None       ,			a = None       ,			**a       ,			)       ->    Optional[int]:
								super().__init__(self       ,			**a					)
								snake_case_				=							repo_info
								snake_case_				=							token
								snake_case_				=							None




	def 							_UpperCamelCase			(       self					)       ->    Tuple:
								if self.dir_cache is None:
															snake_case_				=							{}
															for hf_file in self.repo_info.siblings:
																						# TODO(QL): add sizes
																						snake_case_				=							{
																						    'name': hf_file.rfilename,
																						    'size': None,
																						    'type': 'file',
																						}
																						self.dir_cache.update(
																						    {
																						        str(a					): {'name': str(a					), 'size': None, 'type': 'directory'}
																						        for d in list(PurePosixPath(hf_file.rfilename					).parents					)[:-1]
																						    }					)




	def 							_UpperCamelCase			(       self       ,			a       ,			a = "rb"       ,			**a       ,			)       ->    List[Any]:
								if not isinstance(self.repo_info       ,			a					):
															raise NotImplementedError(F'''Open is only implemented for dataset repositories, but got {self.repo_info}'''					)
								snake_case_				=							hf_hub_url(self.repo_info.id       ,			a       ,			revision=self.repo_info.sha					)
								return fsspec.open(
								    a       ,			mode=a       ,			headers=get_authentication_headers_for_url(a       ,			use_auth_token=self.token					)       ,			client_kwargs={'trust_env': True}       ,			).open()




	def 							_UpperCamelCase			(       self       ,			a       ,			**a					)       ->    Tuple:
								self._get_dirs()
								snake_case_				=							self._strip_protocol(a					)
								if path in self.dir_cache:
															return self.dir_cache[path]
								else:
															raise FileNotFoundError(a					)






	def 							_UpperCamelCase			(       self       ,			a       ,			a=False       ,			**a					)       ->    str:
								self._get_dirs()
								snake_case_				=							PurePosixPath(path.strip('/'					)					)
								snake_case_				=							{}
								for p, f in self.dir_cache.items():
															snake_case_				=							PurePosixPath(p.strip('/'					)					)
															snake_case_				=							p.parent
															if root == path:
																						snake_case_				=							f
								snake_case_				=							list(paths.values()					)
								if detail:
															return out
								else:
															return sorted(f['name'] for f in out					)













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


lowercase  							=					{
    "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 						__UpperCAmelCase			(     a_							,     a_							,     a_							,     a_=None):
							# Initialise PyTorch model
							snake_case_				=							XLNetConfig.from_json_file(a_)

							snake_case_				=							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}''')
														snake_case_				=							finetuning_task
														snake_case_				=							GLUE_TASKS_NUM_LABELS[finetuning_task]
														snake_case_				=							XLNetForSequenceClassification(a_)
							elif "squad" in finetuning_task:
														snake_case_				=							finetuning_task
														snake_case_				=							XLNetForQuestionAnswering(a_)
							else:
														snake_case_				=							XLNetLMHeadModel(a_)

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

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


if __name__ == "__main__":
	lowercase  							=					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",
	)
	lowercase  							=					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
	)





