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


A_      =      {
    "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    (       UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase,		UpperCAmelCase=None       )->       Tuple:



			"""simple docstring"""







			lowercase							=		XLNetConfig.from_json_file(UpperCAmelCase       )

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

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

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


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






from __future__ import annotations

from collections import deque






class      __lowercase					:

						def __init__(	self				:      Dict							,							__lowerCamelCase				:      list[str]   )							->		List[str]:


									'''simple docstring'''

									lowercase							=		[]
									self.adlist.append(
									    {'''value''': '''''', '''next_states''': [], '''fail_state''': 0, '''output''': []}   )

									for keyword in keywords:
												self.add_keyword(__lowerCamelCase   )
									self.set_fail_transitions()

						def        __a		(	self				:      str							,							__lowerCamelCase				:      int							,							__lowerCamelCase				:      str   )							->		int | None:


									'''simple docstring'''

									for state in self.adlist[current_state]["next_states"]:
												if char == self.adlist[state]["value"]:
															return state
									return None

						def        __a		(	self				:      List[Any]							,							__lowerCamelCase				:      str   )							->		None:


									'''simple docstring'''

									lowercase							=		0
									for character in keyword:
												lowercase							=		self.find_next_state(__lowerCamelCase							,							__lowerCamelCase   )
												if next_state is None:
															self.adlist.append(
															    {
															        '''value''': character,
															        '''next_states''': [],
															        '''fail_state''': 0,
															        '''output''': [],
															    }   )
															self.adlist[current_state]["next_states"].append(len(self.adlist   ) - 1   )
															lowercase							=		len(self.adlist   ) - 1
												else:
															lowercase							=		next_state
									self.adlist[current_state]["output"].append(__lowerCamelCase   )

						def        __a		(	self				:      int   )							->		None:


									'''simple docstring'''

									lowercase							=		deque()
									for node in self.adlist[0]["next_states"]:
												q.append(__lowerCamelCase   )
												lowercase							=		0
									while q:
												lowercase							=		q.popleft()
												for child in self.adlist[r]["next_states"]:
															q.append(__lowerCamelCase   )
															lowercase							=		self.adlist[r]['''fail_state''']
															while (
															    self.find_next_state(__lowerCamelCase							,							self.adlist[child]['''value''']   ) is None
															    and state != 0
															):
																		lowercase							=		self.adlist[state]['''fail_state''']
															lowercase							=		self.find_next_state(
															    __lowerCamelCase							,							self.adlist[child]['''value''']   )
															if self.adlist[child]["fail_state"] is None:
																		lowercase							=		0
															lowercase							=		(
															    self.adlist[child]['''output''']
															    + self.adlist[self.adlist[child]['''fail_state''']]['''output''']
															)



						def        __a		(	self				:      List[str]							,							__lowerCamelCase				:      str   )							->		dict[str, list[int]]:


									'''simple docstring'''

									lowercase							=		{}  # returns a dict with keywords and list of its occurrences
									lowercase							=		0
									for i in range(len(__lowerCamelCase   )   ):
												while (
												    self.find_next_state(__lowerCamelCase							,							string[i]   ) is None
												    and current_state != 0
												):
															lowercase							=		self.adlist[current_state]['''fail_state''']
												lowercase							=		self.find_next_state(__lowerCamelCase							,							string[i]   )
												if next_state is None:
															lowercase							=		0
												else:
															lowercase							=		next_state
															for key in self.adlist[current_state]["output"]:
																		if key not in result:
																					lowercase							=		[]
																		result[key].append(i - len(__lowerCamelCase   ) + 1   )
									return result


if __name__ == "__main__":
						import doctest

						doctest.testmod()





