






import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType




class  UpperCamelCase_      (			snake_case_				):



	'''simple docstring'''




	lowerCAmelCase													= ['''image_processor''', '''tokenizer''']
	lowerCAmelCase													= '''LayoutLMv3ImageProcessor'''
	lowerCAmelCase													= ('''LayoutLMv3Tokenizer''', '''LayoutLMv3TokenizerFast''')




	def __init__(       self       ,			a=None       ,			a=None       ,			**a					)       ->    Optional[Any]:
								snake_case_				=							None
								if "feature_extractor" in kwargs:
															warnings.warn(
															    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
															    ' instead.'       ,			a       ,			)
															snake_case_				=							kwargs.pop('feature_extractor'					)

								snake_case_				=							image_processor if image_processor is not None else feature_extractor
								if image_processor is None:
															raise ValueError('You need to specify an `image_processor`.'					)
								if tokenizer is None:
															raise ValueError('You need to specify a `tokenizer`.'					)

								super().__init__(a       ,			a					)




	def __call__(       self       ,			a       ,			a = None       ,			a = None       ,			a = None       ,			a = None       ,			a = True       ,			a = False       ,			a = None       ,			a = None       ,			a = 0       ,			a = None       ,			a = None       ,			a = None       ,			a = False       ,			a = False       ,			a = False       ,			a = False       ,			a = True       ,			a = None       ,			**a       ,			)       ->    BatchEncoding:
								# verify input
								if self.image_processor.apply_ocr and (boxes is not None):
															raise ValueError(
															    'You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True.'					)

								if self.image_processor.apply_ocr and (word_labels is not None):
															raise ValueError(
															    'You cannot provide word labels if you initialized the image processor with apply_ocr set to True.'					)

								# first, apply the image processor
								snake_case_				=							self.image_processor(images=a       ,			return_tensors=a					)

								# second, apply the tokenizer
								if text is not None and self.image_processor.apply_ocr and text_pair is None:
															if isinstance(a       ,			a					):
																						snake_case_				=							[text]  # add batch dimension (as the image processor always adds a batch dimension)
															snake_case_				=							features['words']

								snake_case_				=							self.tokenizer(
								    text=text if text is not None else features['words']       ,			text_pair=text_pair if text_pair is not None else None       ,			boxes=boxes if boxes is not None else features['boxes']       ,			word_labels=a       ,			add_special_tokens=a       ,			padding=a       ,			truncation=a       ,			max_length=a       ,			stride=a       ,			pad_to_multiple_of=a       ,			return_token_type_ids=a       ,			return_attention_mask=a       ,			return_overflowing_tokens=a       ,			return_special_tokens_mask=a       ,			return_offsets_mapping=a       ,			return_length=a       ,			verbose=a       ,			return_tensors=a       ,			**a       ,			)

								# add pixel values
								snake_case_				=							features.pop('pixel_values'					)
								if return_overflowing_tokens is True:
															snake_case_				=							self.get_overflowing_images(a       ,			encoded_inputs['overflow_to_sample_mapping']					)
								snake_case_				=							images

								return encoded_inputs




	def 							_UpperCamelCase			(       self       ,			a       ,			a					)       ->    Optional[int]:
								# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
								snake_case_				=							[]
								for sample_idx in overflow_to_sample_mapping:
															images_with_overflow.append(images[sample_idx]					)

								if len(a					) != len(a					):
															raise ValueError(
															    'Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'
															    F''' {len(a					)} and {len(a					)}'''					)

								return images_with_overflow




	def 							_UpperCamelCase			(       self       ,			*a       ,			**a					)       ->    str:
								return self.tokenizer.batch_decode(*a       ,			**a					)




	def 							_UpperCamelCase			(       self       ,			*a       ,			**a					)       ->    str:
								return self.tokenizer.decode(*a       ,			**a					)




	@property
	def 							_UpperCamelCase			(       self					)       ->    Dict:
								return ["input_ids", "bbox", "attention_mask", "pixel_values"]




	@property
	def 							_UpperCamelCase			(       self					)       ->    str:
								warnings.warn(
								    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'       ,			a       ,			)
								return self.image_processor_class






	@property
	def 							_UpperCamelCase			(       self					)       ->    int:
								warnings.warn(
								    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'       ,			a       ,			)
								return self.image_processor













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
	)





