
"""simple docstring"""

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     lowercase_	(					__lowerCAmelCase							):



 '''simple docstring'''
 UpperCAmelCase   :					int				  =     ['''image_processor''', '''tokenizer''']
 UpperCAmelCase   :					Optional[Any]				  =     '''LayoutLMv2ImageProcessor'''
 UpperCAmelCase   :					Optional[Any]				  =     ('''LayoutXLMTokenizer''', '''LayoutXLMTokenizerFast''')

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

       _A        =       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__(_UpperCAmelCase   ,							_UpperCAmelCase		)

 def __call__(			self	:	List[Any]   ,							_UpperCAmelCase	:	Tuple   ,							_UpperCAmelCase	:	Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None   ,							_UpperCAmelCase	:	Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None   ,							_UpperCAmelCase	:	Union[List[List[int]], List[List[List[int]]]] = None   ,							_UpperCAmelCase	:	Optional[Union[List[int], List[List[int]]]] = None   ,							_UpperCAmelCase	:	bool = True   ,							_UpperCAmelCase	:	Union[bool, str, PaddingStrategy] = False   ,							_UpperCAmelCase	:	Union[bool, str, TruncationStrategy] = None   ,							_UpperCAmelCase	:	Optional[int] = None   ,							_UpperCAmelCase	:	int = 0   ,							_UpperCAmelCase	:	Optional[int] = None   ,							_UpperCAmelCase	:	Optional[bool] = None   ,							_UpperCAmelCase	:	Optional[bool] = None   ,							_UpperCAmelCase	:	bool = False   ,							_UpperCAmelCase	:	bool = False   ,							_UpperCAmelCase	:	bool = False   ,							_UpperCAmelCase	:	bool = False   ,							_UpperCAmelCase	:	bool = True   ,							_UpperCAmelCase	:	Optional[Union[str, TensorType]] = None   ,							**_UpperCAmelCase	:	Any   ,							):
       # 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.'		)

       if return_overflowing_tokens is True and return_offsets_mapping is False:
             raise ValueError('You cannot return overflowing tokens without returning the offsets mapping.'		)

       # first, apply the image processor
       _A        =       self.image_processor(images=_UpperCAmelCase   ,							return_tensors=_UpperCAmelCase		)

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

       _A        =       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=_UpperCAmelCase   ,							add_special_tokens=_UpperCAmelCase   ,							padding=_UpperCAmelCase   ,							truncation=_UpperCAmelCase   ,							max_length=_UpperCAmelCase   ,							stride=_UpperCAmelCase   ,							pad_to_multiple_of=_UpperCAmelCase   ,							return_token_type_ids=_UpperCAmelCase   ,							return_attention_mask=_UpperCAmelCase   ,							return_overflowing_tokens=_UpperCAmelCase   ,							return_special_tokens_mask=_UpperCAmelCase   ,							return_offsets_mapping=_UpperCAmelCase   ,							return_length=_UpperCAmelCase   ,							verbose=_UpperCAmelCase   ,							return_tensors=_UpperCAmelCase   ,							**_UpperCAmelCase   ,							)

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

       return encoded_inputs

 def       lowerCAmelCase_							(			self	:	str   ,							_UpperCAmelCase	:	Union[str, Any]   ,							_UpperCAmelCase	:	str		):
       # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
       _A        =       []
       for sample_idx in overflow_to_sample_mapping:
             images_with_overflow.append(images[sample_idx]		)

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

       return images_with_overflow

 def       lowerCAmelCase_							(			self	:	Any   ,							*_UpperCAmelCase	:	Optional[int]   ,							**_UpperCAmelCase	:	List[Any]		):
       return self.tokenizer.batch_decode(*_UpperCAmelCase   ,							**_UpperCAmelCase		)

 def       lowerCAmelCase_							(			self	:	Optional[Any]   ,							*_UpperCAmelCase	:	Dict   ,							**_UpperCAmelCase	:	Tuple		):
       return self.tokenizer.decode(*_UpperCAmelCase   ,							**_UpperCAmelCase		)

 @property
 def       lowerCAmelCase_							(			self	:	Optional[Any]		):
       return ["input_ids", "bbox", "attention_mask", "image"]

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



 @property
 def       lowerCAmelCase_							(			self	:	Any		):
       warnings.warn(
           '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'   ,							_UpperCAmelCase   ,							)
       return self.image_processor







"""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


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 						_snake_case					( _snake_case							: str    ,  _snake_case							: Optional[int]    ,  _snake_case							: List[Any]    ,  _snake_case							: Tuple=None							)   -> List[str]:






      '''simple docstring'''


      _A        =       XLNetConfig.from_json_file(_snake_case							)

      _A        =       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        =       finetuning_task
            _A        =       GLUE_TASKS_NUM_LABELS[finetuning_task]
            _A        =       XLNetForSequenceClassification(_snake_case							)
      elif "squad" in finetuning_task:
            _A        =       finetuning_task
            _A        =       XLNetForQuestionAnswering(_snake_case							)
      else:
            _A        =       XLNetLMHeadModel(_snake_case							)

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

      # Save pytorch-model
      _A        =       os.path.join(_snake_case    ,  _snake_case							)
      _A        =       os.path.join(_snake_case    ,  _snake_case							)
      print(F'''Save PyTorch model to {os.path.abspath(_snake_case							)}'''							)
      torch.save(model.state_dict()    ,  _snake_case							)
      print(F'''Save configuration file to {os.path.abspath(_snake_case							)}'''							)
      with open(_snake_case    ,  '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
       )

