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 	__snake_case	( __lowerCAmelCase		):





      '''simple docstring'''






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



      def __init__(					self     :				Dict    ,  _UpperCamelCase     :				Tuple=None    ,  _UpperCamelCase     :				Optional[Any]=None    ,  **_UpperCamelCase     :				Tuple)				->List[Any]:


          """simple docstring"""


          _lowerCamelCase     :		Dict 						=						None
          if "feature_extractor" in kwargs:
              warnings.warn(
                  """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
                  """ instead."""    ,  _UpperCamelCase    ,  )
              _lowerCamelCase     :		List[Any] 						=						kwargs.pop("""feature_extractor""")

          _lowerCamelCase     :		Tuple 						=						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     :				Optional[int]    ,  _UpperCamelCase     :				int    ,  _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     :				Union[str, Any]    ,  )				->BatchEncoding:


          """simple docstring"""


          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
          _lowerCamelCase     :		str 						=						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):
                  _lowerCamelCase     :		List[Any] 						=						[text]  # add batch dimension (as the image processor always adds a batch dimension)
              _lowerCamelCase     :		Union[str, Any] 						=						features["""words"""]

          _lowerCamelCase     :		Optional[Any] 						=						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
          _lowerCamelCase     :		List[Any] 						=						features.pop("""pixel_values""")
          if return_overflowing_tokens is True:
              _lowerCamelCase     :		List[str] 						=						self.get_overflowing_images(_UpperCamelCase    ,  encoded_inputs["""overflow_to_sample_mapping"""])
          _lowerCamelCase     :		int 						=						images

          return encoded_inputs



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Union[str, Any]    ,  _UpperCamelCase     :				Any    ,  _UpperCamelCase     :				List[Any])				->Any:


          """simple docstring"""


          _lowerCamelCase     :		Tuple 						=						[]
          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 	_SCREAMING_SNAKE_CASE    (					self     :				List[str]    ,  *_UpperCamelCase     :				str    ,  **_UpperCamelCase     :				Optional[Any])				->str:


          """simple docstring"""


          return self.tokenizer.batch_decode(*_UpperCamelCase    ,  **_UpperCamelCase)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[Any]    ,  *_UpperCamelCase     :				int    ,  **_UpperCamelCase     :				Any)				->int:


          """simple docstring"""


          return self.tokenizer.decode(*_UpperCamelCase    ,  **_UpperCamelCase)



      @property
      def 	_SCREAMING_SNAKE_CASE    (					self     :				Dict)				->Tuple:


          """simple docstring"""


          return ["input_ids", "bbox", "attention_mask", "pixel_values"]



      @property
      def 	_SCREAMING_SNAKE_CASE    (					self     :				Tuple)				->str:


          """simple docstring"""


          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 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[int])				->Optional[int]:


          """simple docstring"""


          warnings.warn(
              """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""    ,  _UpperCamelCase    ,  )
          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


lowerCAmelCase			:							int     ={
    "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 						A__  (					__A   , __A   , __A   , __A=None	):







    '''simple docstring'''
    # Initialise PyTorch model
    _lowerCamelCase     :		Tuple 						=						XLNetConfig.from_json_file(__A	)

    _lowerCamelCase     :		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}"""	)
        _lowerCamelCase     :		int 						=						finetuning_task
        _lowerCamelCase     :		Union[str, Any] 						=						GLUE_TASKS_NUM_LABELS[finetuning_task]
        _lowerCamelCase     :		int 						=						XLNetForSequenceClassification(__A	)
    elif "squad" in finetuning_task:
        _lowerCamelCase     :		Dict 						=						finetuning_task
        _lowerCamelCase     :		Optional[Any] 						=						XLNetForQuestionAnswering(__A	)
    else:
        _lowerCamelCase     :		Any 						=						XLNetLMHeadModel(__A	)

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

    # Save pytorch-model
    _lowerCamelCase     :		Optional[Any] 						=						os.path.join(__A   , __A	)
    _lowerCamelCase     :		Any 						=						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__":
    lowerCAmelCase			:							Dict     =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",
    )
    lowerCAmelCase			:							Union[str, Any]     =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
    )




