




"""simple docstring"""

from ...configuration_utils import PretrainedConfig
from ...utils import logging


a         =      logging.get_logger(__name__)

a         =      {
    '''naver-clova-ix/donut-base''': '''https://huggingface.co/naver-clova-ix/donut-base/resolve/main/config.json''',
    # See all Donut models at https://huggingface.co/models?filter=donut-swin
}





class     lowercase_	(					__lowerCAmelCase							):



 '''simple docstring'''
 UpperCAmelCase   :					List[Any]				  =     '''donut-swin'''

 UpperCAmelCase   :					int				  =     {
     '''num_attention_heads''': '''num_heads''',
     '''num_hidden_layers''': '''num_layers''',
 }

 def __init__(			self	:	Union[str, Any]   ,							_UpperCAmelCase	:	List[str]=224   ,							_UpperCAmelCase	:	Dict=4   ,							_UpperCAmelCase	:	List[str]=3   ,							_UpperCAmelCase	:	Tuple=96   ,							_UpperCAmelCase	:	Any=[2, 2, 6, 2]   ,							_UpperCAmelCase	:	Optional[int]=[3, 6, 12, 24]   ,							_UpperCAmelCase	:	Tuple=7   ,							_UpperCAmelCase	:	Union[str, Any]=4.0   ,							_UpperCAmelCase	:	List[Any]=True   ,							_UpperCAmelCase	:	str=0.0   ,							_UpperCAmelCase	:	Tuple=0.0   ,							_UpperCAmelCase	:	List[Any]=0.1   ,							_UpperCAmelCase	:	str="gelu"   ,							_UpperCAmelCase	:	Optional[int]=False   ,							_UpperCAmelCase	:	List[str]=0.02   ,							_UpperCAmelCase	:	Optional[Any]=1E-5   ,							**_UpperCAmelCase	:	Union[str, Any]   ,							):
       super().__init__(**_UpperCAmelCase		)

       _A        =       image_size
       _A        =       patch_size
       _A        =       num_channels
       _A        =       embed_dim
       _A        =       depths
       _A        =       len(_UpperCAmelCase		)
       _A        =       num_heads
       _A        =       window_size
       _A        =       mlp_ratio
       _A        =       qkv_bias
       _A        =       hidden_dropout_prob
       _A        =       attention_probs_dropout_prob
       _A        =       drop_path_rate
       _A        =       hidden_act
       _A        =       use_absolute_embeddings
       _A        =       layer_norm_eps
       _A        =       initializer_range
       # we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
       # this indicates the channel dimension after the last stage of the model
       _A        =       int(embed_dim * 2 ** (len(_UpperCAmelCase		) - 1)		)







"""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
       )

