


"""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


__snake_case         =					{
    """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 						__lowerCAmelCase		(		lowercase						:						Tuple		,							lowercase						:						Union[str, Any]		,							lowercase						:						str		,							lowercase						:						Optional[int]=None				)			->		Union[str, Any]:


    """simple docstring"""





    snake_case					:   int         =					XLNetConfig.from_json_file(lowercase				)

    snake_case					:   int         =					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					:   Optional[int]         =					finetuning_task
        snake_case					:   Optional[Any]         =					GLUE_TASKS_NUM_LABELS[finetuning_task]
        snake_case					:   List[str]         =					XLNetForSequenceClassification(lowercase				)
    elif "squad" in finetuning_task:
        snake_case					:   List[str]         =					finetuning_task
        snake_case					:   Any         =					XLNetForQuestionAnswering(lowercase				)
    else:
        snake_case					:   str         =					XLNetLMHeadModel(lowercase				)

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

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


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









"""simple docstring"""



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


__snake_case         =					logging.get_logger(__name__)

__snake_case         =					{
    """microsoft/markuplm-base""": """https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json""",
    """microsoft/markuplm-large""": """https://huggingface.co/microsoft/markuplm-large/resolve/main/config.json""",
}







class _lowerCAmelCase       (   snake_case_			):
   __UpperCAmelCase				: Tuple													=						'''markuplm'''







   def __init__(			self		,			UpperCamelCase__=3_0522		,			UpperCamelCase__=768		,			UpperCamelCase__=12		,			UpperCamelCase__=12		,			UpperCamelCase__=3072		,			UpperCamelCase__="gelu"		,			UpperCamelCase__=0.1		,			UpperCamelCase__=0.1		,			UpperCamelCase__=512		,			UpperCamelCase__=2		,			UpperCamelCase__=0.02		,			UpperCamelCase__=1e-12		,			UpperCamelCase__=0		,			UpperCamelCase__=0		,			UpperCamelCase__=2		,			UpperCamelCase__=256		,			UpperCamelCase__=1024		,			UpperCamelCase__=216		,			UpperCamelCase__=1001		,			UpperCamelCase__=32		,			UpperCamelCase__=50		,			UpperCamelCase__="absolute"		,			UpperCamelCase__=True		,			UpperCamelCase__=None		,			**UpperCamelCase__		,			)			->	Any:

       '''simple docstring'''




       super().__init__(
           pad_token_id=UpperCamelCase__		,			bos_token_id=UpperCamelCase__		,			eos_token_id=UpperCamelCase__		,			**UpperCamelCase__		,			)
       snake_case					:   List[str]         =					vocab_size
       snake_case					:   Optional[int]         =					hidden_size
       snake_case					:   List[str]         =					num_hidden_layers
       snake_case					:   Union[str, Any]         =					num_attention_heads
       snake_case					:   Dict         =					hidden_act
       snake_case					:   Tuple         =					intermediate_size
       snake_case					:   int         =					hidden_dropout_prob
       snake_case					:   Tuple         =					attention_probs_dropout_prob
       snake_case					:   Dict         =					max_position_embeddings
       snake_case					:   Optional[Any]         =					type_vocab_size
       snake_case					:   Any         =					initializer_range
       snake_case					:   Union[str, Any]         =					layer_norm_eps
       snake_case					:   List[str]         =					position_embedding_type
       snake_case					:   Optional[Any]         =					use_cache
       snake_case					:   str         =					classifier_dropout
       # additional properties
       snake_case					:   Optional[int]         =					max_depth
       snake_case					:   Optional[int]         =					max_xpath_tag_unit_embeddings
       snake_case					:   str         =					max_xpath_subs_unit_embeddings
       snake_case					:   int         =					tag_pad_id
       snake_case					:   Dict         =					subs_pad_id
       snake_case					:   List[str]         =					xpath_unit_hidden_size





