"""simple docstring"""



import argparse

from transformers import (
    TapasConfig,
    TapasForMaskedLM,
    TapasForQuestionAnswering,
    TapasForSequenceClassification,
    TapasModel,
    TapasTokenizer,
    load_tf_weights_in_tapas,
)
from transformers.utils import logging


logging.set_verbosity_info()



def 	_lowerCamelCase(	a   ,    a   ,    a   ,    a   ,    a				):
       # Initialise PyTorch model.
       # If you want to convert a checkpoint that uses absolute position embeddings, make sure to set reset_position_index_per_cell of
       # TapasConfig to False.

       # initialize configuration from json file
       __a       =    TapasConfig.from_json_file(lowercase_				)
       # set absolute/relative position embeddings parameter
       __a       =    reset_position_index_per_cell

       # set remaining parameters of TapasConfig as well as the model based on the task
       if task == "SQA":
              __a       =    TapasForQuestionAnswering(config=lowercase_				)
       elif task == "WTQ":
              # run_task_main.py hparams
              __a       =    4
              __a       =    True
              # hparam_utils.py hparams
              __a       =    0.66_46_94
              __a       =    0.20_79_51
              __a       =    0.12_11_94
              __a       =    True
              __a       =    True
              __a       =    False
              __a       =    0.0_35_25_13

              __a       =    TapasForQuestionAnswering(config=lowercase_				)
       elif task == "WIKISQL_SUPERVISED":
              # run_task_main.py hparams
              __a       =    4
              __a       =    False
              # hparam_utils.py hparams
              __a       =    3_6.4_5_1_9
              __a       =    0.90_34_21
              __a       =    2_2_2.0_8_8
              __a       =    True
              __a       =    True
              __a       =    True
              __a       =    0.76_31_41

              __a       =    TapasForQuestionAnswering(config=lowercase_				)
       elif task == "TABFACT":
              __a       =    TapasForSequenceClassification(config=lowercase_				)
       elif task == "MLM":
              __a       =    TapasForMaskedLM(config=lowercase_				)
       elif task == "INTERMEDIATE_PRETRAINING":
              __a       =    TapasModel(config=lowercase_				)
       else:
              raise ValueError(F"Task {task} not supported."				)

       print(F"Building PyTorch model from configuration: {config}"				)
       # Load weights from tf checkpoint
       load_tf_weights_in_tapas(lowercase_   ,    lowercase_   ,    lowercase_				)

       # Save pytorch-model (weights and configuration)
       print(F"Save PyTorch model to {pytorch_dump_path}"				)
       model.save_pretrained(lowercase_				)

       # Save tokenizer files
       print(F"Save tokenizer files to {pytorch_dump_path}"				)
       __a       =    TapasTokenizer(vocab_file=tf_checkpoint_path[:-1_0] + "vocab.txt"   ,    model_max_length=5_1_2				)
       tokenizer.save_pretrained(lowercase_				)

       print("Used relative position embeddings:"   ,    model.config.reset_position_index_per_cell				)


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE__:Optional[int]									=			argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       """--task""", default="""SQA""", type=str, help="""Model task for which to convert a checkpoint. Defaults to SQA."""
   )
   parser.add_argument(
       """--reset_position_index_per_cell""",
       default=False,
       action="""store_true""",
       help="""Whether to use relative position embeddings or not. Defaults to True.""",
   )
   parser.add_argument(
       """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
   )
   parser.add_argument(
       """--tapas_config_file""",
       default=None,
       type=str,
       required=True,
       help=(
           """The config json file corresponding to the pre-trained TAPAS model. \n"""
           """This specifies the model architecture."""
       ),
   )
   parser.add_argument(
       """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
   )
   SCREAMING_SNAKE_CASE__:Union[str, Any]									=			parser.parse_args()
   convert_tf_checkpoint_to_pytorch(
       args.task,
       args.reset_position_index_per_cell,
       args.tf_checkpoint_path,
       args.tapas_config_file,
       args.pytorch_dump_path,
   )







import warnings

from ...utils import logging
from .image_processing_deit import DeiTImageProcessor


_lowerCamelCase      							=   logging.get_logger(__name__)







class       __A							(   lowerCamelCase__	):




      """simple docstring"""







      def __init__(		self   ,				*a__   ,				**a__):



             """simple docstring"""





             warnings.warn(
                 '''The class DeiTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
                 ''' use DeiTImageProcessor instead.'''   ,				a__   ,				)
             super().__init__(*a__   ,				**a__)

