


'''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 __snake_case					(      UpperCAmelCase_						:						str					,				UpperCAmelCase_						:						int					,				UpperCAmelCase_						:						Union[str, Any]					,				UpperCAmelCase_						:						List[str]					,				UpperCAmelCase_						:						Any		):
 # 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
 lowerCamelCase_      =  TapasConfig.from_json_file(__UpperCAmelCase		)
 # set absolute/relative position embeddings parameter
 lowerCamelCase_      =  reset_position_index_per_cell

 # set remaining parameters of TapasConfig as well as the model based on the task
 if task == "SQA":
  lowerCamelCase_      =  TapasForQuestionAnswering(config=__UpperCAmelCase		)
 elif task == "WTQ":
  # run_task_main.py hparams
  lowerCamelCase_      =  4
  lowerCamelCase_      =  True
  # hparam_utils.py hparams
  lowerCamelCase_      =  0.66_4694
  lowerCamelCase_      =  0.20_7951
  lowerCamelCase_      =  0.12_1194
  lowerCamelCase_      =  True
  lowerCamelCase_      =  True
  lowerCamelCase_      =  False
  lowerCamelCase_      =  0.035_2513

  lowerCamelCase_      =  TapasForQuestionAnswering(config=__UpperCAmelCase		)
 elif task == "WIKISQL_SUPERVISED":
  # run_task_main.py hparams
  lowerCamelCase_      =  4
  lowerCamelCase_      =  False
  # hparam_utils.py hparams
  lowerCamelCase_      =  36.4519
  lowerCamelCase_      =  0.90_3421
  lowerCamelCase_      =  222.088
  lowerCamelCase_      =  True
  lowerCamelCase_      =  True
  lowerCamelCase_      =  True
  lowerCamelCase_      =  0.76_3141

  lowerCamelCase_      =  TapasForQuestionAnswering(config=__UpperCAmelCase		)
 elif task == "TABFACT":
  lowerCamelCase_      =  TapasForSequenceClassification(config=__UpperCAmelCase		)
 elif task == "MLM":
  lowerCamelCase_      =  TapasForMaskedLM(config=__UpperCAmelCase		)
 elif task == "INTERMEDIATE_PRETRAINING":
  lowerCamelCase_      =  TapasModel(config=__UpperCAmelCase		)
 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(__UpperCAmelCase					,				__UpperCAmelCase					,				__UpperCAmelCase		)

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

 # Save tokenizer files
 print(F'''Save tokenizer files to {pytorch_dump_path}'''		)
 lowerCamelCase_      =  TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + "vocab.txt"					,				model_max_length=512		)
 tokenizer.save_pretrained(__UpperCAmelCase		)

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


if __name__ == "__main__":
     a_  :				Union[str, Any]						= 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."""
     )
     a_  :				str						= 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_imagegpt import ImageGPTImageProcessor


lowerCamelCase__ :   List[str]					=							logging.get_logger(__name__)





class        lowerCamelCase_							(      _SCREAMING_SNAKE_CASE					):


 '''simple docstring'''







 def __init__(							self  :		str				,				*_lowerCAmelCase  :		Optional[Any]				,				**_lowerCAmelCase  :		Optional[Any]						):
       warnings.warn(
           'The class ImageGPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
           ' Please use ImageGPTImageProcessor instead.'				,				_lowerCAmelCase				,				)
       super().__init__(*_lowerCAmelCase				,				**_lowerCAmelCase						)