


'''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_		:		Union[str, Any]   ,							UpperCAmelCase_		:		List[Any]   ,							UpperCAmelCase_		:		List[str]   ,							UpperCAmelCase_		:		List[Any]   ,							UpperCAmelCase_		:		Union[str, Any]						):
 lowerCamelCase_						=    TapasConfig.from_json_file(__a						)
 # 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=__a						)
 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=__a						)
 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=__a						)
 elif task == "TABFACT":
  lowerCamelCase_						=    TapasForSequenceClassification(config=__a						)
 elif task == "MLM":
  lowerCamelCase_						=    TapasForMaskedLM(config=__a						)
 elif task == "INTERMEDIATE_PRETRAINING":
  lowerCamelCase_						=    TapasModel(config=__a						)
 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(__a   ,							__a   ,							__a						)

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

 # 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(__a						)

 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_    :							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,
     )








"""simple docstring"""







import warnings

from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor


a_							=    logging.get_logger(__name__)







class 					A_(SCREAMING_SNAKE_CASE_      ):







			"""simple docstring"""



			def __init__(	self				,   *A				,   **A							):
						warnings.warn(
						    'The class SegformerFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
						    ' Please use SegformerImageProcessor instead.'				,   A				,   )
						super().__init__(*A				,   **A							)





