
'''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_	(				SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_						)     ->     Optional[Any]:




   """simple docstring"""
   # 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
   _SCREAMING_SNAKE_CASE      =				TapasConfig.from_json_file(SCREAMING_SNAKE_CASE_						)
   # set absolute/relative position embeddings parameter
   _SCREAMING_SNAKE_CASE      =				reset_position_index_per_cell

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

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

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

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

   # Save tokenizer files
   print(F"Save tokenizer files to {pytorch_dump_path}"						)
   _SCREAMING_SNAKE_CASE      =				TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + """vocab.txt"""    ,      model_max_length=5_12						)
   tokenizer.save_pretrained(SCREAMING_SNAKE_CASE_						)

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


if __name__ == "__main__":
     UpperCamelCase__      :		List[str]       							=  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."
     )
     UpperCamelCase__      :		Optional[int]       							=  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'''



from pathlib import PurePosixPath
from typing import Optional

import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo

from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url

class    _a				(_lowerCamelCase):





       """simple docstring"""




       SCREAMING_SNAKE_CASE      =  ''
       SCREAMING_SNAKE_CASE      =  'hf-legacy'  # "hf://"" is reserved for hffs






       def __init__(							self       ,							A__ = None       ,							A__ = None       ,							**A__       ,							)    ->			Optional[int]:
          super().__init__(self       ,							**A__      )
          _SCREAMING_SNAKE_CASE      =				repo_info
          _SCREAMING_SNAKE_CASE      =				token
          _SCREAMING_SNAKE_CASE      =				None






       def        UpperCamelCase				(							self      )    ->			Tuple:
          if self.dir_cache is None:
             _SCREAMING_SNAKE_CASE      =				{}
             for hf_file in self.repo_info.siblings:
                # TODO(QL): add sizes
                _SCREAMING_SNAKE_CASE      =				{
                    """name""": hf_file.rfilename,
                    """size""": None,
                    """type""": """file""",
                }
                self.dir_cache.update(
                    {
                        str(A__      ): {"""name""": str(A__      ), """size""": None, """type""": """directory"""}
                        for d in list(PurePosixPath(hf_file.rfilename      ).parents      )[:-1]
                    }      )






       def        UpperCamelCase				(							self       ,							A__       ,							A__ = "rb"       ,							**A__       ,							)    ->			Optional[int]:
          if not isinstance(self.repo_info       ,							A__      ):
             raise NotImplementedError(F"Open is only implemented for dataset repositories, but got {self.repo_info}"      )
          _SCREAMING_SNAKE_CASE      =				hf_hub_url(self.repo_info.id       ,							A__       ,							revision=self.repo_info.sha      )
          return fsspec.open(
              A__       ,							mode=A__       ,							headers=get_authentication_headers_for_url(A__       ,							use_auth_token=self.token      )       ,							client_kwargs={"""trust_env""": True}       ,							).open()






       def        UpperCamelCase				(							self       ,							A__       ,							**A__      )    ->			str:
          self._get_dirs()
          _SCREAMING_SNAKE_CASE      =				self._strip_protocol(A__      )
          if path in self.dir_cache:
             return self.dir_cache[path]
          else:
             raise FileNotFoundError(A__      )






       def        UpperCamelCase				(							self       ,							A__       ,							A__=False       ,							**A__      )    ->			List[Any]:
          self._get_dirs()
          _SCREAMING_SNAKE_CASE      =				PurePosixPath(path.strip("""/"""      )      )
          _SCREAMING_SNAKE_CASE      =				{}
          for p, f in self.dir_cache.items():
             _SCREAMING_SNAKE_CASE      =				PurePosixPath(p.strip("""/"""      )      )
             _SCREAMING_SNAKE_CASE      =				p.parent
             if root == path:
                _SCREAMING_SNAKE_CASE      =				f
          _SCREAMING_SNAKE_CASE      =				list(paths.values()      )
          if detail:
             return out
          else:
             return sorted(f["""name"""] for f in out      )






