"""simple docstring"""


import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List

from ..utils import logging


UpperCAmelCase__     						=  logging.get_logger(__name__)






def 			__UpperCAmelCase    (	lowercase=None      ,lowercase=None						):







  """simple docstring"""
  return field(default_factory=lambda: default      ,metadata=lowercase						)


@dataclass
class 				a :
    _snake_case   :     List[str]   							=							list_field(
        default=[]		,							metadata={
            'help': (
                'Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version'
                ' of all available models'
            )
        }		,							)

    _snake_case   :     List[int]   							=							list_field(
        default=[8]		,							metadata={'help': 'List of batch sizes for which memory and time performance will be evaluated'}      )

    _snake_case   :     List[int]   							=							list_field(
        default=[8, 32, 1_28, 5_12]		,							metadata={'help': 'List of sequence lengths for which memory and time performance will be evaluated'}		,							)

    _snake_case   :     bool   							=							field(
        default=lowerCAmelCase_		,							metadata={'help': 'Whether to benchmark inference of model. Inference can be disabled via --no-inference.'}		,							)
    _snake_case   :     bool   							=							field(
        default=lowerCAmelCase_		,							metadata={'help': 'Whether to run on available cuda devices. Cuda can be disabled via --no-cuda.'}		,							)
    _snake_case   :     bool   							=							field(
        default=lowerCAmelCase_		,							metadata={'help': 'Whether to run on available tpu devices. TPU can be disabled via --no-tpu.'}      )
    _snake_case   :     bool   							=							field(default=lowerCAmelCase_		,							metadata={'help': 'Use FP16 to accelerate inference.'}      )
    _snake_case   :     bool   							=							field(default=lowerCAmelCase_		,							metadata={'help': 'Benchmark training of model'}      )
    _snake_case   :     bool   							=							field(default=lowerCAmelCase_		,							metadata={'help': 'Verbose memory tracing'}      )
    _snake_case   :     bool   							=							field(
        default=lowerCAmelCase_		,							metadata={'help': 'Whether to perform speed measurements. Speed measurements can be disabled via --no-speed.'}		,							)
    _snake_case   :     bool   							=							field(
        default=lowerCAmelCase_		,							metadata={
            'help': 'Whether to perform memory measurements. Memory measurements can be disabled via --no-memory'
        }		,							)
    _snake_case   :     bool   							=							field(default=lowerCAmelCase_		,							metadata={'help': 'Trace memory line by line'}      )
    _snake_case   :     bool   							=							field(default=lowerCAmelCase_		,							metadata={'help': 'Save result to a CSV file'}      )
    _snake_case   :     bool   							=							field(default=lowerCAmelCase_		,							metadata={'help': 'Save all print statements in a log file'}      )
    _snake_case   :     bool   							=							field(default=lowerCAmelCase_		,							metadata={'help': 'Whether to print environment information'}      )
    _snake_case   :     bool   							=							field(
        default=lowerCAmelCase_		,							metadata={
            'help': (
                'Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use'
                ' multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled'
                ' for debugging / testing and on TPU.'
            )
        }		,							)
    _snake_case   :     str   							=							field(
        default=F'''inference_time_{round(time()      )}.csv'''		,							metadata={'help': 'CSV filename used if saving time results to csv.'}		,							)
    _snake_case   :     str   							=							field(
        default=F'''inference_memory_{round(time()      )}.csv'''		,							metadata={'help': 'CSV filename used if saving memory results to csv.'}		,							)
    _snake_case   :     str   							=							field(
        default=F'''train_time_{round(time()      )}.csv'''		,							metadata={'help': 'CSV filename used if saving time results to csv for training.'}		,							)
    _snake_case   :     str   							=							field(
        default=F'''train_memory_{round(time()      )}.csv'''		,							metadata={'help': 'CSV filename used if saving memory results to csv for training.'}		,							)
    _snake_case   :     str   							=							field(
        default=F'''env_info_{round(time()      )}.csv'''		,							metadata={'help': 'CSV filename used if saving environment information.'}		,							)
    _snake_case   :     str   							=							field(
        default=F'''log_{round(time()      )}.csv'''		,							metadata={'help': 'Log filename used if print statements are saved in log.'}		,							)
    _snake_case   :     int   							=							field(default=3		,							metadata={'help': 'Times an experiment will be run.'}      )
    _snake_case   :     bool   							=							field(
        default=lowerCAmelCase_		,							metadata={
            'help': (
                'Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain'
                ' model weights.'
            )
        }		,							)


    def    lowerCAmelCase_     (    self		:     Tuple    ):
      warnings.warn(
          f'''The class {self.__class__} is deprecated. Hugging Face Benchmarking utils'''
          """ are deprecated in general and it is advised to use external Benchmarking libraries """
          """ to benchmark Transformer models."""      , __lowerCAmelCase      , )


    def    lowerCAmelCase_     (    self		:     Union[str, Any]    ):
      return json.dumps(dataclasses.asdict(self    )      , indent=2    )


    @property
    def    lowerCAmelCase_     (    self		:     Union[str, Any]    ):
      if len(self.models    ) <= 0:
        raise ValueError(
            """Please make sure you provide at least one model name / model identifier, *e.g.* `--models"""
            """ bert-base-cased` or `args.models = ['bert-base-cased']."""    )
      return self.models


    @property
    def    lowerCAmelCase_     (    self		:     Any    ):
      if not self.multi_process:
        return False
      elif self.is_tpu:
        logger.info("""Multiprocessing is currently not possible on TPU."""    )
        return False
      else:
        return True

"""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 			__UpperCAmelCase    (	lowercase      ,lowercase      ,lowercase      ,lowercase      ,lowercase						):







  """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
  _UpperCAmelCase         =					TapasConfig.from_json_file(lowercase						)
  # set absolute/relative position embeddings parameter
  _UpperCAmelCase         =					reset_position_index_per_cell

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

    _UpperCAmelCase         =					TapasForQuestionAnswering(config=lowercase						)
  elif task == "WIKISQL_SUPERVISED":
    # run_task_main.py hparams
    _UpperCAmelCase         =					4
    _UpperCAmelCase         =					False
    # hparam_utils.py hparams
    _UpperCAmelCase         =					36.45_19
    _UpperCAmelCase         =					0.90_34_21
    _UpperCAmelCase         =					2_22.0_88
    _UpperCAmelCase         =					True
    _UpperCAmelCase         =					True
    _UpperCAmelCase         =					True
    _UpperCAmelCase         =					0.76_31_41

    _UpperCAmelCase         =					TapasForQuestionAnswering(config=lowercase						)
  elif task == "TABFACT":
    _UpperCAmelCase         =					TapasForSequenceClassification(config=lowercase						)
  elif task == "MLM":
    _UpperCAmelCase         =					TapasForMaskedLM(config=lowercase						)
  elif task == "INTERMEDIATE_PRETRAINING":
    _UpperCAmelCase         =					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}'''						)
  _UpperCAmelCase         =					TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + """vocab.txt"""      ,model_max_length=5_12						)
  tokenizer.save_pretrained(lowercase						)

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


if __name__ == "__main__":
    UpperCAmelCase__     						=  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__     						=  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,
    )
