
"""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__			(							UpperCAmelCase_		,      UpperCAmelCase_		,      UpperCAmelCase_		,      UpperCAmelCase_		,      UpperCAmelCase_				)->							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
       UpperCamelCase					 =		TapasConfig.from_json_file(UpperCAmelCase_				)
       # 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=UpperCAmelCase_				)
       elif task == "WTQ":
              # run_task_main.py hparams
              UpperCamelCase					 =		4
              UpperCamelCase					 =		True
              # hparam_utils.py hparams
              UpperCamelCase					 =		0.66_4694
              UpperCamelCase					 =		0.20_7951
              UpperCamelCase					 =		0.12_1194
              UpperCamelCase					 =		True
              UpperCamelCase					 =		True
              UpperCamelCase					 =		False
              UpperCamelCase					 =		0.035_2513

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

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

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


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE      =     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      =     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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


SCREAMING_SNAKE_CASE      =     {
    """configuration_graphormer""": ["""GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GraphormerConfig"""],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE      =     [
          """GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """GraphormerForGraphClassification""",
          """GraphormerModel""",
          """GraphormerPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_graphormer import GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, GraphormerConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_graphormer import (
                GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                GraphormerForGraphClassification,
                GraphormerModel,
                GraphormerPreTrainedModel,
            )


else:
      import sys

      SCREAMING_SNAKE_CASE      =     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





