"""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    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case,      snake_case,      snake_case,      snake_case):
     # 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
     __snake_case				=							TapasConfig.from_json_file(snake_case)
     # set absolute/relative position embeddings parameter
     __snake_case				=							reset_position_index_per_cell

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

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

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

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

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

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


if __name__ == "__main__":
       __lowercase		:		Dict														=							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."
       )
       __lowercase		:		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"""



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


__lowercase		:		Dict														=							{
    "configuration_vision_text_dual_encoder": ["VisionTextDualEncoderConfig"],
    "processing_vision_text_dual_encoder": ["VisionTextDualEncoderProcessor"],
}


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowercase		:		Optional[int]														=							["VisionTextDualEncoderModel"]


try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowercase		:		Optional[int]														=							["FlaxVisionTextDualEncoderModel"]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowercase		:		Any														=							["TFVisionTextDualEncoderModel"]


if TYPE_CHECKING:
       from .configuration_vision_text_dual_encoder import VisionTextDualEncoderConfig
       from .processing_vision_text_dual_encoder import VisionTextDualEncoderProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_vision_text_dual_encoder import VisionTextDualEncoderModel

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_vision_text_dual_encoder import FlaxVisionTextDualEncoderModel

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_vision_text_dual_encoder import TFVisionTextDualEncoderModel


else:
       import sys

       __lowercase		:		Tuple														=							_LazyModule(__name__, globals()["__file__"], _import_structure)