

from collections import OrderedDict
from typing import List, Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_a		       =   logging.get_logger(__name__)

_a		       =   {
    '''google/efficientnet-b7''': '''https://huggingface.co/google/efficientnet-b7/resolve/main/config.json''',
}






class 							A_ (		lowerCAmelCase_					):
     _lowercase			:     Any								=	'efficientnet'


     def __init__(							self			:		Any			,  UpperCAmelCase			:		int = 3			,  UpperCAmelCase			:		int = 6_0_0			,  UpperCAmelCase			:		float = 2.0			,  UpperCAmelCase			:		float = 3.1			,  UpperCAmelCase			:		int = 8			,  UpperCAmelCase			:		List[int] = [3, 3, 5, 3, 5, 5, 3]			,  UpperCAmelCase			:		List[int] = [3_2, 1_6, 2_4, 4_0, 8_0, 1_1_2, 1_9_2]			,  UpperCAmelCase			:		List[int] = [1_6, 2_4, 4_0, 8_0, 1_1_2, 1_9_2, 3_2_0]			,  UpperCAmelCase			:		List[int] = []			,  UpperCAmelCase			:		List[int] = [1, 2, 2, 2, 1, 2, 1]			,  UpperCAmelCase			:		List[int] = [1, 2, 2, 3, 3, 4, 1]			,  UpperCAmelCase			:		List[int] = [1, 6, 6, 6, 6, 6, 6]			,  UpperCAmelCase			:		float = 0.25			,  UpperCAmelCase			:		str = "swish"			,  UpperCAmelCase			:		int = 2_5_6_0			,  UpperCAmelCase			:		str = "mean"			,  UpperCAmelCase			:		float = 0.02			,  UpperCAmelCase			:		float = 0.001			,  UpperCAmelCase			:		float = 0.99			,  UpperCAmelCase			:		float = 0.5			,  UpperCAmelCase			:		float = 0.2			,  **UpperCAmelCase			:		List[Any]			,  )							->	Optional[Any]:
      super().__init__(**__lowerCAmelCase					)

      __lowerCAmelCase:   Union[str, Any]   	=      num_channels
      __lowerCAmelCase:   Optional[Any]   	=      image_size
      __lowerCAmelCase:   Any   	=      width_coefficient
      __lowerCAmelCase:   Tuple   	=      depth_coefficient
      __lowerCAmelCase:   List[str]   	=      depth_divisor
      __lowerCAmelCase:   Any   	=      kernel_sizes
      __lowerCAmelCase:   int   	=      in_channels
      __lowerCAmelCase:   Dict   	=      out_channels
      __lowerCAmelCase:   Tuple   	=      depthwise_padding
      __lowerCAmelCase:   Union[str, Any]   	=      strides
      __lowerCAmelCase:   Any   	=      num_block_repeats
      __lowerCAmelCase:   Dict   	=      expand_ratios
      __lowerCAmelCase:   Tuple   	=      squeeze_expansion_ratio
      __lowerCAmelCase:   List[Any]   	=      hidden_act
      __lowerCAmelCase:   Tuple   	=      hidden_dim
      __lowerCAmelCase:   str   	=      pooling_type
      __lowerCAmelCase:   List[Any]   	=      initializer_range
      __lowerCAmelCase:   Tuple   	=      batch_norm_eps
      __lowerCAmelCase:   Optional[Any]   	=      batch_norm_momentum
      __lowerCAmelCase:   Optional[int]   	=      dropout_rate
      __lowerCAmelCase:   Optional[Any]   	=      drop_connect_rate
      __lowerCAmelCase:   str   	=      sum(__lowerCAmelCase					) * 4







class 							A_ (		lowerCAmelCase_					):
     _lowercase			:     Dict								=	version.parse('1.11'					)


     @property
     def   UpperCAmelCase  (							self			:		Any					)							->	List[Any]:
      return OrderedDict(
          [
              ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
          ]					)


     @property
     def   UpperCAmelCase  (							self			:		int					)							->	Optional[int]:
      return 1E-5






"""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,
    )
