





from collections import OrderedDict
from typing import Mapping

from packaging import version

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


_snake_case  :   int						   =  logging.get_logger(__name__)

_snake_case  :   List[str]						   =  {
    'facebook/data2vec-vision-base-ft': (
        'https://huggingface.co/facebook/data2vec-vision-base-ft/resolve/main/config.json'
    ),
}



class     _UpperCAmelCase   (   __lowercase   ):

  """simple docstring"""


  a_      =			"data2vec-vision"






  def __init__(    self			:		Optional[Any] ,  lowerCAmelCase_			:		List[Any]=7_6_8 ,  lowerCAmelCase_			:		Tuple=1_2 ,  lowerCAmelCase_			:		Any=1_2 ,  lowerCAmelCase_			:		Optional[int]=3_0_7_2 ,  lowerCAmelCase_			:		Tuple="gelu" ,  lowerCAmelCase_			:		Dict=0.0 ,  lowerCAmelCase_			:		Tuple=0.0 ,  lowerCAmelCase_			:		Dict=0.02 ,  lowerCAmelCase_			:		Optional[int]=1e-12 ,  lowerCAmelCase_			:		int=2_2_4 ,  lowerCAmelCase_			:		Optional[Any]=1_6 ,  lowerCAmelCase_			:		List[str]=3 ,  lowerCAmelCase_			:		Tuple=False ,  lowerCAmelCase_			:		Any=False ,  lowerCAmelCase_			:		Optional[int]=False ,  lowerCAmelCase_			:		str=False ,  lowerCAmelCase_			:		Any=0.1 ,  lowerCAmelCase_			:		int=0.1 ,  lowerCAmelCase_			:		Optional[Any]=True ,  lowerCAmelCase_			:		Union[str, Any]=[3, 5, 7, 1_1] ,  lowerCAmelCase_			:		Any=[1, 2, 3, 6] ,  lowerCAmelCase_			:		int=True ,  lowerCAmelCase_			:		Optional[Any]=0.4 ,  lowerCAmelCase_			:		Tuple=2_5_6 ,  lowerCAmelCase_			:		Optional[Any]=1 ,  lowerCAmelCase_			:		List[Any]=False ,  lowerCAmelCase_			:		Union[str, Any]=2_5_5 ,  **lowerCAmelCase_			:		int ,  )							->						List[str]:
     super().__init__(**snake_case_					)

     __lowerCAmelCase							     =     hidden_size
     __lowerCAmelCase							     =     num_hidden_layers
     __lowerCAmelCase							     =     num_attention_heads
     __lowerCAmelCase							     =     intermediate_size
     __lowerCAmelCase							     =     hidden_act
     __lowerCAmelCase							     =     hidden_dropout_prob
     __lowerCAmelCase							     =     attention_probs_dropout_prob
     __lowerCAmelCase							     =     initializer_range
     __lowerCAmelCase							     =     layer_norm_eps

     __lowerCAmelCase							     =     image_size
     __lowerCAmelCase							     =     patch_size
     __lowerCAmelCase							     =     num_channels
     __lowerCAmelCase							     =     use_mask_token
     __lowerCAmelCase							     =     use_absolute_position_embeddings
     __lowerCAmelCase							     =     use_relative_position_bias
     __lowerCAmelCase							     =     use_shared_relative_position_bias
     __lowerCAmelCase							     =     layer_scale_init_value
     __lowerCAmelCase							     =     drop_path_rate
     __lowerCAmelCase							     =     use_mean_pooling
     # decode head attributes (semantic segmentation)
     __lowerCAmelCase							     =     out_indices
     __lowerCAmelCase							     =     pool_scales
     # auxiliary head attributes (semantic segmentation)
     __lowerCAmelCase							     =     use_auxiliary_head
     __lowerCAmelCase							     =     auxiliary_loss_weight
     __lowerCAmelCase							     =     auxiliary_channels
     __lowerCAmelCase							     =     auxiliary_num_convs
     __lowerCAmelCase							     =     auxiliary_concat_input
     __lowerCAmelCase							     =     semantic_loss_ignore_index





class     _UpperCAmelCase   (   __lowercase   ):

  """simple docstring"""


  a_      =			version.parse("""1.11"""   )






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






  @property
  def     lowercase		(    self			:		Optional[int]					)							->						Any:
     return 1e-4








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   a_   (			lowerCAmelCase_				:      str,     lowerCAmelCase_				:      Optional[int],     lowerCAmelCase_				:      Any,     lowerCAmelCase_				:      List[Any],     lowerCAmelCase_				:      Tuple     ):
   # 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
   __lowerCAmelCase							     =     TapasConfig.from_json_file(lowerCAmelCase_     )
   # set absolute/relative position embeddings parameter
   __lowerCAmelCase							     =     reset_position_index_per_cell

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

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

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

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

   # Save tokenizer files
   print(F"""Save tokenizer files to {pytorch_dump_path}"""     )
   __lowerCAmelCase							     =     TapasTokenizer(vocab_file=tf_checkpoint_path[:-10] + 'vocab.txt',     model_max_length=512     )
   tokenizer.save_pretrained(lowerCAmelCase_     )

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


if __name__ == "__main__":
     _snake_case  :   Union[str, Any]						   =  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.'
     )
     _snake_case  :   Optional[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,
     )

