
"""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 	__a     (		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase,		__lowerCamelCase  ):
   # 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_					:					str			     =    TapasConfig.from_json_file(__lowerCamelCase  )
   # set absolute/relative position embeddings parameter
   UpperCAmelCase_					:					Union[str, Any]			     =    reset_position_index_per_cell

   # set remaining parameters of TapasConfig as well as the model based on the task
   if task == "SQA":
      UpperCAmelCase_					:					str			     =    TapasForQuestionAnswering(config=__lowerCamelCase  )
   elif task == "WTQ":
      # run_task_main.py hparams
      UpperCAmelCase_					:					Union[str, Any]			     =    4
      UpperCAmelCase_					:					int			     =    True
      # hparam_utils.py hparams
      UpperCAmelCase_					:					Optional[Any]			     =    0.66_4694
      UpperCAmelCase_					:					Tuple			     =    0.20_7951
      UpperCAmelCase_					:					Dict			     =    0.12_1194
      UpperCAmelCase_					:					Optional[Any]			     =    True
      UpperCAmelCase_					:					Optional[Any]			     =    True
      UpperCAmelCase_					:					int			     =    False
      UpperCAmelCase_					:					str			     =    0.035_2513

      UpperCAmelCase_					:					List[Any]			     =    TapasForQuestionAnswering(config=__lowerCamelCase  )
   elif task == "WIKISQL_SUPERVISED":
      # run_task_main.py hparams
      UpperCAmelCase_					:					List[str]			     =    4
      UpperCAmelCase_					:					List[str]			     =    False
      # hparam_utils.py hparams
      UpperCAmelCase_					:					List[Any]			     =    36.4519
      UpperCAmelCase_					:					int			     =    0.90_3421
      UpperCAmelCase_					:					Union[str, Any]			     =    222.088
      UpperCAmelCase_					:					List[str]			     =    True
      UpperCAmelCase_					:					str			     =    True
      UpperCAmelCase_					:					str			     =    True
      UpperCAmelCase_					:					Tuple			     =    0.76_3141

      UpperCAmelCase_					:					Dict			     =    TapasForQuestionAnswering(config=__lowerCamelCase  )
   elif task == "TABFACT":
      UpperCAmelCase_					:					List[Any]			     =    TapasForSequenceClassification(config=__lowerCamelCase  )
   elif task == "MLM":
      UpperCAmelCase_					:					Optional[Any]			     =    TapasForMaskedLM(config=__lowerCamelCase  )
   elif task == "INTERMEDIATE_PRETRAINING":
      UpperCAmelCase_					:					int			     =    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}"""  )
   UpperCAmelCase_					:					Union[str, Any]			     =    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__":
    _a		       =							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.'
    )
    _a		       =							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 ...configuration_utils import PretrainedConfig
from ...utils import logging


_a      :		List[Any]           =   logging.get_logger(__name__)

_a      :		Any           =   {
    'microsoft/swinv2-tiny-patch4-window8-256': (
        'https://huggingface.co/microsoft/swinv2-tiny-patch4-window8-256/resolve/main/config.json'
    ),
}
class 						__A							( SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:			int							=		"swinv2"

     _UpperCamelCase							:			List[str]							=		{
         "num_attention_heads": "num_heads",
         "num_hidden_layers": "num_layers",
     }






     def __init__(		self				,     a__=224				,     a__=4				,     a__=3				,     a__=96				,     a__=[2, 2, 6, 2]				,     a__=[3, 6, 12, 24]				,     a__=7				,     a__=4.0				,     a__=True				,     a__=0.0				,     a__=0.0				,     a__=0.1				,     a__="gelu"				,     a__=False				,     a__=0.0_2				,     a__=1e-5				,     a__=32				,     **a__				,     ):
       super().__init__(**a__  )

       _lowerCAmelCase						:       int  			=     image_size
       _lowerCAmelCase						:       Optional[Any]  			=     patch_size
       _lowerCAmelCase						:       Any  			=     num_channels
       _lowerCAmelCase						:       List[Any]  			=     embed_dim
       _lowerCAmelCase						:       Optional[int]  			=     depths
       _lowerCAmelCase						:       List[Any]  			=     len(a__  )
       _lowerCAmelCase						:       Any  			=     num_heads
       _lowerCAmelCase						:       Tuple  			=     window_size
       _lowerCAmelCase						:       Tuple  			=     mlp_ratio
       _lowerCAmelCase						:       Any  			=     qkv_bias
       _lowerCAmelCase						:       Optional[int]  			=     hidden_dropout_prob
       _lowerCAmelCase						:       Tuple  			=     attention_probs_dropout_prob
       _lowerCAmelCase						:       str  			=     drop_path_rate
       _lowerCAmelCase						:       List[str]  			=     hidden_act
       _lowerCAmelCase						:       List[str]  			=     use_absolute_embeddings
       _lowerCAmelCase						:       Optional[int]  			=     layer_norm_eps
       _lowerCAmelCase						:       List[Any]  			=     initializer_range
       _lowerCAmelCase						:       Any  			=     encoder_stride
       # we set the hidden_size attribute in order to make Swinv2 work with VisionEncoderDecoderModel
       # this indicates the channel dimension after the last stage of the model
       _lowerCAmelCase						:       Tuple  			=     int(embed_dim * 2 ** (len(a__  ) - 1)  )
       _lowerCAmelCase						:       Tuple  			=     (0, 0, 0, 0)




