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__  (					__A   , __A   , __A   , __A   , __A	):







    '''simple docstring'''
    _lowerCamelCase     :		Optional[int] 						=						TapasConfig.from_json_file(__A	)
    # set absolute/relative position embeddings parameter
    _lowerCamelCase     :		List[str] 						=						reset_position_index_per_cell

    # set remaining parameters of TapasConfig as well as the model based on the task
    if task == "SQA":
        _lowerCamelCase     :		Optional[int] 						=						TapasForQuestionAnswering(config=__A	)
    elif task == "WTQ":
        # run_task_main.py hparams
        _lowerCamelCase     :		int 						=						4
        _lowerCamelCase     :		Union[str, Any] 						=						True
        # hparam_utils.py hparams
        _lowerCamelCase     :		Any 						=						0.664_694
        _lowerCamelCase     :		int 						=						0.207_951
        _lowerCamelCase     :		Union[str, Any] 						=						0.121_194
        _lowerCamelCase     :		int 						=						True
        _lowerCamelCase     :		List[Any] 						=						True
        _lowerCamelCase     :		List[str] 						=						False
        _lowerCamelCase     :		Tuple 						=						0.0_352_513

        _lowerCamelCase     :		Tuple 						=						TapasForQuestionAnswering(config=__A	)
    elif task == "WIKISQL_SUPERVISED":
        # run_task_main.py hparams
        _lowerCamelCase     :		Dict 						=						4
        _lowerCamelCase     :		List[str] 						=						False
        # hparam_utils.py hparams
        _lowerCamelCase     :		str 						=						36.4_519
        _lowerCamelCase     :		Any 						=						0.903_421
        _lowerCamelCase     :		int 						=						222.088
        _lowerCamelCase     :		Optional[Any] 						=						True
        _lowerCamelCase     :		Optional[int] 						=						True
        _lowerCamelCase     :		Optional[int] 						=						True
        _lowerCamelCase     :		Any 						=						0.763_141

        _lowerCamelCase     :		List[str] 						=						TapasForQuestionAnswering(config=__A	)
    elif task == "TABFACT":
        _lowerCamelCase     :		List[str] 						=						TapasForSequenceClassification(config=__A	)
    elif task == "MLM":
        _lowerCamelCase     :		int 						=						TapasForMaskedLM(config=__A	)
    elif task == "INTERMEDIATE_PRETRAINING":
        _lowerCamelCase     :		Union[str, Any] 						=						TapasModel(config=__A	)
    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(__A   , __A   , __A	)

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

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

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


if __name__ == "__main__":
    lowerCAmelCase			:							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."
    )
    lowerCAmelCase			:							List[str]     =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,
    )





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
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowerCAmelCase			:							Any     =logging.get_logger(__name__)

lowerCAmelCase			:							List[Any]     ={
    "microsoft/swin-tiny-patch4-window7-224": (
        "https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json"
    ),
    # See all Swin models at https://huggingface.co/models?filter=swin
}




class 	__snake_case	( __lowerCAmelCase ,  __lowerCAmelCase		):





      '''simple docstring'''






      _snake_case    		=       'swin'

      _snake_case    		=       {
          'num_attention_heads': 'num_heads',
          'num_hidden_layers': 'num_layers',
      }



      def __init__(					self     :				Optional[int]    ,  _UpperCamelCase     :				List[str]=224    ,  _UpperCamelCase     :				List[str]=4    ,  _UpperCamelCase     :				List[Any]=3    ,  _UpperCamelCase     :				Dict=96    ,  _UpperCamelCase     :				Any=[2, 2, 6, 2]    ,  _UpperCamelCase     :				Any=[3, 6, 12, 24]    ,  _UpperCamelCase     :				Tuple=7    ,  _UpperCamelCase     :				Tuple=4.0    ,  _UpperCamelCase     :				Dict=True    ,  _UpperCamelCase     :				Tuple=0.0    ,  _UpperCamelCase     :				Any=0.0    ,  _UpperCamelCase     :				Optional[int]=0.1    ,  _UpperCamelCase     :				Any="gelu"    ,  _UpperCamelCase     :				str=False    ,  _UpperCamelCase     :				str=0.0_2    ,  _UpperCamelCase     :				Dict=1E-5    ,  _UpperCamelCase     :				List[str]=32    ,  _UpperCamelCase     :				Optional[Any]=None    ,  _UpperCamelCase     :				List[Any]=None    ,  **_UpperCamelCase     :				List[Any]    ,  )				->Tuple:


          """simple docstring"""


          super().__init__(**_UpperCamelCase)

          _lowerCamelCase     :		List[str] 						=						image_size
          _lowerCamelCase     :		Tuple 						=						patch_size
          _lowerCamelCase     :		Dict 						=						num_channels
          _lowerCamelCase     :		Union[str, Any] 						=						embed_dim
          _lowerCamelCase     :		str 						=						depths
          _lowerCamelCase     :		str 						=						len(_UpperCamelCase)
          _lowerCamelCase     :		Optional[Any] 						=						num_heads
          _lowerCamelCase     :		Tuple 						=						window_size
          _lowerCamelCase     :		int 						=						mlp_ratio
          _lowerCamelCase     :		Optional[int] 						=						qkv_bias
          _lowerCamelCase     :		List[str] 						=						hidden_dropout_prob
          _lowerCamelCase     :		str 						=						attention_probs_dropout_prob
          _lowerCamelCase     :		Tuple 						=						drop_path_rate
          _lowerCamelCase     :		List[str] 						=						hidden_act
          _lowerCamelCase     :		Dict 						=						use_absolute_embeddings
          _lowerCamelCase     :		int 						=						layer_norm_eps
          _lowerCamelCase     :		str 						=						initializer_range
          _lowerCamelCase     :		Dict 						=						encoder_stride
          # we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
          # this indicates the channel dimension after the last stage of the model
          _lowerCamelCase     :		int 						=						int(embed_dim * 2 ** (len(_UpperCamelCase) - 1))
          _lowerCamelCase     :		Dict 						=						["""stem"""] + [F"""stage{idx}""" for idx in range(1    ,  len(_UpperCamelCase) + 1)]
          _lowerCamelCase						,							_lowerCamelCase     :		List[str] 						=						get_aligned_output_features_output_indices(
              out_features=_UpperCamelCase    ,  out_indices=_UpperCamelCase    ,  stage_names=self.stage_names)





class 	__snake_case	( __lowerCAmelCase		):





      '''simple docstring'''






      _snake_case    		=       version.parse('1.11'		)



      @property
      def 	_SCREAMING_SNAKE_CASE    (					self     :				Tuple)				->Mapping[str, Mapping[int, str]]:


          """simple docstring"""


          return OrderedDict(
              [
                  ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
              ])




      @property
      def 	_SCREAMING_SNAKE_CASE    (					self     :				Union[str, Any])				->float:


          """simple docstring"""


          return 1E-4




