


"""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 					lowercase      (    A_  ,       A_  ,       A_  ,       A_  ,       A_	)->				Optional[Any]:
  '''simple docstring'''







  a						:		Optional[int]											=      TapasConfig.from_json_file(A_	)
  # set absolute/relative position embeddings parameter
  a						:		str											=      reset_position_index_per_cell

  # set remaining parameters of TapasConfig as well as the model based on the task
  if task == "SQA":
    a						:		Dict											=      TapasForQuestionAnswering(config=A_	)
  elif task == "WTQ":
    # run_task_main.py hparams
    a						:		Any											=      4
    a						:		Dict											=      True
    # hparam_utils.py hparams
    a						:		str											=      0.6_6_4_6_9_4
    a						:		Optional[int]											=      0.2_0_7_9_5_1
    a						:		Optional[Any]											=      0.1_2_1_1_9_4
    a						:		Union[str, Any]											=      True
    a						:		int											=      True
    a						:		Tuple											=      False
    a						:		Dict											=      0.0_3_5_2_5_1_3

    a						:		List[str]											=      TapasForQuestionAnswering(config=A_	)
  elif task == "WIKISQL_SUPERVISED":
    # run_task_main.py hparams
    a						:		Union[str, Any]											=      4
    a						:		List[Any]											=      False
    # hparam_utils.py hparams
    a						:		Dict											=      3_6.4_5_1_9
    a						:		List[str]											=      0.9_0_3_4_2_1
    a						:		Optional[Any]											=      2_2_2.0_8_8
    a						:		Dict											=      True
    a						:		Union[str, Any]											=      True
    a						:		List[str]											=      True
    a						:		List[str]											=      0.7_6_3_1_4_1

    a						:		Any											=      TapasForQuestionAnswering(config=A_	)
  elif task == "TABFACT":
    a						:		int											=      TapasForSequenceClassification(config=A_	)
  elif task == "MLM":
    a						:		int											=      TapasForMaskedLM(config=A_	)
  elif task == "INTERMEDIATE_PRETRAINING":
    a						:		List[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}'''	)
  a						:		Optional[int]											=      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__":
 __lowercase    =				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    =				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 Any, Dict, List, Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline


if is_vision_available():
 from PIL import Image

 from ..image_utils import load_image

if is_torch_available():
 import torch

 from transformers.modeling_outputs import BaseModelOutput

 from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING

__lowercase    =				logging.get_logger(__name__)






@add_end_docstrings(_a  )
class 						_A  (  _a  ):







    """simple docstring"""




    def __init__( self    :   List[Any]    ,			**__UpperCAmelCase    :   List[Any]):
      super().__init__(**__UpperCAmelCase)

      if self.framework == "tf":
        raise ValueError(f'''The {self.__class__} is only available in PyTorch.''')

      requires_backends(self    ,			"vision")
      self.check_model_type(__UpperCAmelCase)






    def __call__( self    :   str    ,			__UpperCAmelCase    :   Union[str, "Image.Image", List[Dict[str, Any]]]    ,			__UpperCAmelCase    :   Union[str, List[str]] = None    ,			**__UpperCAmelCase    :   List[Any]    ,			):
      if "text_queries" in kwargs:
        a						:		List[Any]											=      kwargs.pop("text_queries")

      if isinstance(__UpperCAmelCase    ,			(str, Image.Image)):
        a						:		Any											=      {"image": image, "candidate_labels": candidate_labels}
      else:
        a						:		Optional[int]											=      image
      a						:		Optional[int]											=      super().__call__(__UpperCAmelCase    ,			**__UpperCAmelCase)
      return results






    def      __snake_case							( self    :   Optional[int]    ,			**__UpperCAmelCase    :   List[Any]):
      a						:		str											=      {}
      if "threshold" in kwargs:
        a						:		Dict											=      kwargs["threshold"]
      if "top_k" in kwargs:
        a						:		str											=      kwargs["top_k"]
      return {}, {}, postprocess_params






    def      __snake_case							( self    :   List[Any]    ,			__UpperCAmelCase    :   Optional[Any]):
      a						:		Union[str, Any]											=      load_image(inputs["image"])
      a						:		Any											=      inputs["candidate_labels"]
      if isinstance(__UpperCAmelCase    ,			__UpperCAmelCase):
        a						:		Optional[Any]											=      candidate_labels.split(",")

      a						:		Union[str, Any]											=      torch.tensor([[image.height, image.width]]    ,			dtype=torch.intaa)
      for i, candidate_label in enumerate(__UpperCAmelCase):
        a						:		int											=      self.tokenizer(__UpperCAmelCase    ,			return_tensors=self.framework)
        a						:		int											=      self.image_processor(__UpperCAmelCase    ,			return_tensors=self.framework)
        yield {
            "is_last": i == len(__UpperCAmelCase) - 1,
            "target_size": target_size,
            "candidate_label": candidate_label,
            **text_inputs,
            **image_features,
        }






    def      __snake_case							( self    :   Dict    ,			__UpperCAmelCase    :   Optional[int]):
      a						:		List[Any]											=      model_inputs.pop("target_size")
      a						:		Optional[int]											=      model_inputs.pop("candidate_label")
      a						:		List[Any]											=      model_inputs.pop("is_last")

      a						:		List[Any]											=      self.model(**__UpperCAmelCase)

      a						:		Union[str, Any]											=      {"target_size": target_size, "candidate_label": candidate_label, "is_last": is_last, **outputs}
      return model_outputs






    def      __snake_case							( self    :   str    ,			__UpperCAmelCase    :   Dict    ,			__UpperCAmelCase    :   Optional[int]=0.1    ,			__UpperCAmelCase    :   List[str]=None):
      a						:		Dict											=      []
      for model_output in model_outputs:
        a						:		int											=      model_output["candidate_label"]
        a						:		Any											=      BaseModelOutput(__UpperCAmelCase)
        a						:		Optional[Any]											=      self.image_processor.post_process_object_detection(
            outputs=__UpperCAmelCase    ,			threshold=__UpperCAmelCase    ,			target_sizes=model_output["target_size"])[0]

        for index in outputs["scores"].nonzero():
          a						:		Any											=      outputs["scores"][index].item()
          a						:		str											=      self._get_bounding_box(outputs["boxes"][index][0])

          a						:		Optional[Any]											=      {"score": score, "label": label, "box": box}
          results.append(__UpperCAmelCase)

      a						:		str											=      sorted(__UpperCAmelCase    ,			key=lambda __UpperCAmelCase: x["score"]    ,			reverse=__UpperCAmelCase)
      if top_k:
        a						:		Union[str, Any]											=      results[:top_k]

      return results






    def      __snake_case							( self    :   Union[str, Any]    ,			__UpperCAmelCase    :   "torch.Tensor"):
      if self.framework != "pt":
        raise ValueError("The ZeroShotObjectDetectionPipeline is only available in PyTorch.")
      a							,		a							,		a							,		a						:		List[Any]											=      box.int().tolist()
      a						:		str											=      {
          "xmin": xmin,
          "ymin": ymin,
          "xmax": xmax,
          "ymax": ymax,
      }
      return bbox






