





'''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, Pipeline


if is_vision_available():
  from ..image_utils import load_image


if is_torch_available():
  import torch

  from ..models.auto.modeling_auto import MODEL_FOR_OBJECT_DETECTION_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING

__SCREAMING_SNAKE_CASE     :Optional[int]       =				logging.get_logger(__name__)


__SCREAMING_SNAKE_CASE     :Tuple       =				Dict[str, Any]
__SCREAMING_SNAKE_CASE     :Optional[int]       =				List[Prediction]







@add_end_docstrings(lowerCAmelCase_			)
class   A_	(  lowerCAmelCase_			):




 def __init__(					self       :    List[str]	,			*snake_case_       :    List[Any]	,			**snake_case_       :    Union[str, Any]    ):
      super().__init__(*snake_case_	,			**snake_case_    )

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

      requires_backends(self	,			"vision"    )
      self.check_model_type(
          dict(MODEL_FOR_OBJECT_DETECTION_MAPPING.items() + MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.items()    )    )




 def      lowercase	(					self       :    List[Any]	,			**snake_case_       :    Optional[Any]    ):
      _UpperCAmelCase     							=   {}
      if "threshold" in kwargs:
           _UpperCAmelCase     							=   kwargs["threshold"]
      return {}, {}, postprocess_kwargs




 def __call__(					self       :    Tuple	,			*snake_case_       :    Optional[int]	,			**snake_case_       :    Optional[int]    ):
      return super().__call__(*snake_case_	,			**snake_case_    )




 def      lowercase	(					self       :    Any	,			snake_case_       :    Dict    ):
      _UpperCAmelCase     							=   load_image(snake_case_    )
      _UpperCAmelCase     							=   torch.IntTensor([[image.height, image.width]]    )
      _UpperCAmelCase     							=   self.image_processor(images=[image]	,			return_tensors="pt"    )
      if self.tokenizer is not None:
           _UpperCAmelCase     							=   self.tokenizer(text=inputs["words"]	,			boxes=inputs["boxes"]	,			return_tensors="pt"    )
      _UpperCAmelCase     							=   target_size
      return inputs




 def      lowercase	(					self       :    Optional[int]	,			snake_case_       :    str    ):
      _UpperCAmelCase     							=   model_inputs.pop("target_size"    )
      _UpperCAmelCase     							=   self.model(**snake_case_    )
      _UpperCAmelCase     							=   outputs.__class__({"target_size": target_size, **outputs}    )
      if self.tokenizer is not None:
           _UpperCAmelCase     							=   model_inputs["bbox"]
      return model_outputs




 def      lowercase	(					self       :    Dict	,			snake_case_       :    Optional[Any]	,			snake_case_       :    str=0.9    ):
      _UpperCAmelCase     							=   model_outputs["target_size"]
      if self.tokenizer is not None:
           # This is a LayoutLMForTokenClassification variant.
           # The OCR got the boxes and the model classified the words.
           _UpperCAmelCase							,  _UpperCAmelCase     							=   target_size[0].tolist()

           def unnormalize(snake_case_       :    List[Any]    ):
                return self._get_bounding_box(
                    torch.Tensor(
                        [
                            (width * bbox[0] / 1_0_0_0),
                            (height * bbox[1] / 1_0_0_0),
                            (width * bbox[2] / 1_0_0_0),
                            (height * bbox[3] / 1_0_0_0),
                        ]    )    )

           _UpperCAmelCase							,  _UpperCAmelCase     							=   model_outputs["logits"].squeeze(0    ).softmax(dim=-1    ).max(dim=-1    )
           _UpperCAmelCase     							=   [self.model.config.idalabel[prediction] for prediction in classes.tolist()]
           _UpperCAmelCase     							=   [unnormalize(snake_case_    ) for bbox in model_outputs["bbox"].squeeze(0    )]
           _UpperCAmelCase     							=   ["score", "label", "box"]
           _UpperCAmelCase     							=   [dict(zip(snake_case_	,			snake_case_    )    ) for vals in zip(scores.tolist()	,			snake_case_	,			snake_case_    ) if vals[0] > threshold]
      else:
           # This is a regular ForObjectDetectionModel
           _UpperCAmelCase     							=   self.image_processor.post_process_object_detection(snake_case_	,			snake_case_	,			snake_case_    )
           _UpperCAmelCase     							=   raw_annotations[0]
           _UpperCAmelCase     							=   raw_annotation["scores"]
           _UpperCAmelCase     							=   raw_annotation["labels"]
           _UpperCAmelCase     							=   raw_annotation["boxes"]

           _UpperCAmelCase     							=   scores.tolist()
           _UpperCAmelCase     							=   [self.model.config.idalabel[label.item()] for label in labels]
           _UpperCAmelCase     							=   [self._get_bounding_box(snake_case_    ) for box in boxes]

           # {"scores": [...], ...} --> [{"score":x, ...}, ...]
           _UpperCAmelCase     							=   ["score", "label", "box"]
           _UpperCAmelCase     							=   [
               dict(zip(snake_case_	,			snake_case_    )    )
               for vals in zip(raw_annotation["scores"]	,			raw_annotation["labels"]	,			raw_annotation["boxes"]    )
           ]

      return annotation




 def      lowercase	(					self       :    Optional[int]	,			snake_case_       :    "torch.Tensor"    ):
      if self.framework != "pt":
           raise ValueError("The ObjectDetectionPipeline is only available in PyTorch."    )
      _UpperCAmelCase							,  _UpperCAmelCase							,  _UpperCAmelCase							,  _UpperCAmelCase     							=   box.int().tolist()
      _UpperCAmelCase     							=   {
          "xmin": xmin,
          "ymin": ymin,
          "xmax": xmax,
          "ymax": ymax,
      }
      return bbox











'''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							:      int			,     __lowercase							:      Dict			,     __lowercase							:      str			,     __lowercase							:      Optional[Any]			,     __lowercase							:      str       )		->       List[str]:


     '''simple docstring'''



     _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_4694
          _UpperCAmelCase     							=   0.20_7951
          _UpperCAmelCase     							=   0.12_1194
          _UpperCAmelCase     							=   True
          _UpperCAmelCase     							=   True
          _UpperCAmelCase     							=   False
          _UpperCAmelCase     							=   0.035_2513

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

          _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=512       )
     tokenizer.save_pretrained(__lowercase       )

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


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




