


'''simple docstring'''
import unittest

from transformers import (
    MODEL_FOR_OBJECT_DETECTION_MAPPING,
    AutoFeatureExtractor,
    AutoModelForObjectDetection,
    ObjectDetectionPipeline,
    is_vision_available,
    pipeline,
)
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_pytesseract,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
 from PIL import Image
else:

 class 						_lowerCamelCase					:




  '''simple docstring'''


  @staticmethod
  def  snake_case__   (      *__lowercase      ,      **__lowercase						):

   """simple docstring"""







   pass

@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 						_lowerCamelCase					(    unittest.TestCase				):




 '''simple docstring'''
 __lowercase				:		Optional[Any]    							=						MODEL_FOR_OBJECT_DETECTION_MAPPING


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase      ,      __lowercase						):

  """simple docstring"""







  __A  :		Union[str, Any]       =			ObjectDetectionPipeline(model=__lowercase      ,      image_processor=__lowercase						)
  return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase						):

  """simple docstring"""







  __A  :		List[str]       =			object_detector('./tests/fixtures/tests_samples/COCO/000000039769.png'      ,      threshold=0.0						)

  self.assertGreater(len(__lowercase						)      ,      0						)
  for detected_object in outputs:
   self.assertEqual(
       __lowercase      ,      {
           'score': ANY(__lowercase						),
           'label': ANY(__lowercase						),
           'box': {'xmin': ANY(__lowercase						), 'ymin': ANY(__lowercase						), 'xmax': ANY(__lowercase						), 'ymax': ANY(__lowercase						)},
       }      ,      )

  import datasets

  __A  :		Optional[int]       =			datasets.load_dataset('hf-internal-testing/fixtures_image_utils'      ,      'image'      ,      split='test'						)

  __A  :		str       =			[
      Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'						),
      'http://images.cocodataset.org/val2017/000000039769.jpg',
      # RGBA
      dataset[0]['file'],
      # LA
      dataset[1]['file'],
      # L
      dataset[2]['file'],
  ]
  __A  :		Optional[Any]       =			object_detector(__lowercase      ,      threshold=0.0						)

  self.assertEqual(len(__lowercase						)      ,      len(__lowercase						)						)
  for outputs in batch_outputs:
   self.assertGreater(len(__lowercase						)      ,      0						)
   for detected_object in outputs:
    self.assertEqual(
        __lowercase      ,      {
            'score': ANY(__lowercase						),
            'label': ANY(__lowercase						),
            'box': {'xmin': ANY(__lowercase						), 'ymin': ANY(__lowercase						), 'xmax': ANY(__lowercase						), 'ymax': ANY(__lowercase						)},
        }      ,      )


 @require_tf
 @unittest.skip('Object detection not implemented in TF'						)
 def  snake_case__   (      self						):

  """simple docstring"""







  pass


 @require_torch
 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		List[str]       =			'hf-internal-testing/tiny-detr-mobilenetsv3'

  __A  :		List[str]       =			AutoModelForObjectDetection.from_pretrained(__lowercase						)
  __A  :		List[str]       =			AutoFeatureExtractor.from_pretrained(__lowercase						)
  __A  :		Optional[int]       =			ObjectDetectionPipeline(model=__lowercase      ,      feature_extractor=__lowercase						)

  __A  :		Union[str, Any]       =			object_detector('http://images.cocodataset.org/val2017/000000039769.jpg'      ,      threshold=0.0						)

  self.assertEqual(
      nested_simplify(__lowercase      ,      decimals=4						)      ,      [
          {'score': 0.3_3_7_6, 'label': 'LABEL_0', 'box': {'xmin': 159, 'ymin': 120, 'xmax': 480, 'ymax': 359}},
          {'score': 0.3_3_7_6, 'label': 'LABEL_0', 'box': {'xmin': 159, 'ymin': 120, 'xmax': 480, 'ymax': 359}},
      ]      ,      )

  __A  :		Union[str, Any]       =			object_detector(
      [
          'http://images.cocodataset.org/val2017/000000039769.jpg',
          'http://images.cocodataset.org/val2017/000000039769.jpg',
      ]      ,      threshold=0.0      ,      )

  self.assertEqual(
      nested_simplify(__lowercase      ,      decimals=4						)      ,      [
          [
              {'score': 0.3_3_7_6, 'label': 'LABEL_0', 'box': {'xmin': 159, 'ymin': 120, 'xmax': 480, 'ymax': 359}},
              {'score': 0.3_3_7_6, 'label': 'LABEL_0', 'box': {'xmin': 159, 'ymin': 120, 'xmax': 480, 'ymax': 359}},
          ],
          [
              {'score': 0.3_3_7_6, 'label': 'LABEL_0', 'box': {'xmin': 159, 'ymin': 120, 'xmax': 480, 'ymax': 359}},
              {'score': 0.3_3_7_6, 'label': 'LABEL_0', 'box': {'xmin': 159, 'ymin': 120, 'xmax': 480, 'ymax': 359}},
          ],
      ]      ,      )


 @require_torch
 @slow
 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		List[Any]       =			'facebook/detr-resnet-50'

  __A  :		List[str]       =			AutoModelForObjectDetection.from_pretrained(__lowercase						)
  __A  :		int       =			AutoFeatureExtractor.from_pretrained(__lowercase						)
  __A  :		Any       =			ObjectDetectionPipeline(model=__lowercase      ,      feature_extractor=__lowercase						)

  __A  :		Dict       =			object_detector('http://images.cocodataset.org/val2017/000000039769.jpg'						)
  self.assertEqual(
      nested_simplify(__lowercase      ,      decimals=4						)      ,      [
          {'score': 0.9_9_8_2, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
          {'score': 0.9_9_6_0, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
          {'score': 0.9_9_5_5, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
          {'score': 0.9_9_8_8, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
          {'score': 0.9_9_8_7, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
      ]      ,      )

  __A  :		Optional[Any]       =			object_detector(
      [
          'http://images.cocodataset.org/val2017/000000039769.jpg',
          'http://images.cocodataset.org/val2017/000000039769.jpg',
      ]						)
  self.assertEqual(
      nested_simplify(__lowercase      ,      decimals=4						)      ,      [
          [
              {'score': 0.9_9_8_2, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
              {'score': 0.9_9_6_0, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
              {'score': 0.9_9_5_5, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
              {'score': 0.9_9_8_8, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
              {'score': 0.9_9_8_7, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
          ],
          [
              {'score': 0.9_9_8_2, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
              {'score': 0.9_9_6_0, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
              {'score': 0.9_9_5_5, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
              {'score': 0.9_9_8_8, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
              {'score': 0.9_9_8_7, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
          ],
      ]      ,      )


 @require_torch
 @slow
 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Tuple       =			'facebook/detr-resnet-50'

  __A  :		List[Any]       =			pipeline('object-detection'      ,      model=__lowercase						)

  __A  :		Any       =			object_detector('http://images.cocodataset.org/val2017/000000039769.jpg'						)
  self.assertEqual(
      nested_simplify(__lowercase      ,      decimals=4						)      ,      [
          {'score': 0.9_9_8_2, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
          {'score': 0.9_9_6_0, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
          {'score': 0.9_9_5_5, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
          {'score': 0.9_9_8_8, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
          {'score': 0.9_9_8_7, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
      ]      ,      )

  __A  :		Optional[int]       =			object_detector(
      [
          'http://images.cocodataset.org/val2017/000000039769.jpg',
          'http://images.cocodataset.org/val2017/000000039769.jpg',
      ]						)
  self.assertEqual(
      nested_simplify(__lowercase      ,      decimals=4						)      ,      [
          [
              {'score': 0.9_9_8_2, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
              {'score': 0.9_9_6_0, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
              {'score': 0.9_9_5_5, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
              {'score': 0.9_9_8_8, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
              {'score': 0.9_9_8_7, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
          ],
          [
              {'score': 0.9_9_8_2, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
              {'score': 0.9_9_6_0, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
              {'score': 0.9_9_5_5, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
              {'score': 0.9_9_8_8, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
              {'score': 0.9_9_8_7, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
          ],
      ]      ,      )


 @require_torch
 @slow
 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		str       =			0.9_9_8_5
  __A  :		Union[str, Any]       =			'facebook/detr-resnet-50'

  __A  :		str       =			pipeline('object-detection'      ,      model=__lowercase						)

  __A  :		int       =			object_detector('http://images.cocodataset.org/val2017/000000039769.jpg'      ,      threshold=__lowercase						)
  self.assertEqual(
      nested_simplify(__lowercase      ,      decimals=4						)      ,      [
          {'score': 0.9_9_8_8, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
          {'score': 0.9_9_8_7, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
      ]      ,      )







 @require_torch
 @require_pytesseract
 @slow
 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Union[str, Any]       =			'Narsil/layoutlmv3-finetuned-funsd'
  __A  :		Optional[Any]       =			0.9_9_9_3

  __A  :		str       =			pipeline('object-detection'      ,      model=__lowercase      ,      threshold=__lowercase						)

  __A  :		List[str]       =			object_detector(
      'https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png'						)
  self.assertEqual(
      nested_simplify(__lowercase      ,      decimals=4						)      ,      [
          {'score': 0.9_9_9_3, 'label': 'I-ANSWER', 'box': {'xmin': 294, 'ymin': 254, 'xmax': 343, 'ymax': 264}},
          {'score': 0.9_9_9_3, 'label': 'I-ANSWER', 'box': {'xmin': 294, 'ymin': 254, 'xmax': 343, 'ymax': 264}},
      ]      ,      )










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

 # set remaining parameters of TapasConfig as well as the model based on the task
 if task == "SQA":
  __A  :		Optional[int]       =			TapasForQuestionAnswering(config=UpperCamelCase__			)
 elif task == "WTQ":
  # run_task_main.py hparams
  __A  :		List[str]       =			4
  __A  :		Any       =			True
  # hparam_utils.py hparams
  __A  :		Any       =			0.664694
  __A  :		Dict       =			0.207951
  __A  :		Optional[Any]       =			0.121194
  __A  :		Any       =			True
  __A  :		Any       =			True
  __A  :		int       =			False
  __A  :		Optional[int]       =			0.0352513

  __A  :		List[Any]       =			TapasForQuestionAnswering(config=UpperCamelCase__			)
 elif task == "WIKISQL_SUPERVISED":
  # run_task_main.py hparams
  __A  :		Tuple       =			4
  __A  :		Optional[int]       =			False
  # hparam_utils.py hparams
  __A  :		List[str]       =			36.4519
  __A  :		Union[str, Any]       =			0.903421
  __A  :		List[str]       =			222.088
  __A  :		Optional[Any]       =			True
  __A  :		Optional[int]       =			True
  __A  :		Union[str, Any]       =			True
  __A  :		str       =			0.763141

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

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

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

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


if __name__ == "__main__":
 UpperCAmelCase_ :     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.'
 )
 UpperCAmelCase_ :     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,
 )






