



'''simple docstring'''






import argparse
import json
from collections import OrderedDict

import torch
from huggingface_hub import cached_download, hf_hub_url

from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def    lowerCamelCase__ (		_A       ):
  a		:      Any       =  []
  embed.append(
      (
          f"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight""",
          f"""stage{idx}.patch_embed.proj.weight""",
      )       )
  embed.append(
      (
          f"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias""",
          f"""stage{idx}.patch_embed.proj.bias""",
      )       )
  embed.append(
      (
          f"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight""",
          f"""stage{idx}.patch_embed.norm.weight""",
      )       )
  embed.append(
      (
          f"""cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias""",
          f"""stage{idx}.patch_embed.norm.bias""",
      )       )
  return embed
def    lowerCamelCase__ (		_A				,    _A       ):
  a		:      Tuple       =  []
  attention_weights.append(
      (
          f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight""",
          f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight""",
      )       )
  attention_weights.append(
      (
          f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight""",
          f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight""",
      )       )
  attention_weights.append(
      (
          f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias""",
          f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias""",
      )       )
  attention_weights.append(
      (
          f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean""",
          f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean""",
      )       )
  attention_weights.append(
      (
          f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var""",
          f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var""",
      )       )
  attention_weights.append(
      (
          f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked""",
          f"""stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked""",
      )       )
  attention_weights.append(
      (
          f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight""",
          f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight""",
      )       )
  attention_weights.append(
      (
          f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight""",
          f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight""",
      )       )
  attention_weights.append(
      (
          f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias""",
          f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias""",
      )       )
  attention_weights.append(
      (
          f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean""",
          f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean""",
      )       )
  attention_weights.append(
      (
          f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var""",
          f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var""",
      )       )
  attention_weights.append(
      (
          f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked""",
          f"""stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked""",
      )       )
  attention_weights.append(
      (
          f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight""",
          f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight""",
      )       )
  attention_weights.append(
      (
          f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight""",
          f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight""",
      )       )
  attention_weights.append(
      (
          f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias""",
          f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias""",
      )       )
  attention_weights.append(
      (
          f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean""",
          f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean""",
      )       )
  attention_weights.append(
      (
          f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var""",
          f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var""",
      )       )
  attention_weights.append(
      (
          f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked""",
          f"""stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked""",
      )       )
  attention_weights.append(
      (
          f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight""",
          f"""stage{idx}.blocks.{cnt}.attn.proj_q.weight""",
      )       )
  attention_weights.append(
      (
          f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias""",
          f"""stage{idx}.blocks.{cnt}.attn.proj_q.bias""",
      )       )
  attention_weights.append(
      (
          f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight""",
          f"""stage{idx}.blocks.{cnt}.attn.proj_k.weight""",
      )       )
  attention_weights.append(
      (
          f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias""",
          f"""stage{idx}.blocks.{cnt}.attn.proj_k.bias""",
      )       )
  attention_weights.append(
      (
          f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight""",
          f"""stage{idx}.blocks.{cnt}.attn.proj_v.weight""",
      )       )
  attention_weights.append(
      (
          f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias""",
          f"""stage{idx}.blocks.{cnt}.attn.proj_v.bias""",
      )       )
  attention_weights.append(
      (
          f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight""",
          f"""stage{idx}.blocks.{cnt}.attn.proj.weight""",
      )       )
  attention_weights.append(
      (
          f"""cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias""",
          f"""stage{idx}.blocks.{cnt}.attn.proj.bias""",
      )       )
  attention_weights.append(
      (f"""cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight""", f"""stage{idx}.blocks.{cnt}.mlp.fc1.weight""")       )
  attention_weights.append(
      (f"""cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias""", f"""stage{idx}.blocks.{cnt}.mlp.fc1.bias""")       )
  attention_weights.append(
      (f"""cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight""", f"""stage{idx}.blocks.{cnt}.mlp.fc2.weight""")       )
  attention_weights.append(
      (f"""cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias""", f"""stage{idx}.blocks.{cnt}.mlp.fc2.bias""")       )
  attention_weights.append(
      (f"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight""", f"""stage{idx}.blocks.{cnt}.norm1.weight""")       )
  attention_weights.append(
      (f"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias""", f"""stage{idx}.blocks.{cnt}.norm1.bias""")       )
  attention_weights.append(
      (f"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight""", f"""stage{idx}.blocks.{cnt}.norm2.weight""")       )
  attention_weights.append(
      (f"""cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias""", f"""stage{idx}.blocks.{cnt}.norm2.bias""")       )
  return attention_weights
def    lowerCamelCase__ (		_A       ):
  a		:      Optional[Any]       =  []
  token.append((f"""cvt.encoder.stages.{idx}.cls_token""", 'stage2.cls_token')       )
  return token
def    lowerCamelCase__ (		):
  a		:      Any       =  []
  head.append(('layernorm.weight', 'norm.weight')       )
  head.append(('layernorm.bias', 'norm.bias')       )
  head.append(('classifier.weight', 'head.weight')       )
  head.append(('classifier.bias', 'head.bias')       )
  return head
def    lowerCamelCase__ (		_A				,    _A				,    _A				,    _A       ):
  a		:      Any       =  'imagenet-1k-id2label.json'
  a		:      Union[str, Any]       =  1000

  a		:      List[str]       =  'huggingface/label-files'
  a		:      List[str]       =  num_labels
  a		:      str       =  json.load(open(cached_download(hf_hub_url(lowerCamelCase_				,    lowerCamelCase_				,    repo_type='dataset'       )       )				,    'r'       )       )
  a		:      Optional[Any]       =  {int(lowerCamelCase_       ): v for k, v in idalabel.items()}

  a		:      List[Any]       =  idalabel
  a		:      Any       =  {v: k for k, v in idalabel.items()}

  a		:      Dict       =  CvtConfig(num_labels=lowerCamelCase_				,    idalabel=lowerCamelCase_				,    labelaid=lowerCamelCase_       )

  # For depth size 13 (13 = 1+2+10)
  if cvt_model.rsplit('/'				,    1       )[-1][4:6] == "13":
    a		:      Optional[int]       =  [1, 2, 10]

  # For depth size 21 (21 = 1+4+16)
  elif cvt_model.rsplit('/'				,    1       )[-1][4:6] == "21":
    a		:      Tuple       =  [1, 4, 16]

  # For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
  else:
    a		:      List[str]       =  [2, 2, 20]
    a		:      str       =  [3, 12, 16]
    a		:      Dict       =  [192, 768, 1024]

  a		:      Optional[Any]       =  CvtForImageClassification(lowerCamelCase_       )
  a		:      Any       =  AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'       )
  a		:      List[str]       =  image_size
  a		:      Any       =  torch.load(lowerCamelCase_				,    map_location=torch.device('cpu'       )       )

  a		:      str       =  OrderedDict()
  a		:      Optional[Any]       =  []

  for idx in range(len(config.depth       )       ):
    if config.cls_token[idx]:
      a		:      List[Any]       =  list_of_state_dict + cls_token(lowerCamelCase_       )
    a		:      Optional[int]       =  list_of_state_dict + embeddings(lowerCamelCase_       )
    for cnt in range(config.depth[idx]       ):
      a		:      Dict       =  list_of_state_dict + attention(lowerCamelCase_				,    lowerCamelCase_       )

  a		:      Tuple       =  list_of_state_dict + final()
  for gg in list_of_state_dict:
    print(lowerCamelCase_       )
  for i in range(len(lowerCamelCase_       )       ):
    a		:      List[str]       =  original_weights[list_of_state_dict[i][1]]

  model.load_state_dict(lowerCamelCase_       )
  model.save_pretrained(lowerCamelCase_       )
  image_processor.save_pretrained(lowerCamelCase_       )


# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al

if __name__ == "__main__":
     lowerCAmelCase:      List[str]		 =		argparse.ArgumentParser()
     parser.add_argument(
         '--cvt_model',
         default='cvt-w24',
         type=str,
         help='Name of the cvt model you\'d like to convert.',
     )
     parser.add_argument(
         '--image_size',
         default=3_8_4,
         type=int,
         help='Input Image Size',
     )
     parser.add_argument(
         '--cvt_file_name',
         default=r'cvtmodels\CvT-w24-384x384-IN-22k.pth',
         type=str,
         help='Input Image Size',
     )
     parser.add_argument(
         '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
     )

     lowerCAmelCase:      str		 =		parser.parse_args()
     convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)




'''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 							a__:







         @staticmethod
         def     lowercase_ (   *__snake_case				:						int		,    **__snake_case				:						str			):
           pass

@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 							a__(      unittest.TestCase			):
    lowercase__				=					MODEL_FOR_OBJECT_DETECTION_MAPPING







    def     lowercase_ (   self				:						Optional[Any]		,    __snake_case				:						Union[str, Any]		,    __snake_case				:						int		,    __snake_case				:						Tuple			):
      a		:      Tuple       =  ObjectDetectionPipeline(model=__snake_case		,    image_processor=__snake_case			)
      return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]







    def     lowercase_ (   self				:						Optional[Any]		,    __snake_case				:						str		,    __snake_case				:						Optional[Any]			):
      a		:      Any       =  object_detector('./tests/fixtures/tests_samples/COCO/000000039769.png'		,    threshold=0.0			)

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

      import datasets

      a		:      Any       =  datasets.load_dataset('hf-internal-testing/fixtures_image_utils'		,    'image'		,    split='test'			)

      a		:      Tuple       =  [
          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		:      List[Any]       =  object_detector(__snake_case		,    threshold=0.0			)

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







    @require_tf
    @unittest.skip('Object detection not implemented in TF'			)
    def     lowercase_ (   self				:						List[str]			):
      pass







    @require_torch
    def     lowercase_ (   self				:						Tuple			):
      a		:      Union[str, Any]       =  'hf-internal-testing/tiny-detr-mobilenetsv3'

      a		:      str       =  AutoModelForObjectDetection.from_pretrained(__snake_case			)
      a		:      str       =  AutoFeatureExtractor.from_pretrained(__snake_case			)
      a		:      Any       =  ObjectDetectionPipeline(model=__snake_case		,    feature_extractor=__snake_case			)

      a		:      str       =  object_detector('http://images.cocodataset.org/val2017/000000039769.jpg'		,    threshold=0.0			)

      self.assertEqual(
          nested_simplify(__snake_case		,    decimals=4			)		,    [
              {'score': 0.3376, 'label': 'LABEL_0', 'box': {'xmin': 1_59, 'ymin': 1_20, 'xmax': 4_80, 'ymax': 3_59}},
              {'score': 0.3376, 'label': 'LABEL_0', 'box': {'xmin': 1_59, 'ymin': 1_20, 'xmax': 4_80, 'ymax': 3_59}},
          ]		,    )

      a		:      Dict       =  object_detector(
          [
              'http://images.cocodataset.org/val2017/000000039769.jpg',
              'http://images.cocodataset.org/val2017/000000039769.jpg',
          ]		,    threshold=0.0		,    )

      self.assertEqual(
          nested_simplify(__snake_case		,    decimals=4			)		,    [
              [
                  {'score': 0.3376, 'label': 'LABEL_0', 'box': {'xmin': 1_59, 'ymin': 1_20, 'xmax': 4_80, 'ymax': 3_59}},
                  {'score': 0.3376, 'label': 'LABEL_0', 'box': {'xmin': 1_59, 'ymin': 1_20, 'xmax': 4_80, 'ymax': 3_59}},
              ],
              [
                  {'score': 0.3376, 'label': 'LABEL_0', 'box': {'xmin': 1_59, 'ymin': 1_20, 'xmax': 4_80, 'ymax': 3_59}},
                  {'score': 0.3376, 'label': 'LABEL_0', 'box': {'xmin': 1_59, 'ymin': 1_20, 'xmax': 4_80, 'ymax': 3_59}},
              ],
          ]		,    )







    @require_torch
    @slow
    def     lowercase_ (   self				:						Optional[int]			):
      a		:      Union[str, Any]       =  'facebook/detr-resnet-50'

      a		:      str       =  AutoModelForObjectDetection.from_pretrained(__snake_case			)
      a		:      List[Any]       =  AutoFeatureExtractor.from_pretrained(__snake_case			)
      a		:      str       =  ObjectDetectionPipeline(model=__snake_case		,    feature_extractor=__snake_case			)

      a		:      List[Any]       =  object_detector('http://images.cocodataset.org/val2017/000000039769.jpg'			)
      self.assertEqual(
          nested_simplify(__snake_case		,    decimals=4			)		,    [
              {'score': 0.9982, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 1_75, 'ymax': 1_17}},
              {'score': 0.9960, 'label': 'remote', 'box': {'xmin': 3_33, 'ymin': 72, 'xmax': 3_68, 'ymax': 1_87}},
              {'score': 0.9955, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 6_39, 'ymax': 4_73}},
              {'score': 0.9988, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 3_14, 'ymax': 4_70}},
              {'score': 0.9987, 'label': 'cat', 'box': {'xmin': 3_45, 'ymin': 23, 'xmax': 6_40, 'ymax': 3_68}},
          ]		,    )

      a		:      List[str]       =  object_detector(
          [
              'http://images.cocodataset.org/val2017/000000039769.jpg',
              'http://images.cocodataset.org/val2017/000000039769.jpg',
          ]			)
      self.assertEqual(
          nested_simplify(__snake_case		,    decimals=4			)		,    [
              [
                  {'score': 0.9982, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 1_75, 'ymax': 1_17}},
                  {'score': 0.9960, 'label': 'remote', 'box': {'xmin': 3_33, 'ymin': 72, 'xmax': 3_68, 'ymax': 1_87}},
                  {'score': 0.9955, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 6_39, 'ymax': 4_73}},
                  {'score': 0.9988, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 3_14, 'ymax': 4_70}},
                  {'score': 0.9987, 'label': 'cat', 'box': {'xmin': 3_45, 'ymin': 23, 'xmax': 6_40, 'ymax': 3_68}},
              ],
              [
                  {'score': 0.9982, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 1_75, 'ymax': 1_17}},
                  {'score': 0.9960, 'label': 'remote', 'box': {'xmin': 3_33, 'ymin': 72, 'xmax': 3_68, 'ymax': 1_87}},
                  {'score': 0.9955, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 6_39, 'ymax': 4_73}},
                  {'score': 0.9988, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 3_14, 'ymax': 4_70}},
                  {'score': 0.9987, 'label': 'cat', 'box': {'xmin': 3_45, 'ymin': 23, 'xmax': 6_40, 'ymax': 3_68}},
              ],
          ]		,    )







    @require_torch
    @slow
    def     lowercase_ (   self				:						Any			):
      a		:      Any       =  'facebook/detr-resnet-50'

      a		:      int       =  pipeline('object-detection'		,    model=__snake_case			)

      a		:      Dict       =  object_detector('http://images.cocodataset.org/val2017/000000039769.jpg'			)
      self.assertEqual(
          nested_simplify(__snake_case		,    decimals=4			)		,    [
              {'score': 0.9982, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 1_75, 'ymax': 1_17}},
              {'score': 0.9960, 'label': 'remote', 'box': {'xmin': 3_33, 'ymin': 72, 'xmax': 3_68, 'ymax': 1_87}},
              {'score': 0.9955, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 6_39, 'ymax': 4_73}},
              {'score': 0.9988, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 3_14, 'ymax': 4_70}},
              {'score': 0.9987, 'label': 'cat', 'box': {'xmin': 3_45, 'ymin': 23, 'xmax': 6_40, 'ymax': 3_68}},
          ]		,    )

      a		:      int       =  object_detector(
          [
              'http://images.cocodataset.org/val2017/000000039769.jpg',
              'http://images.cocodataset.org/val2017/000000039769.jpg',
          ]			)
      self.assertEqual(
          nested_simplify(__snake_case		,    decimals=4			)		,    [
              [
                  {'score': 0.9982, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 1_75, 'ymax': 1_17}},
                  {'score': 0.9960, 'label': 'remote', 'box': {'xmin': 3_33, 'ymin': 72, 'xmax': 3_68, 'ymax': 1_87}},
                  {'score': 0.9955, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 6_39, 'ymax': 4_73}},
                  {'score': 0.9988, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 3_14, 'ymax': 4_70}},
                  {'score': 0.9987, 'label': 'cat', 'box': {'xmin': 3_45, 'ymin': 23, 'xmax': 6_40, 'ymax': 3_68}},
              ],
              [
                  {'score': 0.9982, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 1_75, 'ymax': 1_17}},
                  {'score': 0.9960, 'label': 'remote', 'box': {'xmin': 3_33, 'ymin': 72, 'xmax': 3_68, 'ymax': 1_87}},
                  {'score': 0.9955, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 6_39, 'ymax': 4_73}},
                  {'score': 0.9988, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 3_14, 'ymax': 4_70}},
                  {'score': 0.9987, 'label': 'cat', 'box': {'xmin': 3_45, 'ymin': 23, 'xmax': 6_40, 'ymax': 3_68}},
              ],
          ]		,    )







    @require_torch
    @slow
    def     lowercase_ (   self				:						Optional[int]			):
      a		:      Optional[Any]       =  0.9985
      a		:      Optional[int]       =  'facebook/detr-resnet-50'

      a		:      List[Any]       =  pipeline('object-detection'		,    model=__snake_case			)

      a		:      Any       =  object_detector('http://images.cocodataset.org/val2017/000000039769.jpg'		,    threshold=__snake_case			)
      self.assertEqual(
          nested_simplify(__snake_case		,    decimals=4			)		,    [
              {'score': 0.9988, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 3_14, 'ymax': 4_70}},
              {'score': 0.9987, 'label': 'cat', 'box': {'xmin': 3_45, 'ymin': 23, 'xmax': 6_40, 'ymax': 3_68}},
          ]		,    )







    @require_torch
    @require_pytesseract
    @slow
    def     lowercase_ (   self				:						Dict			):
      a		:      Optional[int]       =  'Narsil/layoutlmv3-finetuned-funsd'
      a		:      Optional[int]       =  0.9993

      a		:      List[Any]       =  pipeline('object-detection'		,    model=__snake_case		,    threshold=__snake_case			)

      a		:      Union[str, Any]       =  object_detector(
          'https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png'			)
      self.assertEqual(
          nested_simplify(__snake_case		,    decimals=4			)		,    [
              {'score': 0.9993, 'label': 'I-ANSWER', 'box': {'xmin': 2_94, 'ymin': 2_54, 'xmax': 3_43, 'ymax': 2_64}},
              {'score': 0.9993, 'label': 'I-ANSWER', 'box': {'xmin': 2_94, 'ymin': 2_54, 'xmax': 3_43, 'ymax': 2_64}},
          ]		,    )