





import argparse

import requests
import torch
from PIL import Image
from torchvision.transforms import Compose, Normalize, Resize, ToTensor

from transformers import SwinaSRConfig, SwinaSRForImageSuperResolution, SwinaSRImageProcessor




def _a   (    SCREAMING_SNAKE_CASE__     :    Union[str, Any]  )			->							int:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__  :     Tuple           =		SwinaSRConfig()

							if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
														SCREAMING_SNAKE_CASE__  :     List[Any]           =		4
							elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
														SCREAMING_SNAKE_CASE__  :     Tuple           =		4
														SCREAMING_SNAKE_CASE__  :     Union[str, Any]           =		48
														SCREAMING_SNAKE_CASE__  :     List[str]           =		'pixelshuffle_aux'
							elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
														SCREAMING_SNAKE_CASE__  :     Tuple           =		[6, 6, 6, 6]
														SCREAMING_SNAKE_CASE__  :     Dict           =		60
														SCREAMING_SNAKE_CASE__  :     Tuple           =		[6, 6, 6, 6]
														SCREAMING_SNAKE_CASE__  :     Tuple           =		'pixelshuffledirect'
							elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
														SCREAMING_SNAKE_CASE__  :     Any           =		4
														SCREAMING_SNAKE_CASE__  :     Optional[int]           =		'nearest+conv'
							elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
														SCREAMING_SNAKE_CASE__  :     Union[str, Any]           =		1
														SCREAMING_SNAKE_CASE__  :     List[Any]           =		1
														SCREAMING_SNAKE_CASE__  :     int           =		1_26
														SCREAMING_SNAKE_CASE__  :     str           =		7
														SCREAMING_SNAKE_CASE__  :     List[Any]           =		2_5_5.0
														SCREAMING_SNAKE_CASE__  :     Optional[int]           =		''

							return config




def _a   (    SCREAMING_SNAKE_CASE__     :    List[Any]					, SCREAMING_SNAKE_CASE__     :    List[Any]  )			->							List[Any]:

							'''simple docstring'''






							if "patch_embed.proj" in name and "layers" not in name:
														SCREAMING_SNAKE_CASE__  :     Optional[Any]           =		name.replace("patch_embed.proj"					, "embeddings.patch_embeddings.projection"  )
							if "patch_embed.norm" in name:
														SCREAMING_SNAKE_CASE__  :     Dict           =		name.replace("patch_embed.norm"					, "embeddings.patch_embeddings.layernorm"  )
							if "layers" in name:
														SCREAMING_SNAKE_CASE__  :     Any           =		name.replace("layers"					, "encoder.stages"  )
							if "residual_group.blocks" in name:
														SCREAMING_SNAKE_CASE__  :     Tuple           =		name.replace("residual_group.blocks"					, "layers"  )
							if "attn.proj" in name:
														SCREAMING_SNAKE_CASE__  :     List[Any]           =		name.replace("attn.proj"					, "attention.output.dense"  )
							if "attn" in name:
														SCREAMING_SNAKE_CASE__  :     str           =		name.replace("attn"					, "attention.self"  )
							if "norm1" in name:
														SCREAMING_SNAKE_CASE__  :     List[Any]           =		name.replace("norm1"					, "layernorm_before"  )
							if "norm2" in name:
														SCREAMING_SNAKE_CASE__  :     Union[str, Any]           =		name.replace("norm2"					, "layernorm_after"  )
							if "mlp.fc1" in name:
														SCREAMING_SNAKE_CASE__  :     Tuple           =		name.replace("mlp.fc1"					, "intermediate.dense"  )
							if "mlp.fc2" in name:
														SCREAMING_SNAKE_CASE__  :     Optional[int]           =		name.replace("mlp.fc2"					, "output.dense"  )
							if "q_bias" in name:
														SCREAMING_SNAKE_CASE__  :     List[Any]           =		name.replace("q_bias"					, "query.bias"  )
							if "k_bias" in name:
														SCREAMING_SNAKE_CASE__  :     Optional[int]           =		name.replace("k_bias"					, "key.bias"  )
							if "v_bias" in name:
														SCREAMING_SNAKE_CASE__  :     Any           =		name.replace("v_bias"					, "value.bias"  )
							if "cpb_mlp" in name:
														SCREAMING_SNAKE_CASE__  :     str           =		name.replace("cpb_mlp"					, "continuous_position_bias_mlp"  )
							if "patch_embed.proj" in name:
														SCREAMING_SNAKE_CASE__  :     Dict           =		name.replace("patch_embed.proj"					, "patch_embed.projection"  )

							if name == "norm.weight":
														SCREAMING_SNAKE_CASE__  :     int           =		'layernorm.weight'
							if name == "norm.bias":
														SCREAMING_SNAKE_CASE__  :     Tuple           =		'layernorm.bias'

							if "conv_first" in name:
														SCREAMING_SNAKE_CASE__  :     List[Any]           =		name.replace("conv_first"					, "first_convolution"  )

							if (
							    "upsample" in name
							    or "conv_before_upsample" in name
							    or "conv_bicubic" in name
							    or "conv_up" in name
							    or "conv_hr" in name
							    or "conv_last" in name
							    or "aux" in name
							):
														# heads
														if "conv_last" in name:
																					SCREAMING_SNAKE_CASE__  :     List[str]           =		name.replace("conv_last"					, "final_convolution"  )
														if config.upsampler in ["pixelshuffle", "pixelshuffle_aux", "nearest+conv"]:
																					if "conv_before_upsample.0" in name:
																												SCREAMING_SNAKE_CASE__  :     Tuple           =		name.replace("conv_before_upsample.0"					, "conv_before_upsample"  )
																					if "upsample.0" in name:
																												SCREAMING_SNAKE_CASE__  :     int           =		name.replace("upsample.0"					, "upsample.convolution_0"  )
																					if "upsample.2" in name:
																												SCREAMING_SNAKE_CASE__  :     List[str]           =		name.replace("upsample.2"					, "upsample.convolution_1"  )
																					SCREAMING_SNAKE_CASE__  :     List[Any]           =		'upsample.' + name
														elif config.upsampler == "pixelshuffledirect":
																					SCREAMING_SNAKE_CASE__  :     str           =		name.replace("upsample.0.weight"					, "upsample.conv.weight"  )
																					SCREAMING_SNAKE_CASE__  :     int           =		name.replace("upsample.0.bias"					, "upsample.conv.bias"  )
														else:
																					pass
							else:
														SCREAMING_SNAKE_CASE__  :     Optional[Any]           =		'swin2sr.' + name

							return name




def _a   (    SCREAMING_SNAKE_CASE__     :    str					, SCREAMING_SNAKE_CASE__     :    Union[str, Any]  )			->							Any:

							'''simple docstring'''






							for key in orig_state_dict.copy().keys():
														SCREAMING_SNAKE_CASE__  :     str           =		orig_state_dict.pop(_lowerCAmelCase  )

														if "qkv" in key:
																					SCREAMING_SNAKE_CASE__  :     Optional[int]           =		key.split("."  )
																					SCREAMING_SNAKE_CASE__  :     Dict           =		int(key_split[1]  )
																					SCREAMING_SNAKE_CASE__  :     str           =		int(key_split[4]  )
																					SCREAMING_SNAKE_CASE__  :     Union[str, Any]           =		config.embed_dim

																					if "weight" in key:
																												SCREAMING_SNAKE_CASE__  :     Any           =		val[:dim, :]
																												SCREAMING_SNAKE_CASE__  :     Optional[int]           =		val[dim : dim * 2, :]
																												SCREAMING_SNAKE_CASE__  :     Tuple           =		val[-dim:, :]
																					else:
																												SCREAMING_SNAKE_CASE__  :     int           =		val[:dim]
																												SCREAMING_SNAKE_CASE__  :     str           =		val[dim : dim * 2]
																												SCREAMING_SNAKE_CASE__  :     List[Any]           =		val[-dim:]
																					pass
														else:
																					SCREAMING_SNAKE_CASE__  :     List[Any]           =		val

							return orig_state_dict




def _a   (    SCREAMING_SNAKE_CASE__     :    List[Any]					, SCREAMING_SNAKE_CASE__     :    Dict					, SCREAMING_SNAKE_CASE__     :    str  )			->							List[str]:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__  :     List[Any]           =		get_config(_lowerCAmelCase  )
							SCREAMING_SNAKE_CASE__  :     Tuple           =		SwinaSRForImageSuperResolution(_lowerCAmelCase  )
							model.eval()

							SCREAMING_SNAKE_CASE__  :     Optional[Any]           =		torch.hub.load_state_dict_from_url(_lowerCAmelCase					, map_location="cpu"  )
							SCREAMING_SNAKE_CASE__  :     Optional[int]           =		convert_state_dict(_lowerCAmelCase					, _lowerCAmelCase  )
							SCREAMING_SNAKE_CASE__  :     str           =		model.load_state_dict(_lowerCAmelCase					, strict=_lowerCAmelCase  )

							if len(_lowerCAmelCase  ) > 0:
														raise ValueError("Missing keys when converting: {}".format(_lowerCAmelCase  )  )
							for key in unexpected_keys:
														if not ("relative_position_index" in key or "relative_coords_table" in key or "self_mask" in key):
																					raise ValueError(f'''Unexpected key {key} in state_dict'''  )

														# verify values
							SCREAMING_SNAKE_CASE__  :     List[str]           =		'https://github.com/mv-lab/swin2sr/blob/main/testsets/real-inputs/shanghai.jpg?raw=true'
							SCREAMING_SNAKE_CASE__  :     str           =		Image.open(requests.get(_lowerCAmelCase					, stream=_lowerCAmelCase  ).raw  ).convert("RGB"  )
							SCREAMING_SNAKE_CASE__  :     Optional[Any]           =		SwinaSRImageProcessor()
							# pixel_values = processor(image, return_tensors="pt").pixel_values

							SCREAMING_SNAKE_CASE__  :     List[str]           =		1_26 if 'Jpeg' in checkpoint_url else 2_56
							SCREAMING_SNAKE_CASE__  :     Optional[Any]           =		Compose(
							    [
							        Resize((image_size, image_size)  ),
							        ToTensor(),
							        Normalize(mean=[0.4_8_5, 0.4_5_6, 0.4_0_6]					, std=[0.2_2_9, 0.2_2_4, 0.2_2_5]  ),
							    ]  )
							SCREAMING_SNAKE_CASE__  :     str           =		transforms(_lowerCAmelCase  ).unsqueeze(0  )

							if config.num_channels == 1:
														SCREAMING_SNAKE_CASE__  :     str           =		pixel_values[:, 0, :, :].unsqueeze(1  )

							SCREAMING_SNAKE_CASE__  :     Dict           =		model(_lowerCAmelCase  )

							# assert values
							if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
														SCREAMING_SNAKE_CASE__  :     Tuple           =		torch.Size([1, 3, 5_12, 5_12]  )
														SCREAMING_SNAKE_CASE__  :     Optional[int]           =		torch.tensor(
														    [[-0.7_0_8_7, -0.7_1_3_8, -0.6_7_2_1], [-0.8_3_4_0, -0.8_0_9_5, -0.7_2_9_8], [-0.9_1_4_9, -0.8_4_1_4, -0.7_9_4_0]]  )
							elif "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
														SCREAMING_SNAKE_CASE__  :     Optional[int]           =		torch.Size([1, 3, 10_24, 10_24]  )
														SCREAMING_SNAKE_CASE__  :     Any           =		torch.tensor(
														    [[-0.7_7_7_5, -0.8_1_0_5, -0.8_9_3_3], [-0.7_7_6_4, -0.8_3_5_6, -0.9_2_2_5], [-0.7_9_7_6, -0.8_6_8_6, -0.9_5_7_9]]  )
							elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
														# TODO values didn't match exactly here
														SCREAMING_SNAKE_CASE__  :     List[Any]           =		torch.Size([1, 3, 10_24, 10_24]  )
														SCREAMING_SNAKE_CASE__  :     Any           =		torch.tensor(
														    [[-0.8_0_3_5, -0.7_5_0_4, -0.7_4_9_1], [-0.8_5_3_8, -0.8_1_2_4, -0.7_7_8_2], [-0.8_8_0_4, -0.8_6_5_1, -0.8_4_9_3]]  )
							elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
														SCREAMING_SNAKE_CASE__  :     Optional[int]           =		torch.Size([1, 3, 5_12, 5_12]  )
														SCREAMING_SNAKE_CASE__  :     Optional[Any]           =		torch.tensor(
														    [[-0.7_6_6_9, -0.8_6_6_2, -0.8_7_6_7], [-0.8_8_1_0, -0.9_9_6_2, -0.9_8_2_0], [-0.9_3_4_0, -1.0_3_2_2, -1.1_1_4_9]]  )
							elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
														SCREAMING_SNAKE_CASE__  :     List[str]           =		torch.Size([1, 3, 10_24, 10_24]  )
														SCREAMING_SNAKE_CASE__  :     Optional[int]           =		torch.tensor(
														    [[-0.5_2_3_8, -0.5_5_5_7, -0.6_3_2_1], [-0.6_0_1_6, -0.5_9_0_3, -0.6_3_9_1], [-0.6_2_4_4, -0.6_3_3_4, -0.6_8_8_9]]  )

							assert (
							    outputs.reconstruction.shape == expected_shape
							), f'''Shape of reconstruction should be {expected_shape}, but is {outputs.reconstruction.shape}'''
							assert torch.allclose(outputs.reconstruction[0, 0, :3, :3]					, _lowerCAmelCase					, atol=1E-3  )
							print("Looks ok!"  )

							SCREAMING_SNAKE_CASE__  :     int           =		{
							    'https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X2_64.pth': (
							        'swin2SR-classical-sr-x2-64'
							    ),
							    'https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X4_64.pth': (
							        'swin2SR-classical-sr-x4-64'
							    ),
							    'https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_CompressedSR_X4_48.pth': (
							        'swin2SR-compressed-sr-x4-48'
							    ),
							    'https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_Lightweight_X2_64.pth': (
							        'swin2SR-lightweight-x2-64'
							    ),
							    'https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR.pth': (
							        'swin2SR-realworld-sr-x4-64-bsrgan-psnr'
							    ),
							}
							SCREAMING_SNAKE_CASE__  :     Union[str, Any]           =		url_to_name[checkpoint_url]

							if pytorch_dump_folder_path is not None:
														print(f'''Saving model {model_name} to {pytorch_dump_folder_path}'''  )
														model.save_pretrained(_lowerCAmelCase  )
														print(f'''Saving image processor to {pytorch_dump_folder_path}'''  )
														processor.save_pretrained(_lowerCAmelCase  )

							if push_to_hub:
														model.push_to_hub(f'''caidas/{model_name}'''  )
														processor.push_to_hub(f'''caidas/{model_name}'''  )


if __name__ == "__main__":
		_lowerCamelCase   :							Tuple    				=   argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--checkpoint_url''',
		    default='''https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X2_64.pth''',
		    type=str,
		    help='''URL of the original Swin2SR checkpoint you\'d like to convert.''',
		)
		parser.add_argument(
		    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
		)
		parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Whether to push the converted model to the hub.''')

		_lowerCamelCase   :							int    				=   parser.parse_args()
		convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)











'''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 		_lowercase	(					*_a :      Optional[Any]     ,      **_a :      Optional[Any]  )   ->   Tuple:
       pass






@is_pipeline_test
@require_vision
@require_timm
@require_torch
class    lowerCamelCase_       (							unittest.TestCase	):
  """simple docstring"""




  a_				   =MODEL_FOR_OBJECT_DETECTION_MAPPING




  def 		_lowercase	(					self :      List[Any]     ,      _a :      int     ,      _a :      str     ,      _a :      Tuple  )   ->   List[Any]:
    __lowerCamelCase			:			List[str]       			=					ObjectDetectionPipeline(model=_a     ,      image_processor=_a  )
    return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]




  def 		_lowercase	(					self :      Optional[int]     ,      _a :      int     ,      _a :      Optional[Any]  )   ->   Any:
    __lowerCamelCase			:			int       			=					object_detector('./tests/fixtures/tests_samples/COCO/000000039769.png'     ,      threshold=0.0  )

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

    import datasets

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

    __lowerCamelCase			:			List[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'],
    ]
    __lowerCamelCase			:			Dict       			=					object_detector(_a     ,      threshold=0.0  )

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




  @require_tf
  @unittest.skip('Object detection not implemented in TF'  )
  def 		_lowercase	(					self :      Optional[Any]  )   ->   Dict:
    pass




  @require_torch
  def 		_lowercase	(					self :      int  )   ->   Optional[int]:
    __lowerCamelCase			:			Dict       			=					'hf-internal-testing/tiny-detr-mobilenetsv3'

    __lowerCamelCase			:			str       			=					AutoModelForObjectDetection.from_pretrained(_a  )
    __lowerCamelCase			:			Optional[Any]       			=					AutoFeatureExtractor.from_pretrained(_a  )
    __lowerCamelCase			:			str       			=					ObjectDetectionPipeline(model=_a     ,      feature_extractor=_a  )

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

    self.assertEqual(
        nested_simplify(_a     ,      decimals=4  )     ,      [
            {'score': 0.3376, 'label': 'LABEL_0', 'box': {'xmin': 159, 'ymin': 120, 'xmax': 480, 'ymax': 359}},
            {'score': 0.3376, 'label': 'LABEL_0', 'box': {'xmin': 159, 'ymin': 120, 'xmax': 480, 'ymax': 359}},
        ]     ,      )

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

    self.assertEqual(
        nested_simplify(_a     ,      decimals=4  )     ,      [
            [
                {'score': 0.3376, 'label': 'LABEL_0', 'box': {'xmin': 159, 'ymin': 120, 'xmax': 480, 'ymax': 359}},
                {'score': 0.3376, 'label': 'LABEL_0', 'box': {'xmin': 159, 'ymin': 120, 'xmax': 480, 'ymax': 359}},
            ],
            [
                {'score': 0.3376, 'label': 'LABEL_0', 'box': {'xmin': 159, 'ymin': 120, 'xmax': 480, 'ymax': 359}},
                {'score': 0.3376, 'label': 'LABEL_0', 'box': {'xmin': 159, 'ymin': 120, 'xmax': 480, 'ymax': 359}},
            ],
        ]     ,      )




  @require_torch
  @slow
  def 		_lowercase	(					self :      Union[str, Any]  )   ->   Dict:
    __lowerCamelCase			:			Tuple       			=					'facebook/detr-resnet-50'

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

    __lowerCamelCase			:			int       			=					object_detector('http://images.cocodataset.org/val2017/000000039769.jpg'  )
    self.assertEqual(
        nested_simplify(_a     ,      decimals=4  )     ,      [
            {'score': 0.9982, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
            {'score': 0.9960, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
            {'score': 0.9955, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
            {'score': 0.9988, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
            {'score': 0.9987, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
        ]     ,      )

    __lowerCamelCase			:			List[Any]       			=					object_detector(
        [
            'http://images.cocodataset.org/val2017/000000039769.jpg',
            'http://images.cocodataset.org/val2017/000000039769.jpg',
        ]  )
    self.assertEqual(
        nested_simplify(_a     ,      decimals=4  )     ,      [
            [
                {'score': 0.9982, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
                {'score': 0.9960, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
                {'score': 0.9955, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
                {'score': 0.9988, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
                {'score': 0.9987, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
            ],
            [
                {'score': 0.9982, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
                {'score': 0.9960, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
                {'score': 0.9955, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
                {'score': 0.9988, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
                {'score': 0.9987, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
            ],
        ]     ,      )




  @require_torch
  @slow
  def 		_lowercase	(					self :      Tuple  )   ->   Optional[Any]:
    __lowerCamelCase			:			Any       			=					'facebook/detr-resnet-50'

    __lowerCamelCase			:			List[str]       			=					pipeline('object-detection'     ,      model=_a  )

    __lowerCamelCase			:			int       			=					object_detector('http://images.cocodataset.org/val2017/000000039769.jpg'  )
    self.assertEqual(
        nested_simplify(_a     ,      decimals=4  )     ,      [
            {'score': 0.9982, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
            {'score': 0.9960, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
            {'score': 0.9955, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
            {'score': 0.9988, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
            {'score': 0.9987, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
        ]     ,      )

    __lowerCamelCase			:			List[Any]       			=					object_detector(
        [
            'http://images.cocodataset.org/val2017/000000039769.jpg',
            'http://images.cocodataset.org/val2017/000000039769.jpg',
        ]  )
    self.assertEqual(
        nested_simplify(_a     ,      decimals=4  )     ,      [
            [
                {'score': 0.9982, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
                {'score': 0.9960, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
                {'score': 0.9955, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
                {'score': 0.9988, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
                {'score': 0.9987, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
            ],
            [
                {'score': 0.9982, 'label': 'remote', 'box': {'xmin': 40, 'ymin': 70, 'xmax': 175, 'ymax': 117}},
                {'score': 0.9960, 'label': 'remote', 'box': {'xmin': 333, 'ymin': 72, 'xmax': 368, 'ymax': 187}},
                {'score': 0.9955, 'label': 'couch', 'box': {'xmin': 0, 'ymin': 1, 'xmax': 639, 'ymax': 473}},
                {'score': 0.9988, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
                {'score': 0.9987, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
            ],
        ]     ,      )




  @require_torch
  @slow
  def 		_lowercase	(					self :      int  )   ->   Tuple:
    __lowerCamelCase			:			Optional[Any]       			=					0.9985
    __lowerCamelCase			:			Optional[Any]       			=					'facebook/detr-resnet-50'

    __lowerCamelCase			:			Optional[int]       			=					pipeline('object-detection'     ,      model=_a  )

    __lowerCamelCase			:			Dict       			=					object_detector('http://images.cocodataset.org/val2017/000000039769.jpg'     ,      threshold=_a  )
    self.assertEqual(
        nested_simplify(_a     ,      decimals=4  )     ,      [
            {'score': 0.9988, 'label': 'cat', 'box': {'xmin': 13, 'ymin': 52, 'xmax': 314, 'ymax': 470}},
            {'score': 0.9987, 'label': 'cat', 'box': {'xmin': 345, 'ymin': 23, 'xmax': 640, 'ymax': 368}},
        ]     ,      )




  @require_torch
  @require_pytesseract
  @slow
  def 		_lowercase	(					self :      int  )   ->   Any:
    __lowerCamelCase			:			Optional[int]       			=					'Narsil/layoutlmv3-finetuned-funsd'
    __lowerCamelCase			:			Tuple       			=					0.9993

    __lowerCamelCase			:			List[str]       			=					pipeline('object-detection'     ,      model=_a     ,      threshold=_a  )

    __lowerCamelCase			:			Tuple       			=					object_detector(
        'https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png'  )
    self.assertEqual(
        nested_simplify(_a     ,      decimals=4  )     ,      [
            {'score': 0.9993, 'label': 'I-ANSWER', 'box': {'xmin': 294, 'ymin': 254, 'xmax': 343, 'ymax': 264}},
            {'score': 0.9993, 'label': 'I-ANSWER', 'box': {'xmin': 294, 'ymin': 254, 'xmax': 343, 'ymax': 264}},
        ]     ,      )






