



'''simple docstring'''





import os
import re
import shutil
import sys
import tempfile
import unittest

import black


_UpperCamelCase											=		os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If DDPMSchedulerOutput is changed in scheduling_ddpm.py, this code needs to be manually updated.
_UpperCamelCase											=		'    \"""\n    Output class for the scheduler\'s step function output.\n\n    Args:\n        prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n            Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the\n            denoising loop.\n        pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n            The predicted denoised sample (x_{0}) based on the model output from the current timestep.\n            `pred_original_sample` can be used to preview progress or for guidance.\n    \"""\n\n    prev_sample: torch.FloatTensor\n    pred_original_sample: Optional[torch.FloatTensor] = None\n'






class    lowerCamelCase_       (							unittest.TestCase	):
  """simple docstring"""




  def 		_lowercase	(					self :      str  )   ->   Union[str, Any]:
    __lowerCamelCase			:			int       			=					tempfile.mkdtemp()
    os.makedirs(os.path.join(self.diffusers_dir     ,      'schedulers/'  )  )
    __lowerCamelCase			:			Tuple       			=					self.diffusers_dir
    shutil.copy(
        os.path.join(_a     ,      'src/diffusers/schedulers/scheduling_ddpm.py'  )     ,      os.path.join(self.diffusers_dir     ,      'schedulers/scheduling_ddpm.py'  )     ,      )




  def 		_lowercase	(					self :      Dict  )   ->   List[str]:
    __lowerCamelCase			:			int       			=					'src/diffusers'
    shutil.rmtree(self.diffusers_dir  )




  def 		_lowercase	(					self :      List[Any]     ,      _a :      Tuple     ,      _a :      List[str]     ,      _a :      List[str]     ,      _a :      int=None  )   ->   Dict:
    __lowerCamelCase			:			Union[str, Any]       			=					comment + f'\nclass {class_name}(nn.Module):\n' + class_code
    if overwrite_result is not None:
      __lowerCamelCase			:			int       			=					comment + f'\nclass {class_name}(nn.Module):\n' + overwrite_result
    __lowerCamelCase			:			Dict       			=					black.Mode(target_versions={black.TargetVersion.PYaa}     ,      line_length=119  )
    __lowerCamelCase			:			Optional[Any]       			=					black.format_str(_a     ,      mode=_a  )
    __lowerCamelCase			:			Optional[Any]       			=					os.path.join(self.diffusers_dir     ,      'new_code.py'  )
    with open(_a     ,      'w'     ,      newline='\n'  ) as f:
      f.write(_a  )
    if overwrite_result is None:
      self.assertTrue(len(check_copies.is_copy_consistent(_a  )  ) == 0  )
    else:
      check_copies.is_copy_consistent(f.name     ,      overwrite=_a  )
      with open(_a     ,      'r'  ) as f:
        self.assertTrue(f.read()     ,      _a  )




  def 		_lowercase	(					self :      int  )   ->   Dict:
    __lowerCamelCase			:			Union[str, Any]       			=					check_copies.find_code_in_diffusers('schedulers.scheduling_ddpm.DDPMSchedulerOutput'  )
    self.assertEqual(_a     ,      _a  )




  def 		_lowercase	(					self :      Any  )   ->   int:
    # Base copy consistency
    self.check_copy_consistency(
        '# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput'     ,      'DDPMSchedulerOutput'     ,      REFERENCE_CODE + '\n'     ,      )

    # With no empty line at the end
    self.check_copy_consistency(
        '# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput'     ,      'DDPMSchedulerOutput'     ,      _a     ,      )

    # Copy consistency with rename
    self.check_copy_consistency(
        '# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test'     ,      'TestSchedulerOutput'     ,      re.sub('DDPM'     ,      'Test'     ,      _a  )     ,      )

    # Copy consistency with a really long name
    __lowerCamelCase			:			Optional[int]       			=					'TestClassWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason'
    self.check_copy_consistency(
        f'# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->{long_class_name}'     ,      f'{long_class_name}SchedulerOutput'     ,      re.sub('Bert'     ,      _a     ,      _a  )     ,      )

    # Copy consistency with overwrite
    self.check_copy_consistency(
        '# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test'     ,      'TestSchedulerOutput'     ,      _a     ,      overwrite_result=re.sub('DDPM'     ,      'Test'     ,      _a  )     ,      )











'''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}},
        ]     ,      )






