






import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import DeformableDetrImageProcessor
class 		SCREAMING_SNAKE_CASE__	(  unittest.TestCase     ):

  '''simple docstring'''







  def __init__(      self		:			str       ,				lowerCamelCase		:			Any       ,				lowerCamelCase		:			List[str]=7       ,				lowerCamelCase		:			Optional[Any]=3       ,				lowerCamelCase		:			Dict=30       ,				lowerCamelCase		:			List[Any]=400       ,				lowerCamelCase		:			int=True       ,				lowerCamelCase		:			Tuple=None       ,				lowerCamelCase		:			str=True       ,				lowerCamelCase		:			Optional[int]=[0.5, 0.5, 0.5]       ,				lowerCamelCase		:			Any=[0.5, 0.5, 0.5]       ,				lowerCamelCase		:			List[Any]=True       ,				lowerCamelCase		:			Tuple=1 / 255       ,				lowerCamelCase		:			str=True       ,				)						->	Union[str, Any]:


         """simple docstring"""
         _UpperCAmelCase							=    size if size is not None else {'shortest_edge': 18, 'longest_edge': 1333}
         _UpperCAmelCase							=    parent
         _UpperCAmelCase							=    batch_size
         _UpperCAmelCase							=    num_channels
         _UpperCAmelCase							=    min_resolution
         _UpperCAmelCase							=    max_resolution
         _UpperCAmelCase							=    do_resize
         _UpperCAmelCase							=    size
         _UpperCAmelCase							=    do_normalize
         _UpperCAmelCase							=    image_mean
         _UpperCAmelCase							=    image_std
         _UpperCAmelCase							=    do_rescale
         _UpperCAmelCase							=    rescale_factor
         _UpperCAmelCase							=    do_pad


  def  lowerCamelCase       (      self		:			Optional[Any]							)						->	Optional[int]:


         """simple docstring"""
         return {
             "do_resize": self.do_resize,
             "size": self.size,
             "do_normalize": self.do_normalize,
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_rescale": self.do_rescale,
             "rescale_factor": self.rescale_factor,
             "do_pad": self.do_pad,
         }





  def  lowerCamelCase       (      self		:			Tuple       ,				lowerCamelCase		:			Optional[Any]       ,				lowerCamelCase		:			Optional[Any]=False							)						->	Dict:


         """simple docstring"""

         if not batched:
                _UpperCAmelCase							=    image_inputs[0]
                if isinstance(snake_case__       ,				Image.Image							):
                       _UpperCAmelCase							=    image.size
                else:
                       _UpperCAmelCase							=    image.shape[1], image.shape[2]
                if w < h:
                       _UpperCAmelCase							=    int(self.size["""shortest_edge"""] * h / w							)
                       _UpperCAmelCase							=    self.size['shortest_edge']
                elif w > h:
                       _UpperCAmelCase							=    self.size['shortest_edge']
                       _UpperCAmelCase							=    int(self.size["""shortest_edge"""] * w / h							)
                else:
                       _UpperCAmelCase							=    self.size['shortest_edge']
                       _UpperCAmelCase							=    self.size['shortest_edge']

         else:
                _UpperCAmelCase							=    []
                for image in image_inputs:
                       _UpperCAmelCase							=    self.get_expected_values([image]							)
                       expected_values.append((expected_height, expected_width)							)
                _UpperCAmelCase							=    max(snake_case__       ,				key=lambda lowerCamelCase							: item[0]							)[0]
                _UpperCAmelCase							=    max(snake_case__       ,				key=lambda lowerCamelCase							: item[1]							)[1]

         return expected_height, expected_width



@require_torch
@require_vision
class 		SCREAMING_SNAKE_CASE__	(  SCREAMING_SNAKE_CASE_					,      unittest.TestCase     ):

  '''simple docstring'''







  _lowerCamelCase    =  DeformableDetrImageProcessor if is_vision_available() else None


  def  lowerCamelCase       (      self		:			Union[str, Any]							)						->	Dict:


         """simple docstring"""
         _UpperCAmelCase							=    DeformableDetrImageProcessingTester(self							)


  @property
  def  lowerCamelCase       (      self		:			int							)						->	List[str]:


         """simple docstring"""
         return self.image_processor_tester.prepare_image_processor_dict()


  def  lowerCamelCase       (      self		:			Any							)						->	Any:


         """simple docstring"""
         _UpperCAmelCase							=    self.image_processing_class(**self.image_processor_dict							)
         self.assertTrue(hasattr(snake_case__       ,				"""image_mean"""							)							)
         self.assertTrue(hasattr(snake_case__       ,				"""image_std"""							)							)
         self.assertTrue(hasattr(snake_case__       ,				"""do_normalize"""							)							)
         self.assertTrue(hasattr(snake_case__       ,				"""do_resize"""							)							)
         self.assertTrue(hasattr(snake_case__       ,				"""do_rescale"""							)							)
         self.assertTrue(hasattr(snake_case__       ,				"""do_pad"""							)							)
         self.assertTrue(hasattr(snake_case__       ,				"""size"""							)							)


  def  lowerCamelCase       (      self		:			List[Any]							)						->	Optional[Any]:


         """simple docstring"""
         _UpperCAmelCase							=    self.image_processing_class.from_dict(self.image_processor_dict							)
         self.assertEqual(image_processor.size       ,				{"""shortest_edge""": 18, """longest_edge""": 1333}							)
         self.assertEqual(image_processor.do_pad       ,				snake_case__							)

         _UpperCAmelCase							=    self.image_processing_class.from_dict(
             self.image_processor_dict       ,				size=42       ,				max_size=84       ,				pad_and_return_pixel_mask=snake_case__							)
         self.assertEqual(image_processor.size       ,				{"""shortest_edge""": 42, """longest_edge""": 84}							)
         self.assertEqual(image_processor.do_pad       ,				snake_case__							)


  def  lowerCamelCase       (      self		:			Tuple							)						->	str:


         """simple docstring"""
         pass


  def  lowerCamelCase       (      self		:			Tuple							)						->	List[str]:


         """simple docstring"""
         _UpperCAmelCase							=    self.image_processing_class(**self.image_processor_dict							)
         # create random PIL images
         _UpperCAmelCase							=    prepare_image_inputs(self.image_processor_tester       ,				equal_resolution=snake_case__							)
         for image in image_inputs:
                self.assertIsInstance(snake_case__       ,				Image.Image							)

         # Test not batched input
         _UpperCAmelCase							=    image_processing(image_inputs[0]       ,				return_tensors="""pt"""							).pixel_values

         _UpperCAmelCase							=    self.image_processor_tester.get_expected_values(snake_case__							)

         self.assertEqual(
             encoded_images.shape       ,				(1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,				)

         # Test batched
         _UpperCAmelCase							=    self.image_processor_tester.get_expected_values(snake_case__       ,				batched=snake_case__							)

         _UpperCAmelCase							=    image_processing(snake_case__       ,				return_tensors="""pt"""							).pixel_values
         self.assertEqual(
             encoded_images.shape       ,				(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )       ,				)


  def  lowerCamelCase       (      self		:			Tuple							)						->	Optional[Any]:


         """simple docstring"""
         _UpperCAmelCase							=    self.image_processing_class(**self.image_processor_dict							)
         # create random numpy tensors
         _UpperCAmelCase							=    prepare_image_inputs(self.image_processor_tester       ,				equal_resolution=snake_case__       ,				numpify=snake_case__							)
         for image in image_inputs:
                self.assertIsInstance(snake_case__       ,				np.ndarray							)

         # Test not batched input
         _UpperCAmelCase							=    image_processing(image_inputs[0]       ,				return_tensors="""pt"""							).pixel_values

         _UpperCAmelCase							=    self.image_processor_tester.get_expected_values(snake_case__							)

         self.assertEqual(
             encoded_images.shape       ,				(1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,				)

         # Test batched
         _UpperCAmelCase							=    image_processing(snake_case__       ,				return_tensors="""pt"""							).pixel_values

         _UpperCAmelCase							=    self.image_processor_tester.get_expected_values(snake_case__       ,				batched=snake_case__							)

         self.assertEqual(
             encoded_images.shape       ,				(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )       ,				)


  def  lowerCamelCase       (      self		:			Optional[int]							)						->	Union[str, Any]:


         """simple docstring"""
         _UpperCAmelCase							=    self.image_processing_class(**self.image_processor_dict							)
         # create random PyTorch tensors
         _UpperCAmelCase							=    prepare_image_inputs(self.image_processor_tester       ,				equal_resolution=snake_case__       ,				torchify=snake_case__							)
         for image in image_inputs:
                self.assertIsInstance(snake_case__       ,				torch.Tensor							)

         # Test not batched input
         _UpperCAmelCase							=    image_processing(image_inputs[0]       ,				return_tensors="""pt"""							).pixel_values

         _UpperCAmelCase							=    self.image_processor_tester.get_expected_values(snake_case__							)

         self.assertEqual(
             encoded_images.shape       ,				(1, self.image_processor_tester.num_channels, expected_height, expected_width)       ,				)

         # Test batched
         _UpperCAmelCase							=    image_processing(snake_case__       ,				return_tensors="""pt"""							).pixel_values

         _UpperCAmelCase							=    self.image_processor_tester.get_expected_values(snake_case__       ,				batched=snake_case__							)

         self.assertEqual(
             encoded_images.shape       ,				(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )       ,				)


  @slow
  def  lowerCamelCase       (      self		:			Union[str, Any]							)						->	Optional[Any]:


         """simple docstring"""
         _UpperCAmelCase							=    Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""							)
         with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt"""       ,				"""r"""							) as f:
                _UpperCAmelCase							=    json.loads(f.read()							)

         _UpperCAmelCase							=    {'image_id': 3_9769, 'annotations': target}

         # encode them
         _UpperCAmelCase							=    DeformableDetrImageProcessor()
         _UpperCAmelCase							=    image_processing(images=snake_case__       ,				annotations=snake_case__       ,				return_tensors="""pt"""							)

         # verify pixel values
         _UpperCAmelCase							=    torch.Size([1, 3, 800, 1066]							)
         self.assertEqual(encoding["""pixel_values"""].shape       ,				snake_case__							)

         _UpperCAmelCase							=    torch.tensor([0.2796, 0.3138, 0.3481]							)
         self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]       ,				snake_case__       ,				atol=1E-4							)							)

         # verify area
         _UpperCAmelCase							=    torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438]							)
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]       ,				snake_case__							)							)
         # verify boxes
         _UpperCAmelCase							=    torch.Size([6, 4]							)
         self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape       ,				snake_case__							)
         _UpperCAmelCase							=    torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]							)
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]       ,				snake_case__       ,				atol=1E-3							)							)
         # verify image_id
         _UpperCAmelCase							=    torch.tensor([3_9769]							)
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]       ,				snake_case__							)							)
         # verify is_crowd
         _UpperCAmelCase							=    torch.tensor([0, 0, 0, 0, 0, 0]							)
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]       ,				snake_case__							)							)
         # verify class_labels
         _UpperCAmelCase							=    torch.tensor([75, 75, 63, 65, 17, 17]							)
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]       ,				snake_case__							)							)
         # verify orig_size
         _UpperCAmelCase							=    torch.tensor([480, 640]							)
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]       ,				snake_case__							)							)
         # verify size
         _UpperCAmelCase							=    torch.tensor([800, 1066]							)
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]       ,				snake_case__							)							)





  @slow
  def  lowerCamelCase       (      self		:			Union[str, Any]							)						->	List[str]:


         """simple docstring"""
         _UpperCAmelCase							=    Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""							)
         with open("""./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"""       ,				"""r"""							) as f:
                _UpperCAmelCase							=    json.loads(f.read()							)

         _UpperCAmelCase							=    {'file_name': '000000039769.png', 'image_id': 3_9769, 'segments_info': target}

         _UpperCAmelCase							=    pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic"""							)

         # encode them
         _UpperCAmelCase							=    DeformableDetrImageProcessor(format="""coco_panoptic"""							)
         _UpperCAmelCase							=    image_processing(images=snake_case__       ,				annotations=snake_case__       ,				masks_path=snake_case__       ,				return_tensors="""pt"""							)

         # verify pixel values
         _UpperCAmelCase							=    torch.Size([1, 3, 800, 1066]							)
         self.assertEqual(encoding["""pixel_values"""].shape       ,				snake_case__							)

         _UpperCAmelCase							=    torch.tensor([0.2796, 0.3138, 0.3481]							)
         self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3]       ,				snake_case__       ,				atol=1E-4							)							)

         # verify area
         _UpperCAmelCase							=    torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147]							)
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""]       ,				snake_case__							)							)
         # verify boxes
         _UpperCAmelCase							=    torch.Size([6, 4]							)
         self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape       ,				snake_case__							)
         _UpperCAmelCase							=    torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]							)
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0]       ,				snake_case__       ,				atol=1E-3							)							)
         # verify image_id
         _UpperCAmelCase							=    torch.tensor([3_9769]							)
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""]       ,				snake_case__							)							)
         # verify is_crowd
         _UpperCAmelCase							=    torch.tensor([0, 0, 0, 0, 0, 0]							)
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""]       ,				snake_case__							)							)
         # verify class_labels
         _UpperCAmelCase							=    torch.tensor([17, 17, 63, 75, 75, 93]							)
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""]       ,				snake_case__							)							)
         # verify masks
         _UpperCAmelCase							=    82_2873
         self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item()       ,				snake_case__							)
         # verify orig_size
         _UpperCAmelCase							=    torch.tensor([480, 640]							)
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""]       ,				snake_case__							)							)
         # verify size
         _UpperCAmelCase							=    torch.tensor([800, 1066]							)
         self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""]       ,				snake_case__							)							)



'''simple docstring'''







import os

from bleurt import score  # From: git+https://github.com/google-research/bleurt.git

import datasets


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


lowerCAmelCase     :  List[str]											=    """\
@inproceedings{bleurt,
  title={BLEURT: Learning Robust Metrics for Text Generation},
  author={Thibault Sellam and Dipanjan Das and Ankur P. Parikh},
  booktitle={ACL},
  year={2020},
  url={https://arxiv.org/abs/2004.04696}
}
"""

lowerCAmelCase     :  List[Any]											=    """\
BLEURT a learnt evaluation metric for Natural Language Generation. It is built using multiple phases of transfer learning starting from a pretrained BERT model (Devlin et al. 2018)
and then employing another pre-training phrase using synthetic data. Finally it is trained on WMT human annotations. You may run BLEURT out-of-the-box or fine-tune
it for your specific application (the latter is expected to perform better).

See the project's README at https://github.com/google-research/bleurt#readme for more information.
"""

lowerCAmelCase     :  str											=    """
BLEURT score.

Args:
    `predictions` (list of str): prediction/candidate sentences
    `references` (list of str): reference sentences
    `checkpoint` BLEURT checkpoint. Will default to BLEURT-tiny if None.

Returns:
    'scores': List of scores.
Examples:

    >>> predictions = [\"hello there\", \"general kenobi\"]
    >>> references = [\"hello there\", \"general kenobi\"]
    >>> bleurt = datasets.load_metric(\"bleurt\")
    >>> results = bleurt.compute(predictions=predictions, references=references)
    >>> print([round(v, 2) for v in results[\"scores\"]])
    [1.03, 1.04]
"""

lowerCAmelCase     :  Optional[Any]											=    {
    """bleurt-tiny-128""": """https://storage.googleapis.com/bleurt-oss/bleurt-tiny-128.zip""",
    """bleurt-tiny-512""": """https://storage.googleapis.com/bleurt-oss/bleurt-tiny-512.zip""",
    """bleurt-base-128""": """https://storage.googleapis.com/bleurt-oss/bleurt-base-128.zip""",
    """bleurt-base-512""": """https://storage.googleapis.com/bleurt-oss/bleurt-base-512.zip""",
    """bleurt-large-128""": """https://storage.googleapis.com/bleurt-oss/bleurt-large-128.zip""",
    """bleurt-large-512""": """https://storage.googleapis.com/bleurt-oss/bleurt-large-512.zip""",
    """BLEURT-20-D3""": """https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D3.zip""",
    """BLEURT-20-D6""": """https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D6.zip""",
    """BLEURT-20-D12""": """https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D12.zip""",
    """BLEURT-20""": """https://storage.googleapis.com/bleurt-oss-21/BLEURT-20.zip""",
}
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION     ,       _KWARGS_DESCRIPTION						)
class 						UpperCamelCase__       (			datasets.Metric						):



     """simple docstring"""






     def a						(			self ):

            '''simple docstring'''

            return datasets.MetricInfo(
                description=_DESCRIPTION   ,						citation=_CITATION   ,						homepage='https://github.com/google-research/bleurt'   ,						inputs_description=_KWARGS_DESCRIPTION   ,						features=datasets.Features(
                    {
                        'predictions': datasets.Value('string'   ,						id='sequence' ),
                        'references': datasets.Value('string'   ,						id='sequence' ),
                    } )   ,						codebase_urls=['https://github.com/google-research/bleurt']   ,						reference_urls=['https://github.com/google-research/bleurt', 'https://arxiv.org/abs/2004.04696']   ,						)







     def a						(			self   ,						snake_case__ ):

            '''simple docstring'''

            if self.config_name == "default":
                   logger.warning(
                       'Using default BLEURT-Base checkpoint for sequence maximum length 128. '
                       'You can use a bigger model for better results with e.g.: datasets.load_metric(\'bleurt\', \'bleurt-large-512\').' )
                   _lowerCAmelCase     :				Tuple       =							'bleurt-base-128'

            if self.config_name.lower() in CHECKPOINT_URLS:
                   _lowerCAmelCase     :				int       =							self.config_name.lower()

            elif self.config_name.upper() in CHECKPOINT_URLS:
                   _lowerCAmelCase     :				str       =							self.config_name.upper()

            else:
                   raise KeyError(
                       F'{self.config_name} model not found. You should supply the name of a model checkpoint for bleurt in {CHECKPOINT_URLS.keys()}' )

            # download the model checkpoint specified by self.config_name and set up the scorer
            _lowerCAmelCase     :				Optional[Any]       =							dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name] )
            _lowerCAmelCase     :				str       =							score.BleurtScorer(os.path.join(snake_case__   ,						snake_case__ ) )







     def a						(			self   ,						snake_case__   ,						snake_case__ ):

            '''simple docstring'''

            _lowerCAmelCase     :				Tuple       =							self.scorer.score(references=snake_case__   ,						candidates=snake_case__ )
            return {"scores": scores}

