
"""simple docstring"""


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 DetaImageProcessor



class   __a  (      unittest.TestCase	):






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







              """simple docstring"""
              # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
              UpperCamelCase					 =		size if size is not None else {"shortest_edge": 18, "longest_edge": 1_333}
              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 	_SCREAMING_SNAKE_CASE       (  self :							int			)->  str:







              """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 	_SCREAMING_SNAKE_CASE       (  self :							int       ,      UpperCAmelCase_ :							Optional[int]       ,      UpperCAmelCase_ :							List[Any]=False			)->  int:







              """simple docstring"""
              if not batched:
                     UpperCamelCase					 =		image_inputs[0]
                     if isinstance(UpperCAmelCase_       ,      Image.Image			):
                            UpperCamelCase ,       UpperCamelCase					 =		image.size
                     else:
                            UpperCamelCase ,       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 ,       UpperCamelCase					 =		self.get_expected_values([image]			)
                            expected_values.append((expected_height, expected_width)			)
                     UpperCamelCase					 =		max(UpperCAmelCase_       ,      key=lambda UpperCAmelCase_			: item[0]			)[0]
                     UpperCamelCase					 =		max(UpperCAmelCase_       ,      key=lambda UpperCAmelCase_			: item[1]			)[1]

              return expected_height, expected_width





@require_torch
@require_vision
class   __a  (      _lowerCAmelCase    ,     unittest.TestCase	):
       UpperCamelCase_		:					int   =			DetaImageProcessor if is_vision_available() else None






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







              """simple docstring"""
              UpperCamelCase					 =		DetaImageProcessingTester(self			)






       @property
       def 	_SCREAMING_SNAKE_CASE       (  self :							Any			)->  Dict:







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






       def 	_SCREAMING_SNAKE_CASE       (  self :							List[str]			)->  str:







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






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







              """simple docstring"""
              UpperCamelCase					 =		self.image_processing_class.from_dict(self.image_processor_dict			)
              self.assertEqual(image_processor.size       ,      {"shortest_edge": 18, "longest_edge": 1_333}			)
              self.assertEqual(image_processor.do_pad       ,      UpperCAmelCase_			)






       def 	_SCREAMING_SNAKE_CASE       (  self :							Any			)->  Any:







              """simple docstring"""
              pass






       def 	_SCREAMING_SNAKE_CASE       (  self :							List[Any]			)->  int:







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

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

              UpperCamelCase ,       UpperCamelCase					 =		self.image_processor_tester.get_expected_values(UpperCAmelCase_			)

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

              # Test batched
              UpperCamelCase ,       UpperCamelCase					 =		self.image_processor_tester.get_expected_values(UpperCAmelCase_       ,      batched=UpperCAmelCase_			)

              UpperCamelCase					 =		image_processing(UpperCAmelCase_       ,      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 	_SCREAMING_SNAKE_CASE       (  self :							Tuple			)->  Optional[Any]:







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

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

              UpperCamelCase ,       UpperCamelCase					 =		self.image_processor_tester.get_expected_values(UpperCAmelCase_			)

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

              # Test batched
              UpperCamelCase					 =		image_processing(UpperCAmelCase_       ,      return_tensors="pt"			).pixel_values

              UpperCamelCase ,       UpperCamelCase					 =		self.image_processor_tester.get_expected_values(UpperCAmelCase_       ,      batched=UpperCAmelCase_			)

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






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







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

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

              UpperCamelCase ,       UpperCamelCase					 =		self.image_processor_tester.get_expected_values(UpperCAmelCase_			)

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

              # Test batched
              UpperCamelCase					 =		image_processing(UpperCAmelCase_       ,      return_tensors="pt"			).pixel_values

              UpperCamelCase ,       UpperCamelCase					 =		self.image_processor_tester.get_expected_values(UpperCAmelCase_       ,      batched=UpperCAmelCase_			)

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






       @slow
       def 	_SCREAMING_SNAKE_CASE       (  self :							str			)->  Any:







              """simple docstring"""
              # prepare image and target
              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": 39_769, "annotations": target}

              # encode them
              UpperCamelCase					 =		DetaImageProcessor()
              UpperCamelCase					 =		image_processing(images=UpperCAmelCase_       ,      annotations=UpperCAmelCase_       ,      return_tensors="pt"			)

              # verify pixel values
              UpperCamelCase					 =		torch.Size([1, 3, 800, 1_066]			)
              self.assertEqual(encoding["pixel_values"].shape       ,      UpperCAmelCase_			)

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

              # verify area
              UpperCamelCase					 =		torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]			)
              self.assertTrue(torch.allclose(encoding["labels"][0]["area"]       ,      UpperCAmelCase_			)			)
              # verify boxes
              UpperCamelCase					 =		torch.Size([6, 4]			)
              self.assertEqual(encoding["labels"][0]["boxes"].shape       ,      UpperCAmelCase_			)
              UpperCamelCase					 =		torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]			)
              self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]       ,      UpperCAmelCase_       ,      atol=1e-3			)			)
              # verify image_id
              UpperCamelCase					 =		torch.tensor([39_769]			)
              self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]       ,      UpperCAmelCase_			)			)
              # verify is_crowd
              UpperCamelCase					 =		torch.tensor([0, 0, 0, 0, 0, 0]			)
              self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]       ,      UpperCAmelCase_			)			)
              # verify class_labels
              UpperCamelCase					 =		torch.tensor([75, 75, 63, 65, 17, 17]			)
              self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]       ,      UpperCAmelCase_			)			)
              # verify orig_size
              UpperCamelCase					 =		torch.tensor([480, 640]			)
              self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]       ,      UpperCAmelCase_			)			)
              # verify size
              UpperCamelCase					 =		torch.tensor([800, 1_066]			)
              self.assertTrue(torch.allclose(encoding["labels"][0]["size"]       ,      UpperCAmelCase_			)			)






       @slow
       def 	_SCREAMING_SNAKE_CASE       (  self :							int			)->  Optional[Any]:







              """simple docstring"""
              # prepare image, target and masks_path
              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": 39_769, "segments_info": target}

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

              # encode them
              UpperCamelCase					 =		DetaImageProcessor(format="coco_panoptic"			)
              UpperCamelCase					 =		image_processing(images=UpperCAmelCase_       ,      annotations=UpperCAmelCase_       ,      masks_path=UpperCAmelCase_       ,      return_tensors="pt"			)

              # verify pixel values
              UpperCamelCase					 =		torch.Size([1, 3, 800, 1_066]			)
              self.assertEqual(encoding["pixel_values"].shape       ,      UpperCAmelCase_			)

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

              # verify area
              UpperCamelCase					 =		torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]			)
              self.assertTrue(torch.allclose(encoding["labels"][0]["area"]       ,      UpperCAmelCase_			)			)
              # verify boxes
              UpperCamelCase					 =		torch.Size([6, 4]			)
              self.assertEqual(encoding["labels"][0]["boxes"].shape       ,      UpperCAmelCase_			)
              UpperCamelCase					 =		torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]			)
              self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]       ,      UpperCAmelCase_       ,      atol=1e-3			)			)
              # verify image_id
              UpperCamelCase					 =		torch.tensor([39_769]			)
              self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]       ,      UpperCAmelCase_			)			)
              # verify is_crowd
              UpperCamelCase					 =		torch.tensor([0, 0, 0, 0, 0, 0]			)
              self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]       ,      UpperCAmelCase_			)			)
              # verify class_labels
              UpperCamelCase					 =		torch.tensor([17, 17, 63, 75, 75, 93]			)
              self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]       ,      UpperCAmelCase_			)			)
              # verify masks
              UpperCamelCase					 =		822_873
              self.assertEqual(encoding["labels"][0]["masks"].sum().item()       ,      UpperCAmelCase_			)
              # verify orig_size
              UpperCamelCase					 =		torch.tensor([480, 640]			)
              self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]       ,      UpperCAmelCase_			)			)
              # verify size
              UpperCamelCase					 =		torch.tensor([800, 1_066]			)
              self.assertTrue(torch.allclose(encoding["labels"][0]["size"]       ,      UpperCAmelCase_			)			)







"""simple docstring"""


SCREAMING_SNAKE_CASE      =     {
    """meter""": """m""",
    """kilometer""": """km""",
    """megametre""": """Mm""",
    """gigametre""": """Gm""",
    """terametre""": """Tm""",
    """petametre""": """Pm""",
    """exametre""": """Em""",
    """zettametre""": """Zm""",
    """yottametre""": """Ym""",
}
# Exponent of the factor(meter)
SCREAMING_SNAKE_CASE      =     {
    """m""": 0,
    """km""": 3,
    """Mm""": 6,
    """Gm""": 9,
    """Tm""": 12,
    """Pm""": 15,
    """Em""": 18,
    """Zm""": 21,
    """Ym""": 24,
}



def 	lowerCamelCase__			(							UpperCAmelCase_		,      UpperCAmelCase_		,      UpperCAmelCase_				)->							float:







       """simple docstring"""
       UpperCamelCase					 =		from_type.lower().strip("s"				)
       UpperCamelCase					 =		to_type.lower().strip("s"				)

       UpperCamelCase					 =		UNIT_SYMBOL.get(UpperCAmelCase_		,      UpperCAmelCase_				)
       UpperCamelCase					 =		UNIT_SYMBOL.get(UpperCAmelCase_		,      UpperCAmelCase_				)

       if from_sanitized not in METRIC_CONVERSION:
              UpperCamelCase					 =		(
                  F"Invalid 'from_type' value: {from_type!r}.\n"
                  F"Conversion abbreviations are: {', '.join(UpperCAmelCase_				)}"
              )
              raise ValueError(UpperCAmelCase_				)
       if to_sanitized not in METRIC_CONVERSION:
              UpperCamelCase					 =		(
                  F"Invalid 'to_type' value: {to_type!r}.\n"
                  F"Conversion abbreviations are: {', '.join(UpperCAmelCase_				)}"
              )
              raise ValueError(UpperCAmelCase_				)
       UpperCamelCase					 =		METRIC_CONVERSION[from_sanitized]
       UpperCamelCase					 =		METRIC_CONVERSION[to_sanitized]
       UpperCamelCase					 =		1

       if from_exponent > to_exponent:
              UpperCamelCase					 =		from_exponent - to_exponent
       else:
              UpperCamelCase					 =		-(to_exponent - from_exponent)

       return value * pow(10		,      UpperCAmelCase_				)


if __name__ == "__main__":
      from doctest import testmod

      testmod()





