"""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					:						Any	,  __UpperCAmelCase					:						List[str]	,  __UpperCAmelCase					:						Any=7	,  __UpperCAmelCase					:						Union[str, Any]=3	,  __UpperCAmelCase					:						Any=3_0	,  __UpperCAmelCase					:						Optional[Any]=4_0_0	,  __UpperCAmelCase					:						Optional[int]=True	,  __UpperCAmelCase					:						Union[str, Any]=None	,  __UpperCAmelCase					:						Union[str, Any]=True	,  __UpperCAmelCase					:						Dict=[0.5, 0.5, 0.5]	,  __UpperCAmelCase					:						Optional[int]=[0.5, 0.5, 0.5]	,  __UpperCAmelCase					:						List[Any]=True	,  __UpperCAmelCase					:						Optional[int]=1 / 2_5_5	,  __UpperCAmelCase					:						Tuple=True	,  )						->    List[str]:
         """simple docstring"""



         UpperCAmelCase__								=  size if size is not None else {"shortest_edge": 1_8, "longest_edge": 1_3_3_3}
         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       lowercase_				(self					:						Dict		)						->    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       lowercase_				(self					:						Optional[Any]	,  __UpperCAmelCase					:						List[Any]	,  __UpperCAmelCase					:						Dict=False		)						->    List[str]:
         """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							(    UpperCAmelCase_		,    unittest.TestCase					):
   __UpperCAmelCase			:			str              =      DetaImageProcessor if is_vision_available() else None


   def       lowercase_				(self					:						Optional[Any]		)						->    Optional[int]:
         """simple docstring"""



         UpperCAmelCase__								=  DetaImageProcessingTester(self		)


   @property
   def       lowercase_				(self					:						Optional[Any]		)						->    List[str]:
         """simple docstring"""



         return self.image_processor_tester.prepare_image_processor_dict()


   def       lowercase_				(self					:						List[str]		)						->    Union[str, Any]:
         """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       lowercase_				(self					:						List[str]		)						->    List[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  self.image_processing_class.from_dict(self.image_processor_dict		)
         self.assertEqual(image_processor.size	,  {"shortest_edge": 1_8, "longest_edge": 1_3_3_3}		)
         self.assertEqual(image_processor.do_pad	,  __UpperCAmelCase		)


   def       lowercase_				(self					:						Dict		)						->    Optional[Any]:
         """simple docstring"""



         pass


   def       lowercase_				(self					:						List[Any]		)						->    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=__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       lowercase_				(self					:						List[Any]		)						->    Union[str, 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=__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       lowercase_				(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=__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       lowercase_				(self					:						Optional[Any]		)						->    Optional[int]:
         """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_9_7_6_9, "annotations": target}

         # encode them
         UpperCAmelCase__								=  DetaImageProcessor()
         UpperCAmelCase__								=  image_processing(images=__UpperCAmelCase	,  annotations=__UpperCAmelCase	,  return_tensors="pt"		)

         # verify pixel values
         UpperCAmelCase__								=  torch.Size([1, 3, 8_0_0, 1_0_6_6]		)
         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([3_9_7_6_9]		)
         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([7_5, 7_5, 6_3, 6_5, 1_7, 1_7]		)
         self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]	,  __UpperCAmelCase		)		)
         # verify orig_size
         UpperCAmelCase__								=  torch.tensor([4_8_0, 6_4_0]		)
         self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]	,  __UpperCAmelCase		)		)
         # verify size
         UpperCAmelCase__								=  torch.tensor([8_0_0, 1_0_6_6]		)
         self.assertTrue(torch.allclose(encoding["labels"][0]["size"]	,  __UpperCAmelCase		)		)





   @slow
   def       lowercase_				(self					:						List[Any]		)						->    Tuple:
         """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_9_7_6_9, "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, 8_0_0, 1_0_6_6]		)
         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([3_9_7_6_9]		)
         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([1_7, 1_7, 6_3, 7_5, 7_5, 9_3]		)
         self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]	,  __UpperCAmelCase		)		)
         # verify masks
         UpperCAmelCase__								=  8_2_2_8_7_3
         self.assertEqual(encoding["labels"][0]["masks"].sum().item()	,  __UpperCAmelCase		)
         # verify orig_size
         UpperCAmelCase__								=  torch.tensor([4_8_0, 6_4_0]		)
         self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]	,  __UpperCAmelCase		)		)
         # verify size
         UpperCAmelCase__								=  torch.tensor([8_0_0, 1_0_6_6]		)
         self.assertTrue(torch.allclose(encoding["labels"][0]["size"]	,  __UpperCAmelCase		)		)




from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
      if not (is_transformers_available() and is_torch_available()):
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      from ...utils.dummy_torch_and_transformers_objects import ShapEPipeline
else:
      from .camera import create_pan_cameras
      from .pipeline_shap_e import ShapEPipeline
      from .pipeline_shap_e_img2img import ShapEImgaImgPipeline
      from .renderer import (
          BoundingBoxVolume,
          ImportanceRaySampler,
          MLPNeRFModelOutput,
          MLPNeRSTFModel,
          ShapEParamsProjModel,
          ShapERenderer,
          StratifiedRaySampler,
          VoidNeRFModel,
      )



