

'''simple docstring'''





import hashlib
import unittest

from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
       import torch

if is_vision_available():
       from PIL import Image
else:



       class       __snake_case  :

         @staticmethod
         def 						lowerCAmelCase__     (					*_A						,		**_A):
             pass


def 					_UpperCAmelCase       (  _SCREAMING_SNAKE_CASE					:		Image       ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						hashlib.mda(image.tobytes()       )
    return m.hexdigest()





@is_pipeline_test
@require_vision
@require_timm
@require_torch
class       __snake_case  (    unittest.TestCase       ):
  __lowerCAmelCase     :	str						 =  MODEL_FOR_DEPTH_ESTIMATION_MAPPING

  def 						lowerCAmelCase__     (					self						,		_A						,		_A						,		_A):
      SCREAMING_SNAKE_CASE_									=						DepthEstimationPipeline(model=_A						,		image_processor=_A)
      return depth_estimator, [
          "./tests/fixtures/tests_samples/COCO/000000039769.png",
          "./tests/fixtures/tests_samples/COCO/000000039769.png",
      ]

  def 						lowerCAmelCase__     (					self						,		_A						,		_A):
      SCREAMING_SNAKE_CASE_									=						depth_estimator('./tests/fixtures/tests_samples/COCO/000000039769.png')
      self.assertEqual({'predicted_depth': ANY(torch.Tensor), 'depth': ANY(Image.Image)}						,		_A)
      import datasets

      SCREAMING_SNAKE_CASE_									=						datasets.load_dataset('hf-internal-testing/fixtures_image_utils'						,		'image'						,		split='test')
      SCREAMING_SNAKE_CASE_									=						depth_estimator(
          [
              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'],
          ])
      self.assertEqual(
          [
              {'predicted_depth': ANY(torch.Tensor), 'depth': ANY(Image.Image)},
              {'predicted_depth': ANY(torch.Tensor), 'depth': ANY(Image.Image)},
              {'predicted_depth': ANY(torch.Tensor), 'depth': ANY(Image.Image)},
              {'predicted_depth': ANY(torch.Tensor), 'depth': ANY(Image.Image)},
              {'predicted_depth': ANY(torch.Tensor), 'depth': ANY(Image.Image)},
          ]						,		_A						,		)

  @require_tf
  @unittest.skip('Depth estimation is not implemented in TF')
  def 						lowerCAmelCase__     (					self):
      pass

  @slow
  @require_torch
  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						'Intel/dpt-large'
      SCREAMING_SNAKE_CASE_									=						pipeline('depth-estimation'						,		model=_A)
      SCREAMING_SNAKE_CASE_									=						depth_estimator('http://images.cocodataset.org/val2017/000000039769.jpg')
      SCREAMING_SNAKE_CASE_									=						hashimage(outputs['depth'])

      # This seems flaky.
      # self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
      self.assertEqual(nested_simplify(outputs['predicted_depth'].max().item())						,		29.304)
      self.assertEqual(nested_simplify(outputs['predicted_depth'].min().item())						,		2.6_6_2)


  @require_torch
  def 						lowerCAmelCase__     (					self):
      # This is highly irregular to have no small tests.
      self.skipTest('There is not hf-internal-testing tiny model for either GLPN nor DPT')





import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision
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 BeitImageProcessor



class       __snake_case  (    unittest.TestCase       ):

  def __init__(					self						,		_A						,		_A=7						,		_A=3						,		_A=18						,		_A=30						,		_A=400						,		_A=True						,		_A=None						,		_A=True						,		_A=None						,		_A=True						,		_A=[0.5, 0.5, 0.5]						,		_A=[0.5, 0.5, 0.5]						,		_A=False						,		):
      SCREAMING_SNAKE_CASE_									=						size if size is not None else {'height': 20, 'width': 20}
      SCREAMING_SNAKE_CASE_									=						crop_size if crop_size is not None else {'height': 18, 'width': 18}
      SCREAMING_SNAKE_CASE_									=						parent
      SCREAMING_SNAKE_CASE_									=						batch_size
      SCREAMING_SNAKE_CASE_									=						num_channels
      SCREAMING_SNAKE_CASE_									=						image_size
      SCREAMING_SNAKE_CASE_									=						min_resolution
      SCREAMING_SNAKE_CASE_									=						max_resolution
      SCREAMING_SNAKE_CASE_									=						do_resize
      SCREAMING_SNAKE_CASE_									=						size
      SCREAMING_SNAKE_CASE_									=						do_center_crop
      SCREAMING_SNAKE_CASE_									=						crop_size
      SCREAMING_SNAKE_CASE_									=						do_normalize
      SCREAMING_SNAKE_CASE_									=						image_mean
      SCREAMING_SNAKE_CASE_									=						image_std
      SCREAMING_SNAKE_CASE_									=						do_reduce_labels


  def 						lowerCAmelCase__     (					self):
      return {
          "do_resize": self.do_resize,
          "size": self.size,
          "do_center_crop": self.do_center_crop,
          "crop_size": self.crop_size,
          "do_normalize": self.do_normalize,
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_reduce_labels": self.do_reduce_labels,
      }






def 					_UpperCAmelCase       (  ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						load_dataset('hf-internal-testing/fixtures_ade20k' ,   split='test'       )

    SCREAMING_SNAKE_CASE_									=						Image.open(dataset[0]['file']       )
    SCREAMING_SNAKE_CASE_									=						Image.open(dataset[1]['file']       )

    return image, map


def 					_UpperCAmelCase       (  ):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_									=						load_dataset('hf-internal-testing/fixtures_ade20k' ,   split='test'       )

    SCREAMING_SNAKE_CASE_									=						Image.open(ds[0]['file']       )
    SCREAMING_SNAKE_CASE_									=						Image.open(ds[1]['file']       )
    SCREAMING_SNAKE_CASE_									=						Image.open(ds[2]['file']       )
    SCREAMING_SNAKE_CASE_									=						Image.open(ds[3]['file']       )

    return [imagea, imagea], [mapa, mapa]





@require_torch
@require_vision
class       __snake_case  (    lowerCAmelCase__     , unittest.TestCase       ):
  __lowerCAmelCase     :	Union[str, Any]						 =  BeitImageProcessor if is_vision_available() else None

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						BeitImageProcessingTester(self)

  @property
  def 						lowerCAmelCase__     (					self):
      return self.image_processor_tester.prepare_image_processor_dict()

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						self.image_processing_class(**self.image_processor_dict)
      self.assertTrue(hasattr(_A						,		'do_resize'))
      self.assertTrue(hasattr(_A						,		'size'))
      self.assertTrue(hasattr(_A						,		'do_center_crop'))
      self.assertTrue(hasattr(_A						,		'center_crop'))
      self.assertTrue(hasattr(_A						,		'do_normalize'))
      self.assertTrue(hasattr(_A						,		'image_mean'))
      self.assertTrue(hasattr(_A						,		'image_std'))

  def 						lowerCAmelCase__     (					self):
      SCREAMING_SNAKE_CASE_									=						self.image_processing_class.from_dict(self.image_processor_dict)
      self.assertEqual(image_processor.size						,		{'height': 20, 'width': 20})
      self.assertEqual(image_processor.crop_size						,		{'height': 18, 'width': 18})
      self.assertEqual(image_processor.do_reduce_labels						,		_A)

      SCREAMING_SNAKE_CASE_									=						self.image_processing_class.from_dict(
          self.image_processor_dict						,		size=42						,		crop_size=84						,		reduce_labels=_A)
      self.assertEqual(image_processor.size						,		{'height': 42, 'width': 42})
      self.assertEqual(image_processor.crop_size						,		{'height': 84, 'width': 84})
      self.assertEqual(image_processor.do_reduce_labels						,		_A)

  def 						lowerCAmelCase__     (					self):
      pass

  def 						lowerCAmelCase__     (					self):
      # Initialize image_processing
      SCREAMING_SNAKE_CASE_									=						self.image_processing_class(**self.image_processor_dict)
      # create random PIL images
      SCREAMING_SNAKE_CASE_									=						prepare_image_inputs(self.image_processor_tester						,		equal_resolution=_A)
      for image in image_inputs:
          self.assertIsInstance(_A						,		Image.Image)

      # Test not batched input
      SCREAMING_SNAKE_CASE_									=						image_processing(image_inputs[0]						,		return_tensors='pt').pixel_values
      self.assertEqual(
          encoded_images.shape						,		(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )						,		)

      # Test batched
      SCREAMING_SNAKE_CASE_									=						image_processing(_A						,		return_tensors='pt').pixel_values
      self.assertEqual(
          encoded_images.shape						,		(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )						,		)

  def 						lowerCAmelCase__     (					self):
      # Initialize image_processing
      SCREAMING_SNAKE_CASE_									=						self.image_processing_class(**self.image_processor_dict)
      # create random numpy tensors
      SCREAMING_SNAKE_CASE_									=						prepare_image_inputs(self.image_processor_tester						,		equal_resolution=_A						,		numpify=_A)
      for image in image_inputs:
          self.assertIsInstance(_A						,		np.ndarray)

      # Test not batched input
      SCREAMING_SNAKE_CASE_									=						image_processing(image_inputs[0]						,		return_tensors='pt').pixel_values
      self.assertEqual(
          encoded_images.shape						,		(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )						,		)

      # Test batched
      SCREAMING_SNAKE_CASE_									=						image_processing(_A						,		return_tensors='pt').pixel_values
      self.assertEqual(
          encoded_images.shape						,		(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )						,		)

  def 						lowerCAmelCase__     (					self):
      # Initialize image_processing
      SCREAMING_SNAKE_CASE_									=						self.image_processing_class(**self.image_processor_dict)
      # create random PyTorch tensors
      SCREAMING_SNAKE_CASE_									=						prepare_image_inputs(self.image_processor_tester						,		equal_resolution=_A						,		torchify=_A)
      for image in image_inputs:
          self.assertIsInstance(_A						,		torch.Tensor)

      # Test not batched input
      SCREAMING_SNAKE_CASE_									=						image_processing(image_inputs[0]						,		return_tensors='pt').pixel_values
      self.assertEqual(
          encoded_images.shape						,		(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )						,		)

      # Test batched
      SCREAMING_SNAKE_CASE_									=						image_processing(_A						,		return_tensors='pt').pixel_values
      self.assertEqual(
          encoded_images.shape						,		(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )						,		)

  def 						lowerCAmelCase__     (					self):
      # Initialize image_processing
      SCREAMING_SNAKE_CASE_									=						self.image_processing_class(**self.image_processor_dict)
      # create random PyTorch tensors
      SCREAMING_SNAKE_CASE_									=						prepare_image_inputs(self.image_processor_tester						,		equal_resolution=_A						,		torchify=_A)
      SCREAMING_SNAKE_CASE_									=						[]
      for image in image_inputs:
          self.assertIsInstance(_A						,		torch.Tensor)
          maps.append(torch.zeros(image.shape[-2:]).long())

      # Test not batched input
      SCREAMING_SNAKE_CASE_									=						image_processing(image_inputs[0]						,		maps[0]						,		return_tensors='pt')
      self.assertEqual(
          encoding['pixel_values'].shape						,		(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )						,		)
      self.assertEqual(
          encoding['labels'].shape						,		(
              1,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )						,		)
      self.assertEqual(encoding['labels'].dtype						,		torch.long)
      self.assertTrue(encoding['labels'].min().item() >= 0)
      self.assertTrue(encoding['labels'].max().item() <= 255)

      # Test batched
      SCREAMING_SNAKE_CASE_									=						image_processing(_A						,		_A						,		return_tensors='pt')
      self.assertEqual(
          encoding['pixel_values'].shape						,		(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )						,		)
      self.assertEqual(
          encoding['labels'].shape						,		(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )						,		)
      self.assertEqual(encoding['labels'].dtype						,		torch.long)
      self.assertTrue(encoding['labels'].min().item() >= 0)
      self.assertTrue(encoding['labels'].max().item() <= 255)

      # Test not batched input (PIL images)
      SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						prepare_semantic_single_inputs()

      SCREAMING_SNAKE_CASE_									=						image_processing(_A						,		_A						,		return_tensors='pt')
      self.assertEqual(
          encoding['pixel_values'].shape						,		(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )						,		)
      self.assertEqual(
          encoding['labels'].shape						,		(
              1,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )						,		)
      self.assertEqual(encoding['labels'].dtype						,		torch.long)
      self.assertTrue(encoding['labels'].min().item() >= 0)
      self.assertTrue(encoding['labels'].max().item() <= 255)

      # Test batched input (PIL images)
      SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						prepare_semantic_batch_inputs()

      SCREAMING_SNAKE_CASE_									=						image_processing(_A						,		_A						,		return_tensors='pt')
      self.assertEqual(
          encoding['pixel_values'].shape						,		(
              2,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )						,		)
      self.assertEqual(
          encoding['labels'].shape						,		(
              2,
              self.image_processor_tester.crop_size['height'],
              self.image_processor_tester.crop_size['width'],
          )						,		)
      self.assertEqual(encoding['labels'].dtype						,		torch.long)
      self.assertTrue(encoding['labels'].min().item() >= 0)
      self.assertTrue(encoding['labels'].max().item() <= 255)


  def 						lowerCAmelCase__     (					self):
      # Initialize image_processing
      SCREAMING_SNAKE_CASE_									=						self.image_processing_class(**self.image_processor_dict)

      # ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
      SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_									=						prepare_semantic_single_inputs()
      SCREAMING_SNAKE_CASE_									=						image_processing(_A						,		_A						,		return_tensors='pt')
      self.assertTrue(encoding['labels'].min().item() >= 0)
      self.assertTrue(encoding['labels'].max().item() <= 150)

      SCREAMING_SNAKE_CASE_									=						True
      SCREAMING_SNAKE_CASE_									=						image_processing(_A						,		_A						,		return_tensors='pt')
      self.assertTrue(encoding['labels'].min().item() >= 0)
      self.assertTrue(encoding['labels'].max().item() <= 255)


