



import unittest

import numpy as np

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_video_inputs


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import VivitImageProcessor



class     lowerCAmelCase_		(  unittest.TestCase							):
       def __init__(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_=7,      SCREAMING_SNAKE_CASE_=3,      SCREAMING_SNAKE_CASE_=10,      SCREAMING_SNAKE_CASE_=18,      SCREAMING_SNAKE_CASE_=30,      SCREAMING_SNAKE_CASE_=400,      SCREAMING_SNAKE_CASE_=True,      SCREAMING_SNAKE_CASE_=None,      SCREAMING_SNAKE_CASE_=True,      SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5],      SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5],      SCREAMING_SNAKE_CASE_=None,      )					->		str:
          UpperCamelCase   :  List[str]						     =     size if size is not None else {'shortest_edge': 18}
          UpperCamelCase   :  Dict						     =     crop_size if crop_size is not None else {'height': 18, 'width': 18}

          UpperCamelCase   :  Union[str, Any]						     =     parent
          UpperCamelCase   :  Dict						     =     batch_size
          UpperCamelCase   :  str						     =     num_channels
          UpperCamelCase   :  List[str]						     =     num_frames
          UpperCamelCase   :  Optional[int]						     =     image_size
          UpperCamelCase   :  List[Any]						     =     min_resolution
          UpperCamelCase   :  Tuple						     =     max_resolution
          UpperCamelCase   :  Optional[Any]						     =     do_resize
          UpperCamelCase   :  List[str]						     =     size
          UpperCamelCase   :  List[Any]						     =     do_normalize
          UpperCamelCase   :  Any						     =     image_mean
          UpperCamelCase   :  List[str]						     =     image_std
          UpperCamelCase   :  int						     =     crop_size






       def  snake_case_	(      self	)					->		str:
          return {
              "image_mean": self.image_mean,
              "image_std": self.image_std,
              "do_normalize": self.do_normalize,
              "do_resize": self.do_resize,
              "size": self.size,
              "crop_size": self.crop_size,
          }






@require_torch
@require_vision
class     lowerCAmelCase_		(  __A	,   unittest.TestCase							):
       UpperCAmelCase__    : List[Any]       					=  VivitImageProcessor if is_vision_available() else None
       def  snake_case_	(      self	)					->		Tuple:
          UpperCamelCase   :  Optional[Any]						     =     VivitImageProcessingTester(self	)
       @property
       def  snake_case_	(      self	)					->		str:
          return self.image_processor_tester.prepare_image_processor_dict()
       def  snake_case_	(      self	)					->		Union[str, Any]:
          UpperCamelCase   :  Tuple						     =     self.image_processing_class(**self.image_processor_dict	)
          self.assertTrue(hasattr(__lowercase,      'image_mean'	)	)
          self.assertTrue(hasattr(__lowercase,      'image_std'	)	)
          self.assertTrue(hasattr(__lowercase,      'do_normalize'	)	)
          self.assertTrue(hasattr(__lowercase,      'do_resize'	)	)
          self.assertTrue(hasattr(__lowercase,      'do_center_crop'	)	)
          self.assertTrue(hasattr(__lowercase,      'size'	)	)
       def  snake_case_	(      self	)					->		int:
          UpperCamelCase   :  Tuple						     =     self.image_processing_class.from_dict(self.image_processor_dict	)
          self.assertEqual(image_processor.size,      {'shortest_edge': 18}	)
          self.assertEqual(image_processor.crop_size,      {'height': 18, 'width': 18}	)

          UpperCamelCase   :  Dict						     =     self.image_processing_class.from_dict(self.image_processor_dict,      size=42,      crop_size=84	)
          self.assertEqual(image_processor.size,      {'shortest_edge': 42}	)
          self.assertEqual(image_processor.crop_size,      {'height': 84, 'width': 84}	)
       def  snake_case_	(      self	)					->		Union[str, Any]:
          # Initialize image_processing
          UpperCamelCase   :  List[str]						     =     self.image_processing_class(**self.image_processor_dict	)
          # create random PIL videos
          UpperCamelCase   :  List[str]						     =     prepare_video_inputs(self.image_processor_tester,      equal_resolution=__lowercase	)
          for video in video_inputs:
             self.assertIsInstance(__lowercase,      __lowercase	)
             self.assertIsInstance(video[0],      Image.Image	)

          # Test not batched input
          UpperCamelCase   :  int						     =     image_processing(video_inputs[0],      return_tensors='pt'	).pixel_values
          self.assertEqual(
              encoded_videos.shape,      (
                  1,
                  self.image_processor_tester.num_frames,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['height'],
                  self.image_processor_tester.crop_size['width'],
              ),      )

          # Test batched
          UpperCamelCase   :  Union[str, Any]						     =     image_processing(__lowercase,      return_tensors='pt'	).pixel_values
          self.assertEqual(
              encoded_videos.shape,      (
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_frames,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['height'],
                  self.image_processor_tester.crop_size['width'],
              ),      )
       def  snake_case_	(      self	)					->		int:
          # Initialize image_processing
          UpperCamelCase   :  Union[str, Any]						     =     self.image_processing_class(**self.image_processor_dict	)
          # create random numpy tensors
          UpperCamelCase   :  int						     =     prepare_video_inputs(self.image_processor_tester,      equal_resolution=__lowercase,      numpify=__lowercase	)
          for video in video_inputs:
             self.assertIsInstance(__lowercase,      __lowercase	)
             self.assertIsInstance(video[0],      np.ndarray	)

          # Test not batched input
          UpperCamelCase   :  Dict						     =     image_processing(video_inputs[0],      return_tensors='pt'	).pixel_values
          self.assertEqual(
              encoded_videos.shape,      (
                  1,
                  self.image_processor_tester.num_frames,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['height'],
                  self.image_processor_tester.crop_size['width'],
              ),      )

          # Test batched
          UpperCamelCase   :  Optional[int]						     =     image_processing(__lowercase,      return_tensors='pt'	).pixel_values
          self.assertEqual(
              encoded_videos.shape,      (
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_frames,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['height'],
                  self.image_processor_tester.crop_size['width'],
              ),      )






       def  snake_case_	(      self	)					->		Optional[int]:
          # Initialize image_processing
          UpperCamelCase   :  Tuple						     =     self.image_processing_class(**self.image_processor_dict	)
          # create random PyTorch tensors
          UpperCamelCase   :  Optional[Any]						     =     prepare_video_inputs(self.image_processor_tester,      equal_resolution=__lowercase,      torchify=__lowercase	)
          for video in video_inputs:
             self.assertIsInstance(__lowercase,      __lowercase	)
             self.assertIsInstance(video[0],      torch.Tensor	)

          # Test not batched input
          UpperCamelCase   :  Dict						     =     image_processing(video_inputs[0],      return_tensors='pt'	).pixel_values
          self.assertEqual(
              encoded_videos.shape,      (
                  1,
                  self.image_processor_tester.num_frames,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['height'],
                  self.image_processor_tester.crop_size['width'],
              ),      )

          # Test batched
          UpperCamelCase   :  List[str]						     =     image_processing(__lowercase,      return_tensors='pt'	).pixel_values
          self.assertEqual(
              encoded_videos.shape,      (
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_frames,
                  self.image_processor_tester.num_channels,
                  self.image_processor_tester.crop_size['height'],
                  self.image_processor_tester.crop_size['width'],
              ),      )




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     lowerCAmelCase_		:
           @staticmethod
           def  snake_case_	(      *SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_	)					->		Dict:
              pass
def 		UpperCamelCase     (       snake_case__	:   Image     )     ->					str:
   UpperCamelCase   :  Tuple						     =     hashlib.mda(image.tobytes()     )
   return m.hexdigest()



@is_pipeline_test
@require_vision
@require_timm
@require_torch
class     lowerCAmelCase_		(  unittest.TestCase							):
       UpperCAmelCase__    : str       					=  MODEL_FOR_DEPTH_ESTIMATION_MAPPING
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)					->		Optional[Any]:
          UpperCamelCase   :  List[str]						     =     DepthEstimationPipeline(model=SCREAMING_SNAKE_CASE_,      image_processor=SCREAMING_SNAKE_CASE_	)
          return depth_estimator, [
              "./tests/fixtures/tests_samples/COCO/000000039769.png",
              "./tests/fixtures/tests_samples/COCO/000000039769.png",
          ]
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)					->		Union[str, Any]:
          UpperCamelCase   :  Union[str, Any]						     =     depth_estimator('./tests/fixtures/tests_samples/COCO/000000039769.png'	)
          self.assertEqual({'predicted_depth': ANY(torch.Tensor	), 'depth': ANY(Image.Image	)},      SCREAMING_SNAKE_CASE_	)
          import datasets

          UpperCamelCase   :  int						     =     datasets.load_dataset('hf-internal-testing/fixtures_image_utils',      'image',      split='test'	)
          UpperCamelCase   :  Tuple						     =     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	)},
              ],      SCREAMING_SNAKE_CASE_,      )
       @require_tf
       @unittest.skip('Depth estimation is not implemented in TF'	)
       def  snake_case_	(      self	)					->		Optional[int]:
          pass
       @slow
       @require_torch
       def  snake_case_	(      self	)					->		List[Any]:
          UpperCamelCase   :  Optional[Any]						     =     'Intel/dpt-large'
          UpperCamelCase   :  Tuple						     =     pipeline('depth-estimation',      model=SCREAMING_SNAKE_CASE_	)
          UpperCamelCase   :  Any						     =     depth_estimator('http://images.cocodataset.org/val2017/000000039769.jpg'	)
          UpperCamelCase   :  int						     =     hashimage(outputs['depth']	)

          # This seems flaky.
          # self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
          self.assertEqual(nested_simplify(outputs['predicted_depth'].max().item()	),      29.3_04	)
          self.assertEqual(nested_simplify(outputs['predicted_depth'].min().item()	),      2.6_62	)






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



