



import hashlib
import unittest
from typing import Dict

import numpy as np

from transformers import (
    MODEL_FOR_MASK_GENERATION_MAPPING,
    TF_MODEL_FOR_MASK_GENERATION_MAPPING,
    is_vision_available,
    pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)


if is_vision_available():
    from PIL import Image
else:



    class     lowerCAmelCase_		:
           @staticmethod
           def  snake_case_	(      *SCREAMING_SNAKE_CASE_,      **SCREAMING_SNAKE_CASE_	)					->		Union[str, Any]:
              pass
def 		UpperCamelCase     (       snake_case__	:   Image     )     ->					str:
   UpperCamelCase   :  Tuple						     =     hashlib.mda(image.tobytes()     )
   return m.hexdigest()[:10]
def 		UpperCamelCase     (       snake_case__	:   Image     )     ->					Dict:
   UpperCamelCase   :  Union[str, Any]						     =     np.array(snake_case__     )
   UpperCamelCase   :  Dict						     =     npimg.shape
   return {"hash": hashimage(snake_case__     ), "shape": shape}



@is_pipeline_test
@require_vision
@require_torch
class     lowerCAmelCase_		(  unittest.TestCase							):
       UpperCAmelCase__    : Dict       					=  dict(
           (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()							) if MODEL_FOR_MASK_GENERATION_MAPPING else [])							)
       UpperCAmelCase__    : Union[str, Any]       					=  dict(
           (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()							) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])							)
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)					->		List[Any]:
          UpperCamelCase   :  Dict						     =     MaskGenerationPipeline(model=SCREAMING_SNAKE_CASE_,      image_processor=SCREAMING_SNAKE_CASE_	)
          return image_segmenter, [
              "./tests/fixtures/tests_samples/COCO/000000039769.png",
              "./tests/fixtures/tests_samples/COCO/000000039769.png",
          ]
       def  snake_case_	(      self,      SCREAMING_SNAKE_CASE_,      SCREAMING_SNAKE_CASE_	)					->		Tuple:
          pass
       @require_tf
       @unittest.skip('Image segmentation not implemented in TF'	)
       def  snake_case_	(      self	)					->		Dict:
          pass
       @slow
       @require_torch
       def  snake_case_	(      self	)					->		Optional[int]:
          UpperCamelCase   :  Any						     =     pipeline('mask-generation',      model='facebook/sam-vit-huge'	)

          UpperCamelCase   :  List[Any]						     =     image_segmenter('http://images.cocodataset.org/val2017/000000039769.jpg',      points_per_batch=256	)

          # Shortening by hashing
          UpperCamelCase   :  Tuple						     =     []
          for i, o in enumerate(outputs['masks']	):
             new_outupt += [{"mask": mask_to_test_readable(SCREAMING_SNAKE_CASE_	), "scores": outputs["scores"][i]}]

          # fmt: off
          self.assertEqual(
              nested_simplify(SCREAMING_SNAKE_CASE_,      decimals=4	),      [
                  {'mask': {'hash': '115ad19f5f', 'shape': (480, 640)}, 'scores': 1.04_44},
                  {'mask': {'hash': '6affa964c6', 'shape': (480, 640)}, 'scores': 1.0_21},
                  {'mask': {'hash': 'dfe28a0388', 'shape': (480, 640)}, 'scores': 1.01_67},
                  {'mask': {'hash': 'c0a5f4a318', 'shape': (480, 640)}, 'scores': 1.01_32},
                  {'mask': {'hash': 'fe8065c197', 'shape': (480, 640)}, 'scores': 1.00_53},
                  {'mask': {'hash': 'e2d0b7a0b7', 'shape': (480, 640)}, 'scores': 0.99_67},
                  {'mask': {'hash': '453c7844bd', 'shape': (480, 640)}, 'scores': 0.9_93},
                  {'mask': {'hash': '3d44f2926d', 'shape': (480, 640)}, 'scores': 0.99_09},
                  {'mask': {'hash': '64033ddc3f', 'shape': (480, 640)}, 'scores': 0.98_79},
                  {'mask': {'hash': '801064ff79', 'shape': (480, 640)}, 'scores': 0.98_34},
                  {'mask': {'hash': '6172f276ef', 'shape': (480, 640)}, 'scores': 0.97_16},
                  {'mask': {'hash': 'b49e60e084', 'shape': (480, 640)}, 'scores': 0.96_12},
                  {'mask': {'hash': 'a811e775fd', 'shape': (480, 640)}, 'scores': 0.95_99},
                  {'mask': {'hash': 'a6a8ebcf4b', 'shape': (480, 640)}, 'scores': 0.95_52},
                  {'mask': {'hash': '9d8257e080', 'shape': (480, 640)}, 'scores': 0.95_32},
                  {'mask': {'hash': '32de6454a8', 'shape': (480, 640)}, 'scores': 0.95_16},
                  {'mask': {'hash': 'af3d4af2c8', 'shape': (480, 640)}, 'scores': 0.94_99},
                  {'mask': {'hash': '3c6db475fb', 'shape': (480, 640)}, 'scores': 0.94_83},
                  {'mask': {'hash': 'c290813fb9', 'shape': (480, 640)}, 'scores': 0.94_64},
                  {'mask': {'hash': 'b6f0b8f606', 'shape': (480, 640)}, 'scores': 0.9_43},
                  {'mask': {'hash': '92ce16bfdf', 'shape': (480, 640)}, 'scores': 0.9_43},
                  {'mask': {'hash': 'c749b25868', 'shape': (480, 640)}, 'scores': 0.94_08},
                  {'mask': {'hash': 'efb6cab859', 'shape': (480, 640)}, 'scores': 0.93_35},
                  {'mask': {'hash': '1ff2eafb30', 'shape': (480, 640)}, 'scores': 0.93_26},
                  {'mask': {'hash': '788b798e24', 'shape': (480, 640)}, 'scores': 0.92_62},
                  {'mask': {'hash': 'abea804f0e', 'shape': (480, 640)}, 'scores': 0.89_99},
                  {'mask': {'hash': '7b9e8ddb73', 'shape': (480, 640)}, 'scores': 0.89_86},
                  {'mask': {'hash': 'cd24047c8a', 'shape': (480, 640)}, 'scores': 0.89_84},
                  {'mask': {'hash': '6943e6bcbd', 'shape': (480, 640)}, 'scores': 0.88_73},
                  {'mask': {'hash': 'b5f47c9191', 'shape': (480, 640)}, 'scores': 0.88_71}
              ],      )
          # fmt: on






       @require_torch
       @slow
       def  snake_case_	(      self	)					->		str:
          UpperCamelCase   :  Dict						     =     'facebook/sam-vit-huge'
          UpperCamelCase   :  List[Any]						     =     pipeline('mask-generation',      model=SCREAMING_SNAKE_CASE_	)

          UpperCamelCase   :  List[str]						     =     image_segmenter(
              'http://images.cocodataset.org/val2017/000000039769.jpg',      pred_iou_thresh=1,      points_per_batch=256	)

          # Shortening by hashing
          UpperCamelCase   :  Dict						     =     []
          for i, o in enumerate(outputs['masks']	):
             new_outupt += [{"mask": mask_to_test_readable(SCREAMING_SNAKE_CASE_	), "scores": outputs["scores"][i]}]

          self.assertEqual(
              nested_simplify(SCREAMING_SNAKE_CASE_,      decimals=4	),      [
                  {'mask': {'hash': '115ad19f5f', 'shape': (480, 640)}, 'scores': 1.04_44},
                  {'mask': {'hash': '6affa964c6', 'shape': (480, 640)}, 'scores': 1.02_10},
                  {'mask': {'hash': 'dfe28a0388', 'shape': (480, 640)}, 'scores': 1.01_67},
                  {'mask': {'hash': 'c0a5f4a318', 'shape': (480, 640)}, 'scores': 1.01_32},
                  {'mask': {'hash': 'fe8065c197', 'shape': (480, 640)}, 'scores': 1.00_53},
              ],      )








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'	)



