


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 			SCREAMING_SNAKE_CASE__  :
         @staticmethod
         def 	SCREAMING_SNAKE_CASE							(	*SCREAMING_SNAKE_CASE__    :	List[Any]					,							**SCREAMING_SNAKE_CASE__    :	List[str]					)		->      List[Any]:
                pass







def 	SCREAMING_SNAKE_CASE_						(      __A			:      Image    )     ->		Dict:

       """simple docstring"""

       a_ :		Union[str, Any]							=  hashlib.mda(image.tobytes()    )
       return m.hexdigest()
@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 			SCREAMING_SNAKE_CASE__  (    unittest.TestCase      ):
      snake_case__    :					Tuple					       =						MODEL_FOR_DEPTH_ESTIMATION_MAPPING
      def 	SCREAMING_SNAKE_CASE							(	self    :	List[str]					,							SCREAMING_SNAKE_CASE__    :	Any					,							SCREAMING_SNAKE_CASE__    :	List[Any]					,							SCREAMING_SNAKE_CASE__    :	Optional[int]					)		->      Tuple:
             a_ :		Optional[Any]							=  DepthEstimationPipeline(model=_A					,							image_processor=_A					)
             return depth_estimator, [
                 "./tests/fixtures/tests_samples/COCO/000000039769.png",
                 "./tests/fixtures/tests_samples/COCO/000000039769.png",
             ]
      def 	SCREAMING_SNAKE_CASE							(	self    :	List[str]					,							SCREAMING_SNAKE_CASE__    :	Any					,							SCREAMING_SNAKE_CASE__    :	int					)		->      int:
             a_ :		Tuple							=  depth_estimator('./tests/fixtures/tests_samples/COCO/000000039769.png'					)
             self.assertEqual({'predicted_depth': ANY(torch.Tensor					), 'depth': ANY(Image.Image					)}					,							_A					)
             import datasets

             a_ :		Optional[int]							=  datasets.load_dataset('hf-internal-testing/fixtures_image_utils'					,							'image'					,							split='test'					)
             a_ :		Union[str, Any]							=  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 	SCREAMING_SNAKE_CASE							(	self    :	List[Any]					)		->      Any:
             pass
      @slow
      @require_torch
      def 	SCREAMING_SNAKE_CASE							(	self    :	str					)		->      Optional[Any]:
             a_ :		List[Any]							=  'Intel/dpt-large'
             a_ :		Tuple							=  pipeline('depth-estimation'					,							model=_A					)
             a_ :		Any							=  depth_estimator('http://images.cocodataset.org/val2017/000000039769.jpg'					)
             a_ :		Tuple							=  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()					)					,							2.662					)

      @require_torch
      def 	SCREAMING_SNAKE_CASE							(	self    :	str					)		->      int:
             self.skipTest('There is not hf-internal-testing tiny model for either GLPN nor DPT'					)








import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
   import torch

   from transformers.activations import gelu_new, gelu_python, get_activation
@require_torch
class 			SCREAMING_SNAKE_CASE__  (    unittest.TestCase      ):
      def 	SCREAMING_SNAKE_CASE							(	self    :	Dict					)		->      List[str]:
             a_ :		List[str]							=  torch.tensor([-1_0_0, -1, -0.1, 0, 0.1, 1.0, 1_0_0]					)
             a_ :		Union[str, Any]							=  get_activation('gelu'					)
             self.assertTrue(torch.allclose(gelu_python(SCREAMING_SNAKE_CASE__					)					,							torch_builtin(SCREAMING_SNAKE_CASE__					)					)					)
             self.assertFalse(torch.allclose(gelu_python(SCREAMING_SNAKE_CASE__					)					,							gelu_new(SCREAMING_SNAKE_CASE__					)					)					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Union[str, Any]					)		->      Any:
             a_ :		List[str]							=  torch.tensor([-1_0_0, -1, -0.1, 0, 0.1, 1.0, 1_0_0]					)
             a_ :		Union[str, Any]							=  get_activation('gelu'					)
             a_ :		str							=  get_activation('gelu_10'					)

             a_ :		Tuple							=  torch_builtin(SCREAMING_SNAKE_CASE__					)
             a_ :		str							=  geluaa(SCREAMING_SNAKE_CASE__					)

             a_ :		List[Any]							=  torch.where(y_gelu_aa < 10.0					,							1					,							0					)

             self.assertTrue(torch.max(SCREAMING_SNAKE_CASE__					).item() == 10.0					)
             self.assertTrue(torch.allclose(y_gelu * clipped_mask					,							y_gelu_aa * clipped_mask					)					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Dict					)		->      List[Any]:
             get_activation('gelu'					)
             get_activation('gelu_10'					)
             get_activation('gelu_fast'					)
             get_activation('gelu_new'					)
             get_activation('gelu_python'					)
             get_activation('gelu_pytorch_tanh'					)
             get_activation('linear'					)
             get_activation('mish'					)
             get_activation('quick_gelu'					)
             get_activation('relu'					)
             get_activation('sigmoid'					)
             get_activation('silu'					)
             get_activation('swish'					)
             get_activation('tanh'					)
             with self.assertRaises(SCREAMING_SNAKE_CASE__					):
                    get_activation('bogus'					)
             with self.assertRaises(SCREAMING_SNAKE_CASE__					):
                    get_activation(SCREAMING_SNAKE_CASE__					)

      def 	SCREAMING_SNAKE_CASE							(	self    :	str					)		->      str:
             a_ :		Any							=  get_activation('gelu'					)
             a_ :		Any							=  1
             a_ :		int							=  get_activation('gelu'					)
             self.assertEqual(acta.a					,							1					)
             with self.assertRaises(SCREAMING_SNAKE_CASE__					):
                    a_ :		Tuple							=  acta.a


