



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   :





     """simple docstring"""



     @staticmethod
     def lowercase_		(    *a						,     **a       )						->				str:




       """simple docstring"""







       pass




def      UpperCAmelCase__			(				__snake_case				) ->			str:
  _A							     =     hashlib.mda(image.tobytes()				)
  return m.hexdigest()



@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 		_snake_case   (      unittest.TestCase					):





 """simple docstring"""



 lowerCamelCase_        =      MODEL_FOR_DEPTH_ESTIMATION_MAPPING
 def lowercase_		(    self						,     a						,     a						,     a       )						->				Optional[Any]:




   """simple docstring"""







   _A							     =     DepthEstimationPipeline(model=_lowercase						,     image_processor=_lowercase       )
   return depth_estimator, [
       "./tests/fixtures/tests_samples/COCO/000000039769.png",
       "./tests/fixtures/tests_samples/COCO/000000039769.png",
   ]
 def lowercase_		(    self						,     a						,     a       )						->				Optional[Any]:




   """simple docstring"""







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

   _A							     =     datasets.load_dataset('''hf-internal-testing/fixtures_image_utils'''						,     '''image'''						,     split='''test'''       )
   _A							     =     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       )},
       ]						,     _lowercase						,     )
 @require_tf
 @unittest.skip('''Depth estimation is not implemented in TF'''       )
 def lowercase_		(    self       )						->				List[str]:




   """simple docstring"""







   pass
 @slow
 @require_torch
 def lowercase_		(    self       )						->				Dict:




   """simple docstring"""







   _A							     =     '''Intel/dpt-large'''
   _A							     =     pipeline('''depth-estimation'''						,     model=_lowercase       )
   _A							     =     depth_estimator('''http://images.cocodataset.org/val2017/000000039769.jpg'''       )
   _A							     =     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 lowercase_		(    self       )						->				Any:




   """simple docstring"""







   self.skipTest('''There is not hf-internal-testing tiny model for either GLPN nor DPT'''       )
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


UpperCAmelCase_           =				{
    '''configuration_mega''': ['''MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MegaConfig''', '''MegaOnnxConfig'''],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_           =				[
      '''MEGA_PRETRAINED_MODEL_ARCHIVE_LIST''',
      '''MegaForCausalLM''',
      '''MegaForMaskedLM''',
      '''MegaForMultipleChoice''',
      '''MegaForQuestionAnswering''',
      '''MegaForSequenceClassification''',
      '''MegaForTokenClassification''',
      '''MegaModel''',
      '''MegaPreTrainedModel''',
  ]

if TYPE_CHECKING:
  from .configuration_mega import MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP, MegaConfig, MegaOnnxConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_mega import (
        MEGA_PRETRAINED_MODEL_ARCHIVE_LIST,
        MegaForCausalLM,
        MegaForMaskedLM,
        MegaForMultipleChoice,
        MegaForQuestionAnswering,
        MegaForSequenceClassification,
        MegaForTokenClassification,
        MegaModel,
        MegaPreTrainedModel,
    )

else:
  import sys

  UpperCAmelCase_           =				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)