






"""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     _lowerCAmelCase:





          """simple docstring"""







          @staticmethod
          def       _a   ( *_lowerCamelCase  ,      **_lowerCamelCase							):
            pass

def   snake_case (UpperCAmelCase__  )	->    str:
  UpperCamelCase_: Optional[int]   				=    hashlib.mda(image.tobytes()  )
  return m.hexdigest()




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





       """simple docstring"""







       a    :		Any									=MODEL_FOR_DEPTH_ESTIMATION_MAPPING





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase							):
         UpperCamelCase_: Union[str, Any]   				=    DepthEstimationPipeline(model=_lowerCamelCase  ,      image_processor=_lowerCamelCase							)
         return depth_estimator, [
             "./tests/fixtures/tests_samples/COCO/000000039769.png",
             "./tests/fixtures/tests_samples/COCO/000000039769.png",
         ]





       def       _a   ( self  ,      _lowerCamelCase  ,      _lowerCamelCase							):
         UpperCamelCase_: List[str]   				=    depth_estimator('./tests/fixtures/tests_samples/COCO/000000039769.png'							)
         self.assertEqual({'predicted_depth': ANY(torch.Tensor							), 'depth': ANY(Image.Image							)}  ,      _lowerCamelCase							)
         import datasets

         UpperCamelCase_: List[Any]   				=    datasets.load_dataset('hf-internal-testing/fixtures_image_utils'  ,      'image'  ,      split='test'							)
         UpperCamelCase_: List[str]   				=    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							)},
             ]  ,      _lowerCamelCase  ,      )





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





       @slow
       @require_torch
       def       _a   ( self							):
         UpperCamelCase_: int   				=    'Intel/dpt-large'
         UpperCamelCase_: List[str]   				=    pipeline('depth-estimation'  ,      model=_lowerCamelCase							)
         UpperCamelCase_: List[Any]   				=    depth_estimator('http://images.cocodataset.org/val2017/000000039769.jpg'							)
         UpperCamelCase_: List[Any]   				=    hashimage(outputs['depth']							)

         # This seems flaky.
         # self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
         self.assertEqual(nested_simplify(outputs['predicted_depth'].max().item()							)  ,      2_9.3_0_4							)
         self.assertEqual(nested_simplify(outputs['predicted_depth'].min().item()							)  ,      2.6_6_2							)





       @require_torch
       def       _a   ( 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'							)







from typing import TYPE_CHECKING

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


A_		:		Tuple		     =			{
    'configuration_funnel': ['FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FunnelConfig'],
    'convert_funnel_original_tf_checkpoint_to_pytorch': [],
    'tokenization_funnel': ['FunnelTokenizer'],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A_		:		List[str]		     =			['FunnelTokenizerFast']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A_		:		List[str]		     =			[
       'FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST',
       'FunnelBaseModel',
       'FunnelForMaskedLM',
       'FunnelForMultipleChoice',
       'FunnelForPreTraining',
       'FunnelForQuestionAnswering',
       'FunnelForSequenceClassification',
       'FunnelForTokenClassification',
       'FunnelModel',
       'FunnelPreTrainedModel',
       'load_tf_weights_in_funnel',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   A_		:		str		     =			[
       'TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TFFunnelBaseModel',
       'TFFunnelForMaskedLM',
       'TFFunnelForMultipleChoice',
       'TFFunnelForPreTraining',
       'TFFunnelForQuestionAnswering',
       'TFFunnelForSequenceClassification',
       'TFFunnelForTokenClassification',
       'TFFunnelModel',
       'TFFunnelPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_funnel import FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP, FunnelConfig
   from .tokenization_funnel import FunnelTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_funnel_fast import FunnelTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_funnel import (
          FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
          FunnelBaseModel,
          FunnelForMaskedLM,
          FunnelForMultipleChoice,
          FunnelForPreTraining,
          FunnelForQuestionAnswering,
          FunnelForSequenceClassification,
          FunnelForTokenClassification,
          FunnelModel,
          FunnelPreTrainedModel,
          load_tf_weights_in_funnel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_funnel import (
          TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFFunnelBaseModel,
          TFFunnelForMaskedLM,
          TFFunnelForMultipleChoice,
          TFFunnelForPreTraining,
          TFFunnelForQuestionAnswering,
          TFFunnelForSequenceClassification,
          TFFunnelForTokenClassification,
          TFFunnelModel,
          TFFunnelPreTrainedModel,
      )

else:
   import sys

   A_		:		Any		     =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)