"""simple docstring"""





from argparse import ArgumentParser

from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_to_tf import PTtoTFCommand
from .run import RunCommand
from .serving import ServeCommand
from .user import UserCommands





def        a_  (   ):
       lowercase__    :	List[str]     					=			ArgumentParser('Transformers CLI tool'     ,				usage='transformers-cli <command> [<args>]'  )
       lowercase__    :	int     					=			parser.add_subparsers(help='transformers-cli command helpers'  )

       # Register commands
       ConvertCommand.register_subcommand(_lowerCAmelCase  )
       DownloadCommand.register_subcommand(_lowerCAmelCase  )
       EnvironmentCommand.register_subcommand(_lowerCAmelCase  )
       RunCommand.register_subcommand(_lowerCAmelCase  )
       ServeCommand.register_subcommand(_lowerCAmelCase  )
       UserCommands.register_subcommand(_lowerCAmelCase  )
       AddNewModelCommand.register_subcommand(_lowerCAmelCase  )
       AddNewModelLikeCommand.register_subcommand(_lowerCAmelCase  )
       LfsCommands.register_subcommand(_lowerCAmelCase  )
       PTtoTFCommand.register_subcommand(_lowerCAmelCase  )

       # Let's go
       lowercase__    :	List[Any]     					=			parser.parse_args()

       if not hasattr(_lowerCAmelCase     ,				'func'  ):
              parser.print_help()
              exit(1  )

       # Run
       lowercase__    :	str     					=			args.func(_lowerCAmelCase  )
       service.run()


if __name__ == "__main__":
       main()


"""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   UpperCAmelCase_			:


             @staticmethod
             def 	_UpperCAmelCase (     *a	,    **a							) ->      int:
                    pass





def        a_  (   _lowerCAmelCase						:				Image  ):





       '''simple docstring'''

       lowercase__    :	List[str]     					=			hashlib.mda(image.tobytes()  )
       return m.hexdigest()






@is_pipeline_test
@require_vision
@require_timm
@require_torch
class   UpperCAmelCase_			(					unittest.TestCase):
      lowerCamelCase__	:					Union[str, Any]										=						MODEL_FOR_DEPTH_ESTIMATION_MAPPING


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a							) ->      Dict:
             lowercase__    :	Union[str, 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 	_UpperCAmelCase (     self	,    a	,    a							) ->      Optional[int]:
             lowercase__    :	Tuple     					=			depth_estimator('./tests/fixtures/tests_samples/COCO/000000039769.png'							)
             self.assertEqual({'predicted_depth': ANY(torch.Tensor							), 'depth': ANY(Image.Image							)}	,    a							)
             import datasets

             lowercase__    :	Tuple     					=			datasets.load_dataset('hf-internal-testing/fixtures_image_utils'	,    'image'	,    split='test'							)
             lowercase__    :	List[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 	_UpperCAmelCase (     self							) ->      Optional[int]:
             pass


      @slow
      @require_torch
      def 	_UpperCAmelCase (     self							) ->      Tuple:
             lowercase__    :	Tuple     					=			'Intel/dpt-large'
             lowercase__    :	Optional[int]     					=			pipeline('depth-estimation'	,    model=a							)
             lowercase__    :	List[Any]     					=			depth_estimator('http://images.cocodataset.org/val2017/000000039769.jpg'							)
             lowercase__    :	Optional[Any]     					=			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 	_UpperCAmelCase (     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'							)

