





'''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 					a_     :

          '''simple docstring'''


          @staticmethod
          def 			snake_case_(		*A ,		**A       )		->   Dict:
              pass





def lowerCamelCase						(     __lowerCamelCase :    Image    )						->str:
    _SCREAMING_SNAKE_CASE									=			hashlib.mda(image.tobytes()    )
    return m.hexdigest()






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

    '''simple docstring'''
    UpperCamelCase     = MODEL_FOR_DEPTH_ESTIMATION_MAPPING


    def 			snake_case_(		self ,		A ,		A ,		A       )		->   Optional[int]:
        _SCREAMING_SNAKE_CASE									=			DepthEstimationPipeline(model=A ,		image_processor=A       )
        return depth_estimator, [
            "./tests/fixtures/tests_samples/COCO/000000039769.png",
            "./tests/fixtures/tests_samples/COCO/000000039769.png",
        ]


    def 			snake_case_(		self ,		A ,		A       )		->   Optional[int]:
        _SCREAMING_SNAKE_CASE									=			depth_estimator("""./tests/fixtures/tests_samples/COCO/000000039769.png"""       )
        self.assertEqual({"""predicted_depth""": ANY(torch.Tensor       ), """depth""": ANY(Image.Image       )} ,		A       )
        import datasets

        _SCREAMING_SNAKE_CASE									=			datasets.load_dataset("""hf-internal-testing/fixtures_image_utils""" ,		"""image""" ,		split="""test"""       )
        _SCREAMING_SNAKE_CASE									=			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 			snake_case_(		self       )		->   Optional[int]:
        pass


    @slow
    @require_torch
    def 			snake_case_(		self       )		->   Tuple:
        _SCREAMING_SNAKE_CASE									=			"""Intel/dpt-large"""
        _SCREAMING_SNAKE_CASE									=			pipeline("""depth-estimation""" ,		model=A       )
        _SCREAMING_SNAKE_CASE									=			depth_estimator("""http://images.cocodataset.org/val2017/000000039769.jpg"""       )
        _SCREAMING_SNAKE_CASE									=			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 			snake_case_(		self       )		->   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"""       )










'''simple docstring'''

from typing import List

from .keymap import KEYMAP, get_character





def lowerCamelCase						(     __lowerCamelCase :    str    )						->Optional[int]:

    def decorator(__lowerCamelCase :    int    ):
        _SCREAMING_SNAKE_CASE									=			getattr(__lowerCamelCase					,       """handle_key"""					,       []    )
        handle += [key]
        setattr(__lowerCamelCase					,       """handle_key"""					,       __lowerCamelCase    )
        return func

    return decorator





def lowerCamelCase						(     *__lowerCamelCase :    List[str]    )						->Dict:

    def decorator(__lowerCamelCase :    int    ):
        _SCREAMING_SNAKE_CASE									=			getattr(__lowerCamelCase					,       """handle_key"""					,       []    )
        handle += keys
        setattr(__lowerCamelCase					,       """handle_key"""					,       __lowerCamelCase    )
        return func

    return decorator


class 					a_     (				snake_case_       ):

    '''simple docstring'''


    def __new__(		cls ,		A ,		A ,		A       )		->   int:
        _SCREAMING_SNAKE_CASE									=			super().__new__(cls ,		A ,		A ,		A       )
        if not hasattr(A ,		"""key_handler"""       ):
            setattr(A ,		"""key_handler""" ,		{}       )
        setattr(A ,		"""handle_input""" ,		KeyHandler.handle_input       )

        for value in attrs.values():
            _SCREAMING_SNAKE_CASE									=			getattr(A ,		"""handle_key""" ,		[]       )
            for key in handled_keys:
                _SCREAMING_SNAKE_CASE									=			value
        return new_cls





    @staticmethod
    def 			snake_case_(		cls       )		->   str:
        _SCREAMING_SNAKE_CASE									=			get_character()
        if char != KEYMAP["undefined"]:
            _SCREAMING_SNAKE_CASE									=			ord(A       )
        _SCREAMING_SNAKE_CASE									=			cls.key_handler.get(A       )
        if handler:
            _SCREAMING_SNAKE_CASE									=			char
            return handler(cls       )
        else:
            return None





def lowerCamelCase						(     cls :    Any    )						->Dict:
    return KeyHandler(cls.__name__					,       cls.__bases__					,       cls.__dict__.copy()    )



