



from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_lowercase		:      Dict					=logging.get_logger(__name__)

_lowercase		:      int					={
    "google/vit-base-patch16-224": "https://huggingface.co/vit-base-patch16-224/resolve/main/config.json",
    # See all ViT models at https://huggingface.co/models?filter=vit
}

class    snake_case__				(A__		):







						"""simple docstring"""




						__lowerCAmelCase     :Dict						=     '''vit'''

						def __init__(   self					,   __lowercase=7_6_8					,   __lowercase=1_2					,   __lowercase=1_2					,   __lowercase=3_0_7_2					,   __lowercase="gelu"					,   __lowercase=0.0					,   __lowercase=0.0					,   __lowercase=0.0_2					,   __lowercase=1E-12					,   __lowercase=2_2_4					,   __lowercase=1_6					,   __lowercase=3					,   __lowercase=True					,   __lowercase=1_6					,   **__lowercase					,   )					->      str:
									"""simple docstring"""




									super().__init__(**_snake_case  )

									a__   :						str						       =  hidden_size
									a__   :						List[Any]						       =  num_hidden_layers
									a__   :						Any						       =  num_attention_heads
									a__   :						Union[str, Any]						       =  intermediate_size
									a__   :						Optional[Any]						       =  hidden_act
									a__   :						Union[str, Any]						       =  hidden_dropout_prob
									a__   :						str						       =  attention_probs_dropout_prob
									a__   :						Tuple						       =  initializer_range
									a__   :						Any						       =  layer_norm_eps
									a__   :						Dict						       =  image_size
									a__   :						List[Any]						       =  patch_size
									a__   :						Union[str, Any]						       =  num_channels
									a__   :						str						       =  qkv_bias
									a__   :						Optional[Any]						       =  encoder_stride




class    snake_case__				(A__		):







						"""simple docstring"""




						__lowerCAmelCase     :Optional[Any]						=     version.parse("1.11"		)

						@property
						def 							SCREAMING_SNAKE_CASE__(   self  )					->      Optional[int]:
									"""simple docstring"""




									return OrderedDict(
									    [
									        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
									    ]  )

						@property
						def 							SCREAMING_SNAKE_CASE__(   self  )					->      int:
									"""simple docstring"""




									return 1E-4










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			:







       @staticmethod
       def lowerCamelCase						(      *_snake_case		:						Optional[int]					,						**_snake_case		:						int):

            """simple docstring"""


            pass


def    A						(__A   :    Image       )   ->      str:







     """simple docstring"""


     UpperCAmelCase_          =							hashlib.mda(image.tobytes()       )
     return m.hexdigest()







@is_pipeline_test
@require_vision
@require_timm
@require_torch
class __snake_case			(			unittest.TestCase		):
   UpperCAmelCase__  :    Tuple          =  MODEL_FOR_DEPTH_ESTIMATION_MAPPING







   def lowerCamelCase						(      self		:						Tuple					,						_snake_case		:						Optional[Any]					,						_snake_case		:						Optional[Any]					,						_snake_case		:						List[str]):

        """simple docstring"""


        UpperCAmelCase_          =							DepthEstimationPipeline(model=_snake_case					,						image_processor=_snake_case)
        return depth_estimator, [
            "./tests/fixtures/tests_samples/COCO/000000039769.png",
            "./tests/fixtures/tests_samples/COCO/000000039769.png",
        ]







   def lowerCamelCase						(      self		:						str					,						_snake_case		:						Optional[int]					,						_snake_case		:						List[str]):

        """simple docstring"""


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

        UpperCAmelCase_          =							datasets.load_dataset('''hf-internal-testing/fixtures_image_utils'''					,						'''image'''					,						split='''test''')
        UpperCAmelCase_          =							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)},
            ]					,						_snake_case					,						)







   @require_tf
   @unittest.skip('''Depth estimation is not implemented in TF''')
   def lowerCamelCase						(      self		:						Union[str, Any]):

        """simple docstring"""


        pass







   @slow
   @require_torch
   def lowerCamelCase						(      self		:						List[str]):

        """simple docstring"""


        UpperCAmelCase_          =							'''Intel/dpt-large'''
        UpperCAmelCase_          =							pipeline('''depth-estimation'''					,						model=_snake_case)
        UpperCAmelCase_          =							depth_estimator('''http://images.cocodataset.org/val2017/000000039769.jpg''')
        UpperCAmelCase_          =							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 lowerCamelCase						(      self		:						Optional[Any]):

        """simple docstring"""


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






