
"""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     _SCREAMING_SNAKE_CASE   (			*A_  :   Optional[Any]	,**A_  :   int   )  -> List[str]:
									pass






def    _snake_case					(		snake_case__ :  Image  ):
	A     = hashlib.mda(image.tobytes()  )
	return m.hexdigest()







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



						'''simple docstring'''


						_lowerCamelCase:     Optional[int]   =     MODEL_FOR_DEPTH_ESTIMATION_MAPPING


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


						def     _SCREAMING_SNAKE_CASE   (			self  :   Tuple	,A_  :   Any	,A_  :   Any   )  -> Any:
							A     = depth_estimator('./tests/fixtures/tests_samples/COCO/000000039769.png'   )
							self.assertEqual({'predicted_depth': ANY(torch.Tensor   ), 'depth': ANY(Image.Image   )}	,A_   )
							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   )},
							    ]	,A_	,)


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


						@slow
						@require_torch
						def     _SCREAMING_SNAKE_CASE   (			self  :   Dict   )  -> Optional[Any]:
							A     = 'Intel/dpt-large'
							A     = pipeline('depth-estimation'	,model=A_   )
							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()   )	,29.3_04   )
							self.assertEqual(nested_simplify(outputs['predicted_depth'].min().item()   )	,2.6_62   )




						@require_torch
						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[Any]   )  -> List[Any]:
							# 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 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				 =					logging.get_logger(__name__)

_lowercase				 =					{
    '''google/mobilenet_v1_1.0_224''': '''https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json''',
    '''google/mobilenet_v1_0.75_192''': '''https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json''',
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}







class 					lowerCAmelCase_					(   _lowercase     ):



						'''simple docstring'''


						_lowerCamelCase:     Optional[Any]   =     '''mobilenet_v1'''


						def __init__(			self  :   Optional[int]	,A_  :   Optional[int]=3	,A_  :   Any=224	,A_  :   List[Any]=1.0	,A_  :   Union[str, Any]=8	,A_  :   Union[str, Any]="relu6"	,A_  :   Optional[Any]=True	,A_  :   List[str]=0.9_99	,A_  :   int=0.02	,A_  :   int=0.0_01	,**A_  :   Union[str, Any]	,)  -> Dict:
							super().__init__(**A_   )

							if depth_multiplier <= 0:
								raise ValueError('depth_multiplier must be greater than zero.'   )

							A     = num_channels
							A     = image_size
							A     = depth_multiplier
							A     = min_depth
							A     = hidden_act
							A     = tf_padding
							A     = classifier_dropout_prob
							A     = initializer_range
							A     = layer_norm_eps







class 					lowerCAmelCase_					(   _lowercase     ):



						'''simple docstring'''


						_lowerCamelCase:     Optional[int]   =     version.parse('''1.11'''     )


						@property
						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[int]   )  -> Mapping[str, Mapping[int, str]]:
							return OrderedDict([('pixel_values', {0: 'batch'})]   )


						@property
						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[int]   )  -> Mapping[str, Mapping[int, str]]:
							if self.task == "image-classification":
								return OrderedDict([('logits', {0: 'batch'})]   )
							else:
								return OrderedDict([('last_hidden_state', {0: 'batch'}), ('pooler_output', {0: 'batch'})]   )




						@property
						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[int]   )  -> float:
							return 1e-4