
import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


lowercase						:      int									= logging.get_logger(__name__)

lowercase						:      Optional[int]									= {
    'ut/deta': 'https://huggingface.co/ut/deta/resolve/main/config.json',
}




class 				lowerCamelCase__							(       __lowercase):




 '''simple docstring'''




 _A			=  'deta'
 _A			=  {
     'hidden_size': 'd_model',
     'num_attention_heads': 'encoder_attention_heads',
 }







 def __init__(      self	:Optional[Any]  ,     a	:Union[str, Any]=None  ,     a	:int=9_0_0  ,     a	:Optional[int]=2_0_4_8  ,     a	:Any=6  ,     a	:str=2_0_4_8  ,     a	:List[Any]=8  ,     a	:Tuple=6  ,     a	:Optional[Any]=1_0_2_4  ,     a	:Optional[int]=8  ,     a	:Union[str, Any]=0.0  ,     a	:Optional[Any]=True  ,     a	:Any="relu"  ,     a	:Dict=2_5_6  ,     a	:int=0.1  ,     a	:Dict=0.0  ,     a	:Optional[int]=0.0  ,     a	:Dict=0.02  ,     a	:List[Any]=1.0  ,     a	:Tuple=True  ,     a	:int=False  ,     a	:Optional[int]="sine"  ,     a	:Optional[int]=5  ,     a	:List[Any]=4  ,     a	:int=4  ,     a	:Optional[Any]=True  ,     a	:Optional[Any]=3_0_0  ,     a	:Any=True  ,     a	:Tuple=True  ,     a	:Optional[Any]=1  ,     a	:List[Any]=5  ,     a	:List[str]=2  ,     a	:Tuple=1  ,     a	:int=1  ,     a	:Tuple=5  ,     a	:Optional[int]=2  ,     a	:Optional[Any]=0.1  ,     a	:Dict=0.25  ,     **a	:List[str]  ,     )	->				List[Any]:
        if backbone_config is None:
               logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."		)
               __UpperCamelCase			:     Dict         =      CONFIG_MAPPING["resnet"](out_features=["stage2", "stage3", "stage4"]		)
        else:
               if isinstance(__A  ,     __A		):
                      __UpperCamelCase			:     Dict         =      backbone_config.pop("model_type"		)
                      __UpperCamelCase			:     List[Any]         =      CONFIG_MAPPING[backbone_model_type]
                      __UpperCamelCase			:     Optional[Any]         =      config_class.from_dict(__A		)

        __UpperCamelCase			:     List[str]         =      backbone_config
        __UpperCamelCase			:     Optional[int]         =      num_queries
        __UpperCamelCase			:     List[Any]         =      max_position_embeddings
        __UpperCamelCase			:     Any         =      d_model
        __UpperCamelCase			:     Any         =      encoder_ffn_dim
        __UpperCamelCase			:     Optional[int]         =      encoder_layers
        __UpperCamelCase			:     Tuple         =      encoder_attention_heads
        __UpperCamelCase			:     List[Any]         =      decoder_ffn_dim
        __UpperCamelCase			:     List[Any]         =      decoder_layers
        __UpperCamelCase			:     List[str]         =      decoder_attention_heads
        __UpperCamelCase			:     Tuple         =      dropout
        __UpperCamelCase			:     Optional[int]         =      attention_dropout
        __UpperCamelCase			:     Tuple         =      activation_dropout
        __UpperCamelCase			:     Optional[int]         =      activation_function
        __UpperCamelCase			:     int         =      init_std
        __UpperCamelCase			:     Optional[Any]         =      init_xavier_std
        __UpperCamelCase			:     Tuple         =      encoder_layerdrop
        __UpperCamelCase			:     Tuple         =      auxiliary_loss
        __UpperCamelCase			:     Optional[Any]         =      position_embedding_type
        # deformable attributes
        __UpperCamelCase			:     List[str]         =      num_feature_levels
        __UpperCamelCase			:     Optional[Any]         =      encoder_n_points
        __UpperCamelCase			:     str         =      decoder_n_points
        __UpperCamelCase			:     Any         =      two_stage
        __UpperCamelCase			:     List[Any]         =      two_stage_num_proposals
        __UpperCamelCase			:     int         =      with_box_refine
        __UpperCamelCase			:     Dict         =      assign_first_stage
        if two_stage is True and with_box_refine is False:
               raise ValueError("If two_stage is True, with_box_refine must be True."		)
        # Hungarian matcher
        __UpperCamelCase			:     Optional[Any]         =      class_cost
        __UpperCamelCase			:     List[Any]         =      bbox_cost
        __UpperCamelCase			:     List[str]         =      giou_cost
        # Loss coefficients
        __UpperCamelCase			:     str         =      mask_loss_coefficient
        __UpperCamelCase			:     Union[str, Any]         =      dice_loss_coefficient
        __UpperCamelCase			:     Optional[int]         =      bbox_loss_coefficient
        __UpperCamelCase			:     List[Any]         =      giou_loss_coefficient
        __UpperCamelCase			:     Dict         =      eos_coefficient
        __UpperCamelCase			:     int         =      focal_alpha
        super().__init__(is_encoder_decoder=__A  ,     **__A		)







 @property
 def      _lowerCamelCase       (      self	:str		)	->				Union[str, Any]:
        return self.encoder_attention_heads







 @property
 def      _lowerCamelCase       (      self	:str		)	->				List[Any]:
        return self.d_model







 def      _lowerCamelCase       (      self	:List[str]		)	->				int:
        __UpperCamelCase			:     str         =      copy.deepcopy(self.__dict__		)
        __UpperCamelCase			:     List[Any]         =      self.backbone_config.to_dict()
        __UpperCamelCase			:     List[str]         =      self.__class__.model_type
        return output

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      _lowerCamelCase       (      *a	:Union[str, Any]  ,     **a	:int		)	->				str:
										pass




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Image)      ->					str:






							'''simple docstring'''





							__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			=  MODEL_FOR_DEPTH_ESTIMATION_MAPPING







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

								__UpperCamelCase			:     List[Any]         =      datasets.load_dataset("hf-internal-testing/fixtures_image_utils"  ,     "image"  ,     split="test"		)
								__UpperCamelCase			:     int         =      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      _lowerCamelCase       (      self	:str		)	->				Optional[Any]:
								pass







	@slow
	@require_torch
	def      _lowerCamelCase       (      self	:List[Any]		)	->				List[Any]:
								__UpperCamelCase			:     Dict         =      "Intel/dpt-large"
								__UpperCamelCase			:     int         =      pipeline("depth-estimation"  ,     model=a		)
								__UpperCamelCase			:     Optional[int]         =      depth_estimator("http://images.cocodataset.org/val2017/000000039769.jpg"		)
								__UpperCamelCase			:     Optional[int]         =      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      _lowerCamelCase       (      self	:List[Any]		)	->				List[str]:
								# This is highly irregular to have no small tests.
								self.skipTest("There is not hf-internal-testing tiny model for either GLPN nor DPT"		)