






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 				__a   ( *lowerCamelCase__     :int  ,     **lowerCamelCase__     :Tuple     ):
												pass




def 				A		(					lowercase__  :  str  )		-> Optional[Any]:
	UpperCamelCase__ :int						=      hashlib.mda(image.tobytes()  )
	return m.hexdigest()




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







				"""simple docstring"""




				_snake_case				:   List[str]        =			MODEL_FOR_DEPTH_ESTIMATION_MAPPING







				def 				__a   ( self     :Any  ,     lowerCamelCase__     :Dict  ,     lowerCamelCase__     :Dict  ,     lowerCamelCase__     :str     ):
					UpperCamelCase__ :Tuple						=      DepthEstimationPipeline(model=lowerCamelCase__  ,     image_processor=lowerCamelCase__     )
					return depth_estimator, [
					    "./tests/fixtures/tests_samples/COCO/000000039769.png",
					    "./tests/fixtures/tests_samples/COCO/000000039769.png",
					]







				def 				__a   ( self     :int  ,     lowerCamelCase__     :Any  ,     lowerCamelCase__     :Union[str, Any]     ):
					UpperCamelCase__ :Union[str, Any]						=      depth_estimator("""./tests/fixtures/tests_samples/COCO/000000039769.png"""     )
					self.assertEqual({"""predicted_depth""": ANY(torch.Tensor     ), """depth""": ANY(Image.Image     )}  ,     lowerCamelCase__     )
					import datasets

					UpperCamelCase__ :List[Any]						=      datasets.load_dataset("""hf-internal-testing/fixtures_image_utils"""  ,     """image"""  ,     split="""test"""     )
					UpperCamelCase__ :Optional[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     )},
					    ]  ,     lowerCamelCase__  ,     )







				@require_tf
				@unittest.skip("""Depth estimation is not implemented in TF"""     )
				def 				__a   ( self     :List[str]     ):
					pass







				@slow
				@require_torch
				def 				__a   ( self     :int     ):
					UpperCamelCase__ :Tuple						=      "Intel/dpt-large"
					UpperCamelCase__ :Union[str, Any]						=      pipeline("""depth-estimation"""  ,     model=lowerCamelCase__     )
					UpperCamelCase__ :Union[str, Any]						=      depth_estimator("""http://images.cocodataset.org/val2017/000000039769.jpg"""     )
					UpperCamelCase__ :List[str]						=      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 				__a   ( self     :Tuple     ):
					self.skipTest("""There is not hf-internal-testing tiny model for either GLPN nor DPT"""     )







'''simple docstring'''
import copy

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


UpperCamelCase__			=      logging.get_logger(__name__)

UpperCamelCase__			=      {
    'ut/deta': 'https://huggingface.co/ut/deta/resolve/main/config.json',
}


class 							_UpperCAmelCase    (		snake_case      ):
    __lowerCamelCase: Union[str, Any]									=     'deta'
    __lowerCamelCase: str									=     {
        'hidden_size': 'd_model',
        'num_attention_heads': 'encoder_attention_heads',
    }




    def __init__(      self	:							List[str]	,  a	:							Union[str, Any]=None	,  a	:							Dict=9_0_0	,  a	:							str=2_0_4_8	,  a	:							Union[str, Any]=6	,  a	:							int=2_0_4_8	,  a	:							Optional[int]=8	,  a	:							Optional[Any]=6	,  a	:							Optional[Any]=1_0_2_4	,  a	:							Any=8	,  a	:							Dict=0.0	,  a	:							Optional[int]=True	,  a	:							Union[str, Any]="relu"	,  a	:							str=2_5_6	,  a	:							str=0.1	,  a	:							Any=0.0	,  a	:							Optional[Any]=0.0	,  a	:							str=0.02	,  a	:							int=1.0	,  a	:							Tuple=True	,  a	:							Optional[int]=False	,  a	:							str="sine"	,  a	:							List[Any]=5	,  a	:							str=4	,  a	:							Any=4	,  a	:							Optional[Any]=True	,  a	:							Union[str, Any]=3_0_0	,  a	:							List[Any]=True	,  a	:							Tuple=True	,  a	:							Any=1	,  a	:							int=5	,  a	:							Optional[int]=2	,  a	:							Tuple=1	,  a	:							List[str]=1	,  a	:							Tuple=5	,  a	:							Tuple=2	,  a	:							List[str]=0.1	,  a	:							Tuple=0.25	,  **a	:							Optional[int]	,  ):






       '''simple docstring'''






       if backbone_config is None:
          logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."      )
          lowercase_						:     Optional[int]							     =  CONFIG_MAPPING["resnet"](out_features=["stage2", "stage3", "stage4"]      )
       else:
          if isinstance(a	,  a      ):
             lowercase_						:     Any							     =  backbone_config.pop("model_type"      )
             lowercase_						:     str							     =  CONFIG_MAPPING[backbone_model_type]
             lowercase_						:     Any							     =  config_class.from_dict(a      )

       lowercase_						:     Optional[int]							     =  backbone_config
       lowercase_						:     Dict							     =  num_queries
       lowercase_						:     Tuple							     =  max_position_embeddings
       lowercase_						:     Union[str, Any]							     =  d_model
       lowercase_						:     Optional[Any]							     =  encoder_ffn_dim
       lowercase_						:     Any							     =  encoder_layers
       lowercase_						:     int							     =  encoder_attention_heads
       lowercase_						:     Tuple							     =  decoder_ffn_dim
       lowercase_						:     Dict							     =  decoder_layers
       lowercase_						:     Dict							     =  decoder_attention_heads
       lowercase_						:     List[Any]							     =  dropout
       lowercase_						:     Union[str, Any]							     =  attention_dropout
       lowercase_						:     List[str]							     =  activation_dropout
       lowercase_						:     Optional[Any]							     =  activation_function
       lowercase_						:     str							     =  init_std
       lowercase_						:     Dict							     =  init_xavier_std
       lowercase_						:     List[Any]							     =  encoder_layerdrop
       lowercase_						:     Optional[int]							     =  auxiliary_loss
       lowercase_						:     str							     =  position_embedding_type
       # deformable attributes
       lowercase_						:     Optional[int]							     =  num_feature_levels
       lowercase_						:     List[Any]							     =  encoder_n_points
       lowercase_						:     Optional[Any]							     =  decoder_n_points
       lowercase_						:     List[Any]							     =  two_stage
       lowercase_						:     Any							     =  two_stage_num_proposals
       lowercase_						:     List[str]							     =  with_box_refine
       lowercase_						:     Optional[int]							     =  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
       lowercase_						:     int							     =  class_cost
       lowercase_						:     Optional[int]							     =  bbox_cost
       lowercase_						:     str							     =  giou_cost
       # Loss coefficients
       lowercase_						:     Optional[int]							     =  mask_loss_coefficient
       lowercase_						:     Union[str, Any]							     =  dice_loss_coefficient
       lowercase_						:     Optional[Any]							     =  bbox_loss_coefficient
       lowercase_						:     Optional[Any]							     =  giou_loss_coefficient
       lowercase_						:     Any							     =  eos_coefficient
       lowercase_						:     str							     =  focal_alpha
       super().__init__(is_encoder_decoder=a	,  **a      )




    @property
    def 					lowerCAmelCase__					(      self	:							Union[str, Any]      ):






       '''simple docstring'''






       return self.encoder_attention_heads




    @property
    def 					lowerCAmelCase__					(      self	:							Union[str, Any]      ):






       '''simple docstring'''






       return self.d_model





    def 					lowerCAmelCase__					(      self	:							Optional[Any]      ):






       '''simple docstring'''






       lowercase_						:     List[Any]							     =  copy.deepcopy(self.__dict__      )
       lowercase_						:     Union[str, Any]							     =  self.backbone_config.to_dict()
       lowercase_						:     str							     =  self.__class__.model_type
       return output




