





'''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 :





       @staticmethod
       def      UpperCAmelCase__     (					*lowerCamelCase				:			List[str]				,**lowerCamelCase				:			Optional[Any]    ):

          '''simple docstring'''

          pass

def 						__magic_name__       (							__UpperCAmelCase      )						->					str:

   '''simple docstring'''







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







@is_pipeline_test
@require_vision
@require_timm
@require_torch
class __a (   unittest.TestCase			):
   __UpperCamelCase   : Dict	      =	MODEL_FOR_DEPTH_ESTIMATION_MAPPING





   def      UpperCAmelCase__     (					self				:			List[Any]				,lowerCamelCase				:			str				,lowerCamelCase				:			Tuple				,lowerCamelCase				:			int    ):

      '''simple docstring'''

      __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      UpperCAmelCase__     (					self				:			Union[str, Any]				,lowerCamelCase				:			str				,lowerCamelCase				:			int    ):

      '''simple docstring'''

      __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      UpperCAmelCase__     (					self				:			int    ):

      '''simple docstring'''

      pass





   @slow
   @require_torch
   def      UpperCAmelCase__     (					self				:			int    ):

      '''simple docstring'''

      __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      UpperCAmelCase__     (					self				:			int    ):

      '''simple docstring'''

      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


a        =						logging.get_logger(__name__)

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







class __a (   _snake_case			):
   __UpperCamelCase   : Dict	      =	'deta'
   __UpperCamelCase   : List[str]	      =	{
       'hidden_size': 'd_model',
       'num_attention_heads': 'encoder_attention_heads',
   }





   def __init__(					self				:			Tuple				,lowerCamelCase				:			List[Any]=None				,lowerCamelCase				:			Any=900				,lowerCamelCase				:			int=2048				,lowerCamelCase				:			Any=6				,lowerCamelCase				:			Optional[Any]=2048				,lowerCamelCase				:			str=8				,lowerCamelCase				:			Union[str, Any]=6				,lowerCamelCase				:			List[str]=1024				,lowerCamelCase				:			int=8				,lowerCamelCase				:			Any=0.0				,lowerCamelCase				:			Any=True				,lowerCamelCase				:			Optional[int]="relu"				,lowerCamelCase				:			int=256				,lowerCamelCase				:			Tuple=0.1				,lowerCamelCase				:			Optional[Any]=0.0				,lowerCamelCase				:			Tuple=0.0				,lowerCamelCase				:			List[str]=0.02				,lowerCamelCase				:			Any=1.0				,lowerCamelCase				:			Optional[int]=True				,lowerCamelCase				:			int=False				,lowerCamelCase				:			Optional[Any]="sine"				,lowerCamelCase				:			Dict=5				,lowerCamelCase				:			List[Any]=4				,lowerCamelCase				:			Optional[Any]=4				,lowerCamelCase				:			Any=True				,lowerCamelCase				:			int=300				,lowerCamelCase				:			Any=True				,lowerCamelCase				:			Tuple=True				,lowerCamelCase				:			int=1				,lowerCamelCase				:			Tuple=5				,lowerCamelCase				:			Union[str, Any]=2				,lowerCamelCase				:			Tuple=1				,lowerCamelCase				:			int=1				,lowerCamelCase				:			str=5				,lowerCamelCase				:			Optional[Any]=2				,lowerCamelCase				:			List[Any]=0.1				,lowerCamelCase				:			Union[str, Any]=0.25				,**lowerCamelCase				:			int				,):

      '''simple docstring'''

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

      __SCREAMING_SNAKE_CASE      							=       backbone_config
      __SCREAMING_SNAKE_CASE      							=       num_queries
      __SCREAMING_SNAKE_CASE      							=       max_position_embeddings
      __SCREAMING_SNAKE_CASE      							=       d_model
      __SCREAMING_SNAKE_CASE      							=       encoder_ffn_dim
      __SCREAMING_SNAKE_CASE      							=       encoder_layers
      __SCREAMING_SNAKE_CASE      							=       encoder_attention_heads
      __SCREAMING_SNAKE_CASE      							=       decoder_ffn_dim
      __SCREAMING_SNAKE_CASE      							=       decoder_layers
      __SCREAMING_SNAKE_CASE      							=       decoder_attention_heads
      __SCREAMING_SNAKE_CASE      							=       dropout
      __SCREAMING_SNAKE_CASE      							=       attention_dropout
      __SCREAMING_SNAKE_CASE      							=       activation_dropout
      __SCREAMING_SNAKE_CASE      							=       activation_function
      __SCREAMING_SNAKE_CASE      							=       init_std
      __SCREAMING_SNAKE_CASE      							=       init_xavier_std
      __SCREAMING_SNAKE_CASE      							=       encoder_layerdrop
      __SCREAMING_SNAKE_CASE      							=       auxiliary_loss
      __SCREAMING_SNAKE_CASE      							=       position_embedding_type
      # deformable attributes
      __SCREAMING_SNAKE_CASE      							=       num_feature_levels
      __SCREAMING_SNAKE_CASE      							=       encoder_n_points
      __SCREAMING_SNAKE_CASE      							=       decoder_n_points
      __SCREAMING_SNAKE_CASE      							=       two_stage
      __SCREAMING_SNAKE_CASE      							=       two_stage_num_proposals
      __SCREAMING_SNAKE_CASE      							=       with_box_refine
      __SCREAMING_SNAKE_CASE      							=       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
      __SCREAMING_SNAKE_CASE      							=       class_cost
      __SCREAMING_SNAKE_CASE      							=       bbox_cost
      __SCREAMING_SNAKE_CASE      							=       giou_cost
      # Loss coefficients
      __SCREAMING_SNAKE_CASE      							=       mask_loss_coefficient
      __SCREAMING_SNAKE_CASE      							=       dice_loss_coefficient
      __SCREAMING_SNAKE_CASE      							=       bbox_loss_coefficient
      __SCREAMING_SNAKE_CASE      							=       giou_loss_coefficient
      __SCREAMING_SNAKE_CASE      							=       eos_coefficient
      __SCREAMING_SNAKE_CASE      							=       focal_alpha
      super().__init__(is_encoder_decoder=lowerCamelCase				,**lowerCamelCase    )





   @property
   def      UpperCAmelCase__     (					self				:			Any    ):

      '''simple docstring'''

      return self.encoder_attention_heads





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

      '''simple docstring'''

      return self.d_model





   def      UpperCAmelCase__     (					self				:			Tuple    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       copy.deepcopy(self.__dict__    )
      __SCREAMING_SNAKE_CASE      							=       self.backbone_config.to_dict()
      __SCREAMING_SNAKE_CASE      							=       self.__class__.model_type
      return output





