




import copy

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


snake_case_ : Optional[int]    		=       logging.get_logger(__name__)

snake_case_ : Dict    		=       {
    "SenseTime/deformable-detr": "https://huggingface.co/sensetime/deformable-detr/resolve/main/config.json",
    # See all Deformable DETR models at https://huggingface.co/models?filter=deformable-detr
}







class __snake_case			(			a		):
   UpperCAmelCase__  :    Any          =  '''deformable_detr'''
   UpperCAmelCase__  :    List[Any]          =  {
       '''hidden_size''': '''d_model''',
       '''num_attention_heads''': '''encoder_attention_heads''',
   }







   def __init__(      self		:						Optional[Any]					,						_snake_case		:						List[Any]=True					,						_snake_case		:						Tuple=None					,						_snake_case		:						Dict=3					,						_snake_case		:						Union[str, Any]=300					,						_snake_case		:						str=1024					,						_snake_case		:						Optional[Any]=6					,						_snake_case		:						Optional[Any]=1024					,						_snake_case		:						Optional[Any]=8					,						_snake_case		:						str=6					,						_snake_case		:						Any=1024					,						_snake_case		:						Union[str, Any]=8					,						_snake_case		:						Tuple=0.0					,						_snake_case		:						List[Any]=True					,						_snake_case		:						Optional[int]="relu"					,						_snake_case		:						Dict=256					,						_snake_case		:						Dict=0.1					,						_snake_case		:						Any=0.0					,						_snake_case		:						Optional[Any]=0.0					,						_snake_case		:						Tuple=0.0_2					,						_snake_case		:						Optional[int]=1.0					,						_snake_case		:						List[Any]=True					,						_snake_case		:						Optional[int]=False					,						_snake_case		:						Optional[int]="sine"					,						_snake_case		:						Any="resnet50"					,						_snake_case		:						Dict=True					,						_snake_case		:						Any=False					,						_snake_case		:						List[str]=4					,						_snake_case		:						Any=4					,						_snake_case		:						int=4					,						_snake_case		:						int=False					,						_snake_case		:						int=300					,						_snake_case		:						str=False					,						_snake_case		:						Union[str, Any]=1					,						_snake_case		:						str=5					,						_snake_case		:						Optional[int]=2					,						_snake_case		:						Optional[Any]=1					,						_snake_case		:						Dict=1					,						_snake_case		:						Optional[Any]=5					,						_snake_case		:						List[str]=2					,						_snake_case		:						Tuple=0.1					,						_snake_case		:						List[Any]=0.2_5					,						_snake_case		:						Any=False					,						**_snake_case		:						Optional[int]					,						):

        """simple docstring"""


        if backbone_config is not None and use_timm_backbone:
             raise ValueError('''You can\'t specify both `backbone_config` and `use_timm_backbone`.''')

        if not use_timm_backbone:
             if backbone_config is None:
                  logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.''')
                  UpperCAmelCase_          =							CONFIG_MAPPING['''resnet'''](out_features=['''stage4'''])
             elif isinstance(_snake_case					,						_snake_case):
                  UpperCAmelCase_          =							backbone_config.get('''model_type''')
                  UpperCAmelCase_          =							CONFIG_MAPPING[backbone_model_type]
                  UpperCAmelCase_          =							config_class.from_dict(_snake_case)
        UpperCAmelCase_          =							use_timm_backbone
        UpperCAmelCase_          =							backbone_config
        UpperCAmelCase_          =							num_channels
        UpperCAmelCase_          =							num_queries
        UpperCAmelCase_          =							max_position_embeddings
        UpperCAmelCase_          =							d_model
        UpperCAmelCase_          =							encoder_ffn_dim
        UpperCAmelCase_          =							encoder_layers
        UpperCAmelCase_          =							encoder_attention_heads
        UpperCAmelCase_          =							decoder_ffn_dim
        UpperCAmelCase_          =							decoder_layers
        UpperCAmelCase_          =							decoder_attention_heads
        UpperCAmelCase_          =							dropout
        UpperCAmelCase_          =							attention_dropout
        UpperCAmelCase_          =							activation_dropout
        UpperCAmelCase_          =							activation_function
        UpperCAmelCase_          =							init_std
        UpperCAmelCase_          =							init_xavier_std
        UpperCAmelCase_          =							encoder_layerdrop
        UpperCAmelCase_          =							auxiliary_loss
        UpperCAmelCase_          =							position_embedding_type
        UpperCAmelCase_          =							backbone
        UpperCAmelCase_          =							use_pretrained_backbone
        UpperCAmelCase_          =							dilation
        # deformable attributes
        UpperCAmelCase_          =							num_feature_levels
        UpperCAmelCase_          =							encoder_n_points
        UpperCAmelCase_          =							decoder_n_points
        UpperCAmelCase_          =							two_stage
        UpperCAmelCase_          =							two_stage_num_proposals
        UpperCAmelCase_          =							with_box_refine
        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_          =							class_cost
        UpperCAmelCase_          =							bbox_cost
        UpperCAmelCase_          =							giou_cost
        # Loss coefficients
        UpperCAmelCase_          =							mask_loss_coefficient
        UpperCAmelCase_          =							dice_loss_coefficient
        UpperCAmelCase_          =							bbox_loss_coefficient
        UpperCAmelCase_          =							giou_loss_coefficient
        UpperCAmelCase_          =							eos_coefficient
        UpperCAmelCase_          =							focal_alpha
        UpperCAmelCase_          =							disable_custom_kernels
        super().__init__(is_encoder_decoder=_snake_case					,						**_snake_case)







   @property
   def lowerCamelCase						(      self		:						int):

        """simple docstring"""


        return self.encoder_attention_heads







   @property
   def lowerCamelCase						(      self		:						Optional[Any]):

        """simple docstring"""


        return self.d_model







   def lowerCamelCase						(      self		:						List[str]):

        """simple docstring"""


        UpperCAmelCase_          =							copy.deepcopy(self.__dict__)
        if self.backbone_config is not None:
             UpperCAmelCase_          =							self.backbone_config.to_dict()
        UpperCAmelCase_          =							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 __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''')






