import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig


lowerCAmelCase_:					Optional[Any]     =					logging.get_logger(__name__)

lowerCAmelCase_:					List[str]     =					{
    "Intel/dpt-large": "https://huggingface.co/Intel/dpt-large/resolve/main/config.json",
    # See all DPT models at https://huggingface.co/models?filter=dpt
}






class    a__    (   _lowerCamelCase	):
   snake_case_							=      "dpt"


   def __init__(     self,				_UpperCAmelCase=768,				_UpperCAmelCase=12,				_UpperCAmelCase=12,				_UpperCAmelCase=3072,				_UpperCAmelCase="gelu",				_UpperCAmelCase=0.0,				_UpperCAmelCase=0.0,				_UpperCAmelCase=0.02,				_UpperCAmelCase=1E-12,				_UpperCAmelCase=384,				_UpperCAmelCase=16,				_UpperCAmelCase=3,				_UpperCAmelCase=False,				_UpperCAmelCase=True,				_UpperCAmelCase=[2, 5, 8, 11],				_UpperCAmelCase="project",				_UpperCAmelCase=[4, 2, 1, 0.5],				_UpperCAmelCase=[96, 192, 384, 768],				_UpperCAmelCase=256,				_UpperCAmelCase=-1,				_UpperCAmelCase=False,				_UpperCAmelCase=True,				_UpperCAmelCase=0.4,				_UpperCAmelCase=255,				_UpperCAmelCase=0.1,				_UpperCAmelCase=[1, 1024, 24, 24],				_UpperCAmelCase=[0, 1],				_UpperCAmelCase=None,				**_UpperCAmelCase,				):
        '''simple docstring'''



        super().__init__(**A__  )

        lowercase__				 =							hidden_size
        lowercase__				 =							is_hybrid

        if self.is_hybrid:
             if backbone_config is None:
                  logger.info("Initializing the config with a `BiT` backbone."  )
                  lowercase__				 =							{
                      "global_padding": "same",
                      "layer_type": "bottleneck",
                      "depths": [3, 4, 9],
                      "out_features": ["stage1", "stage2", "stage3"],
                      "embedding_dynamic_padding": True,
                  }
                  lowercase__				 =							BitConfig(**A__  )
             elif isinstance(A__,				A__  ):
                  logger.info("Initializing the config with a `BiT` backbone."  )
                  lowercase__				 =							BitConfig(**A__  )
             elif isinstance(A__,				A__  ):
                  lowercase__				 =							backbone_config
             else:
                  raise ValueError(
                      F'''backbone_config must be a dictionary or a `PretrainedConfig`, got {backbone_config.__class__}.'''  )

             lowercase__				 =							backbone_featmap_shape
             lowercase__				 =							neck_ignore_stages

             if readout_type != "project":
                  raise ValueError("Readout type must be 'project' when using `DPT-hybrid` mode."  )
        else:
             lowercase__				 =							None
             lowercase__				 =							None
             lowercase__				 =							[]

        lowercase__				 =							num_hidden_layers
        lowercase__				 =							num_attention_heads
        lowercase__				 =							intermediate_size
        lowercase__				 =							hidden_act
        lowercase__				 =							hidden_dropout_prob
        lowercase__				 =							attention_probs_dropout_prob
        lowercase__				 =							initializer_range
        lowercase__				 =							layer_norm_eps
        lowercase__				 =							image_size
        lowercase__				 =							patch_size
        lowercase__				 =							num_channels
        lowercase__				 =							qkv_bias
        lowercase__				 =							backbone_out_indices
        if readout_type not in ["ignore", "add", "project"]:
             raise ValueError("Readout_type must be one of ['ignore', 'add', 'project']"  )
        lowercase__				 =							readout_type
        lowercase__				 =							reassemble_factors
        lowercase__				 =							neck_hidden_sizes
        lowercase__				 =							fusion_hidden_size
        lowercase__				 =							head_in_index
        lowercase__				 =							use_batch_norm_in_fusion_residual
        # auxiliary head attributes (semantic segmentation)
        lowercase__				 =							use_auxiliary_head
        lowercase__				 =							auxiliary_loss_weight
        lowercase__				 =							semantic_loss_ignore_index
        lowercase__				 =							semantic_classifier_dropout


   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							copy.deepcopy(self.__dict__  )

        if output["backbone_config"] is not None:
             lowercase__				 =							self.backbone_config.to_dict()

        lowercase__				 =							self.__class__.model_type
        return output





"""simple docstring"""


import unittest

from huggingface_hub import hf_hub_download

from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_decord,
    require_tf,
    require_torch,
    require_torch_or_tf,
    require_vision,
)

from .test_pipelines_common import ANY






@is_pipeline_test
@require_torch_or_tf
@require_vision
@require_decord
class    a__    (   unittest.TestCase	):
   snake_case_							=      MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING


   def 	snake_case__	(     self,				_UpperCAmelCase,				_UpperCAmelCase,				_UpperCAmelCase  ):
        '''simple docstring'''



        lowercase__				 =							hf_hub_download(
            repo_id="nateraw/video-demo",				filename="archery.mp4",				repo_type="dataset"  )
        lowercase__				 =							VideoClassificationPipeline(model=_UpperCAmelCase,				image_processor=_UpperCAmelCase,				top_k=2  )
        lowercase__				 =							[
            example_video_filepath,
            "https://huggingface.co/datasets/nateraw/video-demo/resolve/main/archery.mp4",
        ]
        return video_classifier, examples


   def 	snake_case__	(     self,				_UpperCAmelCase,				_UpperCAmelCase  ):
        '''simple docstring'''



        for example in examples:
             lowercase__				 =							video_classifier(_UpperCAmelCase  )

             self.assertEqual(
                 _UpperCAmelCase,				[
                     {"score": ANY(_UpperCAmelCase  ), "label": ANY(_UpperCAmelCase  )},
                     {"score": ANY(_UpperCAmelCase  ), "label": ANY(_UpperCAmelCase  )},
                 ],				)


   @require_torch
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        lowercase__				 =							"hf-internal-testing/tiny-random-VideoMAEForVideoClassification"
        lowercase__				 =							VideoMAEFeatureExtractor(
            size={"shortest_edge": 10},				crop_size={"height": 10, "width": 10}  )
        lowercase__				 =							pipeline(
            "video-classification",				model=_UpperCAmelCase,				feature_extractor=_UpperCAmelCase,				frame_sampling_rate=4  )

        lowercase__				 =							hf_hub_download(repo_id="nateraw/video-demo",				filename="archery.mp4",				repo_type="dataset"  )
        lowercase__				 =							video_classifier(_UpperCAmelCase,				top_k=2  )
        self.assertEqual(
            nested_simplify(_UpperCAmelCase,				decimals=4  ),				[{"score": 0.5_199, "label": "LABEL_0"}, {"score": 0.4_801, "label": "LABEL_1"}],				)

        lowercase__				 =							video_classifier(
            [
                video_file_path,
                video_file_path,
            ],				top_k=2,				)
        self.assertEqual(
            nested_simplify(_UpperCAmelCase,				decimals=4  ),				[
                [{"score": 0.5_199, "label": "LABEL_0"}, {"score": 0.4_801, "label": "LABEL_1"}],
                [{"score": 0.5_199, "label": "LABEL_0"}, {"score": 0.4_801, "label": "LABEL_1"}],
            ],				)


   @require_tf
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        pass




