"""simple docstring"""




import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig


_a      :		str           =   {
    'facebook/maskformer-swin-base-ade': (
        'https://huggingface.co/facebook/maskformer-swin-base-ade/blob/main/config.json'
    )
    # See all MaskFormer models at https://huggingface.co/models?filter=maskformer
}

_a      :		Optional[int]           =   logging.get_logger(__name__)
class 						__A							( SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:			Any							=		"maskformer"
     _UpperCamelCase							:			Optional[Any]							=		{"hidden_size": "mask_feature_size"}
     _UpperCamelCase							:			Any							=		["resnet", "swin"]
     _UpperCamelCase							:			Dict							=		["detr"]






     def __init__(		self				,     a__ = 256				,     a__ = 256				,     a__ = 0.1				,     a__ = False				,     a__ = None				,     a__ = None				,     a__ = 0.0_2				,     a__ = 1.0				,     a__ = 1.0				,     a__ = 1.0				,     a__ = 2_0.0				,     a__ = None				,     **a__				,     ):
       if backbone_config is None:
         # fall back to https://huggingface.co/microsoft/swin-base-patch4-window12-384-in22k
         _lowerCAmelCase						:       Union[str, Any]  			=     SwinConfig(
             image_size=384				,     in_channels=3				,     patch_size=4				,     embed_dim=128				,     depths=[2, 2, 18, 2]				,     num_heads=[4, 8, 16, 32]				,     window_size=12				,     drop_path_rate=0.3				,     out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]				,     )

       if isinstance(a__				,     a__  ):
         _lowerCAmelCase						:       str  			=     backbone_config.pop("""model_type"""  )
         _lowerCAmelCase						:       List[str]  			=     CONFIG_MAPPING[backbone_model_type]
         _lowerCAmelCase						:       Dict  			=     config_class.from_dict(a__  )

       # verify that the backbone is supported
       if backbone_config.model_type not in self.backbones_supported:
         logger.warning_once(
             F"Backbone {backbone_config.model_type} is not a supported model and may not be compatible with MaskFormer. "
             F"Supported model types: {','.join(self.backbones_supported  )}"  )

       if decoder_config is None:
         # fall back to https://huggingface.co/facebook/detr-resnet-50
         _lowerCAmelCase						:       int  			=     DetrConfig()
       else:
         # verify that the decoder is supported
         _lowerCAmelCase						:       Union[str, Any]  			=     (
             decoder_config.pop("""model_type"""  ) if isinstance(a__				,     a__  ) else decoder_config.model_type
         )
         if decoder_type not in self.decoders_supported:
           raise ValueError(
               F"Transformer Decoder {decoder_type} not supported, please use one of"
               F" {','.join(self.decoders_supported  )}"  )
         if isinstance(a__				,     a__  ):
           _lowerCAmelCase						:       int  			=     CONFIG_MAPPING[decoder_type]
           _lowerCAmelCase						:       Tuple  			=     config_class.from_dict(a__  )

       _lowerCAmelCase						:       List[Any]  			=     backbone_config
       _lowerCAmelCase						:       List[str]  			=     decoder_config
       # main feature dimension for the model
       _lowerCAmelCase						:       int  			=     fpn_feature_size
       _lowerCAmelCase						:       Tuple  			=     mask_feature_size
       # initializer
       _lowerCAmelCase						:       Any  			=     init_std
       _lowerCAmelCase						:       Any  			=     init_xavier_std
       # Hungarian matcher && loss
       _lowerCAmelCase						:       Dict  			=     cross_entropy_weight
       _lowerCAmelCase						:       Union[str, Any]  			=     dice_weight
       _lowerCAmelCase						:       Tuple  			=     mask_weight
       _lowerCAmelCase						:       int  			=     use_auxiliary_loss
       _lowerCAmelCase						:       List[Any]  			=     no_object_weight
       _lowerCAmelCase						:       int  			=     output_auxiliary_logits

       _lowerCAmelCase						:       str  			=     self.decoder_config.encoder_attention_heads
       _lowerCAmelCase						:       int  			=     self.decoder_config.num_hidden_layers
       super().__init__(**a__  )






     @classmethod
     def     __A  (		cls				,     a__				,     a__				,     **a__  ):
       return cls(
           backbone_config=a__				,     decoder_config=a__				,     **a__				,     )






     def     __A  (		self  ):
       _lowerCAmelCase						:       Tuple  			=     copy.deepcopy(self.__dict__  )
       _lowerCAmelCase						:       str  			=     self.backbone_config.to_dict()
       _lowerCAmelCase						:       Dict  			=     self.decoder_config.to_dict()
       _lowerCAmelCase						:       List[Any]  			=     self.__class__.model_type
       return output






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 				lowercase					(		unittest.TestCase   ):
		__lowercase       :       Any	     =				MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING



		def __UpperCamelCase ( self				,      A_				,      A_				,      A_       )  ->					List[Any]:
				"""simple docstring"""
				UpperCamelCase		=     hf_hub_download(
				    repo_id='nateraw/video-demo'				,      filename='archery.mp4'				,      repo_type='dataset'       )
				UpperCamelCase		=     VideoClassificationPipeline(model=A_				,      image_processor=A_				,      top_k=2       )
				UpperCamelCase		=     [
				    example_video_filepath,
				    'https://huggingface.co/datasets/nateraw/video-demo/resolve/main/archery.mp4',
				]
				return video_classifier, examples



		def __UpperCamelCase ( self				,      A_				,      A_       )  ->					Optional[int]:
				"""simple docstring"""
				for example in examples:
						UpperCamelCase		=     video_classifier(A_       )

						self.assertEqual(
						    A_				,      [
						        {'score': ANY(A_       ), 'label': ANY(A_       )},
						        {'score': ANY(A_       ), 'label': ANY(A_       )},
						    ]				,      )



		@require_torch
		def __UpperCamelCase ( self       )  ->					Optional[Any]:
				"""simple docstring"""
				UpperCamelCase		=     'hf-internal-testing/tiny-random-VideoMAEForVideoClassification'
				UpperCamelCase		=     VideoMAEFeatureExtractor(
				    size={'shortest_edge': 10}				,      crop_size={'height': 10, 'width': 10}       )
				UpperCamelCase		=     pipeline(
				    'video-classification'				,      model=A_				,      feature_extractor=A_				,      frame_sampling_rate=4       )

				UpperCamelCase		=     hf_hub_download(repo_id='nateraw/video-demo'				,      filename='archery.mp4'				,      repo_type='dataset'       )
				UpperCamelCase		=     video_classifier(A_				,      top_k=2       )
				self.assertEqual(
				    nested_simplify(A_				,      decimals=4       )				,      [{'score': 0.5199, 'label': 'LABEL_0'}, {'score': 0.4801, 'label': 'LABEL_1'}]				,      )

				UpperCamelCase		=     video_classifier(
				    [
				        video_file_path,
				        video_file_path,
				    ]				,      top_k=2				,      )
				self.assertEqual(
				    nested_simplify(A_				,      decimals=4       )				,      [
				        [{'score': 0.5199, 'label': 'LABEL_0'}, {'score': 0.4801, 'label': 'LABEL_1'}],
				        [{'score': 0.5199, 'label': 'LABEL_0'}, {'score': 0.4801, 'label': 'LABEL_1'}],
				    ]				,      )



		@require_tf
		def __UpperCamelCase ( self       )  ->					Tuple:
				"""simple docstring"""
				pass
