




import torch

from diffusers import KDPMaAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest

class  UpperCAmelCase						(			_UpperCAmelCase   ):
   __SCREAMING_SNAKE_CASE		:							Union[str, Any]							=					(KDPMaAncestralDiscreteScheduler,)
   __SCREAMING_SNAKE_CASE		:							Any							=					10






   def  _a						(	self   :       Union[str, Any]						,					**UpperCamelCase_   :       Any     )->			int:
     _lowercase: int						     =    {
         'num_train_timesteps': 11_00,
         'beta_start': 0.0_001,
         'beta_end': 0.02,
         'beta_schedule': 'linear',
     }

     config.update(**UpperCamelCase_     )
     return config






   def  _a						(	self   :       Tuple     )->			Tuple:






     for timesteps in [10, 50, 1_00, 10_00]:
       self.check_over_configs(num_train_timesteps=UpperCamelCase_     )






   def  _a						(	self   :       List[Any]     )->			Optional[int]:






     for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]						,					[0.0_002, 0.002, 0.02]     ):
       self.check_over_configs(beta_start=UpperCamelCase_						,					beta_end=UpperCamelCase_     )






   def  _a						(	self   :       Any     )->			Tuple:






     for schedule in ["linear", "scaled_linear"]:
       self.check_over_configs(beta_schedule=UpperCamelCase_     )






   def  _a						(	self   :       Optional[Any]     )->			List[Any]:






     if torch_device == "mps":
       return
     _lowercase: Optional[Any]						     =    self.scheduler_classes[0]
     _lowercase: List[Any]						     =    self.get_scheduler_config()
     _lowercase: Optional[int]						     =    scheduler_class(**UpperCamelCase_     )

     scheduler.set_timesteps(self.num_inference_steps     )

     _lowercase: int						     =    torch.manual_seed(0     )

     _lowercase: int						     =    self.dummy_model()
     _lowercase: Optional[Any]						     =    self.dummy_sample_deter * scheduler.init_noise_sigma
     _lowercase: Optional[Any]						     =    sample.to(UpperCamelCase_     )

     for i, t in enumerate(scheduler.timesteps     ):
       _lowercase: Union[str, Any]						     =    scheduler.scale_model_input(UpperCamelCase_						,					UpperCamelCase_     )

       _lowercase: Any						     =    model(UpperCamelCase_						,					UpperCamelCase_     )

       _lowercase: List[str]						     =    scheduler.step(UpperCamelCase_						,					UpperCamelCase_						,					UpperCamelCase_						,					generator=UpperCamelCase_     )
       _lowercase: str						     =    output.prev_sample

     _lowercase: str						     =    torch.sum(torch.abs(UpperCamelCase_     )     )
     _lowercase: Optional[int]						     =    torch.mean(torch.abs(UpperCamelCase_     )     )

     assert abs(result_sum.item() - 1_38_49.38_77     ) < 1E-2
     assert abs(result_mean.item() - 18.0_331     ) < 5E-3






   def  _a						(	self   :       Any     )->			Union[str, Any]:






     for prediction_type in ["epsilon", "v_prediction"]:
       self.check_over_configs(prediction_type=UpperCamelCase_     )






   def  _a						(	self   :       Dict     )->			Union[str, Any]:






     if torch_device == "mps":
       return
     _lowercase: List[str]						     =    self.scheduler_classes[0]
     _lowercase: Any						     =    self.get_scheduler_config(prediction_type='v_prediction'     )
     _lowercase: List[Any]						     =    scheduler_class(**UpperCamelCase_     )

     scheduler.set_timesteps(self.num_inference_steps     )

     _lowercase: int						     =    self.dummy_model()
     _lowercase: int						     =    self.dummy_sample_deter * scheduler.init_noise_sigma
     _lowercase: Any						     =    sample.to(UpperCamelCase_     )

     _lowercase: int						     =    torch.manual_seed(0     )

     for i, t in enumerate(scheduler.timesteps     ):
       _lowercase: str						     =    scheduler.scale_model_input(UpperCamelCase_						,					UpperCamelCase_     )

       _lowercase: Tuple						     =    model(UpperCamelCase_						,					UpperCamelCase_     )

       _lowercase: Optional[Any]						     =    scheduler.step(UpperCamelCase_						,					UpperCamelCase_						,					UpperCamelCase_						,					generator=UpperCamelCase_     )
       _lowercase: Union[str, Any]						     =    output.prev_sample

     _lowercase: Optional[int]						     =    torch.sum(torch.abs(UpperCamelCase_     )     )
     _lowercase: Tuple						     =    torch.mean(torch.abs(UpperCamelCase_     )     )

     assert abs(result_sum.item() - 328.9_970     ) < 1E-2
     assert abs(result_mean.item() - 0.4_284     ) < 1E-3






   def  _a						(	self   :       Optional[int]     )->			Tuple:






     if torch_device == "mps":
       return
     _lowercase: Optional[int]						     =    self.scheduler_classes[0]
     _lowercase: Any						     =    self.get_scheduler_config()
     _lowercase: int						     =    scheduler_class(**UpperCamelCase_     )

     scheduler.set_timesteps(self.num_inference_steps						,					device=UpperCamelCase_     )
     _lowercase: str						     =    torch.manual_seed(0     )

     _lowercase: str						     =    self.dummy_model()
     _lowercase: Union[str, Any]						     =    self.dummy_sample_deter.to(UpperCamelCase_     ) * scheduler.init_noise_sigma

     for t in scheduler.timesteps:
       _lowercase: List[str]						     =    scheduler.scale_model_input(UpperCamelCase_						,					UpperCamelCase_     )

       _lowercase: List[Any]						     =    model(UpperCamelCase_						,					UpperCamelCase_     )

       _lowercase: Dict						     =    scheduler.step(UpperCamelCase_						,					UpperCamelCase_						,					UpperCamelCase_						,					generator=UpperCamelCase_     )
       _lowercase: Optional[Any]						     =    output.prev_sample

     _lowercase: Optional[Any]						     =    torch.sum(torch.abs(UpperCamelCase_     )     )
     _lowercase: List[str]						     =    torch.mean(torch.abs(UpperCamelCase_     )     )

     assert abs(result_sum.item() - 1_38_49.38_18     ) < 1E-1
     assert abs(result_mean.item() - 18.0_331     ) < 1E-3






import copy
from typing import Dict

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
from ..bit import BitConfig


UpperCAmelCase__						:       Dict							=    logging.get_logger(__name__)

UpperCAmelCase__						:       Optional[Any]							=    {
    'google/vit-hybrid-base-bit-384': 'https://huggingface.co/vit-hybrid-base-bit-384/resolve/main/config.json',
    # See all ViT hybrid models at https://huggingface.co/models?filter=vit
}

class      UpperCAmelCase						(			A   ):
   __SCREAMING_SNAKE_CASE		:							int							=					"vit-hybrid"






   def __init__(	self   :       Optional[int]						,					UpperCamelCase_   :       Any=None						,					UpperCamelCase_   :       str=7_68						,					UpperCamelCase_   :       Optional[int]=12						,					UpperCamelCase_   :       str=12						,					UpperCamelCase_   :       Dict=30_72						,					UpperCamelCase_   :       Dict="gelu"						,					UpperCamelCase_   :       str=0.0						,					UpperCamelCase_   :       List[Any]=0.0						,					UpperCamelCase_   :       List[str]=0.02						,					UpperCamelCase_   :       Tuple=1E-12						,					UpperCamelCase_   :       Union[str, Any]=2_24						,					UpperCamelCase_   :       Any=1						,					UpperCamelCase_   :       Optional[Any]=3						,					UpperCamelCase_   :       Optional[Any]=[1, 10_24, 24, 24]						,					UpperCamelCase_   :       Any=True						,					**UpperCamelCase_   :       int						,					)->			Any:
     super().__init__(**UpperCamelCase_     )

     if backbone_config is None:
       logger.info('`backbone_config` is `None`. Initializing the config with a `BiT` backbone.'     )
       _lowercase: Dict						     =    {
           'global_padding': 'same',
           'layer_type': 'bottleneck',
           'depths': [3, 4, 9],
           'out_features': ['stage3'],
           'embedding_dynamic_padding': True,
       }

     if isinstance(UpperCamelCase_						,					UpperCamelCase_     ):
       if "model_type" in backbone_config:
         _lowercase: int						     =    CONFIG_MAPPING[backbone_config['model_type']]
       else:
         logger.info(
             '`model_type` is not found in `backbone_config`. Use `Bit` as the backbone configuration class.'     )
         _lowercase: Optional[int]						     =    BitConfig
       _lowercase: List[Any]						     =    backbone_config_class(**UpperCamelCase_     )

     _lowercase: List[Any]						     =    backbone_featmap_shape
     _lowercase: Dict						     =    backbone_config
     _lowercase: Optional[int]						     =    hidden_size
     _lowercase: List[Any]						     =    num_hidden_layers
     _lowercase: Union[str, Any]						     =    num_attention_heads
     _lowercase: str						     =    intermediate_size
     _lowercase: Optional[int]						     =    hidden_act
     _lowercase: int						     =    hidden_dropout_prob
     _lowercase: List[str]						     =    attention_probs_dropout_prob
     _lowercase: Tuple						     =    initializer_range
     _lowercase: Any						     =    layer_norm_eps
     _lowercase: Any						     =    image_size
     _lowercase: List[Any]						     =    patch_size
     _lowercase: Dict						     =    num_channels
     _lowercase: Tuple						     =    qkv_bias






   def  _a						(	self   :       Optional[int]     )->			Dict[str, any]:
     _lowercase: List[str]						     =    copy.deepcopy(self.__dict__     )
     _lowercase: Tuple						     =    self.backbone_config.to_dict()
     _lowercase: int						     =    self.__class__.model_type
     return output
