

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


lowerCAmelCase							:						Union[str, Any]     			=		logging.get_logger(__name__)

lowerCAmelCase							:						Optional[int]     			=		{
    """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 						__lowercase       (				UpperCAmelCase_			):




			"""simple docstring"""







			_UpperCAmelCase     :							Dict    							=  '''vit-hybrid'''



			def __init__(				self						:  Dict     ,		lowerCAmelCase__						:  List[Any]=None     ,		lowerCAmelCase__						:  List[str]=768     ,		lowerCAmelCase__						:  Optional[Any]=12     ,		lowerCAmelCase__						:  Optional[Any]=12     ,		lowerCAmelCase__						:  Dict=3072     ,		lowerCAmelCase__						:  Optional[int]="gelu"     ,		lowerCAmelCase__						:  int=0.0     ,		lowerCAmelCase__						:  Optional[int]=0.0     ,		lowerCAmelCase__						:  List[str]=0.02     ,		lowerCAmelCase__						:  Any=1E-12     ,		lowerCAmelCase__						:  Any=224     ,		lowerCAmelCase__						:  Dict=1     ,		lowerCAmelCase__						:  str=3     ,		lowerCAmelCase__						:  Tuple=[1, 1024, 24, 24]     ,		lowerCAmelCase__						:  Dict=True     ,		**lowerCAmelCase__						:  Any     ,		):
									super().__init__(**lowerCAmelCase__)

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

									if isinstance(lowerCAmelCase__     ,		lowerCAmelCase__):
															if "model_type" in backbone_config:
																					SCREAMING_SNAKE_CASE_:				Dict							= CONFIG_MAPPING[backbone_config["model_type"]]
															else:
																					logger.info(
																					    "`model_type` is not found in `backbone_config`. Use `Bit` as the backbone configuration class.")
																					SCREAMING_SNAKE_CASE_:				int							= BitConfig
															SCREAMING_SNAKE_CASE_:				Dict							= backbone_config_class(**lowerCAmelCase__)

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




			def  _SCREAMING_SNAKE_CASE    (				self						:  str):
									SCREAMING_SNAKE_CASE_:				Dict							= copy.deepcopy(self.__dict__)
									SCREAMING_SNAKE_CASE_:				int							= self.backbone_config.to_dict()
									SCREAMING_SNAKE_CASE_:				Dict							= self.__class__.model_type
									return output









from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import PNDMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput



class 						__lowercase       (				UpperCAmelCase_			):




			"""simple docstring"""







			_UpperCAmelCase     :							UNetaDModel
			_UpperCAmelCase     :							PNDMScheduler



			def __init__(				self						:  Optional[int]     ,		lowerCAmelCase__						:  UNetaDModel     ,		lowerCAmelCase__						:  PNDMScheduler):
									super().__init__()

									SCREAMING_SNAKE_CASE_:				Dict							= PNDMScheduler.from_config(scheduler.config)

									self.register_modules(unet=lowerCAmelCase__     ,		scheduler=lowerCAmelCase__)




			@torch.no_grad()
			def __call__(				self						:  int     ,		lowerCAmelCase__						:  int = 1     ,		lowerCAmelCase__						:  int = 50     ,		lowerCAmelCase__						:  Optional[Union[torch.Generator, List[torch.Generator]]] = None     ,		lowerCAmelCase__						:  Optional[str] = "pil"     ,		lowerCAmelCase__						:  bool = True     ,		**lowerCAmelCase__						:  str     ,		):
									SCREAMING_SNAKE_CASE_:				Dict							= randn_tensor(
									    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)     ,		generator=lowerCAmelCase__     ,		device=self.device     ,		)

									self.scheduler.set_timesteps(lowerCAmelCase__)
									for t in self.progress_bar(self.scheduler.timesteps):
															SCREAMING_SNAKE_CASE_:				Union[str, Any]							= self.unet(lowerCAmelCase__     ,		lowerCAmelCase__).sample

															SCREAMING_SNAKE_CASE_:				Any							= self.scheduler.step(lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__).prev_sample

									SCREAMING_SNAKE_CASE_:				Optional[Any]							= (image / 2 + 0.5).clamp(0     ,		1)
									SCREAMING_SNAKE_CASE_:				Dict							= image.cpu().permute(0     ,		2     ,		3     ,		1).numpy()
									if output_type == "pil":
															SCREAMING_SNAKE_CASE_:				Any							= self.numpy_to_pil(lowerCAmelCase__)

									if not return_dict:
															return (image,)

									return ImagePipelineOutput(images=lowerCAmelCase__)






