






'''simple docstring'''




from typing import List, Optional, Tuple, Union

import torch

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







class    A_	(a_							):
							"""simple docstring"""




							a__   		=	42
							a__   		=	42

							def __init__(		self			:int       ,							lowerCAmelCase__			:UNetaDModel       ,							lowerCAmelCase__			:KarrasVeScheduler				)				->  str:


										'''simple docstring'''




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






							@torch.no_grad()
							def __call__(		self			:List[str]       ,							lowerCAmelCase__			:int = 1       ,							lowerCAmelCase__			:int = 50       ,							lowerCAmelCase__			:Optional[Union[torch.Generator, List[torch.Generator]]] = None       ,							lowerCAmelCase__			:Optional[str] = "pil"       ,							lowerCAmelCase__			:bool = True       ,							**lowerCAmelCase__			:Optional[Any]       ,							)				->  Union[Tuple, ImagePipelineOutput]:


										'''simple docstring'''




										snake_case_				:					Optional[Any]   =       self.unet.config.sample_size
										snake_case_				:					Optional[int]   =       (batch_size, 3, img_size, img_size)

										snake_case_				:					str   =       self.unet

										# sample x_0 ~ N(0, sigma_0^2 * I)
										snake_case_				:					str   =       randn_tensor(lowerCAmelCase__       ,							generator=lowerCAmelCase__       ,							device=self.device				) * self.scheduler.init_noise_sigma

										self.scheduler.set_timesteps(lowerCAmelCase__				)

										for t in self.progress_bar(self.scheduler.timesteps				):
													# here sigma_t == t_i from the paper
													snake_case_				:					List[str]   =       self.scheduler.schedule[t]
													snake_case_				:					Union[str, Any]   =       self.scheduler.schedule[t - 1] if t > 0 else 0

													# 1. Select temporarily increased noise level sigma_hat
													# 2. Add new noise to move from sample_i to sample_hat
													snake_case_,	snake_case_				:					Union[str, Any]   =       self.scheduler.add_noise_to_input(lowerCAmelCase__       ,							lowerCAmelCase__       ,							generator=lowerCAmelCase__				)

													# 3. Predict the noise residual given the noise magnitude `sigma_hat`
													# The model inputs and output are adjusted by following eq. (213) in [1].
													snake_case_				:					List[Any]   =       (sigma_hat / 2) * model((sample_hat + 1) / 2       ,							sigma_hat / 2				).sample

													# 4. Evaluate dx/dt at sigma_hat
													# 5. Take Euler step from sigma to sigma_prev
													snake_case_				:					str   =       self.scheduler.step(lowerCAmelCase__       ,							lowerCAmelCase__       ,							lowerCAmelCase__       ,							lowerCAmelCase__				)

													if sigma_prev != 0:
																# 6. Apply 2nd order correction
																# The model inputs and output are adjusted by following eq. (213) in [1].
																snake_case_				:					str   =       (sigma_prev / 2) * model((step_output.prev_sample + 1) / 2       ,							sigma_prev / 2				).sample
																snake_case_				:					List[str]   =       self.scheduler.step_correct(
																    lowerCAmelCase__       ,							lowerCAmelCase__       ,							lowerCAmelCase__       ,							lowerCAmelCase__       ,							step_output.prev_sample       ,							step_output["derivative"]       ,							)
													snake_case_				:					Tuple   =       step_output.prev_sample

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

										if not return_dict:
													return (image,)

										return ImagePipelineOutput(images=lowerCAmelCase__				)












'''simple docstring'''




from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCamelCase   :					List[str]   							=					logging.get_logger(__name__)

__lowerCamelCase   :					int   							=					{
    '''microsoft/cvt-13''': '''https://huggingface.co/microsoft/cvt-13/resolve/main/config.json''',
    # See all Cvt models at https://huggingface.co/models?filter=cvt
}







class    A_	(a_							):
							"""simple docstring"""




							a__   		=	'''cvt'''

							def __init__(		self			:List[Any]       ,							lowerCAmelCase__			:Optional[int]=3       ,							lowerCAmelCase__			:Any=[7, 3, 3]       ,							lowerCAmelCase__			:Dict=[4, 2, 2]       ,							lowerCAmelCase__			:Union[str, Any]=[2, 1, 1]       ,							lowerCAmelCase__			:Any=[64, 192, 384]       ,							lowerCAmelCase__			:List[str]=[1, 3, 6]       ,							lowerCAmelCase__			:str=[1, 2, 10]       ,							lowerCAmelCase__			:Any=[4.0, 4.0, 4.0]       ,							lowerCAmelCase__			:int=[0.0, 0.0, 0.0]       ,							lowerCAmelCase__			:Optional[Any]=[0.0, 0.0, 0.0]       ,							lowerCAmelCase__			:Dict=[0.0, 0.0, 0.1]       ,							lowerCAmelCase__			:List[Any]=[True, True, True]       ,							lowerCAmelCase__			:List[Any]=[False, False, True]       ,							lowerCAmelCase__			:Dict=["dw_bn", "dw_bn", "dw_bn"]       ,							lowerCAmelCase__			:Any=[3, 3, 3]       ,							lowerCAmelCase__			:Tuple=[1, 1, 1]       ,							lowerCAmelCase__			:Optional[int]=[2, 2, 2]       ,							lowerCAmelCase__			:Union[str, Any]=[1, 1, 1]       ,							lowerCAmelCase__			:Any=[1, 1, 1]       ,							lowerCAmelCase__			:List[str]=0.0_2       ,							lowerCAmelCase__			:Dict=1E-1_2       ,							**lowerCAmelCase__			:Optional[Any]       ,							)				->  str:


										'''simple docstring'''




										super().__init__(**lowerCAmelCase__				)
										snake_case_				:					int   =       num_channels
										snake_case_				:					int   =       patch_sizes
										snake_case_				:					Optional[Any]   =       patch_stride
										snake_case_				:					Dict   =       patch_padding
										snake_case_				:					Tuple   =       embed_dim
										snake_case_				:					Optional[int]   =       num_heads
										snake_case_				:					Union[str, Any]   =       depth
										snake_case_				:					Optional[int]   =       mlp_ratio
										snake_case_				:					Tuple   =       attention_drop_rate
										snake_case_				:					str   =       drop_rate
										snake_case_				:					Tuple   =       drop_path_rate
										snake_case_				:					Any   =       qkv_bias
										snake_case_				:					Union[str, Any]   =       cls_token
										snake_case_				:					int   =       qkv_projection_method
										snake_case_				:					Any   =       kernel_qkv
										snake_case_				:					Union[str, Any]   =       padding_kv
										snake_case_				:					str   =       stride_kv
										snake_case_				:					Dict   =       padding_q
										snake_case_				:					Tuple   =       stride_q
										snake_case_				:					Any   =       initializer_range
										snake_case_				:					Any   =       layer_norm_eps




