


'''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 						__UpperCamelCase      (						lowercase__							):
							lowercase			:		str   		=  4_2
							lowercase			:		Dict   		=  4_2





							def __init__(  self		:List[Any]  ,_UpperCamelCase		:UNetaDModel  ,_UpperCamelCase		:KarrasVeScheduler			):
									super().__init__()
									self.register_modules(unet=lowercase_  ,scheduler=lowercase_			)





							@torch.no_grad()
							def __call__(  self		:int  ,_UpperCamelCase		:int = 1  ,_UpperCamelCase		:int = 5_0  ,_UpperCamelCase		:Optional[Union[torch.Generator, List[torch.Generator]]] = None  ,_UpperCamelCase		:Optional[str] = "pil"  ,_UpperCamelCase		:bool = True  ,**_UpperCamelCase		:Dict  ,):
									snake_case_		:							List[Any]			       =						self.unet.config.sample_size
									snake_case_		:							Union[str, Any]			       =						(batch_size, 3, img_size, img_size)

									snake_case_		:							Dict			       =						self.unet

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

									self.scheduler.set_timesteps(lowercase_			)

									for t in self.progress_bar(self.scheduler.timesteps			):
											# here sigma_t == t_i from the paper
											snake_case_		:							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_		:							Dict			       =						self.scheduler.add_noise_to_input(lowercase_  ,lowercase_  ,generator=lowercase_			)

											# 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_		:							Optional[int]			       =						self.scheduler.step(lowercase_  ,lowercase_  ,lowercase_  ,lowercase_			)

											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_		:							Tuple			       =						self.scheduler.step_correct(
													    lowercase_  ,lowercase_  ,lowercase_  ,lowercase_  ,step_output.prev_sample  ,step_output["""derivative"""]  ,)
											snake_case_		:							Any			       =						step_output.prev_sample

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

									if not return_dict:
											return (image,)

									return ImagePipelineOutput(images=lowercase_			)



'''simple docstring'''






import torch

from transformers import AutoModel
class 						__UpperCamelCase	(						torch.nn.Module							):





							def __init__(  self		:Union[str, Any]  ,_UpperCamelCase		:Tuple="sayef/fsner-bert-base-uncased"			):
									super(_UpperCamelCase  ,self			).__init__()

									snake_case_		:							Dict			       =						AutoModel.from_pretrained(_UpperCamelCase  ,return_dict=_UpperCamelCase			)
									snake_case_		:							Dict			       =						torch.nn.CosineSimilarity(3  ,1E-0_8			)
									snake_case_		:							int			       =						torch.nn.Softmax(dim=1			)





							def 		a__   (  self		:Optional[Any]  ,**_UpperCamelCase		:Optional[Any]			):
									return self.bert(**_UpperCamelCase			).last_hidden_state





							def 		a__   (  self		:List[Any]  ,_UpperCamelCase		:Dict			):
									return token_embeddings.sum(2  ,keepdim=_UpperCamelCase			)





							def 		a__   (  self		:Optional[Any]  ,_UpperCamelCase		:List[str]  ,_UpperCamelCase		:List[str]  ,_UpperCamelCase		:List[str]=1			):
									return self.softmax(T * self.cos(_UpperCamelCase  ,_UpperCamelCase			)			)





							def 		a__   (  self		:Optional[Any]  ,_UpperCamelCase		:Optional[Any]  ,_UpperCamelCase		:int			):
									snake_case_		:							Union[str, Any]			       =						W_supports["""sizes"""].tolist()
									snake_case_		:							Dict			       =						W_supports["""start_token_id"""].item()
									snake_case_		:							int			       =						W_supports["""end_token_id"""].item()

									del W_supports["sizes"]
									del W_supports["start_token_id"]
									del W_supports["end_token_id"]

									snake_case_		:							int			       =						self.BERT(**_UpperCamelCase			)
									snake_case_		:							int			       =						self.BERT(**_UpperCamelCase			)

									snake_case_		:							Any			       =						None
									snake_case_		:							Any			       =						None

									snake_case_		:							Union[str, Any]			       =						W_supports["""input_ids"""] == start_token_id
									snake_case_		:							Tuple			       =						W_supports["""input_ids"""] == end_token_id

									for i, size in enumerate(_UpperCamelCase			):
											if i == 0:
													snake_case_		:							str			       =						0
											else:
													snake_case_		:							Optional[Any]			       =						support_sizes[i - 1]

											snake_case_		:							Dict			       =						S[s : s + size][start_token_masks[s : s + size]]
											snake_case_		:							Optional[Any]			       =						S[s : s + size][end_token_masks[s : s + size]]

											snake_case_		:							int			       =						torch.matmul(q[i]  ,s_start.T			).sum(1			).softmax(0			)
											snake_case_		:							List[str]			       =						torch.matmul(q[i]  ,s_end.T			).sum(1			).softmax(0			)

											if p_starts is not None:
													snake_case_		:							List[str]			       =						torch.vstack((p_starts, p_start)			)
													snake_case_		:							Tuple			       =						torch.vstack((p_ends, p_end)			)
											else:
													snake_case_		:							Any			       =						p_start
													snake_case_		:							List[str]			       =						p_end

									return p_starts, p_ends