


from typing import List, Optional, Tuple, Union

import torch

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




class    UpperCAmelCase					(       __snake_case    ):
			lowercase						= 42
			lowercase						= 42




			def __init__(						self							:		List[str]       ,       __magic_name__							:		UNetaDModel       ,       __magic_name__							:		ScoreSdeVeScheduler						):







						"""simple docstring"""







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





			@torch.no_grad()
			def __call__(						self							:		str       ,       __magic_name__							:		int = 1       ,       __magic_name__							:		int = 2_0_0_0       ,       __magic_name__							:		Optional[Union[torch.Generator, List[torch.Generator]]] = None       ,       __magic_name__							:		Optional[str] = "pil"       ,       __magic_name__							:		bool = True       ,       **__magic_name__							:		int       ,       ):







						"""simple docstring"""







						UpperCamelCase         =					self.unet.config.sample_size
						UpperCamelCase         =					(batch_size, 3, img_size, img_size)

						UpperCamelCase         =					self.unet

						UpperCamelCase         =					randn_tensor(__magic_name__       ,       generator=__magic_name__						) * self.scheduler.init_noise_sigma
						UpperCamelCase         =					sample.to(self.device						)

						self.scheduler.set_timesteps(__magic_name__						)
						self.scheduler.set_sigmas(__magic_name__						)

						for i, t in enumerate(self.progress_bar(self.scheduler.timesteps						)						):
									UpperCamelCase         =					self.scheduler.sigmas[i] * torch.ones(shape[0]       ,       device=self.device						)

									# correction step
									for _ in range(self.scheduler.config.correct_steps						):
												UpperCamelCase         =					self.unet(__magic_name__       ,       __magic_name__						).sample
												UpperCamelCase         =					self.scheduler.step_correct(__magic_name__       ,       __magic_name__       ,       generator=__magic_name__						).prev_sample

									# prediction step
									UpperCamelCase         =					model(__magic_name__       ,       __magic_name__						).sample
									UpperCamelCase         =					self.scheduler.step_pred(__magic_name__       ,       __magic_name__       ,       __magic_name__       ,       generator=__magic_name__						)

									UpperCamelCase  ,  UpperCamelCase         =					output.prev_sample, output.prev_sample_mean

						UpperCamelCase         =					sample_mean.clamp(0       ,       1						)
						UpperCamelCase         =					sample.cpu().permute(0       ,       2       ,       3       ,       1						).numpy()
						if output_type == "pil":
									UpperCamelCase         =					self.numpy_to_pil(__magic_name__						)

						if not return_dict:
									return (sample,)

						return ImagePipelineOutput(images=__magic_name__						)








import torch

from transformers import AutoModel




class    UpperCAmelCase					(       torch.nn.Module    ):




			def __init__(						self							:		int       ,       __magic_name__							:		List[Any]="sayef/fsner-bert-base-uncased"						):







						"""simple docstring"""







						super(__magic_name__       ,       self						).__init__()

						UpperCamelCase         =					AutoModel.from_pretrained(__magic_name__       ,       return_dict=__magic_name__						)
						UpperCamelCase         =					torch.nn.CosineSimilarity(3       ,       1e-08						)
						UpperCamelCase         =					torch.nn.Softmax(dim=1						)




			def   lowerCamelCase_					(						self							:		Optional[int]       ,       **__magic_name__							:		List[Any]						):







						"""simple docstring"""







						return self.bert(**__magic_name__						).last_hidden_state




			def   lowerCamelCase_					(						self							:		Tuple       ,       __magic_name__							:		int						):







						"""simple docstring"""







						return token_embeddings.sum(2       ,       keepdim=__magic_name__						)




			def   lowerCamelCase_					(						self							:		Optional[Any]       ,       __magic_name__							:		List[str]       ,       __magic_name__							:		Union[str, Any]       ,       __magic_name__							:		Optional[Any]=1						):







						"""simple docstring"""







						return self.softmax(T * self.cos(__magic_name__       ,       __magic_name__						)						)





			def   lowerCamelCase_					(						self							:		Union[str, Any]       ,       __magic_name__							:		List[Any]       ,       __magic_name__							:		List[str]						):







						"""simple docstring"""







						UpperCamelCase         =					W_supports["""sizes"""].tolist()
						UpperCamelCase         =					W_supports["""start_token_id"""].item()
						UpperCamelCase         =					W_supports["""end_token_id"""].item()

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

						UpperCamelCase         =					self.BERT(**__magic_name__						)
						UpperCamelCase         =					self.BERT(**__magic_name__						)

						UpperCamelCase         =					None
						UpperCamelCase         =					None

						UpperCamelCase         =					W_supports["""input_ids"""] == start_token_id
						UpperCamelCase         =					W_supports["""input_ids"""] == end_token_id

						for i, size in enumerate(__magic_name__						):
									if i == 0:
												UpperCamelCase         =					0
									else:
												UpperCamelCase         =					support_sizes[i - 1]

									UpperCamelCase         =					S[s : s + size][start_token_masks[s : s + size]]
									UpperCamelCase         =					S[s : s + size][end_token_masks[s : s + size]]

									UpperCamelCase         =					torch.matmul(q[i]       ,       s_start.T						).sum(1						).softmax(0						)
									UpperCamelCase         =					torch.matmul(q[i]       ,       s_end.T						).sum(1						).softmax(0						)

									if p_starts is not None:
												UpperCamelCase         =					torch.vstack((p_starts, p_start)						)
												UpperCamelCase         =					torch.vstack((p_ends, p_end)						)
									else:
												UpperCamelCase         =					p_start
												UpperCamelCase         =					p_end

						return p_starts, p_ends




