






import inspect
from typing import List, Optional, Tuple, Union

import torch

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






class 					SCREAMING_SNAKE_CASE_  (       _a ):
       """simple docstring"""


       def __init__(				self	:str,      snake_case	:VQModel,      snake_case	:UNetaDModel,      snake_case	:DDIMScheduler):



          """simple docstring"""

          super().__init__()
          self.register_modules(vqvae=snake_case,      unet=snake_case,      scheduler=snake_case)





       @torch.no_grad()
       def __call__(				self	:Any,      snake_case	:int = 1,      snake_case	:Optional[Union[torch.Generator, List[torch.Generator]]] = None,      snake_case	:float = 0.0,      snake_case	:int = 50,      snake_case	:Optional[str] = "pil",      snake_case	:bool = True,      **snake_case	:List[Any],      ):



          """simple docstring"""

          _lowercase              =randn_tensor(
              (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size),      generator=snake_case,      )
          _lowercase              =latents.to(self.device)

          # scale the initial noise by the standard deviation required by the scheduler
          _lowercase              =latents * self.scheduler.init_noise_sigma

          self.scheduler.set_timesteps(snake_case)

          # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
          _lowercase              ='eta' in set(inspect.signature(self.scheduler.step).parameters.keys())

          _lowercase              ={}
          if accepts_eta:
             _lowercase              =eta

          for t in self.progress_bar(self.scheduler.timesteps):
             _lowercase              =self.scheduler.scale_model_input(snake_case,      snake_case)
             # predict the noise residual
             _lowercase              =self.unet(snake_case,      snake_case).sample
             # compute the previous noisy sample x_t -> x_t-1
             _lowercase              =self.scheduler.step(snake_case,      snake_case,      snake_case,      **snake_case).prev_sample

          # decode the image latents with the VAE
          _lowercase              =self.vqvae.decode(snake_case).sample

          _lowercase              =(image / 2 + 0.5).clamp(0,      1)
          _lowercase              =image.cpu().permute(0,      2,      3,      1).numpy()
          if output_type == "pil":
             _lowercase              =self.numpy_to_pil(snake_case)

          if not return_dict:
             return (image,)

          return ImagePipelineOutput(images=snake_case)










import torch

from transformers import AutoModel






class 					SCREAMING_SNAKE_CASE_  (       torch.nn.Module ):
       """simple docstring"""


       def __init__(				self	:Dict,      snake_case	:str="sayef/fsner-bert-base-uncased"):



          """simple docstring"""

          super(snake_case,      self).__init__()

          _lowercase              =AutoModel.from_pretrained(snake_case,      return_dict=snake_case)
          _lowercase              =torch.nn.CosineSimilarity(3,      1e-0_8)
          _lowercase              =torch.nn.Softmax(dim=1)





       def 				UpperCamelCase__						(				self	:str,      **snake_case	:int):



          """simple docstring"""

          return self.bert(**snake_case).last_hidden_state





       def 				UpperCamelCase__						(				self	:Union[str, Any],      snake_case	:Optional[Any]):



          """simple docstring"""

          return token_embeddings.sum(2,      keepdim=snake_case)





       def 				UpperCamelCase__						(				self	:List[Any],      snake_case	:int,      snake_case	:Dict,      snake_case	:Dict=1):



          """simple docstring"""

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





       def 				UpperCamelCase__						(				self	:List[str],      snake_case	:int,      snake_case	:List[str]):



          """simple docstring"""

          _lowercase              =W_supports['sizes'].tolist()
          _lowercase              =W_supports['start_token_id'].item()
          _lowercase              =W_supports['end_token_id'].item()

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

          _lowercase              =self.BERT(**snake_case)
          _lowercase              =self.BERT(**snake_case)

          _lowercase              =None
          _lowercase              =None

          _lowercase              =W_supports['input_ids'] == start_token_id
          _lowercase              =W_supports['input_ids'] == end_token_id

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

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

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

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

          return p_starts, p_ends


