


'''simple docstring'''






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 	a       (       _SCREAMING_SNAKE_CASE    ):
 def __init__(	self ,			__magic_name__ ,			__magic_name__ ,			__magic_name__  )   ->							Any:
      super().__init__()
      self.register_modules(vqvae=__magic_name__ ,			unet=__magic_name__ ,			scheduler=__magic_name__  )




 @torch.no_grad()
 def __call__(	self ,			__magic_name__ = 1 ,			__magic_name__ = None ,			__magic_name__ = 0.0 ,			__magic_name__ = 50 ,			__magic_name__ = "pil" ,			__magic_name__ = True ,			**__magic_name__ ,			)   ->							Union[Tuple, ImagePipelineOutput]:
      _a      =							randn_tensor(
          (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size) ,			generator=__magic_name__ ,			)
      _a      =							latents.to(self.device  )

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

      self.scheduler.set_timesteps(__magic_name__  )

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

      _a      =							{}
      if accepts_eta:
           _a      =							eta

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

      # decode the image latents with the VAE
      _a      =							self.vqvae.decode(__magic_name__  ).sample

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

      if not return_dict:
           return (image,)

      return ImagePipelineOutput(images=__magic_name__  )





'''simple docstring'''






import unicodedata
from dataclasses import dataclass
from typing import Optional, Union

import numpy as np

from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase






def 		_A			(lowerCAmelCase__					:int			,  lowerCAmelCase__					:Any			,  lowerCAmelCase__					:Optional[Any]			,  lowerCAmelCase__					:Dict						)				->					Optional[int]:




     '''simple docstring'''



     if isinstance(lowerCAmelCase__			,  lowerCAmelCase__						):
          _a      =							np.full((len(lowerCAmelCase__						), sequence_length, 2)			,  lowerCAmelCase__						)
     else:
          _a      =							np.full((len(lowerCAmelCase__						), sequence_length)			,  lowerCAmelCase__						)

     for i, tensor in enumerate(lowerCAmelCase__						):
          if padding_side == "right":
               if isinstance(lowerCAmelCase__			,  lowerCAmelCase__						):
                    _a      =							tensor[:sequence_length]
               else:
                    _a      =							tensor[:sequence_length]
          else:
               if isinstance(lowerCAmelCase__			,  lowerCAmelCase__						):
                    _a      =							tensor[:sequence_length]
               else:
                    _a      =							tensor[:sequence_length]

     return out_tensor.tolist()






def 		_A			(lowerCAmelCase__					:Any						)				->					Union[str, Any]:




     '''simple docstring'''



     _a      =							ord(lowerCAmelCase__						)
     if (cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 1_23 and cp <= 1_26):
          return True
     _a      =							unicodedata.category(lowerCAmelCase__						)
     if cat.startswith('P'						):
          return True
     return False






@dataclass
class 	a       (       _SCREAMING_SNAKE_CASE    ):
 _lowerCAmelCase	   =		42
 _lowerCAmelCase	   =		True
 _lowerCAmelCase	   =		None
 _lowerCAmelCase	   =		None
 _lowerCAmelCase	   =		-1_0_0
 _lowerCAmelCase	   =		"pt"
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							Any:
      import torch

      _a      =							'label' if 'label' in features[0].keys() else 'labels'
      _a      =							[feature[label_name] for feature in features] if label_name in features[0].keys() else None
      _a      =							self.tokenizer.pad(
          __magic_name__ ,			padding=self.padding ,			max_length=self.max_length ,			pad_to_multiple_of=self.pad_to_multiple_of ,			return_tensors='pt' if labels is None else None ,			)

      if labels is None:
           return batch

      _a      =							torch.tensor(batch['entity_ids']  ).shape[1]
      _a      =							self.tokenizer.padding_side
      if padding_side == "right":
           _a      =							[
               list(__magic_name__  ) + [self.label_pad_token_id] * (sequence_length - len(__magic_name__  )) for label in labels
           ]
      else:
           _a      =							[
               [self.label_pad_token_id] * (sequence_length - len(__magic_name__  )) + list(__magic_name__  ) for label in labels
           ]

      _a      =							[feature['ner_tags'] for feature in features]
      _a      =							padding_tensor(__magic_name__ ,			-1 ,			__magic_name__ ,			__magic_name__  )
      _a      =							[feature['original_entity_spans'] for feature in features]
      _a      =							padding_tensor(__magic_name__ ,			(-1, -1) ,			__magic_name__ ,			__magic_name__  )
      _a      =							{k: torch.tensor(__magic_name__ ,			dtype=torch.intaa  ) for k, v in batch.items()}

      return batch

