'''simple docstring'''





from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


SCREAMING_SNAKE_CASE_:					str  =logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:					Any  ={
    'camembert-base': 'https://huggingface.co/camembert-base/resolve/main/config.json',
    'umberto-commoncrawl-cased-v1': (
        'https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json'
    ),
    'umberto-wikipedia-uncased-v1': (
        'https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json'
    ),
}






class 	__A				(			_UpperCamelCase     ):
    a__	:						Any	       =  'camembert'



    def __init__(self   :   int       ,      __a   :   Tuple=30522       ,      __a   :   Union[str, Any]=768       ,      __a   :   str=12       ,      __a   :   Any=12       ,      __a   :   str=3072       ,      __a   :   Any="gelu"       ,      __a   :   int=0.1       ,      __a   :   Union[str, Any]=0.1       ,      __a   :   Dict=512       ,      __a   :   Any=2       ,      __a   :   Any=0.02       ,      __a   :   int=1E-12       ,      __a   :   str=1       ,      __a   :   int=0       ,      __a   :   List[Any]=2       ,      __a   :   int="absolute"       ,      __a   :   List[str]=True       ,      __a   :   Tuple=None       ,      **__a   :   Dict       ,      ):
         super().__init__(pad_token_id=_UpperCAmelCase       ,      bos_token_id=_UpperCAmelCase       ,      eos_token_id=_UpperCAmelCase       ,      **_UpperCAmelCase			)

         UpperCAmelCase_	   =       vocab_size
         UpperCAmelCase_	   =       hidden_size
         UpperCAmelCase_	   =       num_hidden_layers
         UpperCAmelCase_	   =       num_attention_heads
         UpperCAmelCase_	   =       hidden_act
         UpperCAmelCase_	   =       intermediate_size
         UpperCAmelCase_	   =       hidden_dropout_prob
         UpperCAmelCase_	   =       attention_probs_dropout_prob
         UpperCAmelCase_	   =       max_position_embeddings
         UpperCAmelCase_	   =       type_vocab_size
         UpperCAmelCase_	   =       initializer_range
         UpperCAmelCase_	   =       layer_norm_eps
         UpperCAmelCase_	   =       position_embedding_type
         UpperCAmelCase_	   =       use_cache
         UpperCAmelCase_	   =       classifier_dropout






class 	__A				(			_UpperCamelCase     ):



    @property
    def  _lowercase						(self   :   Any			):
         if self.task == "multiple-choice":
              UpperCAmelCase_	   =       {0: 'batch', 1: 'choice', 2: 'sequence'}
         else:
              UpperCAmelCase_	   =       {0: 'batch', 1: 'sequence'}
         return OrderedDict(
             [
                 ("input_ids", dynamic_axis),
                 ("attention_mask", dynamic_axis),
             ]			)





'''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				(			UpperCamelCase__     ):



    def __init__(self   :   Union[str, Any]       ,      __a   :   VQModel       ,      __a   :   UNetaDModel       ,      __a   :   DDIMScheduler			):
         super().__init__()
         self.register_modules(vqvae=__a       ,      unet=__a       ,      scheduler=__a			)






    @torch.no_grad()
    def __call__(self   :   str       ,      __a   :   int = 1       ,      __a   :   Optional[Union[torch.Generator, List[torch.Generator]]] = None       ,      __a   :   float = 0.0       ,      __a   :   int = 50       ,      __a   :   Optional[str] = "pil"       ,      __a   :   bool = True       ,      **__a   :   Optional[int]       ,      ):
         UpperCAmelCase_	   =       randn_tensor(
             (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)       ,      generator=__a       ,      )
         UpperCAmelCase_	   =       latents.to(self.device			)

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

         self.scheduler.set_timesteps(__a			)

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

         UpperCAmelCase_	   =       {}
         if accepts_eta:
              UpperCAmelCase_	   =       eta

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

         # decode the image latents with the VAE
         UpperCAmelCase_	   =       self.vqvae.decode(__a			).sample

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

         if not return_dict:
              return (image,)

         return ImagePipelineOutput(images=__a			)




