





from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


A   : Optional[Any]			=				transforms.Compose(
    [
        transforms.Resize((2_5_6, 2_5_6)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)







def        __lowerCamelCase (							__a       :Union[str, Any]   )		->    Tuple:






  """simple docstring"""

  if isinstance(SCREAMING_SNAKE_CASE_   ,					torch.Tensor   ):
    return image
  elif isinstance(SCREAMING_SNAKE_CASE_   ,					PIL.Image.Image   ):
    A__  				=       [image]

  A__  				=       [trans(img.convert("""RGB"""   )   ) for img in image]
  A__  				=       torch.stack(SCREAMING_SNAKE_CASE_   )
  return image






class   A      (lowerCamelCase_   ):
   '''simple docstring'''




   def __init__(      self       :   Optional[int]							,     __lowerCAmelCase       :   Tuple							,     __lowerCAmelCase       :   List[Any]       )     ->     str:







     """simple docstring"""






     super().__init__()

     # make sure scheduler can always be converted to DDIM
     A__  				=       DDIMScheduler.from_config(scheduler.config       )

     self.register_modules(unet=lowerCAmelCase__							,     scheduler=lowerCAmelCase__       )






   def        a_	(      self       :   Tuple							,     __lowerCAmelCase       :   Dict       )     ->     int:







     """simple docstring"""






     if strength < 0 or strength > 1:
       raise ValueError(f'The value of strength should in [0.0, 1.0] but is {strength}'       )






   def        a_	(      self       :   List[Any]							,     __lowerCAmelCase       :   Tuple							,     __lowerCAmelCase       :   List[str]							,     __lowerCAmelCase       :   Union[str, Any]       )     ->     List[str]:







     """simple docstring"""






     A__  				=       min(int(num_inference_steps * strength       )							,     lowerCAmelCase__       )

     A__  				=       max(num_inference_steps - init_timestep							,     0       )
     A__  				=       self.scheduler.timesteps[t_start:]

     return timesteps, num_inference_steps - t_start






   def        a_	(      self       :   Tuple							,     __lowerCAmelCase       :   Optional[Any]							,     __lowerCAmelCase       :   Optional[Any]							,     __lowerCAmelCase       :   Any							,     __lowerCAmelCase       :   List[Any]							,     __lowerCAmelCase       :   Tuple							,     __lowerCAmelCase       :   int=None       )     ->     List[str]:







     """simple docstring"""






     if not isinstance(lowerCAmelCase__							,     (torch.Tensor, PIL.Image.Image, list)       ):
       raise ValueError(
           f'`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(lowerCAmelCase__       )}'       )

     A__  				=       image.to(device=lowerCAmelCase__							,     dtype=lowerCAmelCase__       )

     if isinstance(lowerCAmelCase__							,     lowerCAmelCase__       ) and len(lowerCAmelCase__       ) != batch_size:
       raise ValueError(
           f'You have passed a list of generators of length {len(lowerCAmelCase__       )}, but requested an effective batch'
           f' size of {batch_size}. Make sure the batch size matches the length of the generators.'       )

     A__  				=       init_latents.shape
     A__  				=       randn_tensor(lowerCAmelCase__							,     generator=lowerCAmelCase__							,     device=lowerCAmelCase__							,     dtype=lowerCAmelCase__       )

     # get latents
     print("""add noise to latents at timestep"""							,     lowerCAmelCase__       )
     A__  				=       self.scheduler.add_noise(lowerCAmelCase__							,     lowerCAmelCase__							,     lowerCAmelCase__       )
     A__  				=       init_latents

     return latents







   @torch.no_grad()
   def __call__(      self       :   List[Any]							,     __lowerCAmelCase       :   int = None							,     __lowerCAmelCase       :   str = 0.8							,     __lowerCAmelCase       :   int = 1							,     __lowerCAmelCase       :   Any = None							,     __lowerCAmelCase       :   List[Any] = 0.0							,     __lowerCAmelCase       :   Dict = 50							,     __lowerCAmelCase       :   Union[str, Any] = None							,     __lowerCAmelCase       :   Optional[int] = "pil"							,     __lowerCAmelCase       :   Optional[Any] = True							,     )     ->     Union[ImagePipelineOutput, Tuple]:







     """simple docstring"""






     self.check_inputs(lowerCAmelCase__       )

     # 2. Preprocess image
     A__  				=       preprocess(lowerCAmelCase__       )

     # 3. set timesteps
     self.scheduler.set_timesteps(lowerCAmelCase__							,     device=self.device       )
     A__				,      A__  				=       self.get_timesteps(lowerCAmelCase__							,     lowerCAmelCase__							,     self.device       )
     A__  				=       timesteps[:1].repeat(lowerCAmelCase__       )

     # 4. Prepare latent variables
     A__  				=       self.prepare_latents(lowerCAmelCase__							,     lowerCAmelCase__							,     lowerCAmelCase__							,     self.unet.dtype							,     self.device							,     lowerCAmelCase__       )
     A__  				=       latents

     # 5. Denoising loop
     for t in self.progress_bar(lowerCAmelCase__       ):
       # 1. predict noise model_output
       A__  				=       self.unet(lowerCAmelCase__							,     lowerCAmelCase__       ).sample

       # 2. predict previous mean of image x_t-1 and add variance depending on eta
       # eta corresponds to η in paper and should be between [0, 1]
       # do x_t -> x_t-1
       A__  				=       self.scheduler.step(
           lowerCAmelCase__							,     lowerCAmelCase__							,     lowerCAmelCase__							,     eta=lowerCAmelCase__							,     use_clipped_model_output=lowerCAmelCase__							,     generator=lowerCAmelCase__							,     ).prev_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(lowerCAmelCase__       )

     if not return_dict:
       return (image, latent_timestep.item())

     return ImagePipelineOutput(images=lowerCAmelCase__       )










from collections import OrderedDict
from typing import Mapping

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


A   : Tuple			=				logging.get_logger(__name__)

A   : Optional[int]			=				{
    '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/config.json''',
    '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/config.json''',
    '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/config.json''',
    '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/config.json''',
    '''roberta-base-openai-detector''': '''https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json''',
    '''roberta-large-openai-detector''': '''https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json''',
}


class   A      (SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''




			__lowerCamelCase     :	Union[str, Any]          =   '''roberta'''






			def __init__(      self       :   Any							,     __lowerCAmelCase       :   Tuple=5_02_65							,     __lowerCAmelCase       :   Optional[int]=7_68							,     __lowerCAmelCase       :   Union[str, Any]=12							,     __lowerCAmelCase       :   Dict=12							,     __lowerCAmelCase       :   Optional[Any]=30_72							,     __lowerCAmelCase       :   Any="gelu"							,     __lowerCAmelCase       :   Optional[Any]=0.1							,     __lowerCAmelCase       :   Optional[int]=0.1							,     __lowerCAmelCase       :   Optional[Any]=5_12							,     __lowerCAmelCase       :   int=2							,     __lowerCAmelCase       :   List[str]=0.0_2							,     __lowerCAmelCase       :   Dict=1e-12							,     __lowerCAmelCase       :   Optional[int]=1							,     __lowerCAmelCase       :   Tuple=0							,     __lowerCAmelCase       :   int=2							,     __lowerCAmelCase       :   Dict="absolute"							,     __lowerCAmelCase       :   Optional[Any]=True							,     __lowerCAmelCase       :   Union[str, Any]=None							,     **__lowerCAmelCase       :   str							,     )     ->     str:







					"""simple docstring"""






					super().__init__(pad_token_id=__lowerCAmelCase							,     bos_token_id=__lowerCAmelCase							,     eos_token_id=__lowerCAmelCase							,     **__lowerCAmelCase       )

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


class   A      (SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''




			@property
			def        a_	(      self       :   int       )     ->     Mapping[str, Mapping[int, str]]:







					"""simple docstring"""






					if self.task == "multiple-choice":
							A__  				=       {0: """batch""", 1: """choice""", 2: """sequence"""}
					else:
							A__  				=       {0: """batch""", 1: """sequence"""}
					return OrderedDict(
					    [
					        ("""input_ids""", dynamic_axis),
					        ("""attention_mask""", dynamic_axis),
					    ]       )



