


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   :					Tuple	=      transforms.Compose(
    [
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)

def    lowercase_	(	_A    :					Optional[int]		):

      """simple docstring"""




      if isinstance(_A					,      torch.Tensor		):
            return image
      elif isinstance(_A					,      PIL.Image.Image		):
            lowerCamelCase__	:       str					=						[image]

      lowerCamelCase__	:       Tuple					=						[trans(img.convert("RGB"		)		) for img in image]
      lowerCamelCase__	:       int					=						torch.stack(_A		)
      return image







class     _lowercase				(							lowercase__):


       """simple docstring"""




       def __init__(     self							:    Optional[int]      ,   __lowerCamelCase							:    Any      ,   __lowerCamelCase							:    int ):


             '''simple docstring'''



             super().__init__()

             # make sure scheduler can always be converted to DDIM
             lowerCamelCase__	:       List[str]					=						DDIMScheduler.from_config(scheduler.config )

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





       def 		lowerCAmelCase  (     self							:    int      ,   __lowerCamelCase							:    Any ):


             '''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 		lowerCAmelCase  (     self							:    str      ,   __lowerCamelCase							:    List[Any]      ,   __lowerCamelCase							:    Dict      ,   __lowerCamelCase							:    Dict ):


             '''simple docstring'''



             lowerCamelCase__	:       List[Any]					=						min(int(num_inference_steps * strength )      ,   __lowerCamelCase )

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

             return timesteps, num_inference_steps - t_start





       def 		lowerCAmelCase  (     self							:    Optional[Any]      ,   __lowerCamelCase							:    List[str]      ,   __lowerCamelCase							:    Optional[Any]      ,   __lowerCamelCase							:    List[Any]      ,   __lowerCamelCase							:    Optional[Any]      ,   __lowerCamelCase							:    Dict      ,   __lowerCamelCase							:    Optional[int]=None ):


             '''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 )}" )

             lowerCamelCase__	:       List[Any]					=						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." )

             lowerCamelCase__	:       Optional[int]					=						init_latents.shape
             lowerCamelCase__	:       List[str]					=						randn_tensor(__lowerCamelCase      ,   generator=__lowerCamelCase      ,   device=__lowerCamelCase      ,   dtype=__lowerCamelCase )

             # get latents
             print("add noise to latents at timestep"      ,   __lowerCamelCase )
             lowerCamelCase__	:       int					=						self.scheduler.add_noise(__lowerCamelCase      ,   __lowerCamelCase      ,   __lowerCamelCase )
             lowerCamelCase__	:       str					=						init_latents

             return latents





       @torch.no_grad()
       def __call__(     self							:    Tuple      ,   __lowerCamelCase							:    Union[torch.FloatTensor, PIL.Image.Image] = None      ,   __lowerCamelCase							:    float = 0.8      ,   __lowerCamelCase							:    int = 1      ,   __lowerCamelCase							:    Optional[Union[torch.Generator, List[torch.Generator]]] = None      ,   __lowerCamelCase							:    float = 0.0      ,   __lowerCamelCase							:    int = 50      ,   __lowerCamelCase							:    Optional[bool] = None      ,   __lowerCamelCase							:    Optional[str] = "pil"      ,   __lowerCamelCase							:    bool = True      ,   ):


             '''simple docstring'''



             self.check_inputs(__lowerCamelCase )

             # 2. Preprocess image
             lowerCamelCase__	:       List[Any]					=						preprocess(__lowerCamelCase )

             # 3. set timesteps
             self.scheduler.set_timesteps(__lowerCamelCase      ,   device=self.device )
             lowerCamelCase__					,     lowerCamelCase__	:       Optional[Any]					=						self.get_timesteps(__lowerCamelCase      ,   __lowerCamelCase      ,   self.device )
             lowerCamelCase__	:       List[str]					=						timesteps[:1].repeat(__lowerCamelCase )

             # 4. Prepare latent variables
             lowerCamelCase__	:       Optional[Any]					=						self.prepare_latents(__lowerCamelCase      ,   __lowerCamelCase      ,   __lowerCamelCase      ,   self.unet.dtype      ,   self.device      ,   __lowerCamelCase )
             lowerCamelCase__	:       Tuple					=						latents

             # 5. Denoising loop
             for t in self.progress_bar(__lowerCamelCase ):
                   # 1. predict noise model_output
                   lowerCamelCase__	:       List[str]					=						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
                   lowerCamelCase__	:       Union[str, Any]					=						self.scheduler.step(
                       __lowerCamelCase      ,   __lowerCamelCase      ,   __lowerCamelCase      ,   eta=__lowerCamelCase      ,   use_clipped_model_output=__lowerCamelCase      ,   generator=__lowerCamelCase      ,   ).prev_sample

             lowerCamelCase__	:       List[Any]					=						(image / 2 + 0.5).clamp(0      ,   1 )
             lowerCamelCase__	:       Optional[int]					=						image.cpu().permute(0      ,   2      ,   3      ,   1 ).numpy()
             if output_type == "pil":
                   lowerCamelCase__	:       int					=						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   :					Dict	=      logging.get_logger(__name__)

A   :					Union[str, Any]	=      {
    "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     _lowercase				(							lowercase__):


       """simple docstring"""




       A__      		=    "roberta"





       def __init__(     self							:    int      ,   __lowerCamelCase							:    Dict=50265      ,   __lowerCamelCase							:    Optional[int]=768      ,   __lowerCamelCase							:    Optional[Any]=12      ,   __lowerCamelCase							:    Optional[int]=12      ,   __lowerCamelCase							:    int=3072      ,   __lowerCamelCase							:    Dict="gelu"      ,   __lowerCamelCase							:    Union[str, Any]=0.1      ,   __lowerCamelCase							:    int=0.1      ,   __lowerCamelCase							:    Tuple=512      ,   __lowerCamelCase							:    List[str]=2      ,   __lowerCamelCase							:    Any=0.0_2      ,   __lowerCamelCase							:    Optional[int]=1E-1_2      ,   __lowerCamelCase							:    List[Any]=1      ,   __lowerCamelCase							:    int=0      ,   __lowerCamelCase							:    Any=2      ,   __lowerCamelCase							:    Tuple="absolute"      ,   __lowerCamelCase							:    Tuple=True      ,   __lowerCamelCase							:    str=None      ,   **__lowerCamelCase							:    Optional[Any]      ,   ):


             '''simple docstring'''



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

             lowerCamelCase__	:       List[Any]					=						vocab_size
             lowerCamelCase__	:       str					=						hidden_size
             lowerCamelCase__	:       int					=						num_hidden_layers
             lowerCamelCase__	:       Optional[Any]					=						num_attention_heads
             lowerCamelCase__	:       Optional[Any]					=						hidden_act
             lowerCamelCase__	:       Any					=						intermediate_size
             lowerCamelCase__	:       Tuple					=						hidden_dropout_prob
             lowerCamelCase__	:       Any					=						attention_probs_dropout_prob
             lowerCamelCase__	:       Tuple					=						max_position_embeddings
             lowerCamelCase__	:       int					=						type_vocab_size
             lowerCamelCase__	:       Any					=						initializer_range
             lowerCamelCase__	:       Dict					=						layer_norm_eps
             lowerCamelCase__	:       int					=						position_embedding_type
             lowerCamelCase__	:       Any					=						use_cache
             lowerCamelCase__	:       int					=						classifier_dropout







class     _lowercase				(							lowercase__):


       """simple docstring"""




       @property
       def 		lowerCAmelCase  (     self							:    Union[str, Any] ):


             '''simple docstring'''



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


