





'''simple docstring'''




# limitations under the License.


from typing import Optional, Tuple, Union

import torch

from diffusers import DiffusionPipeline, ImagePipelineOutput


class 			SCREAMING_SNAKE_CASE   (a__							):


     def __init__(						self						,     _UpperCAmelCase						,     _UpperCAmelCase):

          '''simple docstring'''






          super().__init__()
          self.register_modules(unet=_UpperCAmelCase						,     scheduler=_UpperCAmelCase)


     @torch.no_grad()
     def __call__(						self						,     _UpperCAmelCase = 1						,     _UpperCAmelCase = None						,     _UpperCAmelCase = 50						,     _UpperCAmelCase = "pil"						,     _UpperCAmelCase = True						,     **_UpperCAmelCase						,     ):

          '''simple docstring'''






          __A						:			Tuple								=	torch.randn(
              (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)						,     generator=_UpperCAmelCase						,     )
          __A						:			Any								=	image.to(self.device)

          # set step values
          self.scheduler.set_timesteps(_UpperCAmelCase)

          for t in self.progress_bar(self.scheduler.timesteps):
               # 1. predict noise model_output
               __A						:			List[Any]								=	self.unet(_UpperCAmelCase						,     _UpperCAmelCase).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						:			Optional[Any]								=	self.scheduler.step(_UpperCAmelCase						,     _UpperCAmelCase						,     _UpperCAmelCase).prev_sample

          __A						:			Dict								=	(image / 2 + 0.5).clamp(0						,     1)
          __A						:			Any								=	image.cpu().permute(0						,     2						,     3						,     1).numpy()
          if output_type == "pil":
               __A						:			Optional[int]								=	self.numpy_to_pil(_UpperCAmelCase)

          if not return_dict:
               return (image,), "This is a local test"

          return ImagePipelineOutput(images=_UpperCAmelCase), "This is a local test"






'''simple docstring'''




from collections import OrderedDict
from typing import Mapping

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


lowercase__   :				Optional[Any]      =   logging.get_logger(__name__)

lowercase__   :				str      =   {
    '''andreasmadsen/efficient_mlm_m0.40''': (
        '''https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json'''
    ),
}


class 			SCREAMING_SNAKE_CASE   (a__							):
     lowerCAmelCase		      =	'''roberta-prelayernorm'''


     def __init__(						self						,     _UpperCAmelCase=5_0265						,     _UpperCAmelCase=768						,     _UpperCAmelCase=12						,     _UpperCAmelCase=12						,     _UpperCAmelCase=3072						,     _UpperCAmelCase="gelu"						,     _UpperCAmelCase=0.1						,     _UpperCAmelCase=0.1						,     _UpperCAmelCase=512						,     _UpperCAmelCase=2						,     _UpperCAmelCase=0.02						,     _UpperCAmelCase=1e-1_2						,     _UpperCAmelCase=1						,     _UpperCAmelCase=0						,     _UpperCAmelCase=2						,     _UpperCAmelCase="absolute"						,     _UpperCAmelCase=True						,     _UpperCAmelCase=None						,     **_UpperCAmelCase						,     ):

          '''simple docstring'''






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

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




class 			SCREAMING_SNAKE_CASE   (a__							):


     @property
     def 							SCREAMING_SNAKE_CASE  (						self):

          '''simple docstring'''






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