


from collections import OrderedDict
from typing import Mapping

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


lowerCAmelCase__ :			Any         =logging.get_logger(__name__)

lowerCAmelCase__ :			Tuple         ={
    'google/rembert': 'https://huggingface.co/google/rembert/resolve/main/config.json',
    # See all RemBERT models at https://huggingface.co/models?filter=rembert
}







class   __lowercase						(__SCREAMING_SNAKE_CASE     ):





							"""simple docstring"""







							_UpperCAmelCase						     =	"""rembert"""






							def __init__(			self ,	lowerCAmelCase__=2_5_0_3_0_0 ,	lowerCAmelCase__=1_1_5_2 ,	lowerCAmelCase__=3_2 ,	lowerCAmelCase__=1_8 ,	lowerCAmelCase__=2_5_6 ,	lowerCAmelCase__=1_6_6_4 ,	lowerCAmelCase__=4_6_0_8 ,	lowerCAmelCase__="gelu" ,	lowerCAmelCase__=0.0 ,	lowerCAmelCase__=0.0 ,	lowerCAmelCase__=0.1 ,	lowerCAmelCase__=5_1_2 ,	lowerCAmelCase__=2 ,	lowerCAmelCase__=0.02 ,	lowerCAmelCase__=1E-12 ,	lowerCAmelCase__=True ,	lowerCAmelCase__=0 ,	lowerCAmelCase__=3_1_2 ,	lowerCAmelCase__=3_1_3 ,	**lowerCAmelCase__ ,	):





										"""simple docstring"""




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

										SCREAMING_SNAKE_CASE_		:    Tuple 		=				vocab_size
										SCREAMING_SNAKE_CASE_		:    List[Any] 		=				input_embedding_size
										SCREAMING_SNAKE_CASE_		:    Any 		=				output_embedding_size
										SCREAMING_SNAKE_CASE_		:    Tuple 		=				max_position_embeddings
										SCREAMING_SNAKE_CASE_		:    Tuple 		=				hidden_size
										SCREAMING_SNAKE_CASE_		:    Dict 		=				num_hidden_layers
										SCREAMING_SNAKE_CASE_		:    List[str] 		=				num_attention_heads
										SCREAMING_SNAKE_CASE_		:    Any 		=				intermediate_size
										SCREAMING_SNAKE_CASE_		:    List[str] 		=				hidden_act
										SCREAMING_SNAKE_CASE_		:    Union[str, Any] 		=				hidden_dropout_prob
										SCREAMING_SNAKE_CASE_		:    List[str] 		=				attention_probs_dropout_prob
										SCREAMING_SNAKE_CASE_		:    Any 		=				classifier_dropout_prob
										SCREAMING_SNAKE_CASE_		:    int 		=				initializer_range
										SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				type_vocab_size
										SCREAMING_SNAKE_CASE_		:    Tuple 		=				layer_norm_eps
										SCREAMING_SNAKE_CASE_		:    int 		=				use_cache
										SCREAMING_SNAKE_CASE_		:    Any 		=				False







class   __lowercase						(__SCREAMING_SNAKE_CASE     ):





							"""simple docstring"""







							@property
							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




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






							@property
							def 				UpperCamelCase__    (			self					):





										"""simple docstring"""




										return 1E-4









from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import PNDMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class   __lowercase						(__SCREAMING_SNAKE_CASE     ):





							"""simple docstring"""







							_UpperCAmelCase						     =	42
							_UpperCAmelCase						     =	42






							def __init__(			self ,	lowerCAmelCase__ ,	lowerCAmelCase__					):





										"""simple docstring"""




										super().__init__()

										SCREAMING_SNAKE_CASE_		:    str 		=				PNDMScheduler.from_config(scheduler.config					)

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






							@torch.no_grad()
							def __call__(			self ,	lowerCAmelCase__ = 1 ,	lowerCAmelCase__ = 5_0 ,	lowerCAmelCase__ = None ,	lowerCAmelCase__ = "pil" ,	lowerCAmelCase__ = True ,	**lowerCAmelCase__ ,	):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:    Tuple 		=				randn_tensor(
										    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size) ,	generator=lowerCAmelCase__ ,	device=self.device ,	)

										self.scheduler.set_timesteps(lowerCAmelCase__					)
										for t in self.progress_bar(self.scheduler.timesteps					):
													SCREAMING_SNAKE_CASE_		:    Dict 		=				self.unet(lowerCAmelCase__ ,	lowerCAmelCase__					).sample

													SCREAMING_SNAKE_CASE_		:    Optional[Any] 		=				self.scheduler.step(lowerCAmelCase__ ,	lowerCAmelCase__ ,	lowerCAmelCase__					).prev_sample

										SCREAMING_SNAKE_CASE_		:    Dict 		=				(image / 2 + 0.5).clamp(0 ,	1					)
										SCREAMING_SNAKE_CASE_		:    Any 		=				image.cpu().permute(0 ,	2 ,	3 ,	1					).numpy()
										if output_type == "pil":
													SCREAMING_SNAKE_CASE_		:    str 		=				self.numpy_to_pil(lowerCAmelCase__					)

										if not return_dict:
													return (image,)

										return ImagePipelineOutput(images=lowerCAmelCase__					)





