



'''simple docstring'''
from collections import OrderedDict
from typing import Mapping

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


snake_case_ :	str         =  logging.get_logger(__name__)

snake_case_ :	Any         =  {
    'kssteven/ibert-roberta-base': 'https://huggingface.co/kssteven/ibert-roberta-base/resolve/main/config.json',
    'kssteven/ibert-roberta-large': 'https://huggingface.co/kssteven/ibert-roberta-large/resolve/main/config.json',
    'kssteven/ibert-roberta-large-mnli': (
        'https://huggingface.co/kssteven/ibert-roberta-large-mnli/resolve/main/config.json'
    ),
}






class 							lowercase__	(							lowercase   ):
	lowercase__  				=			"""ibert"""





	def __init__( self					:				Dict ,lowerCamelCase__					:				str=30522 ,lowerCamelCase__					:				List[Any]=768 ,lowerCamelCase__					:				Optional[Any]=12 ,lowerCamelCase__					:				str=12 ,lowerCamelCase__					:				List[str]=3072 ,lowerCamelCase__					:				Dict="gelu" ,lowerCamelCase__					:				Any=0.1 ,lowerCamelCase__					:				Optional[int]=0.1 ,lowerCamelCase__					:				List[str]=512 ,lowerCamelCase__					:				int=2 ,lowerCamelCase__					:				int=0.0_2 ,lowerCamelCase__					:				List[Any]=1E-12 ,lowerCamelCase__					:				Optional[int]=1 ,lowerCamelCase__					:				List[str]=0 ,lowerCamelCase__					:				int=2 ,lowerCamelCase__					:				Any="absolute" ,lowerCamelCase__					:				str=False ,lowerCamelCase__					:				Any="none" ,**lowerCamelCase__					:				Dict ,):







							'''simple docstring'''
							super().__init__(pad_token_id=lowerCamelCase__ ,bos_token_id=lowerCamelCase__ ,eos_token_id=lowerCamelCase__ ,**lowerCamelCase__ )

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






class 							lowercase__	(							lowercase   ):





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







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







'''simple docstring'''




import torch

from diffusers import StableDiffusionPipeline


lowerCamelCase							:Tuple				    =					'''path-to-your-trained-model'''
lowerCamelCase							:Optional[int]				    =					StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to('''cuda''')

lowerCamelCase							:Optional[int]				    =					'''A photo of sks dog in a bucket'''
lowerCamelCase							:List[Any]				    =					pipe(prompt, num_inference_steps=5_0, guidance_scale=7.5).images[0]

image.save('''dog-bucket.png''')