
from collections import OrderedDict
from typing import Mapping

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


SCREAMING_SNAKE_CASE							:str							 =				logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							:Tuple							 =				{
    '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 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_					    =     "roberta"






	def __init__(       self					: Optional[int]							,A					: Optional[int]=5_02_65							,A					: Union[str, Any]=7_68							,A					: Tuple=12							,A					: Any=12							,A					: List[Any]=30_72							,A					: str="gelu"							,A					: Dict=0.1							,A					: Union[str, Any]=0.1							,A					: Dict=5_12							,A					: Union[str, Any]=2							,A					: Tuple=0.02							,A					: Dict=1E-12							,A					: Optional[Any]=1							,A					: int=0							,A					: Union[str, Any]=2							,A					: str="absolute"							,A					: Optional[int]=True							,A					: Dict=None							,**A					: Optional[Any]							,):
					super().__init__(pad_token_id=A							,bos_token_id=A							,eos_token_id=A							,**A )

					__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 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





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


import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image


if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('9.1.0'):
					SCREAMING_SNAKE_CASE							:Any							 =				{
					    'linear': PIL.Image.Resampling.BILINEAR,
					    'bilinear': PIL.Image.Resampling.BILINEAR,
					    'bicubic': PIL.Image.Resampling.BICUBIC,
					    'lanczos': PIL.Image.Resampling.LANCZOS,
					    'nearest': PIL.Image.Resampling.NEAREST,
					}
else:
					SCREAMING_SNAKE_CASE							:int							 =				{
					    'linear': PIL.Image.LINEAR,
					    'bilinear': PIL.Image.BILINEAR,
					    'bicubic': PIL.Image.BICUBIC,
					    'lanczos': PIL.Image.LANCZOS,
					    'nearest': PIL.Image.NEAREST,
					}


def 						UpperCAmelCase			(	a_						)		->	Optional[Any]:







				"""simple docstring"""



				__A   			= (images / 2 + 0.5).clamp(0		,   1						)
				__A   			= images.cpu().permute(0		,   2		,   3		,   1						).float().numpy()
				__A   			= numpy_to_pil(a_						)
				return images


def 						UpperCAmelCase			(	a_						)		->	int:







				"""simple docstring"""



				if images.ndim == 3:
								__A   			= images[None, ...]
				__A   			= (images * 2_5_5).round().astype("uint8"						)
				if images.shape[-1] == 1:
								# special case for grayscale (single channel) images
								__A   			= [Image.fromarray(image.squeeze()		,   mode="L"						) for image in images]
				else:
								__A   			= [Image.fromarray(a_						) for image in images]

				return pil_images
