

import inspect
from typing import Optional, Union

import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    DPMSolverMultistepScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.utils import (
    PIL_INTERPOLATION,
    randn_tensor,
)




def 					_lowercase     (				a_				:					Optional[int]					,a_				:					List[Any]					,a_				:					Any				)				->	str:




					'''simple docstring'''







					if isinstance(a_					,torch.Tensor				):
										return image
					elif isinstance(a_					,PIL.Image.Image				):
										__magic_name__							=    [image]

					if isinstance(image[0]					,PIL.Image.Image				):
										__magic_name__							=    [np.array(i.resize((w, h)					,resample=PIL_INTERPOLATION['lanczos']				)				)[None, :] for i in image]
										__magic_name__							=    np.concatenate(a_					,axis=0				)
										__magic_name__							=    np.array(a_				).astype(np.floataa				) / 255.0
										__magic_name__							=    image.transpose(0					,3					,1					,2				)
										__magic_name__							=    2.0 * image - 1.0
										__magic_name__							=    torch.from_numpy(a_				)
					elif isinstance(image[0]					,torch.Tensor				):
										__magic_name__							=    torch.cat(a_					,dim=0				)
					return image




def 					_lowercase     (				a_				:					Dict					,a_				:					Optional[Any]					,a_				:					Any					,a_				:					int=0.9995				)				->	Optional[Any]:




					'''simple docstring'''







					if not isinstance(a_					,np.ndarray				):
										__magic_name__							=    True
										__magic_name__							=    va.device
										__magic_name__							=    va.cpu().numpy()
										__magic_name__							=    va.cpu().numpy()

					__magic_name__							=    np.sum(va * va / (np.linalg.norm(a_				) * np.linalg.norm(a_				))				)
					if np.abs(a_				) > DOT_THRESHOLD:
										__magic_name__							=    (1 - t) * va + t * va
					else:
										__magic_name__							=    np.arccos(a_				)
										__magic_name__							=    np.sin(a_				)
										__magic_name__							=    theta_a * t
										__magic_name__							=    np.sin(a_				)
										__magic_name__							=    np.sin(theta_a - theta_t				) / sin_theta_a
										__magic_name__							=    sin_theta_t / sin_theta_a
										__magic_name__							=    sa * va + sa * va

					if inputs_are_torch:
										__magic_name__							=    torch.from_numpy(a_				).to(a_				)

					return va




def 					_lowercase     (				a_				:					Tuple					,a_				:					Union[str, Any]				)				->	List[Any]:




					'''simple docstring'''







					__magic_name__							=    F.normalize(a_					,dim=-1				)
					__magic_name__							=    F.normalize(a_					,dim=-1				)
					return (x - y).norm(dim=-1				).div(2				).arcsin().pow(2				).mul(2				)




def 					_lowercase     (				a_				:					Any					,a_				:					str				)				->	Any:




					'''simple docstring'''







					for param in model.parameters():
										__magic_name__							=    value


class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE			):







	def __init__(   self: Optional[Any]  , __UpperCamelCase: AutoencoderKL  , __UpperCamelCase: CLIPTextModel  , __UpperCamelCase: CLIPModel  , __UpperCamelCase: CLIPTokenizer  , __UpperCamelCase: UNetaDConditionModel  , __UpperCamelCase: Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler, DPMSolverMultistepScheduler]  , __UpperCamelCase: CLIPFeatureExtractor  , __UpperCamelCase: Tuple=None  , __UpperCamelCase: Union[str, Any]=None  , __UpperCamelCase: Optional[int]=None  , ):

						'''simple docstring'''





						super().__init__()
						self.register_modules(
						    vae=__UpperCamelCase  , text_encoder=__UpperCamelCase  , clip_model=__UpperCamelCase  , tokenizer=__UpperCamelCase  , unet=__UpperCamelCase  , scheduler=__UpperCamelCase  , feature_extractor=__UpperCamelCase  , coca_model=__UpperCamelCase  , coca_tokenizer=__UpperCamelCase  , coca_transform=__UpperCamelCase  , )
						__magic_name__							=    (
						    feature_extractor.size
						    if isinstance(feature_extractor.size  , __UpperCamelCase      )
						    else feature_extractor.size['shortest_edge']
						)
						__magic_name__							=    transforms.Normalize(mean=feature_extractor.image_mean  , std=feature_extractor.image_std      )
						set_requires_grad(self.text_encoder  , __UpperCamelCase      )
						set_requires_grad(self.clip_model  , __UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Union[str, Any]  , __UpperCamelCase: Optional[Union[str, int]] = "auto"      ):

						'''simple docstring'''





						if slice_size == "auto":
											# half the attention head size is usually a good trade-off between
											# speed and memory
											__magic_name__							=    self.unet.config.attention_head_dim // 2
						self.unet.set_attention_slice(__UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: int      ):

						'''simple docstring'''





						self.enable_attention_slicing(__UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: int      ):

						'''simple docstring'''





						set_requires_grad(self.vae  , __UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Dict      ):

						'''simple docstring'''





						set_requires_grad(self.vae  , __UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: str      ):

						'''simple docstring'''





						set_requires_grad(self.unet  , __UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Dict      ):

						'''simple docstring'''





						set_requires_grad(self.unet  , __UpperCamelCase      )







	def 	_SCREAMING_SNAKE_CASE							(   self: List[Any]  , __UpperCamelCase: Dict  , __UpperCamelCase: List[str]  , __UpperCamelCase: Any      ):

						'''simple docstring'''





						__magic_name__							=    min(int(num_inference_steps * strength      )  , __UpperCamelCase      )

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

						return timesteps, num_inference_steps - t_start







	def 	_SCREAMING_SNAKE_CASE							(   self: str  , __UpperCamelCase: List[str]  , __UpperCamelCase: Union[str, Any]  , __UpperCamelCase: List[Any]  , __UpperCamelCase: Union[str, Any]  , __UpperCamelCase: Optional[int]  , __UpperCamelCase: Tuple=None      ):

						'''simple docstring'''





						if not isinstance(__UpperCamelCase  , torch.Tensor      ):
											raise ValueError(F'`image` has to be of type `torch.Tensor` but is {type(__UpperCamelCase      )}'      )

						__magic_name__							=    image.to(device=__UpperCamelCase  , dtype=__UpperCamelCase      )

						if isinstance(__UpperCamelCase  , __UpperCamelCase      ):
											__magic_name__							=    [
											    self.vae.encode(image[i : i + 1]      ).latent_dist.sample(generator[i]      ) for i in range(__UpperCamelCase      )
											]
											__magic_name__							=    torch.cat(__UpperCamelCase  , dim=0      )
						else:
											__magic_name__							=    self.vae.encode(__UpperCamelCase      ).latent_dist.sample(__UpperCamelCase      )

						# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
						__magic_name__							=    0.18215 * init_latents
						__magic_name__							=    init_latents.repeat_interleave(__UpperCamelCase  , dim=0      )

						__magic_name__							=    randn_tensor(init_latents.shape  , generator=__UpperCamelCase  , device=__UpperCamelCase  , dtype=__UpperCamelCase      )

						# get latents
						__magic_name__							=    self.scheduler.add_noise(__UpperCamelCase  , __UpperCamelCase  , __UpperCamelCase      )
						__magic_name__							=    init_latents

						return latents







	def 	_SCREAMING_SNAKE_CASE							(   self: int  , __UpperCamelCase: int      ):

						'''simple docstring'''





						__magic_name__							=    self.coca_transform(__UpperCamelCase      ).unsqueeze(0      )
						with torch.no_grad(), torch.cuda.amp.autocast():
											__magic_name__							=    self.coca_model.generate(transformed_image.to(device=self.device  , dtype=self.coca_model.dtype      )      )
						__magic_name__							=    self.coca_tokenizer.decode(generated[0].cpu().numpy()      )
						return generated.split('<end_of_text>'      )[0].replace('<start_of_text>'  , ''      ).rstrip(' .,'      )







	def 	_SCREAMING_SNAKE_CASE							(   self: Dict  , __UpperCamelCase: Tuple  , __UpperCamelCase: Union[str, Any]      ):

						'''simple docstring'''





						__magic_name__							=    self.feature_extractor.preprocess(__UpperCamelCase      )
						__magic_name__							=    torch.from_numpy(clip_image_input['pixel_values'][0]      ).unsqueeze(0      ).to(self.device      ).half()
						__magic_name__							=    self.clip_model.get_image_features(__UpperCamelCase      )
						__magic_name__							=    image_embeddings_clip / image_embeddings_clip.norm(p=2  , dim=-1  , keepdim=__UpperCamelCase      )
						__magic_name__							=    image_embeddings_clip.repeat_interleave(__UpperCamelCase  , dim=0      )
						return image_embeddings_clip







	@torch.enable_grad()
	def 	_SCREAMING_SNAKE_CASE							(   self: List[Any]  , __UpperCamelCase: Optional[int]  , __UpperCamelCase: Optional[Any]  , __UpperCamelCase: Tuple  , __UpperCamelCase: Optional[int]  , __UpperCamelCase: int  , __UpperCamelCase: Optional[int]  , __UpperCamelCase: Optional[int]  , ):

						'''simple docstring'''





						__magic_name__							=    latents.detach().requires_grad_()

						__magic_name__							=    self.scheduler.scale_model_input(__UpperCamelCase  , __UpperCamelCase      )

						# predict the noise residual
						__magic_name__							=    self.unet(__UpperCamelCase  , __UpperCamelCase  , encoder_hidden_states=__UpperCamelCase      ).sample

						if isinstance(self.scheduler  , (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)      ):
											__magic_name__							=    self.scheduler.alphas_cumprod[timestep]
											__magic_name__							=    1 - alpha_prod_t
											# compute predicted original sample from predicted noise also called
											# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
											__magic_name__							=    (latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

											__magic_name__							=    torch.sqrt(__UpperCamelCase      )
											__magic_name__							=    pred_original_sample * (fac) + latents * (1 - fac)
						elif isinstance(self.scheduler  , __UpperCamelCase      ):
											__magic_name__							=    self.scheduler.sigmas[index]
											__magic_name__							=    latents - sigma * noise_pred
						else:
											raise ValueError(F'scheduler type {type(self.scheduler      )} not supported'      )

						# Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
						__magic_name__							=    1 / 0.18215 * sample
						__magic_name__							=    self.vae.decode(__UpperCamelCase      ).sample
						__magic_name__							=    (image / 2 + 0.5).clamp(0  , 1      )

						__magic_name__							=    transforms.Resize(self.feature_extractor_size      )(__UpperCamelCase      )
						__magic_name__							=    self.normalize(__UpperCamelCase      ).to(latents.dtype      )

						__magic_name__							=    self.clip_model.get_image_features(__UpperCamelCase      )
						__magic_name__							=    image_embeddings_clip / image_embeddings_clip.norm(p=2  , dim=-1  , keepdim=__UpperCamelCase      )

						__magic_name__							=    spherical_dist_loss(__UpperCamelCase  , __UpperCamelCase      ).mean() * clip_guidance_scale

						__magic_name__							=    -torch.autograd.grad(__UpperCamelCase  , __UpperCamelCase      )[0]

						if isinstance(self.scheduler  , __UpperCamelCase      ):
											__magic_name__							=    latents.detach() + grads * (sigma**2)
											__magic_name__							=    noise_pred_original
						else:
											__magic_name__							=    noise_pred_original - torch.sqrt(__UpperCamelCase      ) * grads
						return noise_pred, latents







	@torch.no_grad()
	def __call__(   self: Dict  , __UpperCamelCase: Union[torch.FloatTensor, PIL.Image.Image]  , __UpperCamelCase: Union[torch.FloatTensor, PIL.Image.Image]  , __UpperCamelCase: Optional[str] = None  , __UpperCamelCase: Optional[str] = None  , __UpperCamelCase: Optional[int] = 5_12  , __UpperCamelCase: Optional[int] = 5_12  , __UpperCamelCase: float = 0.6  , __UpperCamelCase: Optional[int] = 50  , __UpperCamelCase: Optional[float] = 7.5  , __UpperCamelCase: Optional[int] = 1  , __UpperCamelCase: float = 0.0  , __UpperCamelCase: Optional[float] = 1_00  , __UpperCamelCase: Optional[torch.Generator] = None  , __UpperCamelCase: Optional[str] = "pil"  , __UpperCamelCase: bool = True  , __UpperCamelCase: float = 0.8  , __UpperCamelCase: float = 0.1  , __UpperCamelCase: float = 0.1  , ):

						'''simple docstring'''





						if isinstance(__UpperCamelCase  , __UpperCamelCase      ) and len(__UpperCamelCase      ) != batch_size:
											raise ValueError(F'You have passed {batch_size} batch_size, but only {len(__UpperCamelCase      )} generators.'      )

						if height % 8 != 0 or width % 8 != 0:
											raise ValueError(F'`height` and `width` have to be divisible by 8 but are {height} and {width}.'      )

						if isinstance(__UpperCamelCase  , torch.Generator      ) and batch_size > 1:
											__magic_name__							=    [generator] + [None] * (batch_size - 1)

						__magic_name__							=    [
						    ('model', self.coca_model is None),
						    ('tokenizer', self.coca_tokenizer is None),
						    ('transform', self.coca_transform is None),
						]
						__magic_name__							=    [x[0] for x in coca_is_none if x[1]]
						__magic_name__							=    ', '.join(__UpperCamelCase      )
						# generate prompts with coca model if prompt is None
						if content_prompt is None:
											if len(__UpperCamelCase      ):
																raise ValueError(
																    F'Content prompt is None and CoCa [{coca_is_none_str}] is None.'
																    F'Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.'      )
											__magic_name__							=    self.get_image_description(__UpperCamelCase      )
						if style_prompt is None:
											if len(__UpperCamelCase      ):
																raise ValueError(
																    F'Style prompt is None and CoCa [{coca_is_none_str}] is None.'
																    F' Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.'      )
											__magic_name__							=    self.get_image_description(__UpperCamelCase      )

						# get prompt text embeddings for content and style
						__magic_name__							=    self.tokenizer(
						    __UpperCamelCase  , padding='max_length'  , max_length=self.tokenizer.model_max_length  , truncation=__UpperCamelCase  , return_tensors='pt'  , )
						__magic_name__							=    self.text_encoder(content_text_input.input_ids.to(self.device      )      )[0]

						__magic_name__							=    self.tokenizer(
						    __UpperCamelCase  , padding='max_length'  , max_length=self.tokenizer.model_max_length  , truncation=__UpperCamelCase  , return_tensors='pt'  , )
						__magic_name__							=    self.text_encoder(style_text_input.input_ids.to(self.device      )      )[0]

						__magic_name__							=    slerp(__UpperCamelCase  , __UpperCamelCase  , __UpperCamelCase      )

						# duplicate text embeddings for each generation per prompt
						__magic_name__							=    text_embeddings.repeat_interleave(__UpperCamelCase  , dim=0      )

						# set timesteps
						__magic_name__							=    'offset' in set(inspect.signature(self.scheduler.set_timesteps      ).parameters.keys()      )
						__magic_name__							=    {}
						if accepts_offset:
											__magic_name__							=    1

						self.scheduler.set_timesteps(__UpperCamelCase  , **__UpperCamelCase      )
						# Some schedulers like PNDM have timesteps as arrays
						# It's more optimized to move all timesteps to correct device beforehand
						self.scheduler.timesteps.to(self.device      )

						__magic_name__,	__magic_name__							=    self.get_timesteps(__UpperCamelCase  , __UpperCamelCase  , self.device      )
						__magic_name__							=    timesteps[:1].repeat(__UpperCamelCase      )

						# Preprocess image
						__magic_name__							=    preprocess(__UpperCamelCase  , __UpperCamelCase  , __UpperCamelCase      )
						__magic_name__							=    self.prepare_latents(
						    __UpperCamelCase  , __UpperCamelCase  , __UpperCamelCase  , text_embeddings.dtype  , self.device  , __UpperCamelCase      )

						__magic_name__							=    preprocess(__UpperCamelCase  , __UpperCamelCase  , __UpperCamelCase      )
						__magic_name__							=    self.prepare_latents(
						    __UpperCamelCase  , __UpperCamelCase  , __UpperCamelCase  , text_embeddings.dtype  , self.device  , __UpperCamelCase      )

						__magic_name__							=    slerp(__UpperCamelCase  , __UpperCamelCase  , __UpperCamelCase      )

						if clip_guidance_scale > 0:
											__magic_name__							=    self.get_clip_image_embeddings(__UpperCamelCase  , __UpperCamelCase      )
											__magic_name__							=    self.get_clip_image_embeddings(__UpperCamelCase  , __UpperCamelCase      )
											__magic_name__							=    slerp(
											    __UpperCamelCase  , __UpperCamelCase  , __UpperCamelCase      )

						# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
						# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
						# corresponds to doing no classifier free guidance.
						__magic_name__							=    guidance_scale > 1.0
						# get unconditional embeddings for classifier free guidance
						if do_classifier_free_guidance:
											__magic_name__							=    content_text_input.input_ids.shape[-1]
											__magic_name__							=    self.tokenizer(['']  , padding='max_length'  , max_length=__UpperCamelCase  , return_tensors='pt'      )
											__magic_name__							=    self.text_encoder(uncond_input.input_ids.to(self.device      )      )[0]
											# duplicate unconditional embeddings for each generation per prompt
											__magic_name__							=    uncond_embeddings.repeat_interleave(__UpperCamelCase  , dim=0      )

											# For classifier free guidance, we need to do two forward passes.
											# Here we concatenate the unconditional and text embeddings into a single batch
											# to avoid doing two forward passes
											__magic_name__							=    torch.cat([uncond_embeddings, text_embeddings]      )

						# get the initial random noise unless the user supplied it

						# Unlike in other pipelines, latents need to be generated in the target device
						# for 1-to-1 results reproducibility with the CompVis implementation.
						# However this currently doesn't work in `mps`.
						__magic_name__							=    (batch_size, self.unet.config.in_channels, height // 8, width // 8)
						__magic_name__							=    text_embeddings.dtype
						if latents is None:
											if self.device.type == "mps":
																# randn does not work reproducibly on mps
																__magic_name__							=    torch.randn(__UpperCamelCase  , generator=__UpperCamelCase  , device='cpu'  , dtype=__UpperCamelCase      ).to(
																    self.device      )
											else:
																__magic_name__							=    torch.randn(__UpperCamelCase  , generator=__UpperCamelCase  , device=self.device  , dtype=__UpperCamelCase      )
						else:
											if latents.shape != latents_shape:
																raise ValueError(F'Unexpected latents shape, got {latents.shape}, expected {latents_shape}'      )
											__magic_name__							=    latents.to(self.device      )

						# scale the initial noise by the standard deviation required by the scheduler
						__magic_name__							=    latents * self.scheduler.init_noise_sigma

						# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
						# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
						# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
						# and should be between [0, 1]
						__magic_name__							=    'eta' in set(inspect.signature(self.scheduler.step      ).parameters.keys()      )
						__magic_name__							=    {}
						if accepts_eta:
											__magic_name__							=    eta

						# check if the scheduler accepts generator
						__magic_name__							=    'generator' in set(inspect.signature(self.scheduler.step      ).parameters.keys()      )
						if accepts_generator:
											__magic_name__							=    generator

						with self.progress_bar(total=__UpperCamelCase      ):
											for i, t in enumerate(__UpperCamelCase      ):
																# expand the latents if we are doing classifier free guidance
																__magic_name__							=    torch.cat([latents] * 2      ) if do_classifier_free_guidance else latents
																__magic_name__							=    self.scheduler.scale_model_input(__UpperCamelCase  , __UpperCamelCase      )

																# predict the noise residual
																__magic_name__							=    self.unet(__UpperCamelCase  , __UpperCamelCase  , encoder_hidden_states=__UpperCamelCase      ).sample

																# perform classifier free guidance
																if do_classifier_free_guidance:
																					__magic_name__,	__magic_name__							=    noise_pred.chunk(2      )
																					__magic_name__							=    noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

																# perform clip guidance
																if clip_guidance_scale > 0:
																					__magic_name__							=    (
																					    text_embeddings.chunk(2      )[1] if do_classifier_free_guidance else text_embeddings
																					)
																					__magic_name__,	__magic_name__							=    self.cond_fn(
																					    __UpperCamelCase  , __UpperCamelCase  , __UpperCamelCase  , __UpperCamelCase  , __UpperCamelCase  , __UpperCamelCase  , __UpperCamelCase  , )

																# compute the previous noisy sample x_t -> x_t-1
																__magic_name__							=    self.scheduler.step(__UpperCamelCase  , __UpperCamelCase  , __UpperCamelCase  , **__UpperCamelCase      ).prev_sample

        # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
						__magic_name__							=    1 / 0.18215 * latents
						__magic_name__							=    self.vae.decode(__UpperCamelCase      ).sample

						__magic_name__							=    (image / 2 + 0.5).clamp(0  , 1      )
						__magic_name__							=    image.cpu().permute(0  , 2  , 3  , 1      ).numpy()

						if output_type == "pil":
											__magic_name__							=    self.numpy_to_pil(__UpperCamelCase      )

						if not return_dict:
											return (image, None)

						return StableDiffusionPipelineOutput(images=__UpperCamelCase  , nsfw_content_detected=__UpperCamelCase      )







from collections import OrderedDict
from typing import Mapping

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


A__						=							logging.get_logger(__name__)

A__						=							{
    "camembert-base": "https://huggingface.co/camembert-base/resolve/main/config.json",
    "umberto-commoncrawl-cased-v1": (
        "https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json"
    ),
    "umberto-wikipedia-uncased-v1": (
        "https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json"
    ),
}

class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE			):
	_lowercase  :   Optional[int]       				=  "camembert"







	def __init__(   self: Optional[Any]  , __UpperCamelCase: Any=3_05_22  , __UpperCamelCase: Tuple=7_68  , __UpperCamelCase: str=12  , __UpperCamelCase: Optional[int]=12  , __UpperCamelCase: List[str]=30_72  , __UpperCamelCase: Any="gelu"  , __UpperCamelCase: Optional[int]=0.1  , __UpperCamelCase: Any=0.1  , __UpperCamelCase: str=5_12  , __UpperCamelCase: Dict=2  , __UpperCamelCase: str=0.02  , __UpperCamelCase: List[Any]=1E-12  , __UpperCamelCase: List[Any]=1  , __UpperCamelCase: Any=0  , __UpperCamelCase: Union[str, Any]=2  , __UpperCamelCase: Dict="absolute"  , __UpperCamelCase: Any=True  , __UpperCamelCase: Any=None  , **__UpperCamelCase: Union[str, Any]  , ):

						'''simple docstring'''





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

						__magic_name__							=    vocab_size
						__magic_name__							=    hidden_size
						__magic_name__							=    num_hidden_layers
						__magic_name__							=    num_attention_heads
						__magic_name__							=    hidden_act
						__magic_name__							=    intermediate_size
						__magic_name__							=    hidden_dropout_prob
						__magic_name__							=    attention_probs_dropout_prob
						__magic_name__							=    max_position_embeddings
						__magic_name__							=    type_vocab_size
						__magic_name__							=    initializer_range
						__magic_name__							=    layer_norm_eps
						__magic_name__							=    position_embedding_type
						__magic_name__							=    use_cache
						__magic_name__							=    classifier_dropout




class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE			):







	@property
	def 	_SCREAMING_SNAKE_CASE							(   self: Dict      ):

						'''simple docstring'''





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




