

"""simple docstring"""




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 	lowerCAmelCase    (       __UpperCamelCase				,     __UpperCamelCase				,     __UpperCamelCase   ):
 '''simple docstring'''





 if isinstance(__UpperCamelCase				,     torch.Tensor   ):
  return image
 elif isinstance(__UpperCamelCase				,     PIL.Image.Image   ):
  UpperCAmelCase__       :	List[Any]        =       [image]

 if isinstance(image[0]				,     PIL.Image.Image   ):
  UpperCAmelCase__       :	Optional[int]        =       [np.array(i.resize((w, h)				,     resample=PIL_INTERPOLATION["""lanczos"""]   )   )[None, :] for i in image]
  UpperCAmelCase__       :	Union[str, Any]        =       np.concatenate(__UpperCamelCase				,     axis=0   )
  UpperCAmelCase__       :	str        =       np.array(__UpperCamelCase   ).astype(np.floataa   ) / 255.0
  UpperCAmelCase__       :	List[str]        =       image.transpose(0				,     3				,     1				,     2   )
  UpperCAmelCase__       :	Optional[int]        =       2.0 * image - 1.0
  UpperCAmelCase__       :	List[Any]        =       torch.from_numpy(__UpperCamelCase   )
 elif isinstance(image[0]				,     torch.Tensor   ):
  UpperCAmelCase__       :	List[Any]        =       torch.cat(__UpperCamelCase				,     dim=0   )
 return image
def 	lowerCAmelCase    (       __UpperCamelCase				,     __UpperCamelCase				,     __UpperCamelCase				,     __UpperCamelCase=0.9995   ):
 '''simple docstring'''





 if not isinstance(__UpperCamelCase				,     np.ndarray   ):
  UpperCAmelCase__       :	Any        =       True
  UpperCAmelCase__       :	Optional[int]        =       va.device
  UpperCAmelCase__       :	Tuple        =       va.cpu().numpy()
  UpperCAmelCase__       :	Optional[int]        =       va.cpu().numpy()

 UpperCAmelCase__       :	Dict        =       np.sum(va * va / (np.linalg.norm(__UpperCamelCase   ) * np.linalg.norm(__UpperCamelCase   ))   )
 if np.abs(__UpperCamelCase   ) > DOT_THRESHOLD:
  UpperCAmelCase__       :	Tuple        =       (1 - t) * va + t * va
 else:
  UpperCAmelCase__       :	str        =       np.arccos(__UpperCamelCase   )
  UpperCAmelCase__       :	int        =       np.sin(__UpperCamelCase   )
  UpperCAmelCase__       :	Union[str, Any]        =       theta_a * t
  UpperCAmelCase__       :	int        =       np.sin(__UpperCamelCase   )
  UpperCAmelCase__       :	Optional[int]        =       np.sin(theta_a - theta_t   ) / sin_theta_a
  UpperCAmelCase__       :	Tuple        =       sin_theta_t / sin_theta_a
  UpperCAmelCase__       :	List[Any]        =       sa * va + sa * va

 if inputs_are_torch:
  UpperCAmelCase__       :	Dict        =       torch.from_numpy(__UpperCamelCase   ).to(__UpperCamelCase   )

 return va
def 	lowerCAmelCase    (       __UpperCamelCase				,     __UpperCamelCase   ):
 '''simple docstring'''





 UpperCAmelCase__       :	str        =       F.normalize(__UpperCamelCase				,     dim=-1   )
 UpperCAmelCase__       :	Union[str, Any]        =       F.normalize(__UpperCamelCase				,     dim=-1   )
 return (x - y).norm(dim=-1   ).div(2   ).arcsin().pow(2   ).mul(2   )
def 	lowerCAmelCase    (       __UpperCamelCase				,     __UpperCamelCase   ):
 '''simple docstring'''





 for param in model.parameters():
  UpperCAmelCase__       :	Any        =       value







class   __lowercase      (     __lowerCamelCase       ):

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






   '''simple docstring'''
   super().__init__()
   self.register_modules(
       vae=A       ,text_encoder=A       ,clip_model=A       ,tokenizer=A       ,unet=A       ,scheduler=A       ,feature_extractor=A       ,coca_model=A       ,coca_tokenizer=A       ,coca_transform=A       ,)
   UpperCAmelCase__       :	List[Any]        =       (
       feature_extractor.size
       if isinstance(feature_extractor.size       ,A     )
       else feature_extractor.size["""shortest_edge"""]
   )
   UpperCAmelCase__       :	str        =       transforms.Normalize(mean=feature_extractor.image_mean       ,std=feature_extractor.image_std     )
   set_requires_grad(self.text_encoder       ,A     )
   set_requires_grad(self.clip_model       ,A     )

  def  __lowercase							(	self		:      Optional[int]       ,A		:      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
    UpperCAmelCase__       :	int        =       self.unet.config.attention_head_dim // 2
   self.unet.set_attention_slice(A     )

  def  __lowercase							(	self		:      int     ):






   '''simple docstring'''
   self.enable_attention_slicing(A     )

  def  __lowercase							(	self		:      List[str]     ):






   '''simple docstring'''
   set_requires_grad(self.vae       ,A     )

  def  __lowercase							(	self		:      List[Any]     ):






   '''simple docstring'''
   set_requires_grad(self.vae       ,A     )

  def  __lowercase							(	self		:      List[str]     ):






   '''simple docstring'''
   set_requires_grad(self.unet       ,A     )

  def  __lowercase							(	self		:      Union[str, Any]     ):






   '''simple docstring'''
   set_requires_grad(self.unet       ,A     )

  def  __lowercase							(	self		:      Dict       ,A		:      str       ,A		:      List[Any]       ,A		:      int     ):






   '''simple docstring'''
   # get the original timestep using init_timestep
   UpperCAmelCase__       :	Any        =       min(int(num_inference_steps * strength     )       ,A     )

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

   return timesteps, num_inference_steps - t_start

  def  __lowercase							(	self		:      str       ,A		:      Optional[int]       ,A		:      Dict       ,A		:      int       ,A		:      Optional[int]       ,A		:      Optional[Any]       ,A		:      int=None     ):






   '''simple docstring'''
   if not isinstance(A       ,torch.Tensor     ):
    raise ValueError(f"`image` has to be of type `torch.Tensor` but is {type(A     )}"     )

   UpperCAmelCase__       :	int        =       image.to(device=A       ,dtype=A     )

   if isinstance(A       ,A     ):
    UpperCAmelCase__       :	List[Any]        =       [
        self.vae.encode(image[i : i + 1]     ).latent_dist.sample(generator[i]     ) for i in range(A     )
    ]
    UpperCAmelCase__       :	Union[str, Any]        =       torch.cat(A       ,dim=0     )
   else:
    UpperCAmelCase__       :	List[Any]        =       self.vae.encode(A     ).latent_dist.sample(A     )

   # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
   UpperCAmelCase__       :	Any        =       0.1_8_2_1_5 * init_latents
   UpperCAmelCase__       :	Tuple        =       init_latents.repeat_interleave(A       ,dim=0     )

   UpperCAmelCase__       :	Any        =       randn_tensor(init_latents.shape       ,generator=A       ,device=A       ,dtype=A     )

   # get latents
   UpperCAmelCase__       :	Optional[Any]        =       self.scheduler.add_noise(A       ,A       ,A     )
   UpperCAmelCase__       :	Union[str, Any]        =       init_latents

   return latents

  def  __lowercase							(	self		:      List[Any]       ,A		:      List[str]     ):






   '''simple docstring'''
   UpperCAmelCase__       :	Tuple        =       self.coca_transform(A     ).unsqueeze(0     )
   with torch.no_grad(), torch.cuda.amp.autocast():
    UpperCAmelCase__       :	Optional[Any]        =       self.coca_model.generate(transformed_image.to(device=self.device       ,dtype=self.coca_model.dtype     )     )
   UpperCAmelCase__       :	str        =       self.coca_tokenizer.decode(generated[0].cpu().numpy()     )
   return generated.split("""<end_of_text>"""     )[0].replace("""<start_of_text>"""       ,""""""     ).rstrip(""" .,"""     )

  def  __lowercase							(	self		:      str       ,A		:      List[str]       ,A		:      Any     ):






   '''simple docstring'''
   UpperCAmelCase__       :	List[str]        =       self.feature_extractor.preprocess(A     )
   UpperCAmelCase__       :	List[Any]        =       torch.from_numpy(clip_image_input["""pixel_values"""][0]     ).unsqueeze(0     ).to(self.device     ).half()
   UpperCAmelCase__       :	Optional[Any]        =       self.clip_model.get_image_features(A     )
   UpperCAmelCase__       :	Dict        =       image_embeddings_clip / image_embeddings_clip.norm(p=2       ,dim=-1       ,keepdim=A     )
   UpperCAmelCase__       :	Tuple        =       image_embeddings_clip.repeat_interleave(A       ,dim=0     )
   return image_embeddings_clip

  @torch.enable_grad()
  def  __lowercase							(	self		:      Any       ,A		:      List[Any]       ,A		:      List[Any]       ,A		:      int       ,A		:      int       ,A		:      int       ,A		:      List[str]       ,A		:      Optional[int]       ,):






   '''simple docstring'''
   UpperCAmelCase__       :	Tuple        =       latents.detach().requires_grad_()

   UpperCAmelCase__       :	Tuple        =       self.scheduler.scale_model_input(A       ,A     )

   # predict the noise residual
   UpperCAmelCase__       :	List[Any]        =       self.unet(A       ,A       ,encoder_hidden_states=A     ).sample

   if isinstance(self.scheduler       ,(PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)     ):
    UpperCAmelCase__       :	str        =       self.scheduler.alphas_cumprod[timestep]
    UpperCAmelCase__       :	Any        =       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
    UpperCAmelCase__       :	Dict        =       (latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

    UpperCAmelCase__       :	int        =       torch.sqrt(A     )
    UpperCAmelCase__       :	List[Any]        =       pred_original_sample * (fac) + latents * (1 - fac)
   elif isinstance(self.scheduler       ,A     ):
    UpperCAmelCase__       :	List[Any]        =       self.scheduler.sigmas[index]
    UpperCAmelCase__       :	Any        =       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
   UpperCAmelCase__       :	List[Any]        =       1 / 0.1_8_2_1_5 * sample
   UpperCAmelCase__       :	Union[str, Any]        =       self.vae.decode(A     ).sample
   UpperCAmelCase__       :	Optional[int]        =       (image / 2 + 0.5).clamp(0       ,1     )

   UpperCAmelCase__       :	Tuple        =       transforms.Resize(self.feature_extractor_size     )(A     )
   UpperCAmelCase__       :	List[Any]        =       self.normalize(A     ).to(latents.dtype     )

   UpperCAmelCase__       :	Union[str, Any]        =       self.clip_model.get_image_features(A     )
   UpperCAmelCase__       :	Optional[int]        =       image_embeddings_clip / image_embeddings_clip.norm(p=2       ,dim=-1       ,keepdim=A     )

   UpperCAmelCase__       :	Union[str, Any]        =       spherical_dist_loss(A       ,A     ).mean() * clip_guidance_scale

   UpperCAmelCase__       :	List[Any]        =       -torch.autograd.grad(A       ,A     )[0]

   if isinstance(self.scheduler       ,A     ):
    UpperCAmelCase__       :	List[str]        =       latents.detach() + grads * (sigma**2)
    UpperCAmelCase__       :	Optional[Any]        =       noise_pred_original
   else:
    UpperCAmelCase__       :	Tuple        =       noise_pred_original - torch.sqrt(A     ) * grads
   return noise_pred, latents




  @torch.no_grad()
  def __call__(	self		:      Dict       ,A		:      Union[torch.FloatTensor, PIL.Image.Image]       ,A		:      Union[torch.FloatTensor, PIL.Image.Image]       ,A		:      Optional[str] = None       ,A		:      Optional[str] = None       ,A		:      Optional[int] = 512       ,A		:      Optional[int] = 512       ,A		:      float = 0.6       ,A		:      Optional[int] = 50       ,A		:      Optional[float] = 7.5       ,A		:      Optional[int] = 1       ,A		:      float = 0.0       ,A		:      Optional[float] = 100       ,A		:      Optional[torch.Generator] = None       ,A		:      Optional[str] = "pil"       ,A		:      bool = True       ,A		:      float = 0.8       ,A		:      float = 0.1       ,A		:      float = 0.1       ,):






   '''simple docstring'''
   if isinstance(A       ,A     ) and len(A     ) != batch_size:
    raise ValueError(f"You have passed {batch_size} batch_size, but only {len(A     )} 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(A       ,torch.Generator     ) and batch_size > 1:
    UpperCAmelCase__       :	int        =       [generator] + [None] * (batch_size - 1)

   UpperCAmelCase__       :	Union[str, Any]        =       [
       ("""model""", self.coca_model is None),
       ("""tokenizer""", self.coca_tokenizer is None),
       ("""transform""", self.coca_transform is None),
   ]
   UpperCAmelCase__       :	str        =       [x[0] for x in coca_is_none if x[1]]
   UpperCAmelCase__       :	Optional[Any]        =       """, """.join(A     )
   # generate prompts with coca model if prompt is None
   if content_prompt is None:
    if len(A     ):
     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."     )
    UpperCAmelCase__       :	Union[str, Any]        =       self.get_image_description(A     )
   if style_prompt is None:
    if len(A     ):
     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."     )
    UpperCAmelCase__       :	Optional[Any]        =       self.get_image_description(A     )

   # get prompt text embeddings for content and style
   UpperCAmelCase__       :	Any        =       self.tokenizer(
       A       ,padding="""max_length"""       ,max_length=self.tokenizer.model_max_length       ,truncation=A       ,return_tensors="""pt"""       ,)
   UpperCAmelCase__       :	List[Any]        =       self.text_encoder(content_text_input.input_ids.to(self.device     )     )[0]

   UpperCAmelCase__       :	List[str]        =       self.tokenizer(
       A       ,padding="""max_length"""       ,max_length=self.tokenizer.model_max_length       ,truncation=A       ,return_tensors="""pt"""       ,)
   UpperCAmelCase__       :	List[str]        =       self.text_encoder(style_text_input.input_ids.to(self.device     )     )[0]

   UpperCAmelCase__       :	Tuple        =       slerp(A       ,A       ,A     )

   # duplicate text embeddings for each generation per prompt
   UpperCAmelCase__       :	Any        =       text_embeddings.repeat_interleave(A       ,dim=0     )

   # set timesteps
   UpperCAmelCase__       :	List[Any]        =       """offset""" in set(inspect.signature(self.scheduler.set_timesteps     ).parameters.keys()     )
   UpperCAmelCase__       :	Any        =       {}
   if accepts_offset:
    UpperCAmelCase__       :	List[Any]        =       1

   self.scheduler.set_timesteps(A       ,**A     )
   # 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     )

   UpperCAmelCase__ ,   UpperCAmelCase__       :	Tuple        =       self.get_timesteps(A       ,A       ,self.device     )
   UpperCAmelCase__       :	List[str]        =       timesteps[:1].repeat(A     )

   # Preprocess image
   UpperCAmelCase__       :	Tuple        =       preprocess(A       ,A       ,A     )
   UpperCAmelCase__       :	str        =       self.prepare_latents(
       A       ,A       ,A       ,text_embeddings.dtype       ,self.device       ,A     )

   UpperCAmelCase__       :	Tuple        =       preprocess(A       ,A       ,A     )
   UpperCAmelCase__       :	Dict        =       self.prepare_latents(
       A       ,A       ,A       ,text_embeddings.dtype       ,self.device       ,A     )

   UpperCAmelCase__       :	int        =       slerp(A       ,A       ,A     )

   if clip_guidance_scale > 0:
    UpperCAmelCase__       :	List[Any]        =       self.get_clip_image_embeddings(A       ,A     )
    UpperCAmelCase__       :	Any        =       self.get_clip_image_embeddings(A       ,A     )
    UpperCAmelCase__       :	Optional[Any]        =       slerp(
        A       ,A       ,A     )

   # 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.
   UpperCAmelCase__       :	Dict        =       guidance_scale > 1.0
   # get unconditional embeddings for classifier free guidance
   if do_classifier_free_guidance:
    UpperCAmelCase__       :	Dict        =       content_text_input.input_ids.shape[-1]
    UpperCAmelCase__       :	List[Any]        =       self.tokenizer([""""""]       ,padding="""max_length"""       ,max_length=A       ,return_tensors="""pt"""     )
    UpperCAmelCase__       :	Union[str, Any]        =       self.text_encoder(uncond_input.input_ids.to(self.device     )     )[0]
    # duplicate unconditional embeddings for each generation per prompt
    UpperCAmelCase__       :	Optional[int]        =       uncond_embeddings.repeat_interleave(A       ,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
    UpperCAmelCase__       :	Optional[int]        =       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`.
   UpperCAmelCase__       :	Dict        =       (batch_size, self.unet.config.in_channels, height // 8, width // 8)
   UpperCAmelCase__       :	List[Any]        =       text_embeddings.dtype
   if latents is None:
    if self.device.type == "mps":
     # randn does not work reproducibly on mps
     UpperCAmelCase__       :	Union[str, Any]        =       torch.randn(A       ,generator=A       ,device="""cpu"""       ,dtype=A     ).to(
         self.device     )
    else:
     UpperCAmelCase__       :	Optional[int]        =       torch.randn(A       ,generator=A       ,device=self.device       ,dtype=A     )
   else:
    if latents.shape != latents_shape:
     raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {latents_shape}"     )
    UpperCAmelCase__       :	List[Any]        =       latents.to(self.device     )

   # scale the initial noise by the standard deviation required by the scheduler
   UpperCAmelCase__       :	Optional[int]        =       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]
   UpperCAmelCase__       :	Union[str, Any]        =       """eta""" in set(inspect.signature(self.scheduler.step     ).parameters.keys()     )
   UpperCAmelCase__       :	Optional[int]        =       {}
   if accepts_eta:
    UpperCAmelCase__       :	Union[str, Any]        =       eta

   # check if the scheduler accepts generator
   UpperCAmelCase__       :	str        =       """generator""" in set(inspect.signature(self.scheduler.step     ).parameters.keys()     )
   if accepts_generator:
    UpperCAmelCase__       :	Optional[int]        =       generator

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

     # predict the noise residual
     UpperCAmelCase__       :	Tuple        =       self.unet(A       ,A       ,encoder_hidden_states=A     ).sample

     # perform classifier free guidance
     if do_classifier_free_guidance:
      UpperCAmelCase__ ,   UpperCAmelCase__       :	Union[str, Any]        =       noise_pred.chunk(2     )
      UpperCAmelCase__       :	List[str]        =       noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

     # perform clip guidance
     if clip_guidance_scale > 0:
      UpperCAmelCase__       :	Optional[int]        =       (
          text_embeddings.chunk(2     )[1] if do_classifier_free_guidance else text_embeddings
      )
      UpperCAmelCase__ ,   UpperCAmelCase__       :	Optional[Any]        =       self.cond_fn(
          A       ,A       ,A       ,A       ,A       ,A       ,A       ,)

     # compute the previous noisy sample x_t -> x_t-1
     UpperCAmelCase__       :	List[Any]        =       self.scheduler.step(A       ,A       ,A       ,**A     ).prev_sample

        # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
   UpperCAmelCase__       :	Any        =       1 / 0.1_8_2_1_5 * latents
   UpperCAmelCase__       :	int        =       self.vae.decode(A     ).sample

   UpperCAmelCase__       :	Any        =       (image / 2 + 0.5).clamp(0       ,1     )
   UpperCAmelCase__       :	Optional[Any]        =       image.cpu().permute(0       ,2       ,3       ,1     ).numpy()

   if output_type == "pil":
    UpperCAmelCase__       :	int        =       self.numpy_to_pil(A     )

   if not return_dict:
    return (image, None)

   return StableDiffusionPipelineOutput(images=A       ,nsfw_content_detected=A     )







from ...configuration_utils import PretrainedConfig
from ...utils import logging


__magic_name__  							=	logging.get_logger(__name__)

__magic_name__  							=	{
    '''microsoft/biogpt''': '''https://huggingface.co/microsoft/biogpt/resolve/main/config.json''',
    # See all BioGPT models at https://huggingface.co/models?filter=biogpt
}




class        _SCREAMING_SNAKE_CASE	(   __UpperCamelCase			):
      _A    :		Dict     						=  'biogpt'
      def __init__(      self		,						lowerCamelCase=4_23_84		,						lowerCamelCase=10_24		,						lowerCamelCase=24		,						lowerCamelCase=16		,						lowerCamelCase=40_96		,						lowerCamelCase="gelu"		,						lowerCamelCase=0.1		,						lowerCamelCase=0.1		,						lowerCamelCase=10_24		,						lowerCamelCase=0.0_2		,						lowerCamelCase=1e-12		,						lowerCamelCase=True		,						lowerCamelCase=True		,						lowerCamelCase=0.0		,						lowerCamelCase=0.0		,						lowerCamelCase=1		,						lowerCamelCase=0		,						lowerCamelCase=2		,						**lowerCamelCase		,						):
          snake_case__    =					vocab_size
          snake_case__    =					max_position_embeddings
          snake_case__    =					hidden_size
          snake_case__    =					num_hidden_layers
          snake_case__    =					num_attention_heads
          snake_case__    =					intermediate_size
          snake_case__    =					hidden_act
          snake_case__    =					hidden_dropout_prob
          snake_case__    =					attention_probs_dropout_prob
          snake_case__    =					initializer_range
          snake_case__    =					layer_norm_eps
          snake_case__    =					scale_embedding
          snake_case__    =					use_cache
          snake_case__    =					layerdrop
          snake_case__    =					activation_dropout
          super().__init__(pad_token_id=lowerCamelCase		,						bos_token_id=lowerCamelCase		,						eos_token_id=lowerCamelCase		,						**lowerCamelCase			)



