

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 TYPE_CHECKING, Any, Mapping, Optional

from packaging import version

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


if TYPE_CHECKING:
						from ...processing_utils import ProcessorMixin
						from ...utils import TensorType


A__						=							logging.get_logger(__name__)

A__						=							{
    "microsoft/layoutlmv3-base": "https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json",
}

class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE			):
	_lowercase  :   Optional[Any]       				=  "layoutlmv3"







	def __init__(   self: Tuple  , __UpperCamelCase: List[Any]=5_02_65  , __UpperCamelCase: Union[str, Any]=7_68  , __UpperCamelCase: Optional[int]=12  , __UpperCamelCase: List[str]=12  , __UpperCamelCase: List[Any]=30_72  , __UpperCamelCase: Union[str, Any]="gelu"  , __UpperCamelCase: Union[str, Any]=0.1  , __UpperCamelCase: Union[str, Any]=0.1  , __UpperCamelCase: List[Any]=5_12  , __UpperCamelCase: int=2  , __UpperCamelCase: Union[str, Any]=0.02  , __UpperCamelCase: Dict=1E-5  , __UpperCamelCase: int=1  , __UpperCamelCase: Dict=0  , __UpperCamelCase: Tuple=2  , __UpperCamelCase: Any=10_24  , __UpperCamelCase: Union[str, Any]=1_28  , __UpperCamelCase: Dict=1_28  , __UpperCamelCase: Any=True  , __UpperCamelCase: Any=32  , __UpperCamelCase: int=1_28  , __UpperCamelCase: List[str]=64  , __UpperCamelCase: Union[str, Any]=2_56  , __UpperCamelCase: int=True  , __UpperCamelCase: Optional[int]=True  , __UpperCamelCase: Union[str, Any]=True  , __UpperCamelCase: Union[str, Any]=2_24  , __UpperCamelCase: Tuple=3  , __UpperCamelCase: Any=16  , __UpperCamelCase: List[Any]=None  , **__UpperCamelCase: str  , ):

						'''simple docstring'''





						super().__init__(
						    vocab_size=__UpperCamelCase  , hidden_size=__UpperCamelCase  , num_hidden_layers=__UpperCamelCase  , num_attention_heads=__UpperCamelCase  , intermediate_size=__UpperCamelCase  , hidden_act=__UpperCamelCase  , hidden_dropout_prob=__UpperCamelCase  , attention_probs_dropout_prob=__UpperCamelCase  , max_position_embeddings=__UpperCamelCase  , type_vocab_size=__UpperCamelCase  , initializer_range=__UpperCamelCase  , layer_norm_eps=__UpperCamelCase  , pad_token_id=__UpperCamelCase  , bos_token_id=__UpperCamelCase  , eos_token_id=__UpperCamelCase  , **__UpperCamelCase  , )
						__magic_name__							=    max_ad_position_embeddings
						__magic_name__							=    coordinate_size
						__magic_name__							=    shape_size
						__magic_name__							=    has_relative_attention_bias
						__magic_name__							=    rel_pos_bins
						__magic_name__							=    max_rel_pos
						__magic_name__							=    has_spatial_attention_bias
						__magic_name__							=    rel_ad_pos_bins
						__magic_name__							=    max_rel_ad_pos
						__magic_name__							=    text_embed
						__magic_name__							=    visual_embed
						__magic_name__							=    input_size
						__magic_name__							=    num_channels
						__magic_name__							=    patch_size
						__magic_name__							=    classifier_dropout




class 				__UpperCamelCase				(					SCREAMING_SNAKE_CASE			):
	_lowercase  :   Any       				=  version.parse("1.12"			)







	@property
	def 	_SCREAMING_SNAKE_CASE							(   self: Any      ):

						'''simple docstring'''





						if self.task in ["question-answering", "sequence-classification"]:
											return OrderedDict(
											    [
											        ('input_ids', {0: 'batch', 1: 'sequence'}),
											        ('attention_mask', {0: 'batch', 1: 'sequence'}),
											        ('bbox', {0: 'batch', 1: 'sequence'}),
											        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
											    ]      )
						else:
											return OrderedDict(
											    [
											        ('input_ids', {0: 'batch', 1: 'sequence'}),
											        ('bbox', {0: 'batch', 1: 'sequence'}),
											        ('attention_mask', {0: 'batch', 1: 'sequence'}),
											        ('pixel_values', {0: 'batch', 1: 'num_channels'}),
											    ]      )







	@property
	def 	_SCREAMING_SNAKE_CASE							(   self: int      ):

						'''simple docstring'''





						return 1E-5







	@property
	def 	_SCREAMING_SNAKE_CASE							(   self: Optional[int]      ):

						'''simple docstring'''





						return 12







	def 	_SCREAMING_SNAKE_CASE							(   self: Tuple  , __UpperCamelCase: "ProcessorMixin"  , __UpperCamelCase: int = -1  , __UpperCamelCase: int = -1  , __UpperCamelCase: bool = False  , __UpperCamelCase: Optional["TensorType"] = None  , __UpperCamelCase: int = 3  , __UpperCamelCase: int = 40  , __UpperCamelCase: int = 40  , ):

						'''simple docstring'''





						setattr(processor.image_processor  , 'apply_ocr'  , __UpperCamelCase      )

						# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
						__magic_name__							=    compute_effective_axis_dimension(
						    __UpperCamelCase  , fixed_dimension=OnnxConfig.default_fixed_batch  , num_token_to_add=0      )
						# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
						__magic_name__							=    processor.tokenizer.num_special_tokens_to_add(__UpperCamelCase      )
						__magic_name__							=    compute_effective_axis_dimension(
						    __UpperCamelCase  , fixed_dimension=OnnxConfig.default_fixed_sequence  , num_token_to_add=__UpperCamelCase      )
						# Generate dummy inputs according to compute batch and sequence
						__magic_name__							=    [[' '.join([processor.tokenizer.unk_token]      ) * seq_length]] * batch_size

						# Generate dummy bounding boxes
						__magic_name__							=    [[[48, 84, 73, 1_28]]] * batch_size

						# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
						# batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
						__magic_name__							=    self._generate_dummy_images(__UpperCamelCase  , __UpperCamelCase  , __UpperCamelCase  , __UpperCamelCase      )

						__magic_name__							=    dict(
						    processor(
						        __UpperCamelCase  , text=__UpperCamelCase  , boxes=__UpperCamelCase  , return_tensors=__UpperCamelCase  , )      )

						return inputs




