






"""simple docstring"""







import inspect
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch
import torch.utils.checkpoint

from ...models import UNetaDModel, VQModel
from ...schedulers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






def       _lowerCAmelCase       (      lowerCAmelCase		):







    '''simple docstring'''
    UpperCAmelCase    ,       UpperCAmelCase	    =			image.size
    UpperCAmelCase    ,       UpperCAmelCase	    =			(x - x % 32 for x in (w, h))  # resize to integer multiple of 32
    UpperCAmelCase	    =			image.resize((w, h)	,      resample=PIL_INTERPOLATION["""lanczos"""]		)
    UpperCAmelCase	    =			np.array(lowerCAmelCase		).astype(np.floataa		) / 255.0
    UpperCAmelCase	    =			image[None].transpose(0	,      3	,      1	,      2		)
    UpperCAmelCase	    =			torch.from_numpy(lowerCAmelCase		)
    return 2.0 * image - 1.0





class   UpperCamelCase_ (       a_  ):



  def __init__(      self ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		)			->			Dict:



      """simple docstring"""





      super().__init__()
      self.register_modules(vqvae=snake_case__ ,		unet=snake_case__ ,		scheduler=snake_case__				)



  @torch.no_grad()
  def __call__(      self ,		snake_case__ = None ,		snake_case__ = 1 ,		snake_case__ = 1_00 ,		snake_case__ = 0.0 ,		snake_case__ = None ,		snake_case__ = "pil" ,		snake_case__ = True ,		)			->			Union[Tuple, ImagePipelineOutput]:



      """simple docstring"""





      if isinstance(snake_case__ ,		PIL.Image.Image				):
          UpperCAmelCase	    =			1
      elif isinstance(snake_case__ ,		torch.Tensor				):
          UpperCAmelCase	    =			image.shape[0]
      else:
          raise ValueError(f'''`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(snake_case__				)}'''				)

      if isinstance(snake_case__ ,		PIL.Image.Image				):
          UpperCAmelCase	    =			preprocess(snake_case__				)

      UpperCAmelCase    ,       UpperCAmelCase	    =			image.shape[-2:]

      # in_channels should be 6: 3 for latents, 3 for low resolution image
      UpperCAmelCase	    =			(batch_size, self.unet.config.in_channels // 2, height, width)
      UpperCAmelCase	    =			next(self.unet.parameters()				).dtype

      UpperCAmelCase	    =			randn_tensor(snake_case__ ,		generator=snake_case__ ,		device=self.device ,		dtype=snake_case__				)

      UpperCAmelCase	    =			image.to(device=self.device ,		dtype=snake_case__				)

      # set timesteps and move to the correct device
      self.scheduler.set_timesteps(snake_case__ ,		device=self.device				)
      UpperCAmelCase	    =			self.scheduler.timesteps

      # scale the initial noise by the standard deviation required by the scheduler
      UpperCAmelCase	    =			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	    =			"""eta""" in set(inspect.signature(self.scheduler.step				).parameters.keys()				)
      UpperCAmelCase	    =			{}
      if accepts_eta:
          UpperCAmelCase	    =			eta

      for t in self.progress_bar(snake_case__				):
          # concat latents and low resolution image in the channel dimension.
          UpperCAmelCase	    =			torch.cat([latents, image] ,		dim=1				)
          UpperCAmelCase	    =			self.scheduler.scale_model_input(snake_case__ ,		snake_case__				)
          # predict the noise residual
          UpperCAmelCase	    =			self.unet(snake_case__ ,		snake_case__				).sample
          # compute the previous noisy sample x_t -> x_t-1
          UpperCAmelCase	    =			self.scheduler.step(snake_case__ ,		snake_case__ ,		snake_case__ ,		**snake_case__				).prev_sample

      # decode the image latents with the VQVAE
      UpperCAmelCase	    =			self.vqvae.decode(snake_case__				).sample
      UpperCAmelCase	    =			torch.clamp(snake_case__ ,		-1.0 ,		1.0				)
      UpperCAmelCase	    =			image / 2 + 0.5
      UpperCAmelCase	    =			image.cpu().permute(0 ,		2 ,		3 ,		1				).numpy()

      if output_type == "pil":
          UpperCAmelCase	    =			self.numpy_to_pil(snake_case__				)

      if not return_dict:
          return (image,)

      return ImagePipelineOutput(images=snake_case__				)








"""simple docstring"""







from collections import OrderedDict
from typing import Mapping

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


lowerCAmelCase_	:    int            =      logging.get_logger(__name__)

lowerCAmelCase_	:    List[Any]            =      {
    '''facebook/xlm-roberta-xl''': '''https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json''',
    '''facebook/xlm-roberta-xxl''': '''https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json''',
    # See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}





class   UpperCamelCase_ (       a_  ):
  _A       :					int				=	'xlm-roberta-xl'



  def __init__(      self ,		snake_case__=25_08_80 ,		snake_case__=25_60 ,		snake_case__=36 ,		snake_case__=32 ,		snake_case__=1_02_40 ,		snake_case__="gelu" ,		snake_case__=0.1 ,		snake_case__=0.1 ,		snake_case__=5_14 ,		snake_case__=1 ,		snake_case__=0.02 ,		snake_case__=1e-05 ,		snake_case__=1 ,		snake_case__=0 ,		snake_case__=2 ,		snake_case__="absolute" ,		snake_case__=True ,		snake_case__=None ,		**snake_case__ ,		)			->			Union[str, Any]:



         """simple docstring"""





         super().__init__(pad_token_id=snake_case__ ,		bos_token_id=snake_case__ ,		eos_token_id=snake_case__ ,		**snake_case__				)
         UpperCAmelCase	    =			vocab_size
         UpperCAmelCase	    =			hidden_size
         UpperCAmelCase	    =			num_hidden_layers
         UpperCAmelCase	    =			num_attention_heads
         UpperCAmelCase	    =			hidden_act
         UpperCAmelCase	    =			intermediate_size
         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	    =			position_embedding_type
         UpperCAmelCase	    =			use_cache
         UpperCAmelCase	    =			classifier_dropout





class   UpperCamelCase_ (       a_  ):



  @property
  def 		UpperCamelCase_				(      self				)			->			Mapping[str, Mapping[int, str]]:



         """simple docstring"""





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