


from collections import OrderedDict
from typing import Mapping

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


SCREAMING_SNAKE_CASE							:		int       =					logging.get_logger(__name__)

SCREAMING_SNAKE_CASE							:		Dict       =					{
    '''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       (		lowercase__  ):


   '''simple docstring'''







   lowercase       :						Any          ="""xlm-roberta-xl"""
   def __init__(  self ,				UpperCamelCase_=25_0880 ,				UpperCamelCase_=2560 ,				UpperCamelCase_=36 ,				UpperCamelCase_=32 ,				UpperCamelCase_=1_0240 ,				UpperCamelCase_="gelu" ,				UpperCamelCase_=0.1 ,				UpperCamelCase_=0.1 ,				UpperCamelCase_=514 ,				UpperCamelCase_=1 ,				UpperCamelCase_=0.02 ,				UpperCamelCase_=1E-05 ,				UpperCamelCase_=1 ,				UpperCamelCase_=0 ,				UpperCamelCase_=2 ,				UpperCamelCase_="absolute" ,				UpperCamelCase_=True ,				UpperCamelCase_=None ,				**UpperCamelCase_ ,				):
        super().__init__(pad_token_id=lowercase__ ,				bos_token_id=lowercase__ ,				eos_token_id=lowercase__ ,				**lowercase__		)
        lowercase_							:Dict			     =							vocab_size
        lowercase_							:List[str]			     =							hidden_size
        lowercase_							:str			     =							num_hidden_layers
        lowercase_							:Optional[int]			     =							num_attention_heads
        lowercase_							:List[str]			     =							hidden_act
        lowercase_							:Tuple			     =							intermediate_size
        lowercase_							:Optional[Any]			     =							hidden_dropout_prob
        lowercase_							:Optional[int]			     =							attention_probs_dropout_prob
        lowercase_							:List[str]			     =							max_position_embeddings
        lowercase_							:Union[str, Any]			     =							type_vocab_size
        lowercase_							:Optional[Any]			     =							initializer_range
        lowercase_							:Dict			     =							layer_norm_eps
        lowercase_							:str			     =							position_embedding_type
        lowercase_							:Tuple			     =							use_cache
        lowercase_							:Any			     =							classifier_dropout






class   UpperCamelCase       (		lowercase__  ):


   '''simple docstring'''







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


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

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


_lowercase:	List[str]       			=		logging.get_logger(__name__)  # pylint: disable=invalid-name






def _lowerCamelCase  (		snake_case  ):
							warnings.warn(
							    'The preprocess method is deprecated and will be removed in a future version. Please'
							    ' use VaeImageProcessor.preprocess instead'     , snake_case     , )
							if isinstance(snake_case     , torch.Tensor  ):
														return image
							elif isinstance(snake_case     , PIL.Image.Image  ):
														_lowerCAmelCase							=							[image]

							if isinstance(image[0]     , PIL.Image.Image  ):
														_lowerCAmelCase			,		_lowerCAmelCase							=							image[0].size
														_lowerCAmelCase			,		_lowerCAmelCase							=							(x - x % 8 for x in (w, h))  # resize to integer multiple of 8

														_lowerCAmelCase							=							[np.array(i.resize((w, h)     , resample=PIL_INTERPOLATION['lanczos']  )  )[None, :] for i in image]
														_lowerCAmelCase							=							np.concatenate(snake_case     , axis=0  )
														_lowerCAmelCase							=							np.array(snake_case  ).astype(np.floataa  ) / 2_55.0
														_lowerCAmelCase							=							image.transpose(0     , 3     , 1     , 2  )
														_lowerCAmelCase							=							2.0 * image - 1.0
														_lowerCAmelCase							=							torch.from_numpy(snake_case  )
							elif isinstance(image[0]     , torch.Tensor  ):
														_lowerCAmelCase							=							torch.cat(snake_case     , dim=0  )
							return image






def _lowerCamelCase  (		snake_case  ):
							if isinstance(snake_case     , torch.Tensor  ):
														return mask
							elif isinstance(snake_case     , PIL.Image.Image  ):
														_lowerCAmelCase							=							[mask]

							if isinstance(mask[0]     , PIL.Image.Image  ):
														_lowerCAmelCase			,		_lowerCAmelCase							=							mask[0].size
														_lowerCAmelCase			,		_lowerCAmelCase							=							(x - x % 32 for x in (w, h))  # resize to integer multiple of 32
														_lowerCAmelCase							=							[np.array(m.convert('L'  ).resize((w, h)     , resample=PIL_INTERPOLATION['nearest']  )  )[None, :] for m in mask]
														_lowerCAmelCase							=							np.concatenate(snake_case     , axis=0  )
														_lowerCAmelCase							=							mask.astype(np.floataa  ) / 2_55.0
														_lowerCAmelCase							=							0
														_lowerCAmelCase							=							1
														_lowerCAmelCase							=							torch.from_numpy(snake_case  )
							elif isinstance(mask[0]     , torch.Tensor  ):
														_lowerCAmelCase							=							torch.cat(snake_case     , dim=0  )
							return mask



class        lowerCamelCase__				(   UpperCAmelCase				):
			UpperCamelCase__    							=42
			UpperCamelCase__    							=42





			def __init__(       self	:	int  ,  lowercase__	:	List[Any]  ,  lowercase__	:	Optional[Any]						):
										super().__init__()
										self.register_modules(unet=lowercase__  ,  scheduler=lowercase__						)





			@torch.no_grad()
			def __call__(       self	:	Any  ,  lowercase__	:	Union[torch.Tensor, PIL.Image.Image]  ,  lowercase__	:	Union[torch.Tensor, PIL.Image.Image]  ,  lowercase__	:	int = 2_50  ,  lowercase__	:	float = 0.0  ,  lowercase__	:	int = 10  ,  lowercase__	:	int = 10  ,  lowercase__	:	Optional[Union[torch.Generator, List[torch.Generator]]] = None  ,  lowercase__	:	Optional[str] = "pil"  ,  lowercase__	:	bool = True  ,  ):
										_lowerCAmelCase							=							image

										_lowerCAmelCase							=							_preprocess_image(lowercase__						)
										_lowerCAmelCase							=							original_image.to(device=self.device  ,  dtype=self.unet.dtype						)
										_lowerCAmelCase							=							_preprocess_mask(lowercase__						)
										_lowerCAmelCase							=							mask_image.to(device=self.device  ,  dtype=self.unet.dtype						)

										_lowerCAmelCase							=							original_image.shape[0]

										# sample gaussian noise to begin the loop
										if isinstance(lowercase__  ,  lowercase__						) and len(lowercase__						) != batch_size:
																	raise ValueError(
																	    f'You have passed a list of generators of length {len(lowercase__						)}, but requested an effective batch'
																	    f' size of {batch_size}. Make sure the batch size matches the length of the generators.'						)

										_lowerCAmelCase							=							original_image.shape
										_lowerCAmelCase							=							randn_tensor(lowercase__  ,  generator=lowercase__  ,  device=self.device  ,  dtype=self.unet.dtype						)

										# set step values
										self.scheduler.set_timesteps(lowercase__  ,  lowercase__  ,  lowercase__  ,  self.device						)
										_lowerCAmelCase							=							eta

										_lowerCAmelCase							=							self.scheduler.timesteps[0] + 1
										_lowerCAmelCase							=							generator[0] if isinstance(lowercase__  ,  lowercase__						) else generator
										for i, t in enumerate(self.progress_bar(self.scheduler.timesteps						)						):
																	if t < t_last:
																								# predict the noise residual
																								_lowerCAmelCase							=							self.unet(lowercase__  ,  lowercase__						).sample
																								# compute previous image: x_t -> x_t-1
																								_lowerCAmelCase							=							self.scheduler.step(lowercase__  ,  lowercase__  ,  lowercase__  ,  lowercase__  ,  lowercase__  ,  lowercase__						).prev_sample

																	else:
																								# compute the reverse: x_t-1 -> x_t
																								_lowerCAmelCase							=							self.scheduler.undo_step(lowercase__  ,  lowercase__  ,  lowercase__						)
																	_lowerCAmelCase							=							t

										_lowerCAmelCase							=							(image / 2 + 0.5).clamp(0  ,  1						)
										_lowerCAmelCase							=							image.cpu().permute(0  ,  2  ,  3  ,  1						).numpy()
										if output_type == "pil":
																	_lowerCAmelCase							=							self.numpy_to_pil(lowercase__						)

										if not return_dict:
																	return (image,)

										return ImagePipelineOutput(images=lowercase__						)






