


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


_lowerCAmelCase:						Tuple   				=       logging.get_logger(__name__)  # pylint: disable=invalid-name






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

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

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






def   _lowercase(					__a				:						int     ):






							if isinstance(UpperCamelCase__    ,     torch.Tensor     ):
														return mask
							elif isinstance(UpperCamelCase__    ,     PIL.Image.Image     ):
														a__      =[mask]

							if isinstance(mask[0]    ,     PIL.Image.Image     ):
														a__    ,     a__      =mask[0].size
														a__    ,     a__      =(x - x % 32 for x in (w, h))  # resize to integer multiple of 32
														a__      =[np.array(m.convert('L'     ).resize((w, h)    ,     resample=PIL_INTERPOLATION['nearest']     )     )[None, :] for m in mask]
														a__      =np.concatenate(UpperCamelCase__    ,     axis=0     )
														a__      =mask.astype(np.floataa     ) / 255.0
														a__      =0
														a__      =1
														a__      =torch.from_numpy(UpperCamelCase__     )
							elif isinstance(mask[0]    ,     torch.Tensor     ):
														a__      =torch.cat(UpperCamelCase__    ,     dim=0     )
							return mask







class       lowercase_					(lowercase_   ):
				snake_case	    =42
				snake_case	    =42





				def __init__(				self       ,  lowercase_       ,  lowercase_)  -> int:
											super().__init__()
											self.register_modules(unet=lowercase_       ,  scheduler=lowercase_)





				@torch.no_grad()
				def __call__(				self       ,  lowercase_       ,  lowercase_       ,  lowercase_ = 250       ,  lowercase_ = 0.0       ,  lowercase_ = 10       ,  lowercase_ = 10       ,  lowercase_ = None       ,  lowercase_ = "pil"       ,  lowercase_ = True       ,  )  -> Dict:
											a__      =image

											a__      =_preprocess_image(lowercase_)
											a__      =original_image.to(device=self.device       ,  dtype=self.unet.dtype)
											a__      =_preprocess_mask(lowercase_)
											a__      =mask_image.to(device=self.device       ,  dtype=self.unet.dtype)

											a__      =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.""")

											a__      =original_image.shape
											a__      =randn_tensor(lowercase_       ,  generator=lowercase_       ,  device=self.device       ,  dtype=self.unet.dtype)

											# set step values
											self.scheduler.set_timesteps(lowercase_       ,  lowercase_       ,  lowercase_       ,  self.device)
											a__      =eta

											a__      =self.scheduler.timesteps[0] + 1
											a__      =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
																									a__      =self.unet(lowercase_       ,  lowercase_).sample
																									# compute previous image: x_t -> x_t-1
																									a__      =self.scheduler.step(lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_).prev_sample

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

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

											if not return_dict:
																		return (image,)

											return ImagePipelineOutput(images=lowercase_)




import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import ClassLabel, Features, Value
from .base import TaskTemplate







@dataclass(frozen=lowercase_     )
class 					lowercase							(			lowercase_     ):
				# `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
				__SCREAMING_SNAKE_CASE   :					str			     =     field(default='''text-classification'''      ,    metadata={'''include_in_asdict_even_if_is_default''': True}     )
				__SCREAMING_SNAKE_CASE   :					ClassVar[Features]			     =     Features({'''text''': Value('''string'''     )}     )
				__SCREAMING_SNAKE_CASE   :					ClassVar[Features]			     =     Features({'''labels''': ClassLabel}     )
				__SCREAMING_SNAKE_CASE   :					str			     =     "text"
				__SCREAMING_SNAKE_CASE   :					str			     =     "labels"




				def        a				(   self ,  snake_case			):
											if self.label_column not in features:
																		raise ValueError(F'''Column {self.label_column} is not present in features.'''			)
											if not isinstance(features[self.label_column] ,  snake_case			):
																		raise ValueError(F'''Column {self.label_column} is not a ClassLabel.'''			)
											snake_case_     							=       copy.deepcopy(self			)
											snake_case_     							=       self.label_schema.copy()
											snake_case_     							=       features[self.label_column]
											snake_case_     							=       label_schema
											return task_template




				@property
				def        a				(   self			):
											return {
											    self.text_column: "text",
											    self.label_column: "labels",
											}




