






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


A__	:			Dict			   = logging.get_logger(__name__)  # pylint: disable=invalid-name






def   a			(						lowerCamelCase_				):






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

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

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






def   a			(						lowerCamelCase_				):






				'''simple docstring'''
				if isinstance(lowerCamelCase_		,   torch.Tensor				):
								return mask
				elif isinstance(lowerCamelCase_		,   PIL.Image.Image				):
								lowercase__    =			[mask]

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




class       _UpperCAmelCase					(       A__	):

				"""simple docstring"""




				lowercase__											=       42
				lowercase__											=       42




				def __init__(     self						:    int,  lowerCamelCase						:    List[Any],  lowerCamelCase						:    int  ):

								'''simple docstring'''
								super().__init__()
								self.register_modules(unet=lowerCamelCase,  scheduler=lowerCamelCase  )




				@torch.no_grad()
				def __call__(     self						:    Tuple,  lowerCamelCase						:    Union[torch.Tensor, PIL.Image.Image],  lowerCamelCase						:    Union[torch.Tensor, PIL.Image.Image],  lowerCamelCase						:    int = 250,  lowerCamelCase						:    float = 0.0,  lowerCamelCase						:    int = 10,  lowerCamelCase						:    int = 10,  lowerCamelCase						:    Optional[Union[torch.Generator, List[torch.Generator]]] = None,  lowerCamelCase						:    Optional[str] = "pil",  lowerCamelCase						:    bool = True,  ):

								'''simple docstring'''
								lowercase__    =			image

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

								lowercase__    =			original_image.shape[0]

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

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

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

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

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

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

								if not return_dict:
												return (image,)

								return ImagePipelineOutput(images=lowerCamelCase  )












from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class       _UpperCAmelCase					(       A__	):

				"""simple docstring"""




				lowercase__											=       ["""image_processor""", """tokenizer"""]
				lowercase__											=       """BlipImageProcessor"""
				lowercase__											=       ("""BertTokenizer""", """BertTokenizerFast""")




				def __init__(     self						:    Dict,  lowerCamelCase						:    Dict,  lowerCamelCase						:    str  ):

								'''simple docstring'''
								lowercase__    =			False
								super().__init__(lowerCamelCase,  lowerCamelCase  )
								lowercase__    =			self.image_processor




				def __call__(     self						:    int,  lowerCamelCase						:    ImageInput = None,  lowerCamelCase						:    Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,  lowerCamelCase						:    bool = True,  lowerCamelCase						:    Union[bool, str, PaddingStrategy] = False,  lowerCamelCase						:    Union[bool, str, TruncationStrategy] = None,  lowerCamelCase						:    Optional[int] = None,  lowerCamelCase						:    int = 0,  lowerCamelCase						:    Optional[int] = None,  lowerCamelCase						:    Optional[bool] = None,  lowerCamelCase						:    bool = False,  lowerCamelCase						:    bool = False,  lowerCamelCase						:    bool = False,  lowerCamelCase						:    bool = False,  lowerCamelCase						:    bool = False,  lowerCamelCase						:    bool = True,  lowerCamelCase						:    Optional[Union[str, TensorType]] = None,  **lowerCamelCase						:    Any,  ):

								'''simple docstring'''
								if images is None and text is None:
												raise ValueError('''You have to specify either images or text.'''  )

								# Get only text
								if images is None:
												lowercase__    =			self.tokenizer
												lowercase__    =			self.tokenizer(
												    text=lowerCamelCase,  add_special_tokens=lowerCamelCase,  padding=lowerCamelCase,  truncation=lowerCamelCase,  max_length=lowerCamelCase,  stride=lowerCamelCase,  pad_to_multiple_of=lowerCamelCase,  return_attention_mask=lowerCamelCase,  return_overflowing_tokens=lowerCamelCase,  return_special_tokens_mask=lowerCamelCase,  return_offsets_mapping=lowerCamelCase,  return_token_type_ids=lowerCamelCase,  return_length=lowerCamelCase,  verbose=lowerCamelCase,  return_tensors=lowerCamelCase,  **lowerCamelCase,  )
												return text_encoding

								# add pixel_values
								lowercase__    =			self.image_processor(lowerCamelCase,  return_tensors=lowerCamelCase  )

								if text is not None:
												lowercase__    =			self.tokenizer(
												    text=lowerCamelCase,  add_special_tokens=lowerCamelCase,  padding=lowerCamelCase,  truncation=lowerCamelCase,  max_length=lowerCamelCase,  stride=lowerCamelCase,  pad_to_multiple_of=lowerCamelCase,  return_attention_mask=lowerCamelCase,  return_overflowing_tokens=lowerCamelCase,  return_special_tokens_mask=lowerCamelCase,  return_offsets_mapping=lowerCamelCase,  return_token_type_ids=lowerCamelCase,  return_length=lowerCamelCase,  verbose=lowerCamelCase,  return_tensors=lowerCamelCase,  **lowerCamelCase,  )
								else:
												lowercase__    =			None

								if text_encoding is not None:
												encoding_image_processor.update(lowerCamelCase  )

								return encoding_image_processor




				def 				lowercase__   (     self						:    Tuple,  *lowerCamelCase						:    Union[str, Any],  **lowerCamelCase						:    Optional[int]  ):

								'''simple docstring'''
								return self.tokenizer.batch_decode(*lowerCamelCase,  **lowerCamelCase  )




				def 				lowercase__   (     self						:    List[str],  *lowerCamelCase						:    int,  **lowerCamelCase						:    List[str]  ):

								'''simple docstring'''
								return self.tokenizer.decode(*lowerCamelCase,  **lowerCamelCase  )




				@property
				def 				lowercase__   (     self						:    List[str]  ):

								'''simple docstring'''
								lowercase__    =			self.tokenizer.model_input_names
								lowercase__    =			self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names  )  )




