


'''simple docstring'''



from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class 						_snake_case   (       __lowerCamelCase		):
					lowerCAmelCase :UNetaDModel
					lowerCAmelCase :ScoreSdeVeScheduler




					def __init__( self							,							_lowerCamelCase							,							_lowerCamelCase):
								super().__init__()
								self.register_modules(unet=UpperCamelCase_							,							scheduler=UpperCamelCase_)




					@torch.no_grad()
					def __call__( self							,							_lowerCamelCase = 1							,							_lowerCamelCase = 2000							,							_lowerCamelCase = None							,							_lowerCamelCase = "pil"							,							_lowerCamelCase = True							,							**_lowerCamelCase							,							):
								UpperCAmelCase__					:   str       				=       self.unet.config.sample_size
								UpperCAmelCase__					:   int       				=       (batch_size, 3, img_size, img_size)

								UpperCAmelCase__					:   int       				=       self.unet

								UpperCAmelCase__					:   List[Any]       				=       randn_tensor(UpperCamelCase_							,							generator=UpperCamelCase_) * self.scheduler.init_noise_sigma
								UpperCAmelCase__					:   Union[str, Any]       				=       sample.to(self.device)

								self.scheduler.set_timesteps(UpperCamelCase_)
								self.scheduler.set_sigmas(UpperCamelCase_)

								for i, t in enumerate(self.progress_bar(self.scheduler.timesteps)):
											UpperCAmelCase__					:   Optional[Any]       				=       self.scheduler.sigmas[i] * torch.ones(shape[0]							,							device=self.device)

											# correction step
											for _ in range(self.scheduler.config.correct_steps):
														UpperCAmelCase__					:   int       				=       self.unet(UpperCamelCase_							,							UpperCamelCase_).sample
														UpperCAmelCase__					:   str       				=       self.scheduler.step_correct(UpperCamelCase_							,							UpperCamelCase_							,							generator=UpperCamelCase_).prev_sample

											# prediction step
											UpperCAmelCase__					:   Optional[int]       				=       model(UpperCamelCase_							,							UpperCamelCase_).sample
											UpperCAmelCase__					:   List[Any]       				=       self.scheduler.step_pred(UpperCamelCase_							,							UpperCamelCase_							,							UpperCamelCase_							,							generator=UpperCamelCase_)

											UpperCAmelCase__				,    UpperCAmelCase__					:   List[str]       				=       output.prev_sample, output.prev_sample_mean

								UpperCAmelCase__					:   Tuple       				=       sample_mean.clamp(0							,							1)
								UpperCAmelCase__					:   Optional[Any]       				=       sample.cpu().permute(0							,							2							,							3							,							1).numpy()
								if output_type == "pil":
											UpperCAmelCase__					:   Dict       				=       self.numpy_to_pil(UpperCamelCase_)

								if not return_dict:
											return (sample,)

								return ImagePipelineOutput(images=UpperCamelCase_)



'''simple docstring'''



from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType


class 						_snake_case   (       a__		):
					lowerCAmelCase :Optional[int]						=   ['''image_processor''', '''tokenizer''']
					lowerCAmelCase :Optional[int]						=   '''BridgeTowerImageProcessor'''
					lowerCAmelCase :List[str]						=   ('''RobertaTokenizer''', '''RobertaTokenizerFast''')




					def __init__( self							,							_lowerCamelCase							,							_lowerCamelCase):
								super().__init__(_lowerCamelCase							,							_lowerCamelCase)




					def __call__( self							,							_lowerCamelCase							,							_lowerCamelCase = None							,							_lowerCamelCase = True							,							_lowerCamelCase = False							,							_lowerCamelCase = None							,							_lowerCamelCase = None							,							_lowerCamelCase = 0							,							_lowerCamelCase = None							,							_lowerCamelCase = None							,							_lowerCamelCase = None							,							_lowerCamelCase = False							,							_lowerCamelCase = False							,							_lowerCamelCase = False							,							_lowerCamelCase = False							,							_lowerCamelCase = True							,							_lowerCamelCase = None							,							**_lowerCamelCase							,							):
								UpperCAmelCase__					:   List[str]       				=       self.tokenizer(
								    text=_lowerCamelCase							,							add_special_tokens=_lowerCamelCase							,							padding=_lowerCamelCase							,							truncation=_lowerCamelCase							,							max_length=_lowerCamelCase							,							stride=_lowerCamelCase							,							pad_to_multiple_of=_lowerCamelCase							,							return_token_type_ids=_lowerCamelCase							,							return_attention_mask=_lowerCamelCase							,							return_overflowing_tokens=_lowerCamelCase							,							return_special_tokens_mask=_lowerCamelCase							,							return_offsets_mapping=_lowerCamelCase							,							return_length=_lowerCamelCase							,							verbose=_lowerCamelCase							,							return_tensors=_lowerCamelCase							,							**_lowerCamelCase							,							)
								# add pixel_values + pixel_mask
								UpperCAmelCase__					:   Optional[Any]       				=       self.image_processor(
								    _lowerCamelCase							,							return_tensors=_lowerCamelCase							,							do_normalize=_lowerCamelCase							,							do_center_crop=_lowerCamelCase							,							**_lowerCamelCase)
								encoding.update(_lowerCamelCase)

								return encoding




					def 			snake_case__  ( self							,							*_lowerCamelCase							,							**_lowerCamelCase):
								return self.tokenizer.batch_decode(*_lowerCamelCase							,							**_lowerCamelCase)




					def 			snake_case__  ( self							,							*_lowerCamelCase							,							**_lowerCamelCase):
								return self.tokenizer.decode(*_lowerCamelCase							,							**_lowerCamelCase)




					@property
					def 			snake_case__  ( self):
								UpperCAmelCase__					:   Optional[int]       				=       self.tokenizer.model_input_names
								UpperCAmelCase__					:   str       				=       self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))