






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_ (     snake_case_							):



	'''simple docstring'''




	lowerCAmelCase						=					['''image_processor''', '''tokenizer''']
	lowerCAmelCase						=					'''BlipImageProcessor'''
	lowerCAmelCase						=					('''BertTokenizer''', '''BertTokenizerFast''')




	def __init__(			self			,     a			,     a   )			->       Dict:
								snake_case_           =				False
								super().__init__(a			,     a   )
								snake_case_           =				self.image_processor




	def __call__(			self			,     a = None			,     a = None			,     a = True			,     a = False			,     a = None			,     a = None			,     a = 0			,     a = None			,     a = None			,     a = False			,     a = False			,     a = False			,     a = False			,     a = False			,     a = True			,     a = None			,     **a			,     )			->       BatchEncoding:
								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:
															snake_case_           =				self.tokenizer
															snake_case_           =				self.tokenizer(
															    text=a			,     add_special_tokens=a			,     padding=a			,     truncation=a			,     max_length=a			,     stride=a			,     pad_to_multiple_of=a			,     return_attention_mask=a			,     return_overflowing_tokens=a			,     return_special_tokens_mask=a			,     return_offsets_mapping=a			,     return_token_type_ids=a			,     return_length=a			,     verbose=a			,     return_tensors=a			,     **a			,     )
															return text_encoding

								# add pixel_values
								snake_case_           =				self.image_processor(a			,     return_tensors=a   )

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

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

								return encoding_image_processor




	def 				_UpperCamelCase     (			self			,     *a			,     **a   )			->       Optional[int]:
								return self.tokenizer.batch_decode(*a			,     **a   )




	def 				_UpperCamelCase     (			self			,     *a			,     **a   )			->       Any:
								return self.tokenizer.decode(*a			,     **a   )






	@property
	def 				_UpperCamelCase     (			self   )			->       List[str]:
								snake_case_           =				self.tokenizer.model_input_names
								snake_case_           =				self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names   )   )













from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput




class 					UpperCamelCase_ (     snake_case_							):



	'''simple docstring'''




	def __init__(			self			,     a			,     a   )			->       Optional[Any]:
								super().__init__()

								# make sure scheduler can always be converted to DDIM
								snake_case_           =				DDIMScheduler.from_config(scheduler.config   )

								self.register_modules(unet=a			,     scheduler=a   )






	@torch.no_grad()
	def __call__(			self			,     a = 1			,     a = None			,     a = 0.0			,     a = 50			,     a = None			,     a = "pil"			,     a = True			,     )			->       Union[ImagePipelineOutput, Tuple]:

								# Sample gaussian noise to begin loop
								if isinstance(self.unet.config.sample_size			,     a   ):
															snake_case_           =				(
															    batch_size,
															    self.unet.config.in_channels,
															    self.unet.config.sample_size,
															    self.unet.config.sample_size,
															)
								else:
															snake_case_           =				(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

								if isinstance(a			,     a   ) and len(a   ) != batch_size:
															raise ValueError(
															    F'''You have passed a list of generators of length {len(a   )}, but requested an effective batch'''
															    F''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''   )

								snake_case_           =				randn_tensor(a			,     generator=a			,     device=self.device			,     dtype=self.unet.dtype   )

								# set step values
								self.scheduler.set_timesteps(a   )

								for t in self.progress_bar(self.scheduler.timesteps   ):
															# 1. predict noise model_output
															snake_case_           =				self.unet(a			,     a   ).sample

															# 2. predict previous mean of image x_t-1 and add variance depending on eta
															# eta corresponds to η in paper and should be between [0, 1]
															# do x_t -> x_t-1
															snake_case_           =				self.scheduler.step(
															    a			,     a			,     a			,     eta=a			,     use_clipped_model_output=a			,     generator=a   ).prev_sample

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

								if not return_dict:
															return (image,)

								return ImagePipelineOutput(images=a   )





