






from typing import Optional, Tuple, Union

import torch
from einops import rearrange, reduce

from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.scheduling_ddpm import DDPMSchedulerOutput


UpperCamelCase 				=	8

def        __lowerCamelCase			(		snake_case__       ,snake_case__=BITS		)							->  List[Any]:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE	      =  x.device

						_SCREAMING_SNAKE_CASE	      =  (x * 2_55).int().clamp(0       ,2_55		)

						_SCREAMING_SNAKE_CASE	      =  2 ** torch.arange(bits - 1       ,-1       ,-1       ,device=a_		)
						_SCREAMING_SNAKE_CASE	      =  rearrange(a_       ,"""d -> d 1 1"""		)
						_SCREAMING_SNAKE_CASE	      =  rearrange(a_       ,"""b c h w -> b c 1 h w"""		)

						_SCREAMING_SNAKE_CASE	      =  ((x & mask) != 0).float()
						_SCREAMING_SNAKE_CASE	      =  rearrange(a_       ,"""b c d h w -> b (c d) h w"""		)
						_SCREAMING_SNAKE_CASE	      =  bits * 2 - 1
						return bits

def        __lowerCamelCase			(		snake_case__       ,snake_case__=BITS		)							->  List[Any]:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE	      =  x.device

						_SCREAMING_SNAKE_CASE	      =  (x > 0).int()
						_SCREAMING_SNAKE_CASE	      =  2 ** torch.arange(bits - 1       ,-1       ,-1       ,device=a_       ,dtype=torch.intaa		)

						_SCREAMING_SNAKE_CASE	      =  rearrange(a_       ,"""d -> d 1 1"""		)
						_SCREAMING_SNAKE_CASE	      =  rearrange(a_       ,"""b (c d) h w -> b c d h w"""       ,d=8		)
						_SCREAMING_SNAKE_CASE	      =  reduce(x * mask       ,"""b c d h w -> b c h w"""       ,"""sum"""		)
						return (dec / 2_55).clamp(0.0       ,1.0		)

def        __lowerCamelCase			(		self       ,snake_case__       ,snake_case__       ,snake_case__       ,snake_case__ = 0.0       ,snake_case__ = True       ,snake_case__=None       ,snake_case__ = True       ,)							->  int:






						"""simple docstring"""





						if self.num_inference_steps is None:
												raise ValueError(
												    """Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler"""		)

						# See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
						# Ideally, read DDIM paper in-detail understanding

						# Notation (<variable name> -> <name in paper>
						# - pred_noise_t -> e_theta(x_t, t)
						# - pred_original_sample -> f_theta(x_t, t) or x_0
						# - std_dev_t -> sigma_t
						# - eta -> η
						# - pred_sample_direction -> "direction pointing to x_t"
						# - pred_prev_sample -> "x_t-1"

						# 1. get previous step value (=t-1)
						_SCREAMING_SNAKE_CASE	      =  timestep - self.config.num_train_timesteps // self.num_inference_steps

						# 2. compute alphas, betas
						_SCREAMING_SNAKE_CASE	      =  self.alphas_cumprod[timestep]
						_SCREAMING_SNAKE_CASE	      =  self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

						_SCREAMING_SNAKE_CASE	      =  1 - alpha_prod_t

						# 3. compute predicted original sample from predicted noise also called
						# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
						_SCREAMING_SNAKE_CASE	      =  (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

						# 4. Clip "predicted x_0"
						_SCREAMING_SNAKE_CASE	      =  self.bit_scale
						if self.config.clip_sample:
												_SCREAMING_SNAKE_CASE	      =  torch.clamp(a_       ,-scale       ,a_		)

						# 5. compute variance: "sigma_t(η)" -> see formula (16)
						# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
						_SCREAMING_SNAKE_CASE	      =  self._get_variance(a_       ,a_		)
						_SCREAMING_SNAKE_CASE	      =  eta * variance ** 0.5

						if use_clipped_model_output:
												# the model_output is always re-derived from the clipped x_0 in Glide
												_SCREAMING_SNAKE_CASE	      =  (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5

						# 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
						_SCREAMING_SNAKE_CASE	      =  (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

						# 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
						_SCREAMING_SNAKE_CASE	      =  alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

						if eta > 0:
												# randn_like does not support generator https://github.com/pytorch/pytorch/issues/27072
												_SCREAMING_SNAKE_CASE	      =  model_output.device if torch.is_tensor(a_		) else """cpu"""
												_SCREAMING_SNAKE_CASE	      =  torch.randn(model_output.shape       ,dtype=model_output.dtype       ,generator=a_		).to(a_		)
												_SCREAMING_SNAKE_CASE	      =  self._get_variance(a_       ,a_		) ** 0.5 * eta * noise

												_SCREAMING_SNAKE_CASE	      =  prev_sample + variance

						if not return_dict:
												return (prev_sample,)

						return DDIMSchedulerOutput(prev_sample=a_       ,pred_original_sample=a_		)

def        __lowerCamelCase			(		self       ,snake_case__       ,snake_case__       ,snake_case__       ,snake_case__="epsilon"       ,snake_case__=None       ,snake_case__ = True       ,)							->  Dict:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE	      =  timestep

						if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
												_SCREAMING_SNAKE_CASE      ,     _SCREAMING_SNAKE_CASE	      =  torch.split(a_       ,sample.shape[1]       ,dim=1		)
						else:
												_SCREAMING_SNAKE_CASE	      =  None

						# 1. compute alphas, betas
						_SCREAMING_SNAKE_CASE	      =  self.alphas_cumprod[t]
						_SCREAMING_SNAKE_CASE	      =  self.alphas_cumprod[t - 1] if t > 0 else self.one
						_SCREAMING_SNAKE_CASE	      =  1 - alpha_prod_t
						_SCREAMING_SNAKE_CASE	      =  1 - alpha_prod_t_prev

						# 2. compute predicted original sample from predicted noise also called
						# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
						if prediction_type == "epsilon":
												_SCREAMING_SNAKE_CASE	      =  (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
						elif prediction_type == "sample":
												_SCREAMING_SNAKE_CASE	      =  model_output
						else:
												raise ValueError(F'Unsupported prediction_type {prediction_type}.'		)

						# 3. Clip "predicted x_0"
						_SCREAMING_SNAKE_CASE	      =  self.bit_scale
						if self.config.clip_sample:
												_SCREAMING_SNAKE_CASE	      =  torch.clamp(a_       ,-scale       ,a_		)

						# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
						# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
						_SCREAMING_SNAKE_CASE	      =  (alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
						_SCREAMING_SNAKE_CASE	      =  self.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

						# 5. Compute predicted previous sample µ_t
						# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
						_SCREAMING_SNAKE_CASE	      =  pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

						# 6. Add noise
						_SCREAMING_SNAKE_CASE	      =  0
						if t > 0:
												_SCREAMING_SNAKE_CASE	      =  torch.randn(
												    model_output.size()       ,dtype=model_output.dtype       ,layout=model_output.layout       ,generator=a_		).to(model_output.device		)
												_SCREAMING_SNAKE_CASE	      =  (self._get_variance(a_       ,predicted_variance=a_		) ** 0.5) * noise

						_SCREAMING_SNAKE_CASE	      =  pred_prev_sample + variance

						if not return_dict:
												return (pred_prev_sample,)

						return DDPMSchedulerOutput(prev_sample=a_       ,pred_original_sample=a_		)




class       __UpperCAmelCase		(snake_case_    ):







			def __init__(	self:  List[str]      ,	UpperCAmelCase_:  List[str]      ,	UpperCAmelCase_:  Optional[Any]      ,	UpperCAmelCase_:  Optional[Any] = 1.0      ,	):







									'''simple docstring'''




									super().__init__()
									_SCREAMING_SNAKE_CASE	      =  bit_scale
									_SCREAMING_SNAKE_CASE	      =  (
									    ddim_bit_scheduler_step if isinstance(UpperCAmelCase_      ,	UpperCAmelCase_     ) else ddpm_bit_scheduler_step
									)

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







			@torch.no_grad()
			def __call__(	self:  List[Any]      ,	UpperCAmelCase_:  Tuple = 256      ,	UpperCAmelCase_:  List[str] = 256      ,	UpperCAmelCase_:  Union[str, Any] = 50      ,	UpperCAmelCase_:  Tuple = None      ,	UpperCAmelCase_:  Union[str, Any] = 1      ,	UpperCAmelCase_:  Tuple = "pil"      ,	UpperCAmelCase_:  int = True      ,	**UpperCAmelCase_:  List[str]      ,	):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE	      =  torch.randn(
									    (batch_size, self.unet.config.in_channels, height, width)      ,	generator=UpperCAmelCase_      ,	)
									_SCREAMING_SNAKE_CASE	      =  decimal_to_bits(UpperCAmelCase_     ) * self.bit_scale
									_SCREAMING_SNAKE_CASE	      =  latents.to(self.device     )

									self.scheduler.set_timesteps(UpperCAmelCase_     )

									for t in self.progress_bar(self.scheduler.timesteps     ):
															# predict the noise residual
															_SCREAMING_SNAKE_CASE	      =  self.unet(UpperCAmelCase_      ,	UpperCAmelCase_     ).sample

															# compute the previous noisy sample x_t -> x_t-1
															_SCREAMING_SNAKE_CASE	      =  self.scheduler.step(UpperCAmelCase_      ,	UpperCAmelCase_      ,	UpperCAmelCase_     ).prev_sample

									_SCREAMING_SNAKE_CASE	      =  bits_to_decimal(UpperCAmelCase_     )

									if output_type == "pil":
															_SCREAMING_SNAKE_CASE	      =  self.numpy_to_pil(UpperCAmelCase_     )

									if not return_dict:
															return (image,)

									return ImagePipelineOutput(images=UpperCAmelCase_     )









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					)       ->    Tuple:
								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					)       ->    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					)					)





