




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


__SCREAMING_SNAKE_CASE 						=8

def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase=BITS     ):
							SCREAMING_SNAKE_CASE_   	=    x.device

							SCREAMING_SNAKE_CASE_   	=    (x * 2_5_5).int().clamp(0  ,    2_5_5     )

							SCREAMING_SNAKE_CASE_   	=    2 ** torch.arange(bits - 1  ,    -1  ,    -1  ,    device=_lowerCAmelCase     )
							SCREAMING_SNAKE_CASE_   	=    rearrange(_lowerCAmelCase  ,    '''d -> d 1 1'''     )
							SCREAMING_SNAKE_CASE_   	=    rearrange(_lowerCAmelCase  ,    '''b c h w -> b c 1 h w'''     )

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

def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase=BITS     ):
							SCREAMING_SNAKE_CASE_   	=    x.device

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

							SCREAMING_SNAKE_CASE_   	=    rearrange(_lowerCAmelCase  ,    '''d -> d 1 1'''     )
							SCREAMING_SNAKE_CASE_   	=    rearrange(_lowerCAmelCase  ,    '''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_5_5).clamp(0.0  ,    1.0     )

def 			a   (self  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase = 0.0  ,    _lowerCAmelCase = True  ,    _lowerCAmelCase=None  ,    _lowerCAmelCase = True  ,    ):
							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(_lowerCAmelCase  ,    -scale  ,    _lowerCAmelCase     )

							# 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(_lowerCAmelCase  ,    _lowerCAmelCase     )
							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(_lowerCAmelCase     ) else '''cpu'''
														SCREAMING_SNAKE_CASE_   	=    torch.randn(model_output.shape  ,    dtype=model_output.dtype  ,    generator=_lowerCAmelCase     ).to(_lowerCAmelCase     )
														SCREAMING_SNAKE_CASE_   	=    self._get_variance(_lowerCAmelCase  ,    _lowerCAmelCase     ) ** 0.5 * eta * noise

														SCREAMING_SNAKE_CASE_   	=    prev_sample + variance

							if not return_dict:
														return (prev_sample,)

							return DDIMSchedulerOutput(prev_sample=_lowerCAmelCase  ,    pred_original_sample=_lowerCAmelCase     )

def 			a   (self  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase  ,    _lowerCAmelCase="epsilon"  ,    _lowerCAmelCase=None  ,    _lowerCAmelCase = True  ,    ):
							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(_lowerCAmelCase  ,    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(_lowerCAmelCase  ,    -scale  ,    _lowerCAmelCase     )

							# 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=_lowerCAmelCase     ).to(model_output.device     )
														SCREAMING_SNAKE_CASE_   	=    (self._get_variance(_lowerCAmelCase  ,    predicted_variance=_lowerCAmelCase     ) ** 0.5) * noise

							SCREAMING_SNAKE_CASE_   	=    pred_prev_sample + variance

							if not return_dict:
														return (pred_prev_sample,)

							return DDPMSchedulerOutput(prev_sample=_lowerCAmelCase  ,    pred_original_sample=_lowerCAmelCase     )






class  __magic_name__						(  __UpperCAmelCase):
				'''simple docstring'''







				def __init__(							self:   Optional[Any]		,					_lowerCamelCase:   UNetaDConditionModel		,					_lowerCamelCase:   Union[DDIMScheduler, DDPMScheduler]		,					_lowerCamelCase:   Optional[float] = 1.0		,					):
											super().__init__()
											SCREAMING_SNAKE_CASE_   	=    bit_scale
											SCREAMING_SNAKE_CASE_   	=    (
											    ddim_bit_scheduler_step if isinstance(_lowerCamelCase		,					_lowerCamelCase		) else ddpm_bit_scheduler_step
											)

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





				@torch.no_grad()
				def __call__(							self:   List[Any]		,					_lowerCamelCase:   Optional[int] = 2_56		,					_lowerCamelCase:   Optional[int] = 2_56		,					_lowerCamelCase:   Optional[int] = 50		,					_lowerCamelCase:   Optional[torch.Generator] = None		,					_lowerCamelCase:   Optional[int] = 1		,					_lowerCamelCase:   Optional[str] = "pil"		,					_lowerCamelCase:   bool = True		,					**_lowerCamelCase:   Union[str, Any]		,					):
											SCREAMING_SNAKE_CASE_   	=    torch.randn(
											    (batch_size, self.unet.config.in_channels, height, width)		,					generator=_lowerCamelCase		,					)
											SCREAMING_SNAKE_CASE_   	=    decimal_to_bits(_lowerCamelCase		) * self.bit_scale
											SCREAMING_SNAKE_CASE_   	=    latents.to(self.device		)

											self.scheduler.set_timesteps(_lowerCamelCase		)

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

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

											SCREAMING_SNAKE_CASE_   	=    bits_to_decimal(_lowerCamelCase		)

											if output_type == "pil":
																		SCREAMING_SNAKE_CASE_   	=    self.numpy_to_pil(_lowerCamelCase		)

											if not return_dict:
																		return (image,)

											return ImagePipelineOutput(images=_lowerCamelCase		)








# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
			import torch






class  __magic_name__						(  TensorFormatter[Mapping, "torch.Tensor", Mapping]):
				'''simple docstring'''







				def __init__(							self:   int		,					_lowerCamelCase:   int=None		,					**_lowerCamelCase:   List[Any]		):
											super().__init__(features=_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    torch_tensor_kwargs
											import torch  # noqa import torch at initialization


				def     _A							(							self:   Union[str, Any]		,					_lowerCamelCase:   Optional[Any]		):
											import torch

											if isinstance(_lowerCamelCase		,					_lowerCamelCase		) and column:
																		if all(
																		    isinstance(_lowerCamelCase		,					torch.Tensor		) and x.shape == column[0].shape and x.dtype == column[0].dtype
																		    for x in column		):
																									return torch.stack(_lowerCamelCase		)
											return column


				def     _A							(							self:   Tuple		,					_lowerCamelCase:   List[str]		):
											import torch

											if isinstance(_lowerCamelCase		,					(str, bytes, type(_lowerCamelCase		))		):
																		return value
											elif isinstance(_lowerCamelCase		,					(np.character, np.ndarray)		) and np.issubdtype(value.dtype		,					np.character		):
																		return value.tolist()

											SCREAMING_SNAKE_CASE_   	=    {}

											if isinstance(_lowerCamelCase		,					(np.number, np.ndarray)		) and np.issubdtype(value.dtype		,					np.integer		):
																		SCREAMING_SNAKE_CASE_   	=    {'''dtype''': torch.intaa}
											elif isinstance(_lowerCamelCase		,					(np.number, np.ndarray)		) and np.issubdtype(value.dtype		,					np.floating		):
																		SCREAMING_SNAKE_CASE_   	=    {'''dtype''': torch.floataa}
											elif config.PIL_AVAILABLE and "PIL" in sys.modules:
																		import PIL.Image

																		if isinstance(_lowerCamelCase		,					PIL.Image.Image		):
																									SCREAMING_SNAKE_CASE_   	=    np.asarray(_lowerCamelCase		)
											return torch.tensor(_lowerCamelCase		,					**{**default_dtype, **self.torch_tensor_kwargs}		)


				def     _A							(							self:   Union[str, Any]		,					_lowerCamelCase:   Dict		):
											import torch

											# support for torch, tf, jax etc.
											if hasattr(_lowerCamelCase		,					'''__array__'''		) and not isinstance(_lowerCamelCase		,					torch.Tensor		):
																		SCREAMING_SNAKE_CASE_   	=    data_struct.__array__()
											# support for nested types like struct of list of struct
											if isinstance(_lowerCamelCase		,					np.ndarray		):
																		if data_struct.dtype == object:  # torch tensors cannot be instantied from an array of objects
																									return self._consolidate([self.recursive_tensorize(_lowerCamelCase		) for substruct in data_struct]		)
											elif isinstance(_lowerCamelCase		,					(list, tuple)		):
																		return self._consolidate([self.recursive_tensorize(_lowerCamelCase		) for substruct in data_struct]		)
											return self._tensorize(_lowerCamelCase		)


				def     _A							(							self:   str		,					_lowerCamelCase:   dict		):
											return map_nested(self._recursive_tensorize		,					_lowerCamelCase		,					map_list=_lowerCamelCase		)


				def     _A							(							self:   Optional[int]		,					_lowerCamelCase:   pa.Table		):
											SCREAMING_SNAKE_CASE_   	=    self.numpy_arrow_extractor().extract_row(_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    self.python_features_decoder.decode_row(_lowerCamelCase		)
											return self.recursive_tensorize(_lowerCamelCase		)


				def     _A							(							self:   Optional[Any]		,					_lowerCamelCase:   pa.Table		):
											SCREAMING_SNAKE_CASE_   	=    self.numpy_arrow_extractor().extract_column(_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    self.python_features_decoder.decode_column(_lowerCamelCase		,					pa_table.column_names[0]		)
											SCREAMING_SNAKE_CASE_   	=    self.recursive_tensorize(_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    self._consolidate(_lowerCamelCase		)
											return column





				def     _A							(							self:   Optional[Any]		,					_lowerCamelCase:   pa.Table		):
											SCREAMING_SNAKE_CASE_   	=    self.numpy_arrow_extractor().extract_batch(_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    self.python_features_decoder.decode_batch(_lowerCamelCase		)
											SCREAMING_SNAKE_CASE_   	=    self.recursive_tensorize(_lowerCamelCase		)
											for column_name in batch:
																		SCREAMING_SNAKE_CASE_   	=    self._consolidate(batch[column_name]		)
											return batch


