"""simple docstring"""





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   :						List[str]      =    8





def 				a_     (      _lowerCAmelCase  : Optional[int]   , _lowerCAmelCase  : str=BITS     ):





       '''simple docstring'''

       lowercase__			:		List[Any]       =	x.device

       lowercase__			:		List[str]       =	(x * 255).int().clamp(0   , 255     )

       lowercase__			:		Union[str, Any]       =	2 ** torch.arange(bits - 1   , -1   , -1   , device=_lowerCAmelCase     )
       lowercase__			:		Dict       =	rearrange(_lowerCAmelCase   , 'd -> d 1 1'     )
       lowercase__			:		List[Any]       =	rearrange(_lowerCAmelCase   , 'b c h w -> b c 1 h w'     )

       lowercase__			:		Any       =	((x & mask) != 0).float()
       lowercase__			:		Optional[int]       =	rearrange(_lowerCAmelCase   , 'b c d h w -> b (c d) h w'     )
       lowercase__			:		int       =	bits * 2 - 1
       return bits





def 				a_     (      _lowerCAmelCase  : List[str]   , _lowerCAmelCase  : List[Any]=BITS     ):





       '''simple docstring'''

       lowercase__			:		Dict       =	x.device

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

       lowercase__			:		Union[str, Any]       =	rearrange(_lowerCAmelCase   , 'd -> d 1 1'     )
       lowercase__			:		Tuple       =	rearrange(_lowerCAmelCase   , 'b (c d) h w -> b c d h w'   , d=8     )
       lowercase__			:		Union[str, Any]       =	reduce(x * mask   , 'b c d h w -> b c h w'   , 'sum'     )
       return (dec / 255).clamp(0.0   , 1.0     )





def 				a_     (      self  : List[Any]   , _lowerCAmelCase  : torch.FloatTensor   , _lowerCAmelCase  : int   , _lowerCAmelCase  : torch.FloatTensor   , _lowerCAmelCase  : float = 0.0   , _lowerCAmelCase  : bool = True   , _lowerCAmelCase  : Optional[int]=None   , _lowerCAmelCase  : bool = True   , ):





       '''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)
       lowercase__			:		Union[str, Any]       =	timestep - self.config.num_train_timesteps // self.num_inference_steps

       # 2. compute alphas, betas
       lowercase__			:		Optional[Any]       =	self.alphas_cumprod[timestep]
       lowercase__			:		int       =	self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

       lowercase__			:		Optional[int]       =	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
       lowercase__			:		Tuple       =	(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

       # 4. Clip "predicted x_0"
       lowercase__			:		str       =	self.bit_scale
       if self.config.clip_sample:
              lowercase__			:		Tuple       =	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)
       lowercase__			:		int       =	self._get_variance(_lowerCAmelCase   , _lowerCAmelCase     )
       lowercase__			:		List[str]       =	eta * variance ** 0.5

       if use_clipped_model_output:
              # the model_output is always re-derived from the clipped x_0 in Glide
              lowercase__			:		str       =	(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
       lowercase__			:		Tuple       =	(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
       lowercase__			:		Any       =	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
              lowercase__			:		Optional[Any]       =	model_output.device if torch.is_tensor(_lowerCAmelCase     ) else 'cpu'
              lowercase__			:		Tuple       =	torch.randn(model_output.shape   , dtype=model_output.dtype   , generator=_lowerCAmelCase     ).to(_lowerCAmelCase     )
              lowercase__			:		Tuple       =	self._get_variance(_lowerCAmelCase   , _lowerCAmelCase     ) ** 0.5 * eta * noise

              lowercase__			:		int       =	prev_sample + variance

       if not return_dict:
              return (prev_sample,)

       return DDIMSchedulerOutput(prev_sample=_lowerCAmelCase   , pred_original_sample=_lowerCAmelCase     )





def 				a_     (      self  : List[str]   , _lowerCAmelCase  : torch.FloatTensor   , _lowerCAmelCase  : int   , _lowerCAmelCase  : torch.FloatTensor   , _lowerCAmelCase  : int="epsilon"   , _lowerCAmelCase  : int=None   , _lowerCAmelCase  : bool = True   , ):





       '''simple docstring'''

       lowercase__			:		Dict       =	timestep

       if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
              lowercase__    ,      lowercase__			:		List[Any]       =	torch.split(_lowerCAmelCase   , sample.shape[1]   , dim=1     )
       else:
              lowercase__			:		Tuple       =	None

       # 1. compute alphas, betas
       lowercase__			:		int       =	self.alphas_cumprod[t]
       lowercase__			:		Union[str, Any]       =	self.alphas_cumprod[t - 1] if t > 0 else self.one
       lowercase__			:		Optional[int]       =	1 - alpha_prod_t
       lowercase__			:		Any       =	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":
              lowercase__			:		Optional[int]       =	(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
       elif prediction_type == "sample":
              lowercase__			:		Optional[Any]       =	model_output
       else:
              raise ValueError(f"""Unsupported prediction_type {prediction_type}."""     )

       # 3. Clip "predicted x_0"
       lowercase__			:		Optional[int]       =	self.bit_scale
       if self.config.clip_sample:
              lowercase__			:		Any       =	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
       lowercase__			:		Union[str, Any]       =	(alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
       lowercase__			:		Tuple       =	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
       lowercase__			:		Optional[Any]       =	pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

       # 6. Add noise
       lowercase__			:		Optional[int]       =	0
       if t > 0:
              lowercase__			:		Dict       =	torch.randn(
                  model_output.size()   , dtype=model_output.dtype   , layout=model_output.layout   , generator=_lowerCAmelCase     ).to(model_output.device     )
              lowercase__			:		List[str]       =	(self._get_variance(_lowerCAmelCase   , predicted_variance=_lowerCAmelCase     ) ** 0.5) * noise

       lowercase__			:		Union[str, Any]       =	pred_prev_sample + variance

       if not return_dict:
              return (pred_prev_sample,)

       return DDPMSchedulerOutput(prev_sample=_lowerCAmelCase   , pred_original_sample=_lowerCAmelCase     )






class      UpperCAmelCase_	(    _a):


      def __init__(   self		,			a		,			a		,			a = 1.0		,			)       ->					str:
             super().__init__()
             lowercase__			:		Any       =	bit_scale
             lowercase__			:		str       =	(
                 ddim_bit_scheduler_step if isinstance(a		,			a							) else ddpm_bit_scheduler_step
             )

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


      @torch.no_grad()
      def __call__(   self		,			a = 2_5_6		,			a = 2_5_6		,			a = 5_0		,			a = None		,			a = 1		,			a = "pil"		,			a = True		,			**a		,			)       ->					Union[Tuple, ImagePipelineOutput]:
             lowercase__			:		Optional[Any]       =	torch.randn(
                 (batch_size, self.unet.config.in_channels, height, width)		,			generator=a		,			)
             lowercase__			:		Optional[int]       =	decimal_to_bits(a							) * self.bit_scale
             lowercase__			:		Dict       =	latents.to(self.device							)

             self.scheduler.set_timesteps(a							)

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

                    # compute the previous noisy sample x_t -> x_t-1
                    lowercase__			:		Tuple       =	self.scheduler.step(a		,			a		,			a							).prev_sample

             lowercase__			:		List[str]       =	bits_to_decimal(a							)

             if output_type == "pil":
                    lowercase__			:		Optional[int]       =	self.numpy_to_pil(a							)

             if not return_dict:
                    return (image,)

             return ImagePipelineOutput(images=a							)






"""simple docstring"""







from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import pyarrow as pa


if TYPE_CHECKING:
							from .features import FeatureType
@dataclass
class        __A       :







		'''simple docstring'''





		lowerCAmelCase			:					List[str]
		lowerCAmelCase			:					Optional[str]   	=				None
		# Automatically constructed
		lowerCAmelCase			:					ClassVar[str]   	=				"dict"
		lowerCAmelCase			:					ClassVar[Any]   	=				None
		lowerCAmelCase			:					str   	=				field(default="Translation"     ,init=A_     ,repr=A_    )







		def __call__(	self						:  List[str]						)					->		Any:






				"""simple docstring"""



				return pa.struct({lang: pa.string() for lang in sorted(self.languages						)}						)







		def 					UpperCAmelCase    (	self						:  List[str]						)					->		Union["FeatureType", Dict[str, "FeatureType"]]:






				"""simple docstring"""



				from .features import Value

				return {k: Value('''string'''						) for k in sorted(self.languages						)}





@dataclass
class        __A       :







		'''simple docstring'''





		lowerCAmelCase			:					Optional[List]   	=				None
		lowerCAmelCase			:					Optional[int]   	=				None
		lowerCAmelCase			:					Optional[str]   	=				None
		# Automatically constructed
		lowerCAmelCase			:					ClassVar[str]   	=				"dict"
		lowerCAmelCase			:					ClassVar[Any]   	=				None
		lowerCAmelCase			:					str   	=				field(default="TranslationVariableLanguages"     ,init=A_     ,repr=A_    )







		def 					UpperCAmelCase    (	self						:  List[Any]						)					->		Optional[int]:






				"""simple docstring"""



				lowercase__   :    Optional[int]				=			sorted(set(self.languages						)						) if self.languages else None
				lowercase__   :    Dict				=			len(self.languages						) if self.languages else None







		def __call__(	self						:  List[Any]						)					->		List[Any]:






				"""simple docstring"""



				return pa.struct({'''language''': pa.list_(pa.string()						), '''translation''': pa.list_(pa.string()						)}						)







		def 					UpperCAmelCase    (	self						:  Dict			,_snake_case						:  Tuple						)					->		int:






				"""simple docstring"""



				lowercase__   :    List[Any]				=			set(self.languages						)
				if self.languages and set(_snake_case						) - lang_set:
						raise ValueError(
						    f"""Some languages in example ({", ".join(sorted(set(_snake_case						) - lang_set						)						)}) are not in valid set ({", ".join(_snake_case						)})."""						)

				# Convert dictionary into tuples, splitting out cases where there are
				# multiple translations for a single language.
				lowercase__   :    str				=			[]
				for lang, text in translation_dict.items():
						if isinstance(_snake_case			,_snake_case						):
								translation_tuples.append((lang, text)						)
						else:
								translation_tuples.extend([(lang, el) for el in text]						)

        # Ensure translations are in ascending order by language code.
				lowercase__		,			lowercase__   :    Optional[Any]				=			zip(*sorted(_snake_case						)						)

				return {"language": languages, "translation": translations}







		def 					UpperCAmelCase    (	self						:  List[Any]						)					->		Union["FeatureType", Dict[str, "FeatureType"]]:






				"""simple docstring"""



				from .features import Sequence, Value

				return {
				    "language": Sequence(Value('''string'''						)						),
				    "translation": Sequence(Value('''string'''						)						),
				}
