



import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin



@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class 				lowerCAmelCase_		(  a__							):
       UpperCAmelCase__				:					torch.FloatTensor
       UpperCAmelCase__				:					Optional[torch.FloatTensor]        =  None






def 		UpperCamelCase	(   snake_case__       :    Optional[int]       ,     snake_case__       :    int=0.999       ,     snake_case__       :    str="cosine"       ,     )	->     List[Any]:
   if alpha_transform_type == "cosine":

      def alpha_bar_fn(snake_case__       :    Union[str, Any]	):
         return math.cos((t + 0.008) / 1.008 * math.pi / 2	) ** 2

   elif alpha_transform_type == "exp":

      def alpha_bar_fn(snake_case__       :    str	):
         return math.exp(t * -12.0	)

   else:
      raise ValueError(F"""Unsupported alpha_tranform_type: {alpha_transform_type}"""	)

   UpperCamelCase    :  Dict							=	[]
   for i in range(snake_case__	):
      UpperCamelCase    :  str							=	i / num_diffusion_timesteps
      UpperCamelCase    :  Optional[int]							=	(i + 1) / num_diffusion_timesteps
      betas.append(min(1 - alpha_bar_fn(snake_case__	) / alpha_bar_fn(snake_case__	)       ,     snake_case__	)	)
   return torch.tensor(snake_case__       ,     dtype=torch.floataa	)



class 				lowerCAmelCase_		(  a__       ,    a__							):
       @register_to_config
       def __init__(			self,			SCREAMING_SNAKE_CASE_ = 1000,			SCREAMING_SNAKE_CASE_ = "fixed_small_log",			SCREAMING_SNAKE_CASE_ = True,			SCREAMING_SNAKE_CASE_ = 1.0,			SCREAMING_SNAKE_CASE_ = "epsilon",			SCREAMING_SNAKE_CASE_ = "squaredcos_cap_v2",			)     ->		Tuple:
          if beta_schedule != "squaredcos_cap_v2":
             raise ValueError('UnCLIPScheduler only supports `beta_schedule`: \'squaredcos_cap_v2\''       )

          UpperCamelCase    :  Dict							=	betas_for_alpha_bar(SCREAMING_SNAKE_CASE_       )

          UpperCamelCase    :  Any							=	1.0 - self.betas
          UpperCamelCase    :  Tuple							=	torch.cumprod(self.alphas,			dim=0       )
          UpperCamelCase    :  Tuple							=	torch.tensor(1.0       )

          # standard deviation of the initial noise distribution
          UpperCamelCase    :  Any							=	1.0

          # setable values
          UpperCamelCase    :  Tuple							=	None
          UpperCamelCase    :  Any							=	torch.from_numpy(np.arange(0,			SCREAMING_SNAKE_CASE_       )[::-1].copy()       )

          UpperCamelCase    :  Optional[int]							=	variance_type
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_ = None       )     ->		torch.FloatTensor:
          return sample
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_ = None       )     ->		Any:
          UpperCamelCase    :  List[str]							=	num_inference_steps
          UpperCamelCase    :  Optional[Any]							=	(self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
          UpperCamelCase    :  Dict							=	(np.arange(0,			SCREAMING_SNAKE_CASE_       ) * step_ratio).round()[::-1].copy().astype(np.intaa       )
          UpperCamelCase    :  int							=	torch.from_numpy(SCREAMING_SNAKE_CASE_       ).to(SCREAMING_SNAKE_CASE_       )
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_=None,			SCREAMING_SNAKE_CASE_=None,			SCREAMING_SNAKE_CASE_=None       )     ->		Tuple:
          if prev_timestep is None:
             UpperCamelCase    :  Union[str, Any]							=	t - 1

          UpperCamelCase    :  List[Any]							=	self.alphas_cumprod[t]
          UpperCamelCase    :  Dict							=	self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
          UpperCamelCase    :  Optional[Any]							=	1 - alpha_prod_t
          UpperCamelCase    :  Dict							=	1 - alpha_prod_t_prev

          if prev_timestep == t - 1:
             UpperCamelCase    :  Dict							=	self.betas[t]
          else:
             UpperCamelCase    :  Tuple							=	1 - alpha_prod_t / alpha_prod_t_prev

          # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
          # and sample from it to get previous sample
          # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
          UpperCamelCase    :  List[Any]							=	beta_prod_t_prev / beta_prod_t * beta

          if variance_type is None:
             UpperCamelCase    :  Tuple							=	self.config.variance_type

          # hacks - were probably added for training stability
          if variance_type == "fixed_small_log":
             UpperCamelCase    :  Optional[Any]							=	torch.log(torch.clamp(SCREAMING_SNAKE_CASE_,			min=1e-20       )       )
             UpperCamelCase    :  Any							=	torch.exp(0.5 * variance       )
          elif variance_type == "learned_range":
             # NOTE difference with DDPM scheduler
             UpperCamelCase    :  Tuple							=	variance.log()
             UpperCamelCase    :  Optional[int]							=	beta.log()

             UpperCamelCase    :  Optional[int]							=	(predicted_variance + 1) / 2
             UpperCamelCase    :  Tuple							=	frac * max_log + (1 - frac) * min_log

          return variance
       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_ = None,			SCREAMING_SNAKE_CASE_=None,			SCREAMING_SNAKE_CASE_ = True,			)     ->		Union[UnCLIPSchedulerOutput, Tuple]:
          UpperCamelCase    :  Any							=	timestep

          if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
             UpperCamelCase	,     UpperCamelCase    :  Union[str, Any]							=	torch.split(SCREAMING_SNAKE_CASE_,			sample.shape[1],			dim=1       )
          else:
             UpperCamelCase    :  Optional[int]							=	None

          # 1. compute alphas, betas
          if prev_timestep is None:
             UpperCamelCase    :  int							=	t - 1

          UpperCamelCase    :  List[str]							=	self.alphas_cumprod[t]
          UpperCamelCase    :  Optional[Any]							=	self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
          UpperCamelCase    :  Optional[Any]							=	1 - alpha_prod_t
          UpperCamelCase    :  List[Any]							=	1 - alpha_prod_t_prev

          if prev_timestep == t - 1:
             UpperCamelCase    :  Optional[int]							=	self.betas[t]
             UpperCamelCase    :  Any							=	self.alphas[t]
          else:
             UpperCamelCase    :  Any							=	1 - alpha_prod_t / alpha_prod_t_prev
             UpperCamelCase    :  int							=	1 - beta

          # 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 self.config.prediction_type == "epsilon":
             UpperCamelCase    :  Tuple							=	(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
          elif self.config.prediction_type == "sample":
             UpperCamelCase    :  List[Any]							=	model_output
          else:
             raise ValueError(
                 F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`"""
                 ' for the UnCLIPScheduler.'       )

          # 3. Clip "predicted x_0"
          if self.config.clip_sample:
             UpperCamelCase    :  int							=	torch.clamp(
                 SCREAMING_SNAKE_CASE_,			-self.config.clip_sample_range,			self.config.clip_sample_range       )

          # 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
          UpperCamelCase    :  Union[str, Any]							=	(alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
          UpperCamelCase    :  Any							=	alpha ** 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
          UpperCamelCase    :  int							=	pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

          # 6. Add noise
          UpperCamelCase    :  List[Any]							=	0
          if t > 0:
             UpperCamelCase    :  int							=	randn_tensor(
                 model_output.shape,			dtype=model_output.dtype,			generator=SCREAMING_SNAKE_CASE_,			device=model_output.device       )

             UpperCamelCase    :  Dict							=	self._get_variance(
                 SCREAMING_SNAKE_CASE_,			predicted_variance=SCREAMING_SNAKE_CASE_,			prev_timestep=SCREAMING_SNAKE_CASE_,			)

             if self.variance_type == "fixed_small_log":
                UpperCamelCase    :  Dict							=	variance
             elif self.variance_type == "learned_range":
                UpperCamelCase    :  Any							=	(0.5 * variance).exp()
             else:
                raise ValueError(
                    F"""variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`"""
                    ' for the UnCLIPScheduler.'       )

             UpperCamelCase    :  str							=	variance * variance_noise

          UpperCamelCase    :  int							=	pred_prev_sample + variance

          if not return_dict:
             return (pred_prev_sample,)

          return UnCLIPSchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE_,			pred_original_sample=SCREAMING_SNAKE_CASE_       )






       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_,			)     ->		torch.FloatTensor:
          # Make sure alphas_cumprod and timestep have same device and dtype as original_samples
          UpperCamelCase    :  Any							=	self.alphas_cumprod.to(device=original_samples.device,			dtype=original_samples.dtype       )
          UpperCamelCase    :  Dict							=	timesteps.to(original_samples.device       )

          UpperCamelCase    :  Optional[int]							=	alphas_cumprod[timesteps] ** 0.5
          UpperCamelCase    :  List[Any]							=	sqrt_alpha_prod.flatten()
          while len(sqrt_alpha_prod.shape       ) < len(original_samples.shape       ):
             UpperCamelCase    :  Optional[int]							=	sqrt_alpha_prod.unsqueeze(-1       )

          UpperCamelCase    :  List[str]							=	(1 - alphas_cumprod[timesteps]) ** 0.5
          UpperCamelCase    :  Tuple							=	sqrt_one_minus_alpha_prod.flatten()
          while len(sqrt_one_minus_alpha_prod.shape       ) < len(original_samples.shape       ):
             UpperCamelCase    :  List[str]							=	sqrt_one_minus_alpha_prod.unsqueeze(-1       )

          UpperCamelCase    :  int							=	sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
          return noisy_samples








from typing import Optional

import pyspark

from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader



class 				lowerCAmelCase_		(  a__							):
       def __init__(			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_ = None,			SCREAMING_SNAKE_CASE_ = None,			SCREAMING_SNAKE_CASE_ = True,			SCREAMING_SNAKE_CASE_ = None,			SCREAMING_SNAKE_CASE_ = False,			SCREAMING_SNAKE_CASE_ = None,			SCREAMING_SNAKE_CASE_ = True,			SCREAMING_SNAKE_CASE_ = "arrow",			**SCREAMING_SNAKE_CASE_,			)     ->		Optional[int]:
          super().__init__(
              split=SCREAMING_SNAKE_CASE_,			features=SCREAMING_SNAKE_CASE_,			cache_dir=SCREAMING_SNAKE_CASE_,			keep_in_memory=SCREAMING_SNAKE_CASE_,			streaming=SCREAMING_SNAKE_CASE_,			**SCREAMING_SNAKE_CASE_,			)
          UpperCamelCase    :  List[str]							=	load_from_cache_file
          UpperCamelCase    :  List[str]							=	file_format
          UpperCamelCase    :  Optional[int]							=	Spark(
              df=SCREAMING_SNAKE_CASE_,			features=SCREAMING_SNAKE_CASE_,			cache_dir=SCREAMING_SNAKE_CASE_,			working_dir=SCREAMING_SNAKE_CASE_,			**SCREAMING_SNAKE_CASE_,			)






       def 					snake_case_       (			self       )     ->		int:
          if self.streaming:
             return self.builder.as_streaming_dataset(split=self.split       )
          UpperCamelCase    :  Union[str, Any]							=	None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
          self.builder.download_and_prepare(
              download_mode=SCREAMING_SNAKE_CASE_,			file_format=self._file_format,			)
          return self.builder.as_dataset(split=self.split       )



