

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
class  __lowerCamelCase				(	__snake_case	):
      lowerCamelCase_	:      torch.FloatTensor
      lowerCamelCase_	:      torch.FloatTensor



def    UpperCamelCase(					lowercase_			,					lowercase_=0.9_99			,					lowercase_="cosine"			,					)	->				Optional[Any]:





  '''simple docstring'''





  if alpha_transform_type == "cosine":

    def alpha_bar_fn(lowercase_							):
      return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2							) ** 2

  elif alpha_transform_type == "exp":

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

  else:
    raise ValueError(f'''Unsupported alpha_tranform_type: {alpha_transform_type}'''							)

  snake_case_       						=      []
  for i in range(lowercase_							):
    snake_case_       						=      i / num_diffusion_timesteps
    snake_case_       						=      (i + 1) / num_diffusion_timesteps
    betas.append(min(1 - alpha_bar_fn(lowercase_							) / alpha_bar_fn(lowercase_							)			,					lowercase_							)							)
  return torch.tensor(lowercase_			,					dtype=torch.floataa							)







class  __lowerCamelCase				(	__snake_case  ,							__snake_case	):
      lowerCamelCase_	:      List[str]          =					1







      @register_to_config
      def __init__(     self							,  lowerCamelCase = 1000							,  lowerCamelCase = 0.0001							,  lowerCamelCase = 0.02							,  lowerCamelCase = "linear"							,  lowerCamelCase = 0.0							,  lowerCamelCase = None							,  lowerCamelCase = True							,  )						->       Union[str, Any]:
        if trained_betas is not None:
          snake_case_       						=      torch.from_numpy(lowerCamelCase   )
        elif beta_schedule == "linear":
          snake_case_       						=      torch.linspace(lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  dtype=torch.floataa   )
        elif beta_schedule == "scaled_linear":
          # this schedule is very specific to the latent diffusion model.
          snake_case_       						=      (
              torch.linspace(beta_start**0.5							,  beta_end**0.5							,  lowerCamelCase							,  dtype=torch.floataa   ) ** 2
          )
        elif beta_schedule == "squaredcos_cap_v2":
          # Glide cosine schedule
          snake_case_       						=      betas_for_alpha_bar(lowerCamelCase   )
        elif beta_schedule == "sigmoid":
          # GeoDiff sigmoid schedule
          snake_case_       						=      torch.linspace(-6							,  6							,  lowerCamelCase   )
          snake_case_       						=      torch.sigmoid(lowerCamelCase   ) * (beta_end - beta_start) + beta_start
        else:
          raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''   )

        snake_case_       						=      1.0 - self.betas
        snake_case_       						=      torch.cumprod(self.alphas							,  dim=0   )
        snake_case_       						=      torch.tensor(1.0   )

        snake_case_       						=      torch.tensor(1.0   )

        # standard deviation of the initial noise distribution
        snake_case_       						=      1.0

        # setable values
        snake_case_       						=      None
        snake_case_       						=      torch.from_numpy(np.arange(0							,  lowerCamelCase   )[::-1].copy()   )

        snake_case_       						=      eta







      def 	lowerCAmelCase_    (     self							,  lowerCamelCase							,  lowerCamelCase = None   )						->       torch.FloatTensor:
        return sample







      def 	lowerCAmelCase_    (     self							,  lowerCamelCase							,  lowerCamelCase = 10							,  lowerCamelCase = 10							,  lowerCamelCase = None							,  )						->       Optional[Any]:
        snake_case_       						=      min(self.config.num_train_timesteps							,  lowerCamelCase   )
        snake_case_       						=      num_inference_steps

        snake_case_       						=      []

        snake_case_       						=      {}
        for j in range(0							,  num_inference_steps - jump_length							,  lowerCamelCase   ):
          snake_case_       						=      jump_n_sample - 1

        snake_case_       						=      num_inference_steps
        while t >= 1:
          snake_case_       						=      t - 1
          timesteps.append(lowerCamelCase   )

          if jumps.get(lowerCamelCase							,  0   ) > 0:
            snake_case_       						=      jumps[t] - 1
            for _ in range(lowerCamelCase   ):
              snake_case_       						=      t + 1
              timesteps.append(lowerCamelCase   )

        snake_case_       						=      np.array(lowerCamelCase   ) * (self.config.num_train_timesteps // self.num_inference_steps)
        snake_case_       						=      torch.from_numpy(lowerCamelCase   ).to(lowerCamelCase   )







      def 	lowerCAmelCase_    (     self							,  lowerCamelCase   )						->       Tuple:
        snake_case_       						=      t - self.config.num_train_timesteps // self.num_inference_steps

        snake_case_       						=      self.alphas_cumprod[t]
        snake_case_       						=      self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
        snake_case_       						=      1 - alpha_prod_t
        snake_case_       						=      1 - 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
        # Is equivalent to formula (16) in https://arxiv.org/pdf/2010.02502.pdf
        # without eta.
        # variance = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * self.betas[t]
        snake_case_       						=      (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

        return variance







      def 	lowerCAmelCase_    (     self							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase = None							,  lowerCamelCase = True							,  )						->       Union[RePaintSchedulerOutput, Tuple]:
        snake_case_       						=      timestep
        snake_case_       						=      timestep - self.config.num_train_timesteps // self.num_inference_steps

        # 1. compute alphas, betas
        snake_case_       						=      self.alphas_cumprod[t]
        snake_case_       						=      self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
        snake_case_       						=      1 - alpha_prod_t

        # 2. compute predicted original sample from predicted noise also called
        # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
        snake_case_       						=      (sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5

        # 3. Clip "predicted x_0"
        if self.config.clip_sample:
          snake_case_       						=      torch.clamp(lowerCamelCase							,  -1							,  1   )

        # We choose to follow RePaint Algorithm 1 to get x_{t-1}, however we
        # substitute formula (7) in the algorithm coming from DDPM paper
        # (formula (4) Algorithm 2 - Sampling) with formula (12) from DDIM paper.
        # DDIM schedule gives the same results as DDPM with eta = 1.0
        # Noise is being reused in 7. and 8., but no impact on quality has
        # been observed.

        # 5. Add noise
        snake_case_       						=      model_output.device
        snake_case_       						=      randn_tensor(model_output.shape							,  generator=lowerCamelCase							,  device=lowerCamelCase							,  dtype=model_output.dtype   )
        snake_case_       						=      self.eta * self._get_variance(lowerCamelCase   ) ** 0.5

        snake_case_       						=      0
        if t > 0 and self.eta > 0:
          snake_case_       						=      std_dev_t * noise

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

        # 7. compute x_{t-1} of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
        snake_case_       						=      alpha_prod_t_prev**0.5 * pred_original_sample + pred_sample_direction + variance

        # 8. Algorithm 1 Line 5 https://arxiv.org/pdf/2201.09865.pdf
        snake_case_       						=      (alpha_prod_t_prev**0.5) * original_image + ((1 - alpha_prod_t_prev) ** 0.5) * noise

        # 9. Algorithm 1 Line 8 https://arxiv.org/pdf/2201.09865.pdf
        snake_case_       						=      mask * prev_known_part + (1.0 - mask) * prev_unknown_part

        if not return_dict:
          return (
              pred_prev_sample,
              pred_original_sample,
          )

        return RePaintSchedulerOutput(prev_sample=lowerCamelCase							,  pred_original_sample=lowerCamelCase   )







      def 	lowerCAmelCase_    (     self							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase=None   )						->       Any:
        snake_case_       						=      self.config.num_train_timesteps // self.num_inference_steps

        for i in range(lowerCamelCase   ):
          snake_case_       						=      self.betas[timestep + i]
          if sample.device.type == "mps":
            # randn does not work reproducibly on mps
            snake_case_       						=      randn_tensor(sample.shape							,  dtype=sample.dtype							,  generator=lowerCamelCase   )
            snake_case_       						=      noise.to(sample.device   )
          else:
            snake_case_       						=      randn_tensor(sample.shape							,  generator=lowerCamelCase							,  device=sample.device							,  dtype=sample.dtype   )

          # 10. Algorithm 1 Line 10 https://arxiv.org/pdf/2201.09865.pdf
          snake_case_       						=      (1 - beta) ** 0.5 * sample + beta**0.5 * noise

        return sample







      def 	lowerCAmelCase_    (     self							,  lowerCamelCase							,  lowerCamelCase							,  lowerCamelCase							,  )						->       torch.FloatTensor:
        raise NotImplementedError("""Use `DDPMScheduler.add_noise()` to train for sampling with RePaint."""   )







      def __len__(     self   )						->       Optional[Any]:
        return self.config.num_train_timesteps





import json
import os
from dataclasses import dataclass

import numpy as np
import pyarrow as pa

import datasets
from utils import get_duration


UpperCamelCase__              =						1_0_0_0_0_0_0_0_0_0_0_0
UpperCamelCase__              =						1_0_0_0_0

UpperCamelCase__       , UpperCamelCase__              =						os.path.split(__file__)
UpperCamelCase__              =						os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))






def 					snake_case					(							A						,   A			)							->							datasets.Dataset:




	'''simple docstring'''


	lowerCamelCase_							:		int							   =							pa.Table.from_pydict({'col': [0] * chunk_size}			)
	lowerCamelCase_							:		List[str]							   =							pa.concat_tables([table] * (num_examples // chunk_size)			)
	return datasets.Dataset(A						,   fingerprint='table_100B'			)


@dataclass
class 				__SCREAMING_SNAKE_CASE							:
					_lowerCamelCase		  = 42
					_lowerCamelCase		  = 42
					_lowerCamelCase		  = 42
					_lowerCamelCase		  = 42

					def 	lowercase_							(				self					):







						'''simple docstring'''




						lowerCamelCase_							:		Optional[Any]							   =							np.random.default_rng(self.seed					)
						lowerCamelCase_							:		Any							   =							rng.integers(low=self.low ,       high=self.high ,       size=self.size					).tolist()

					def __iter__(				self					):







						'''simple docstring'''




						return iter(self._sampled_values					)





					def __len__(				self					):







						'''simple docstring'''




						return self.size






@get_duration
def 					snake_case					(							A			)							->							Optional[Any]:




	'''simple docstring'''


	lowerCamelCase_							:		Union[str, Any]							   =							dataset[0]






@get_duration
def 					snake_case					(							A			)							->							Any:




	'''simple docstring'''


	lowerCamelCase_							:		List[Any]							   =							dataset[-1]






@get_duration
def 					snake_case					(							A			)							->							Optional[Any]:




	'''simple docstring'''


	lowerCamelCase_							:		Tuple							   =							dataset[range(len(A			) // 2						,   len(A			) // 2 + 1024			)]






@get_duration
def 					snake_case					(							A			)							->							str:




	'''simple docstring'''


	lowerCamelCase_							:		List[str]							   =							dataset[RandIter(0						,   len(A			)						,   1024						,   seed=42			)]






def 					snake_case					(							)							->							List[Any]:




	'''simple docstring'''


	lowerCamelCase_							:		Tuple							   =							{'num examples': SPEED_TEST_N_EXAMPLES}
	lowerCamelCase_							:		Optional[int]							   =							(get_first_row, get_last_row, get_batch_of_aaaa_rows, get_batch_of_aaaa_random_rows)
	print('generating dataset'			)
	lowerCamelCase_							:		List[Any]							   =							generate_100B_dataset(num_examples=A						,   chunk_size=A			)
	print('Functions'			)
	for func in functions:
		print(func.__name__			)
		lowerCamelCase_							:		str							   =							func(A			)

	with open(A						,   'wb'			) as f:
		f.write(json.dumps(A			).encode('utf-8'			)			)


if __name__ == "__main__":  # useful to run the profiler
							benchmark_table_100B()
