'''simple docstring'''

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 							lowercase						(						_snake_case ):
   lowerCamelCase_    					=42
   lowerCamelCase_    					=None




def __a (	__lowerCamelCase			:						Optional[int] ,		__lowerCamelCase			:						List[str]=0.9_9_9 ,		__lowerCamelCase			:						Union[str, Any]="cosine" ,		)	->							str:
      '''simple docstring'''


      if alpha_transform_type == "cosine":

            def alpha_bar_fn(__lowerCamelCase			:						Any				):
                  return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2				) ** 2

      elif alpha_transform_type == "exp":

            def alpha_bar_fn(__lowerCamelCase			:						Optional[Any]				):
                  return math.exp(t * -12.0				)

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

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






class 							lowercase						(						_snake_case ,				_snake_case ):


   @register_to_config
   def __init__(   self				:	Optional[int]   ,		__lowerCAmelCase				:	int = 1000   ,		__lowerCAmelCase				:	str = "fixed_small_log"   ,		__lowerCAmelCase				:	bool = True   ,		__lowerCAmelCase				:	Optional[float] = 1.0   ,		__lowerCAmelCase				:	str = "epsilon"   ,		__lowerCAmelCase				:	str = "squaredcos_cap_v2"   ,		)      ->					List[Any]:

         if beta_schedule != "squaredcos_cap_v2":
               raise ValueError("UnCLIPScheduler only supports `beta_schedule`: 'squaredcos_cap_v2'")

         lowercase_     =      betas_for_alpha_bar(__lowerCAmelCase)

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

         # standard deviation of the initial noise distribution
         lowercase_     =      1.0

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

         lowercase_     =      variance_type


   def __UpperCAmelCase       (   self				:	List[str]   ,		__lowerCAmelCase				:	torch.FloatTensor   ,		__lowerCAmelCase				:	Optional[int] = None)      ->					Any:
         return sample


   def __UpperCAmelCase       (   self				:	List[Any]   ,		__lowerCAmelCase				:	int   ,		__lowerCAmelCase				:	Union[str, torch.device] = None)      ->					List[str]:
         lowercase_     =      num_inference_steps
         lowercase_     =      (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
         lowercase_     =      (np.arange(0   ,		__lowerCAmelCase) * step_ratio).round()[::-1].copy().astype(np.intaa)
         lowercase_     =      torch.from_numpy(__lowerCAmelCase).to(__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	Optional[int]   ,		__lowerCAmelCase				:	Any   ,		__lowerCAmelCase				:	List[str]=None   ,		__lowerCAmelCase				:	Optional[int]=None   ,		__lowerCAmelCase				:	Any=None)      ->					int:

         if prev_timestep is None:
               lowercase_     =      t - 1

         lowercase_     =      self.alphas_cumprod[t]
         lowercase_     =      self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
         lowercase_     =      1 - alpha_prod_t
         lowercase_     =      1 - alpha_prod_t_prev

         if prev_timestep == t - 1:
               lowercase_     =      self.betas[t]
         else:
               lowercase_     =      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
         lowercase_     =      beta_prod_t_prev / beta_prod_t * beta

         if variance_type is None:
               lowercase_     =      self.config.variance_type

         # hacks - were probably added for training stability
         if variance_type == "fixed_small_log":
               lowercase_     =      torch.log(torch.clamp(__lowerCAmelCase   ,		min=1e-20))
               lowercase_     =      torch.exp(0.5 * variance)
         elif variance_type == "learned_range":
               # NOTE difference with DDPM scheduler
               lowercase_     =      variance.log()
               lowercase_     =      beta.log()

               lowercase_     =      (predicted_variance + 1) / 2
               lowercase_     =      frac * max_log + (1 - frac) * min_log

         return variance


   def __UpperCAmelCase       (   self				:	List[Any]   ,		__lowerCAmelCase				:	torch.FloatTensor   ,		__lowerCAmelCase				:	int   ,		__lowerCAmelCase				:	torch.FloatTensor   ,		__lowerCAmelCase				:	Optional[int] = None   ,		__lowerCAmelCase				:	List[str]=None   ,		__lowerCAmelCase				:	bool = True   ,		)      ->					Union[str, Any]:
         lowercase_     =      timestep

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

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

         lowercase_     =      self.alphas_cumprod[t]
         lowercase_     =      self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
         lowercase_     =      1 - alpha_prod_t
         lowercase_     =      1 - alpha_prod_t_prev

         if prev_timestep == t - 1:
               lowercase_     =      self.betas[t]
               lowercase_     =      self.alphas[t]
         else:
               lowercase_     =      1 - alpha_prod_t / alpha_prod_t_prev
               lowercase_     =      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":
               lowercase_     =      (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
         elif self.config.prediction_type == "sample":
               lowercase_     =      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:
               lowercase_     =      torch.clamp(
                   __lowerCAmelCase   ,		-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
         lowercase_     =      (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
         lowercase_     =      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
         lowercase_     =      pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

         # 6. Add noise
         lowercase_     =      0
         if t > 0:
               lowercase_     =      randn_tensor(
                   model_output.shape   ,		dtype=model_output.dtype   ,		generator=__lowerCAmelCase   ,		device=model_output.device)

               lowercase_     =      self._get_variance(
                   __lowerCAmelCase   ,		predicted_variance=__lowerCAmelCase   ,		prev_timestep=__lowerCAmelCase   ,		)

               if self.variance_type == "fixed_small_log":
                     lowercase_     =      variance
               elif self.variance_type == "learned_range":
                     lowercase_     =      (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.")

               lowercase_     =      variance * variance_noise

         lowercase_     =      pred_prev_sample + variance

         if not return_dict:
               return (pred_prev_sample,)

         return UnCLIPSchedulerOutput(prev_sample=__lowerCAmelCase   ,		pred_original_sample=__lowerCAmelCase)


   def __UpperCAmelCase       (   self				:	str   ,		__lowerCAmelCase				:	torch.FloatTensor   ,		__lowerCAmelCase				:	torch.FloatTensor   ,		__lowerCAmelCase				:	torch.IntTensor   ,		)      ->					Optional[int]:
         lowercase_     =      self.alphas_cumprod.to(device=original_samples.device   ,		dtype=original_samples.dtype)
         lowercase_     =      timesteps.to(original_samples.device)

         lowercase_     =      alphas_cumprod[timesteps] ** 0.5
         lowercase_     =      sqrt_alpha_prod.flatten()
         while len(sqrt_alpha_prod.shape) < len(original_samples.shape):
               lowercase_     =      sqrt_alpha_prod.unsqueeze(-1)

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

         lowercase_     =      sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
         return noisy_samples







'''simple docstring'''

import json
import sys


def __a (	__lowerCamelCase			:						Optional[int] ,		__lowerCamelCase			:						Union[str, Any]				)	->							Optional[Any]:
      '''simple docstring'''


      with open(__lowerCamelCase ,		encoding="utf-8"				) as f:
            lowercase_     =      json.load(__lowerCamelCase				)

      lowercase_     =      ["<details>", "<summary>Show updated benchmarks!</summary>", " "]

      for benchmark_name in sorted(__lowerCamelCase				):
            lowercase_     =      results[benchmark_name]

            lowercase_     =      benchmark_name.split("/"				)[-1]
            output_md.append(f'### Benchmark: {benchmark_file_name}'				)

            lowercase_     =      "| metric |"
            lowercase_     =      "|--------|"
            lowercase_     =      "| new / old (diff) |"
            for metric_name in sorted(__lowerCamelCase				):
                  lowercase_     =      benchmark_res[metric_name]
                  lowercase_     =      metric_vals["new"]
                  lowercase_     =      metric_vals.get("old" ,		__lowerCamelCase				)
                  lowercase_     =      metric_vals.get("diff" ,		__lowerCamelCase				)

                  lowercase_     =      f' {new_val:f}' if isinstance(__lowerCamelCase ,		(int, float)				) else "None"

                  if old_val is not None:
                        val_str += f' / {old_val:f}' if isinstance(__lowerCamelCase ,		(int, float)				) else "None"
                  if dif_val is not None:
                        val_str += f' ({dif_val:f})' if isinstance(__lowerCamelCase ,		(int, float)				) else "None"

                  title += " " + metric_name + " |"
                  lines += "---|"
                  value += val_str + " |"

            output_md += [title, lines, value, " "]

      output_md.append("</details>"				)

      with open(__lowerCamelCase ,		"w" ,		encoding="utf-8"				) as f:
            f.writelines("\n".join(__lowerCamelCase				)				)


if __name__ == "__main__":
       lowerCAmelCase_ : List[Any]						  =      sys.argv[1]
       lowerCAmelCase_ : Any						  =      sys.argv[2]

       format_json_to_md(input_json_file, output_md_file)






