






# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.schedulers.scheduling_utils import SchedulerMixin
from diffusers.utils import BaseOutput, deprecate







@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
class 		_A						( UpperCAmelCase_ ):
  lowercase_						:			torch.FloatTensor
  lowercase_						:			Optional[torch.FloatTensor]						=					None







def 				__lowerCamelCase     (							__lowerCAmelCase							:					Any					,							__lowerCAmelCase							:					List[Any]=0.999					,							__lowerCAmelCase							:					str="cosine"					,							)     ->  Optional[Any]:
   if alpha_transform_type == "cosine":

      def alpha_bar_fn(__lowerCAmelCase							:					int							):
         return math.cos((t + 0.008) / 1.008 * math.pi / 2							) ** 2

   elif alpha_transform_type == "exp":

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

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

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







class 		_A						( UpperCAmelCase_	,				UpperCAmelCase_ ):
  lowercase_						:			str						=					1

  @register_to_config
  def __init__(    self						:	str   ,				lowerCamelCase__						:	int = 10_00   ,				lowerCamelCase__						:	float = 0.0001   ,				lowerCamelCase__						:	float = 0.02   ,				lowerCamelCase__						:	str = "linear"   ,				lowerCamelCase__						:	Optional[Union[np.ndarray, List[float]]] = None   ,				lowerCamelCase__						:	bool = True   ,				lowerCamelCase__						:	bool = True   ,				lowerCamelCase__						:	int = 0   ,				lowerCamelCase__						:	str = "epsilon"   ,				lowerCamelCase__						:	float = 1.0   ,				**lowerCamelCase__						:	Optional[int]   ,				):
     """simple docstring"""

     if kwargs.get("""set_alpha_to_one"""   ,				lowerCamelCase__			) is not None:
        __UpperCamelCase	:			Optional[Any]					=					(
            """The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead."""
        )
        deprecate("""set_alpha_to_one"""   ,				"""1.0.0"""   ,				lowerCamelCase__   ,				standard_warn=lowerCamelCase__			)
        __UpperCamelCase	:			int					=					kwargs["""set_alpha_to_one"""]
     if trained_betas is not None:
        __UpperCamelCase	:			Union[str, Any]					=					torch.tensor(lowerCamelCase__   ,				dtype=torch.floataa			)
     elif beta_schedule == "linear":
        __UpperCamelCase	:			Union[str, Any]					=					torch.linspace(lowerCamelCase__   ,				lowerCamelCase__   ,				lowerCamelCase__   ,				dtype=torch.floataa			)
     elif beta_schedule == "scaled_linear":
        # this schedule is very specific to the latent diffusion model.
        __UpperCamelCase	:			List[str]					=					(
            torch.linspace(beta_start**0.5   ,				beta_end**0.5   ,				lowerCamelCase__   ,				dtype=torch.floataa			) ** 2
        )
     elif beta_schedule == "squaredcos_cap_v2":
        # Glide cosine schedule
        __UpperCamelCase	:			List[Any]					=					betas_for_alpha_bar(lowerCamelCase__			)
     else:
        raise NotImplementedError(f'{beta_schedule} does is not implemented for {self.__class__}'			)

     __UpperCamelCase	:			List[Any]					=					1.0 - self.betas
     __UpperCamelCase	:			Tuple					=					torch.cumprod(self.alphas   ,				dim=0			)

     # At every step in inverted ddim, we are looking into the next alphas_cumprod
     # For the final step, there is no next alphas_cumprod, and the index is out of bounds
     # `set_alpha_to_zero` decides whether we set this parameter simply to zero
     # in this case, self.step() just output the predicted noise
     # or whether we use the final alpha of the "non-previous" one.
     __UpperCamelCase	:			int					=					torch.tensor(0.0			) if set_alpha_to_zero else self.alphas_cumprod[-1]

     # standard deviation of the initial noise distribution
     __UpperCamelCase	:			Tuple					=					1.0

     # setable values
     __UpperCamelCase	:			Optional[Any]					=					None
     __UpperCamelCase	:			str					=					torch.from_numpy(np.arange(0   ,				lowerCamelCase__			).copy().astype(np.intaa			)			)

  def    a					(    self						:	Optional[int]   ,				lowerCamelCase__						:	torch.FloatTensor   ,				lowerCamelCase__						:	Optional[int] = None			):
     """simple docstring"""

     return sample

  def    a					(    self						:	Tuple   ,				lowerCamelCase__						:	int   ,				lowerCamelCase__						:	Union[str, torch.device] = None			):
     """simple docstring"""

     if num_inference_steps > self.config.num_train_timesteps:
        raise ValueError(
            f'`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'
            f' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'
            f' maximal {self.config.num_train_timesteps} timesteps.'			)

     __UpperCamelCase	:			Any					=					num_inference_steps
     __UpperCamelCase	:			Any					=					self.config.num_train_timesteps // self.num_inference_steps
     # creates integer timesteps by multiplying by ratio
     # casting to int to avoid issues when num_inference_step is power of 3
     __UpperCamelCase	:			Union[str, Any]					=					(np.arange(0   ,				lowerCamelCase__			) * step_ratio).round().copy().astype(np.intaa			)
     __UpperCamelCase	:			Any					=					torch.from_numpy(lowerCamelCase__			).to(lowerCamelCase__			)
     self.timesteps += self.config.steps_offset

  def    a					(    self						:	Tuple   ,				lowerCamelCase__						:	torch.FloatTensor   ,				lowerCamelCase__						:	int   ,				lowerCamelCase__						:	torch.FloatTensor   ,				lowerCamelCase__						:	float = 0.0   ,				lowerCamelCase__						:	bool = False   ,				lowerCamelCase__						:	Optional[torch.FloatTensor] = None   ,				lowerCamelCase__						:	bool = True   ,				):
     """simple docstring"""

     __UpperCamelCase	:			List[str]					=					timestep + self.config.num_train_timesteps // self.num_inference_steps

     # 2. compute alphas, betas
     # change original implementation to exactly match noise levels for analogous forward process
     __UpperCamelCase	:			Any					=					self.alphas_cumprod[timestep]
     __UpperCamelCase	:			Union[str, Any]					=					(
         self.alphas_cumprod[prev_timestep]
         if prev_timestep < self.config.num_train_timesteps
         else self.final_alpha_cumprod
     )

     __UpperCamelCase	:			Tuple					=					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
     if self.config.prediction_type == "epsilon":
        __UpperCamelCase	:			int					=					(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
        __UpperCamelCase	:			List[str]					=					model_output
     elif self.config.prediction_type == "sample":
        __UpperCamelCase	:			List[Any]					=					model_output
        __UpperCamelCase	:			Any					=					(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
     elif self.config.prediction_type == "v_prediction":
        __UpperCamelCase	:			List[str]					=					(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
        __UpperCamelCase	:			Optional[Any]					=					(alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
     else:
        raise ValueError(
            f'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or'
            """ `v_prediction`"""			)

     # 4. Clip or threshold "predicted x_0"
     if self.config.clip_sample:
        __UpperCamelCase	:			List[str]					=					pred_original_sample.clamp(
            -self.config.clip_sample_range   ,				self.config.clip_sample_range			)

     # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
     __UpperCamelCase	:			Optional[int]					=					(1 - alpha_prod_t_prev) ** 0.5 * pred_epsilon

     # 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
     __UpperCamelCase	:			Tuple					=					alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

     if not return_dict:
        return (prev_sample, pred_original_sample)
     return DDIMSchedulerOutput(prev_sample=lowerCamelCase__   ,				pred_original_sample=lowerCamelCase__			)

  def __len__(    self						:	List[Any]			):
     """simple docstring"""

     return self.config.num_train_timesteps











import os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


UpperCamelCase    							=      namedtuple(
    '_TestCommandArgs',
    [
        'dataset',
        'name',
        'cache_dir',
        'data_dir',
        'all_configs',
        'save_infos',
        'ignore_verifications',
        'force_redownload',
        'clear_cache',
    ],
    defaults=[None, None, None, False, False, False, False, False],
)


def 				__lowerCamelCase     (							__lowerCAmelCase							:					List[Any]					,							__lowerCAmelCase							:					Any							)     ->  Optional[int]:
   return (abs(source - target							) / target) < 0.01



@pytest.mark.integration
def 				__lowerCamelCase     (							__lowerCAmelCase							:					str							)     ->  Union[str, Any]:
   __UpperCamelCase	:			Dict					=					_TestCommandArgs(dataset=__lowerCAmelCase					,							all_configs=__lowerCAmelCase					,							save_infos=__lowerCAmelCase							)
   __UpperCamelCase	:			List[Any]					=					TestCommand(*__lowerCAmelCase							)
   test_command.run()
   __UpperCamelCase	:			Any					=					os.path.join(__lowerCAmelCase					,							"""README.md"""							)
   assert os.path.exists(__lowerCAmelCase							)
   __UpperCamelCase	:			Optional[int]					=					DatasetInfosDict.from_directory(__lowerCAmelCase							)
   __UpperCamelCase	:			List[Any]					=					DatasetInfosDict(
       {
           """default""": DatasetInfo(
               features=Features(
                   {
                       """tokens""": Sequence(Value("""string"""							)							),
                       """ner_tags""": Sequence(
                           ClassLabel(names=["""O""", """B-PER""", """I-PER""", """B-ORG""", """I-ORG""", """B-LOC""", """I-LOC"""]							)							),
                       """langs""": Sequence(Value("""string"""							)							),
                       """spans""": Sequence(Value("""string"""							)							),
                   }							)					,							splits=[
                   {
                       """name""": """train""",
                       """num_bytes""": 2351563,
                       """num_examples""": 10000,
                   },
                   {
                       """name""": """validation""",
                       """num_bytes""": 238418,
                       """num_examples""": 1000,
                   },
               ]					,							download_size=3940680					,							dataset_size=2589981					,							)
       }							)
   assert dataset_infos.keys() == expected_dataset_infos.keys()
   for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
      __UpperCamelCase	,     __UpperCamelCase	:			Union[str, Any]					=					getattr(dataset_infos["""default"""]					,							__lowerCAmelCase							), getattr(expected_dataset_infos["""default"""]					,							__lowerCAmelCase							)
      if key == "num_bytes":
         assert is_apercent_close(__lowerCAmelCase					,							__lowerCAmelCase							)
      elif key == "splits":
         assert list(__lowerCAmelCase							) == list(__lowerCAmelCase							)
         for split in result:
            assert result[split].name == expected[split].name
            assert result[split].num_examples == expected[split].num_examples
            assert is_apercent_close(result[split].num_bytes					,							expected[split].num_bytes							)
      else:
         result == expected



