code: 
  '''simple docstring'''
  
  import math
  import warnings
  from dataclasses import dataclass
  from typing import List, Optional, Tuple, Union
  
  import numpy as np
  import torch
  from scipy import integrate
  
  from ..configuration_utils import ConfigMixin, register_to_config
  from ..utils import BaseOutput
  from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin
  
  
  @dataclass
  # Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->LMSDiscrete
  class   a_  (_a    ):
         __lowerCAmelCase    :				torch.FloatTensor
         __lowerCAmelCase    :				Optional[torch.FloatTensor]    =      None
  
  
  
  def 	_UpperCAmelCase				(  _lowerCamelCase						:       str  ,      _lowerCamelCase						:       List[str]=0.999  ,      _lowerCamelCase						:       str="cosine"  ,      )    ->						Union[str, Any]:
    if alpha_transform_type == "cosine":
  
      def alpha_bar_fn(_lowerCamelCase						:       Any		):
        return math.cos((t + 0.008) / 1.008 * math.pi / 2		) ** 2
  
    elif alpha_transform_type == "exp":
  
      def alpha_bar_fn(_lowerCamelCase						:       Optional[int]		):
        return math.exp(t * -12.0		)
  
    else:
      raise ValueError(f'Unsupported alpha_tranform_type: {alpha_transform_type}'		)
  
    _lowerCAmelCase       :  int   				=					[]
    for i in range(_lowerCamelCase		):
      _lowerCAmelCase       :  Union[str, Any]   				=					i / num_diffusion_timesteps
      _lowerCAmelCase       :  List[str]   				=					(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_  (_a							,			_a    ):
         __lowerCAmelCase    :				Any    =      [e.name for e in KarrasDiffusionSchedulers]
         __lowerCAmelCase    :				Optional[Any]    =      1
  
  
  
  
         @register_to_config
         def __init__(     self   ,   snake_case_ = 1_0_0_0   ,   snake_case_ = 0.0001   ,   snake_case_ = 0.02   ,   snake_case_ = "linear"   ,   snake_case_ = None   ,   snake_case_ = False   ,   snake_case_ = "epsilon"   ,   snake_case_ = "linspace"   ,   snake_case_ = 0   ,   ):
           if trained_betas is not None:
             _lowerCAmelCase       :  List[str]   				=					torch.tensor(snake_case_   ,   dtype=torch.floataa  )
           elif beta_schedule == "linear":
             _lowerCAmelCase       :  int   				=					torch.linspace(snake_case_   ,   snake_case_   ,   snake_case_   ,   dtype=torch.floataa  )
           elif beta_schedule == "scaled_linear":
             # this schedule is very specific to the latent diffusion model.
             _lowerCAmelCase       :  List[str]   				=					(
                 torch.linspace(beta_start**0.5   ,   beta_end**0.5   ,   snake_case_   ,   dtype=torch.floataa  ) ** 2
             )
           elif beta_schedule == "squaredcos_cap_v2":
             # Glide cosine schedule
             _lowerCAmelCase       :  List[str]   				=					betas_for_alpha_bar(snake_case_  )
           else:
             raise NotImplementedError(f'{beta_schedule} does is not implemented for {self.__class__}'  )
  
           _lowerCAmelCase       :  Optional[int]   				=					1.0 - self.betas
           _lowerCAmelCase       :  int   				=					torch.cumprod(self.alphas   ,   dim=0  )
  
           _lowerCAmelCase       :  Dict   				=					np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5  )
           _lowerCAmelCase       :  Optional[int]   				=					np.concatenate([sigmas[::-1], [0.0]]  ).astype(np.floataa  )
           _lowerCAmelCase       :  Any   				=					torch.from_numpy(snake_case_  )
  
           # setable values
           _lowerCAmelCase       :  Union[str, Any]   				=					None
           _lowerCAmelCase       :  Optional[int]   				=					use_karras_sigmas
           self.set_timesteps(snake_case_   ,   snake_case_  )
           _lowerCAmelCase       :  List[Any]   				=					[]
           _lowerCAmelCase       :  int   				=					False
  
  
  
  
         @property
         def 							__UpperCamelCase       (     self  ):
           # standard deviation of the initial noise distribution
           if self.config.timestep_spacing in ["linspace", "trailing"]:
             return self.sigmas.max()
  
           return (self.sigmas.max() ** 2 + 1) ** 0.5
  
  
  
  
         def 							__UpperCamelCase       (     self   ,   snake_case_   ,   snake_case_  ):
           if isinstance(snake_case_   ,   torch.Tensor  ):
             _lowerCAmelCase       :  str   				=					timestep.to(self.timesteps.device  )
           _lowerCAmelCase       :  Dict   				=					(self.timesteps == timestep).nonzero().item()
           _lowerCAmelCase       :  Union[str, Any]   				=					self.sigmas[step_index]
           _lowerCAmelCase       :  Optional[Any]   				=					sample / ((sigma**2 + 1) ** 0.5)
           _lowerCAmelCase       :  Union[str, Any]   				=					True
           return sample
  
  
  
  
         def 							__UpperCamelCase       (     self   ,   snake_case_   ,   snake_case_   ,   snake_case_  ):
  
           def lms_derivative(snake_case_  ):
             _lowerCAmelCase       :  Optional[int]   				=					1.0
             for k in range(snake_case_  ):
               if current_order == k:
                 continue
               prod *= (tau - self.sigmas[t - k]) / (self.sigmas[t - current_order] - self.sigmas[t - k])
             return prod
  
           _lowerCAmelCase       :  Union[str, Any]   				=					integrate.quad(snake_case_   ,   self.sigmas[t]   ,   self.sigmas[t + 1]   ,   epsrel=1E-4  )[0]
  
           return integrated_coeff
  
  
  
  
         def 							__UpperCamelCase       (     self   ,   snake_case_   ,   snake_case_ = None  ):
           _lowerCAmelCase       :  Dict   				=					num_inference_steps
  
           # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
           if self.config.timestep_spacing == "linspace":
             _lowerCAmelCase       :  Any   				=					np.linspace(0   ,   self.config.num_train_timesteps - 1   ,   snake_case_   ,   dtype=snake_case_  )[
                 ::-1
             ].copy()
           elif self.config.timestep_spacing == "leading":
             _lowerCAmelCase       :  List[str]   				=					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
             _lowerCAmelCase       :  Union[str, Any]   				=					(np.arange(0   ,   snake_case_  ) * step_ratio).round()[::-1].copy().astype(snake_case_  )
             timesteps += self.config.steps_offset
           elif self.config.timestep_spacing == "trailing":
             _lowerCAmelCase       :  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
             _lowerCAmelCase       :  Optional[int]   				=					(np.arange(self.config.num_train_timesteps   ,   0   ,   -step_ratio  )).round().copy().astype(snake_case_  )
             timesteps -= 1
           else:
             raise ValueError(
                 f'{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'  )
  
           _lowerCAmelCase       :  Tuple   				=					np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5  )
           _lowerCAmelCase       :  Union[str, Any]   				=					np.log(snake_case_  )
           _lowerCAmelCase       :  Optional[int]   				=					np.interp(snake_case_   ,   np.arange(0   ,   len(snake_case_  )  )   ,   snake_case_  )
  
           if self.use_karras_sigmas:
             _lowerCAmelCase       :  List[Any]   				=					self._convert_to_karras(in_sigmas=snake_case_  )
             _lowerCAmelCase       :  Dict   				=					np.array([self._sigma_to_t(snake_case_   ,   snake_case_  ) for sigma in sigmas]  )
  
           _lowerCAmelCase       :  int   				=					np.concatenate([sigmas, [0.0]]  ).astype(np.floataa  )
  
           _lowerCAmelCase       :  int   				=					torch.from_numpy(snake_case_  ).to(device=snake_case_  )
           if str(snake_case_  ).startswith("""mps"""  ):
             # mps does not support float64
             _lowerCAmelCase       :  Any   				=					torch.from_numpy(snake_case_  ).to(snake_case_   ,   dtype=torch.floataa  )
           else:
             _lowerCAmelCase       :  Tuple   				=					torch.from_numpy(snake_case_  ).to(device=snake_case_  )
  
           _lowerCAmelCase       :  Optional[int]   				=					[]
  
  
  
  
         def 							__UpperCamelCase       (     self   ,   snake_case_   ,   snake_case_  ):
           # get log sigma
           _lowerCAmelCase       :  str   				=					np.log(snake_case_  )
  
           # get distribution
           _lowerCAmelCase       :  int   				=					log_sigma - log_sigmas[:, np.newaxis]
  
           # get sigmas range
           _lowerCAmelCase       :  int   				=					np.cumsum((dists >= 0)   ,   axis=0  ).argmax(axis=0  ).clip(max=log_sigmas.shape[0] - 2  )
           _lowerCAmelCase       :  Optional[Any]   				=					low_idx + 1
  
           _lowerCAmelCase       :  Optional[Any]   				=					log_sigmas[low_idx]
           _lowerCAmelCase       :  Union[str, Any]   				=					log_sigmas[high_idx]
  
           # interpolate sigmas
           _lowerCAmelCase       :  List[Any]   				=					(low - log_sigma) / (low - high)
           _lowerCAmelCase       :  Optional[Any]   				=					np.clip(snake_case_   ,   0   ,   1  )
  
           # transform interpolation to time range
           _lowerCAmelCase       :  Dict   				=					(1 - w) * low_idx + w * high_idx
           _lowerCAmelCase       :  Union[str, Any]   				=					t.reshape(sigma.shape  )
           return t
  
  
  
  
         def 							__UpperCamelCase       (     self   ,   snake_case_  ):
           _lowerCAmelCase       :  float   				=					in_sigmas[-1].item()
           _lowerCAmelCase       :  float   				=					in_sigmas[0].item()
  
           _lowerCAmelCase       :  Dict   				=					7.0  # 7.0 is the value used in the paper
           _lowerCAmelCase       :  int   				=					np.linspace(0   ,   1   ,   self.num_inference_steps  )
           _lowerCAmelCase       :  Optional[int]   				=					sigma_min ** (1 / rho)
           _lowerCAmelCase       :  Tuple   				=					sigma_max ** (1 / rho)
           _lowerCAmelCase       :  Union[str, Any]   				=					(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
           return sigmas
  
  
  
  
         def 							__UpperCamelCase       (     self   ,   snake_case_   ,   snake_case_   ,   snake_case_   ,   snake_case_ = 4   ,   snake_case_ = True   ,   ):
           if not self.is_scale_input_called:
             warnings.warn(
                 """The `scale_model_input` function should be called before `step` to ensure correct denoising. """
                 """See `StableDiffusionPipeline` for a usage example."""  )
  
           if isinstance(snake_case_   ,   torch.Tensor  ):
             _lowerCAmelCase       :  List[Any]   				=					timestep.to(self.timesteps.device  )
           _lowerCAmelCase       :  Optional[Any]   				=					(self.timesteps == timestep).nonzero().item()
           _lowerCAmelCase       :  Optional[int]   				=					self.sigmas[step_index]
  
           # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
           if self.config.prediction_type == "epsilon":
             _lowerCAmelCase       :  Union[str, Any]   				=					sample - sigma * model_output
           elif self.config.prediction_type == "v_prediction":
             # * c_out + input * c_skip
             _lowerCAmelCase       :  Union[str, Any]   				=					model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
           elif self.config.prediction_type == "sample":
             _lowerCAmelCase       :  List[Any]   				=					model_output
           else:
             raise ValueError(
                 f'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'  )
  
           # 2. Convert to an ODE derivative
           _lowerCAmelCase       :  Optional[int]   				=					(sample - pred_original_sample) / sigma
           self.derivatives.append(snake_case_  )
           if len(self.derivatives  ) > order:
             self.derivatives.pop(0  )
  
           # 3. Compute linear multistep coefficients
           _lowerCAmelCase       :  Tuple   				=					min(step_index + 1   ,   snake_case_  )
           _lowerCAmelCase       :  List[Any]   				=					[self.get_lms_coefficient(snake_case_   ,   snake_case_   ,   snake_case_  ) for curr_order in range(snake_case_  )]
  
           # 4. Compute previous sample based on the derivatives path
           _lowerCAmelCase       :  Union[str, Any]   				=					sample + sum(
               coeff * derivative for coeff, derivative in zip(snake_case_   ,   reversed(self.derivatives  )  )  )
  
           if not return_dict:
             return (prev_sample,)
  
           return LMSDiscreteSchedulerOutput(prev_sample=snake_case_   ,   pred_original_sample=snake_case_  )
  
  
  
  
         def 							__UpperCamelCase       (     self   ,   snake_case_   ,   snake_case_   ,   snake_case_   ,   ):
           # Make sure sigmas and timesteps have the same device and dtype as original_samples
           _lowerCAmelCase       :  Optional[Any]   				=					self.sigmas.to(device=original_samples.device   ,   dtype=original_samples.dtype  )
           if original_samples.device.type == "mps" and torch.is_floating_point(snake_case_  ):
             # mps does not support float64
             _lowerCAmelCase       :  List[str]   				=					self.timesteps.to(original_samples.device   ,   dtype=torch.floataa  )
             _lowerCAmelCase       :  Union[str, Any]   				=					timesteps.to(original_samples.device   ,   dtype=torch.floataa  )
           else:
             _lowerCAmelCase       :  Tuple   				=					self.timesteps.to(original_samples.device  )
             _lowerCAmelCase       :  str   				=					timesteps.to(original_samples.device  )
  
           _lowerCAmelCase       :  Optional[Any]   				=					[(schedule_timesteps == t).nonzero().item() for t in timesteps]
  
           _lowerCAmelCase       :  int   				=					sigmas[step_indices].flatten()
           while len(sigma.shape  ) < len(original_samples.shape  ):
             _lowerCAmelCase       :  Dict   				=					sigma.unsqueeze(-1  )
  
           _lowerCAmelCase       :  Tuple   				=					original_samples + noise * sigma
           return noisy_samples
  
  
  
  
  
  
  
         def __len__(     self  ):
           return self.config.num_train_timesteps
  
  
  
style_context: 
  '''simple docstring'''
  
  import json
  import os
  import unittest
  
  from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import (
      VOCAB_FILES_NAMES,
      GPTNeoXJapaneseTokenizer,
  )
  from transformers.testing_utils import require_tokenizers, slow
  
  from ...test_tokenization_common import TokenizerTesterMixin
  
  
  @require_tokenizers
  class   a_  (_a							,			unittest.TestCase    ):
         __lowerCAmelCase    :				List[Any]    =      GPTNeoXJapaneseTokenizer
         __lowerCAmelCase    :				Any    =      False
         __lowerCAmelCase    :				List[Any]    =      {"""do_clean_text""": False, """add_prefix_space""": False}
  
  
  
  
         def 							__UpperCamelCase       (     self  ):
           super().setUp()
  
           _lowerCAmelCase       :  Any   				=					[
               """こん""",
               """こんに""",
               """にちは""",
               """ばんは""",
               """世界,㔺界""",
               """、""",
               """。""",
               """<BR>""",
               """<SP>""",
               """<TAB>""",
               """<URL>""",
               """<EMAIL>""",
               """<TEL>""",
               """<DATE>""",
               """<PRICE>""",
               """<BLOCK>""",
               """<KIGOU>""",
               """<U2000U2BFF>""",
               """<|emoji1|>""",
               """<unk>""",
               """<|startoftext|>""",
               """<|endoftext|>""",
           ]
           _lowerCAmelCase       :  Union[str, Any]   				=					{"""emoji""": {"""\ud83d\ude00""": """<|emoji1|>"""}, """emoji_inv""": {"""<|emoji1|>""": """\ud83d\ude00"""}}  # 😀
           _lowerCAmelCase       :  Any   				=					{"""unk_token""": """<unk>"""}
  
           _lowerCAmelCase       :  Optional[int]   				=					os.path.join(self.tmpdirname   ,   VOCAB_FILES_NAMES["""vocab_file"""]  )
           _lowerCAmelCase       :  Any   				=					os.path.join(self.tmpdirname   ,   VOCAB_FILES_NAMES["""emoji_file"""]  )
           with open(self.vocab_file   ,   """w"""   ,   encoding="""utf-8"""  ) as vocab_writer:
             vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens]  )  )
           with open(self.emoji_file   ,   """w"""  ) as emoji_writer:
             emoji_writer.write(json.dumps(snake_case_  )  )
  
  
  
  
         def 							__UpperCamelCase       (     self   ,   **snake_case_  ):
           kwargs.update(self.special_tokens_map  )
           return GPTNeoXJapaneseTokenizer.from_pretrained(self.tmpdirname   ,   **snake_case_  )
  
  
  
  
         def 							__UpperCamelCase       (     self   ,   snake_case_  ):
           _lowerCAmelCase       :  Tuple   				=					"""こんにちは、世界。 \nこんばんは、㔺界。😀"""
           _lowerCAmelCase       :  str   				=					"""こんにちは、世界。 \nこんばんは、世界。😀"""
           return input_text, output_text
  
  
  
  
         def 							__UpperCamelCase       (     self   ,   snake_case_  ):
           _lowerCAmelCase				,					_lowerCAmelCase       :  str   				=					self.get_input_output_texts(snake_case_  )
           _lowerCAmelCase       :  Tuple   				=					tokenizer.encode(snake_case_   ,   add_special_tokens=snake_case_  )
           _lowerCAmelCase       :  Optional[Any]   				=					tokenizer.decode(snake_case_   ,   clean_up_tokenization_spaces=snake_case_  )
           return text, ids
  
  
  
  
         def 							__UpperCamelCase       (     self  ):
           pass  # TODO add if relevant
  
  
  
  
         def 							__UpperCamelCase       (     self  ):
           pass  # TODO add if relevant
  
  
  
  
         def 							__UpperCamelCase       (     self  ):
           pass  # TODO add if relevant
  
  
  
  
         def 							__UpperCamelCase       (     self  ):
           _lowerCAmelCase       :  List[Any]   				=					self.get_tokenizer()
  
           # Testing tokenization
           _lowerCAmelCase       :  Tuple   				=					"""こんにちは、世界。　こんばんは、㔺界。"""
           _lowerCAmelCase       :  Optional[Any]   				=					["""こん""", """にちは""", """、""", """世界""", """。""", """<SP>""", """こん""", """ばんは""", """、""", """㔺界""", """。"""]
           _lowerCAmelCase       :  Tuple   				=					tokenizer.tokenize(snake_case_  )
           self.assertListEqual(snake_case_   ,   snake_case_  )
  
           # Testing conversion to ids without special tokens
           _lowerCAmelCase       :  Optional[int]   				=					[0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6]
           _lowerCAmelCase       :  Optional[int]   				=					tokenizer.convert_tokens_to_ids(snake_case_  )
           self.assertListEqual(snake_case_   ,   snake_case_  )
  
           # Testing conversion to ids with special tokens
           _lowerCAmelCase       :  Optional[Any]   				=					tokens + [tokenizer.unk_token]
           _lowerCAmelCase       :  List[str]   				=					[0, 2, 5, 4, 6, 8, 0, 3, 5, 4, 6, 1_9]
           _lowerCAmelCase       :  Dict   				=					tokenizer.convert_tokens_to_ids(snake_case_  )
           self.assertListEqual(snake_case_   ,   snake_case_  )
  
  
  
  
         @slow
         def 							__UpperCamelCase       (     self  ):
           _lowerCAmelCase       :  int   				=					self.tokenizer_class.from_pretrained("""abeja/gpt-neox-japanese-2.7b"""  )
  
           _lowerCAmelCase       :  List[Any]   				=					tokenizer.encode("""ありがとう。"""   ,   add_special_tokens=snake_case_  )
           _lowerCAmelCase       :  List[str]   				=					tokenizer.encode("""どういたしまして。"""   ,   add_special_tokens=snake_case_  )
  
           _lowerCAmelCase       :  int   				=					tokenizer.build_inputs_with_special_tokens(snake_case_  )
           _lowerCAmelCase       :  Any   				=					tokenizer.build_inputs_with_special_tokens(snake_case_   ,   snake_case_  )
  
           assert encoded_sentence == ids_a
           assert encoded_pair == ids_a + ids_a
  
  
  
  
         def 							__UpperCamelCase       (     self  ):
           # Intentionally convert some words to accommodate character fluctuations unique to Japanese
           pass
  
  
  
  
  
  
  
         def 							__UpperCamelCase       (     self  ):
           # tokenizer has no padding token
           pass
  
  
  