
'''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




'''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


