


'''simple docstring'''






# 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    __snake_case       (   _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    lowercase    =					42
    lowercase    =					None







def 				UpperCamelCase_			(    A__					:   Any							,    A__					:   List[Any]=0.999							,    A__					:   Dict="cosine"							,    ):
  '''simple docstring'''





  if alpha_transform_type == "cosine":

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

  elif alpha_transform_type == "exp":

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

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

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






class    __snake_case       (   _SCREAMING_SNAKE_CASE	,_SCREAMING_SNAKE_CASE):







    """simple docstring"""



    lowercase    =					1

    @register_to_config
    def __init__(   self    :  str    ,						lowerCamelCase    :  int = 10_00    ,						lowerCamelCase    :  float = 0.0_001    ,						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    :  str    ,						)   ->  Any:
      if kwargs.get("""set_alpha_to_one"""    ,						lowerCamelCase		) is not None:
        lowerCAmelCase_			:      Union[str, 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		)
        lowerCAmelCase_			:      Tuple			=   kwargs["""set_alpha_to_one"""]
      if trained_betas is not None:
        lowerCAmelCase_			:      int			=   torch.tensor(lowerCamelCase    ,						dtype=torch.floataa		)
      elif beta_schedule == "linear":
        lowerCAmelCase_			:      Optional[int]			=   torch.linspace(lowerCamelCase    ,						lowerCamelCase    ,						lowerCamelCase    ,						dtype=torch.floataa		)
      elif beta_schedule == "scaled_linear":
        # this schedule is very specific to the latent diffusion model.
        lowerCAmelCase_			:      Tuple			=   (
            torch.linspace(beta_start**0.5    ,						beta_end**0.5    ,						lowerCamelCase    ,						dtype=torch.floataa		) ** 2
        )
      elif beta_schedule == "squaredcos_cap_v2":
        # Glide cosine schedule
        lowerCAmelCase_			:      Any			=   betas_for_alpha_bar(lowerCamelCase		)
      else:
        raise NotImplementedError(F'{beta_schedule} does is not implemented for {self.__class__}'		)

      lowerCAmelCase_			:      int			=   1.0 - self.betas
      lowerCAmelCase_			:      List[str]			=   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.
      lowerCAmelCase_			:      int			=   torch.tensor(0.0		) if set_alpha_to_zero else self.alphas_cumprod[-1]

      # standard deviation of the initial noise distribution
      lowerCAmelCase_			:      Optional[int]			=   1.0

      # setable values
      lowerCAmelCase_			:      Optional[Any]			=   None
      lowerCAmelCase_			:      List[Any]			=   torch.from_numpy(np.arange(0    ,						lowerCamelCase		).copy().astype(np.intaa		)		)

    def __lowercase   (   self    :  Dict    ,						lowerCamelCase    :  torch.FloatTensor    ,						lowerCamelCase    :  Optional[int] = None		)   ->  torch.FloatTensor:
      return sample

    def __lowercase   (   self    :  int    ,						lowerCamelCase    :  int    ,						lowerCamelCase    :  Union[str, torch.device] = None		)   ->  int:

      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.'		)

      lowerCAmelCase_			:      List[Any]			=   num_inference_steps
      lowerCAmelCase_			:      int			=   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[Any]			=   (np.arange(0    ,						lowerCamelCase		) * step_ratio).round().copy().astype(np.intaa		)
      lowerCAmelCase_			:      Tuple			=   torch.from_numpy(lowerCamelCase		).to(lowerCamelCase		)
      self.timesteps += self.config.steps_offset

    def __lowercase   (   self    :  Union[str, Any]    ,						lowerCamelCase    :  torch.FloatTensor    ,						lowerCamelCase    :  int    ,						lowerCamelCase    :  torch.FloatTensor    ,						lowerCamelCase    :  float = 0.0    ,						lowerCamelCase    :  bool = False    ,						lowerCamelCase    :  Optional[torch.FloatTensor] = None    ,						lowerCamelCase    :  bool = True    ,						)   ->  Union[DDIMSchedulerOutput, Tuple]:
      # 1. get previous step value (=t+1)
      lowerCAmelCase_			:      Optional[int]			=   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
      lowerCAmelCase_			:      Union[str, Any]			=   self.alphas_cumprod[timestep]
      lowerCAmelCase_			:      Any			=   (
          self.alphas_cumprod[prev_timestep]
          if prev_timestep < self.config.num_train_timesteps
          else self.final_alpha_cumprod
      )

      lowerCAmelCase_			:      Union[str, Any]			=   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":
        lowerCAmelCase_			:      Tuple			=   (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
        lowerCAmelCase_			:      str			=   model_output
      elif self.config.prediction_type == "sample":
        lowerCAmelCase_			:      List[str]			=   model_output
        lowerCAmelCase_			:      Optional[int]			=   (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
      elif self.config.prediction_type == "v_prediction":
        lowerCAmelCase_			:      Optional[Any]			=   (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
        lowerCAmelCase_			:      Tuple			=   (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:
        lowerCAmelCase_			:      Tuple			=   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
      lowerCAmelCase_			:      List[str]			=   (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
      lowerCAmelCase_			:      Dict			=   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    :  Optional[int]		)   ->  Optional[Any]:
      return self.config.num_train_timesteps







'''simple docstring'''






import json
import os
import unittest

from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin






class    __snake_case       (   _SCREAMING_SNAKE_CASE	,unittest.TestCase):







    """simple docstring"""



    lowercase    =					BioGptTokenizer
    lowercase    =					False

    def __lowercase   (   self    :  List[Any]		)   ->  List[str]:
      super().setUp()

      # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
      lowerCAmelCase_			:      Optional[Any]			=   [
          """l""",
          """o""",
          """w""",
          """e""",
          """r""",
          """s""",
          """t""",
          """i""",
          """d""",
          """n""",
          """w</w>""",
          """r</w>""",
          """t</w>""",
          """lo""",
          """low""",
          """er</w>""",
          """low</w>""",
          """lowest</w>""",
          """newer</w>""",
          """wider</w>""",
          """<unk>""",
      ]
      lowerCAmelCase_			:      int			=   dict(zip(lowerCamelCase    ,						range(len(lowerCamelCase		)		)		)		)
      lowerCAmelCase_			:      int			=   ["""l o 123""", """lo w 1456""", """e r</w> 1789""", """"""]

      lowerCAmelCase_			:      Optional[int]			=   os.path.join(self.tmpdirname    ,						VOCAB_FILES_NAMES["""vocab_file"""]		)
      lowerCAmelCase_			:      Union[str, Any]			=   os.path.join(self.tmpdirname    ,						VOCAB_FILES_NAMES["""merges_file"""]		)
      with open(self.vocab_file    ,						"""w"""		) as fp:
        fp.write(json.dumps(lowerCamelCase		)		)
      with open(self.merges_file    ,						"""w"""		) as fp:
        fp.write("""\n""".join(lowerCamelCase		)		)

    def __lowercase   (   self    :  Union[str, Any]    ,						lowerCamelCase    :  Any		)   ->  List[str]:
      lowerCAmelCase_			:      Dict			=   """lower newer"""
      lowerCAmelCase_			:      str			=   """lower newer"""
      return input_text, output_text

    def __lowercase   (   self    :  Optional[int]		)   ->  str:
      lowerCAmelCase_			:      Any			=   BioGptTokenizer(self.vocab_file    ,						self.merges_file		)

      lowerCAmelCase_			:      int			=   """lower"""
      lowerCAmelCase_			:      str			=   ["""low""", """er</w>"""]
      lowerCAmelCase_			:      Any			=   tokenizer.tokenize(lowerCamelCase		)
      self.assertListEqual(lowerCamelCase    ,						lowerCamelCase		)

      lowerCAmelCase_			:      Dict			=   tokens + ["""<unk>"""]
      lowerCAmelCase_			:      Optional[Any]			=   [14, 15, 20]
      self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase		)    ,						lowerCamelCase		)





    @slow
    def __lowercase   (   self    :  str		)   ->  Optional[Any]:
      lowerCAmelCase_			:      Dict			=   BioGptTokenizer.from_pretrained("""microsoft/biogpt"""		)

      lowerCAmelCase_			:      int			=   tokenizer.encode("""sequence builders"""    ,						add_special_tokens=lowerCamelCase		)
      lowerCAmelCase_			:      Optional[Any]			=   tokenizer.encode("""multi-sequence build"""    ,						add_special_tokens=lowerCamelCase		)

      lowerCAmelCase_			:      str			=   tokenizer.build_inputs_with_special_tokens(lowerCamelCase		)
      lowerCAmelCase_			:      Optional[int]			=   tokenizer.build_inputs_with_special_tokens(lowerCamelCase    ,						lowerCamelCase		)

      self.assertTrue(encoded_sentence == [2] + text		)
      self.assertTrue(encoded_pair == [2] + text + [2] + text_a		)



