

"""simple docstring"""






import json
import os
import re
import unicodedata
from json.encoder import INFINITY
from typing import Any, Dict, List, Optional, Tuple, Union

import numpy as np
import regex

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_flax_available, is_tf_available, is_torch_available, logging
from ...utils.generic import _is_jax, _is_numpy


_lowerCAmelCase						=		logging.get_logger(__name__)

_lowerCAmelCase						=		{
    """artists_file""": """artists.json""",
    """lyrics_file""": """lyrics.json""",
    """genres_file""": """genres.json""",
}

_lowerCAmelCase						=		{
    """artists_file""": {
        """jukebox""": """https://huggingface.co/ArthurZ/jukebox/blob/main/artists.json""",
    },
    """genres_file""": {
        """jukebox""": """https://huggingface.co/ArthurZ/jukebox/blob/main/genres.json""",
    },
    """lyrics_file""": {
        """jukebox""": """https://huggingface.co/ArthurZ/jukebox/blob/main/lyrics.json""",
    },
}

_lowerCAmelCase						=		{
    """jukebox""": 5_1_2,
}







class 	__UpperCamelCase    (    a__						):
  _UpperCAmelCase          =		VOCAB_FILES_NAMES
  _UpperCAmelCase          =		PRETRAINED_VOCAB_FILES_MAP
  _UpperCAmelCase          =		PRETRAINED_LYRIC_TOKENS_SIZES
  _UpperCAmelCase          =		["input_ids", "attention_mask"]

  def __init__(		self    ,_A    ,_A    ,_A    ,_A=["v3", "v2", "v2"]    ,_A=512    ,_A=5    ,_A="<|endoftext|>"    ,**_A    ,):
      '''simple docstring'''

      _lowerCAmelCase       :   Union[str, Any]  =    AddedToken(_A    ,lstrip=_A    ,rstrip=_A      ) if isinstance(_A    ,_A      ) else unk_token
      super().__init__(
          unk_token=_A    ,n_genres=_A    ,version=_A    ,max_n_lyric_tokens=_A    ,**_A    ,)
      _lowerCAmelCase       :   Dict  =    version
      _lowerCAmelCase       :   List[str]  =    max_n_lyric_tokens
      _lowerCAmelCase       :   Tuple  =    n_genres

      with open(_A    ,encoding='utf-8'      ) as vocab_handle:
          _lowerCAmelCase       :   Dict  =    json.load(_A      )

      with open(_A    ,encoding='utf-8'      ) as vocab_handle:
          _lowerCAmelCase       :   int  =    json.load(_A      )

      with open(_A    ,encoding='utf-8'      ) as vocab_handle:
          _lowerCAmelCase       :   List[Any]  =    json.load(_A      )

      _lowerCAmelCase       :   int  =    r'[^A-Za-z0-9.,:;!?\-\'\"()\[\] \t\n]+'
      # In v2, we had a n_vocab=80 and in v3 we missed + and so n_vocab=79 of characters.
      if len(self.lyrics_encoder      ) == 79:
          _lowerCAmelCase       :   int  =    oov.replace(r'\-\''    ,r'\-+\''      )

      _lowerCAmelCase       :   Any  =    regex.compile(_A      )
      _lowerCAmelCase       :   Union[str, Any]  =    {v: k for k, v in self.artists_encoder.items()}
      _lowerCAmelCase       :   Tuple  =    {v: k for k, v in self.genres_encoder.items()}
      _lowerCAmelCase       :   Dict  =    {v: k for k, v in self.lyrics_encoder.items()}

  @property
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      return len(self.artists_encoder      ) + len(self.genres_encoder      ) + len(self.lyrics_encoder      )

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      return dict(self.artists_encoder    ,self.genres_encoder    ,self.lyrics_encoder      )

  def __lowerCamelCase		(		self    ,_A    ,_A    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Optional[Any]  =    [self.artists_encoder.get(_A    ,0      ) for artist in list_artists]
      for genres in range(len(_A      )      ):
          _lowerCAmelCase       :   List[Any]  =    [self.genres_encoder.get(_A    ,0      ) for genre in list_genres[genres]]
          _lowerCAmelCase       :   Tuple  =    list_genres[genres] + [-1] * (self.n_genres - len(list_genres[genres]      ))

      _lowerCAmelCase       :   Union[str, Any]  =    [[self.lyrics_encoder.get(_A    ,0      ) for character in list_lyrics[0]], [], []]
      return artists_id, list_genres, lyric_ids

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      return list(_A      )

  def __lowerCamelCase		(		self    ,_A    ,_A    ,_A    ,**_A      ):
      '''simple docstring'''

      _lowerCAmelCase,					_lowerCAmelCase,					_lowerCAmelCase       :   Any  =    self.prepare_for_tokenization(_A    ,_A    ,_A      )
      _lowerCAmelCase       :   Dict  =    self._tokenize(_A      )
      return artist, genre, lyrics

  def __lowerCamelCase		(		self    ,_A    ,_A    ,_A    ,_A = False      ):
      '''simple docstring'''

      for idx in range(len(self.version      )      ):
          if self.version[idx] == "v3":
              _lowerCAmelCase       :   int  =    artists[idx].lower()
              _lowerCAmelCase       :   Optional[int]  =    [genres[idx].lower()]
          else:
              _lowerCAmelCase       :   int  =    self._normalize(artists[idx]      ) + '.v2'
              _lowerCAmelCase       :   Optional[int]  =    [
                  self._normalize(_A      ) + '.v2' for genre in genres[idx].split('_'      )
              ]  # split is for the full dictionary with combined genres

      if self.version[0] == "v2":
          _lowerCAmelCase       :   List[Any]  =    regex.compile(r'[^A-Za-z0-9.,:;!?\-\'\"()\[\] \t\n]+'      )
          _lowerCAmelCase       :   Union[str, Any]  =    'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789.,:;!?-+\'\"()[] \t\n'
          _lowerCAmelCase       :   int  =    {vocab[index]: index + 1 for index in range(len(_A      )      )}
          _lowerCAmelCase       :   List[str]  =    0
          _lowerCAmelCase       :   Tuple  =    len(_A      ) + 1
          _lowerCAmelCase       :   Optional[Any]  =    self.vocab
          _lowerCAmelCase       :   Optional[Any]  =    {v: k for k, v in self.vocab.items()}
          _lowerCAmelCase       :   Optional[Any]  =    ''
      else:
          _lowerCAmelCase       :   int  =    regex.compile(r'[^A-Za-z0-9.,:;!?\-+\'\"()\[\] \t\n]+'      )

      _lowerCAmelCase       :   str  =    self._run_strip_accents(_A      )
      _lowerCAmelCase       :   List[Any]  =    lyrics.replace('\\'    ,'\n'      )
      _lowerCAmelCase       :   Optional[Any]  =    self.out_of_vocab.sub(''    ,_A      ), [], []
      return artists, genres, lyrics

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Optional[int]  =    unicodedata.normalize('NFD'    ,_A      )
      _lowerCAmelCase       :   Tuple  =    []
      for char in text:
          _lowerCAmelCase       :   Dict  =    unicodedata.category(_A      )
          if cat == "Mn":
              continue
          output.append(_A      )
      return "".join(_A      )

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   int  =    (
          [chr(_A      ) for i in range(ord('a'      )    ,ord('z'      ) + 1      )]
          + [chr(_A      ) for i in range(ord('A'      )    ,ord('Z'      ) + 1      )]
          + [chr(_A      ) for i in range(ord('0'      )    ,ord('9'      ) + 1      )]
          + ['.']
      )
      _lowerCAmelCase       :   Optional[int]  =    frozenset(_A      )
      _lowerCAmelCase       :   List[Any]  =    re.compile(r'_+'      )
      _lowerCAmelCase       :   int  =    ''.join([c if c in accepted else '_' for c in text.lower()]      )
      _lowerCAmelCase       :   Optional[Any]  =    pattern.sub('_'    ,_A      ).strip('_'      )
      return text

  def __lowerCamelCase		(		self    ,_A      ):
      '''simple docstring'''

      return " ".join(_A      )

  def __lowerCamelCase		(		self    ,_A    ,_A = None    ,_A = False      ):
      '''simple docstring'''

      if not isinstance(_A    ,_A      ):
          _lowerCAmelCase       :   List[str]  =    TensorType(_A      )

      # Get a function reference for the correct framework
      if tensor_type == TensorType.TENSORFLOW:
          if not is_tf_available():
              raise ImportError(
                  'Unable to convert output to TensorFlow tensors format, TensorFlow is not installed.'      )
          import tensorflow as tf

          _lowerCAmelCase       :   List[str]  =    tf.constant
          _lowerCAmelCase       :   Any  =    tf.is_tensor
      elif tensor_type == TensorType.PYTORCH:
          if not is_torch_available():
              raise ImportError('Unable to convert output to PyTorch tensors format, PyTorch is not installed.'      )
          import torch

          _lowerCAmelCase       :   Dict  =    torch.tensor
          _lowerCAmelCase       :   List[str]  =    torch.is_tensor
      elif tensor_type == TensorType.JAX:
          if not is_flax_available():
              raise ImportError('Unable to convert output to JAX tensors format, JAX is not installed.'      )
          import jax.numpy as jnp  # noqa: F811

          _lowerCAmelCase       :   str  =    jnp.array
          _lowerCAmelCase       :   List[str]  =    _is_jax
      else:
          _lowerCAmelCase       :   List[str]  =    np.asarray
          _lowerCAmelCase       :   int  =    _is_numpy

      # Do the tensor conversion in batch

      try:
          if prepend_batch_axis:
              _lowerCAmelCase       :   Optional[Any]  =    [inputs]

          if not is_tensor(_A      ):
              _lowerCAmelCase       :   Any  =    as_tensor(_A      )
      except:  # noqa E722
          raise ValueError(
              'Unable to create tensor, you should probably activate truncation and/or padding '
              'with \'padding=True\' \'truncation=True\' to have batched tensors with the same length.'      )

      return inputs

  def __call__(		self    ,_A    ,_A    ,_A=""    ,_A="pt"      ):
      '''simple docstring'''

      _lowerCAmelCase       :   str  =    [0, 0, 0]
      _lowerCAmelCase       :   List[Any]  =    [artist] * len(self.version      )
      _lowerCAmelCase       :   int  =    [genres] * len(self.version      )

      _lowerCAmelCase,					_lowerCAmelCase,					_lowerCAmelCase       :   List[Any]  =    self.tokenize(_A    ,_A    ,_A      )
      _lowerCAmelCase,					_lowerCAmelCase,					_lowerCAmelCase       :   Any  =    self._convert_token_to_id(_A    ,_A    ,_A      )

      _lowerCAmelCase       :   int  =    [-INFINITY] * len(full_tokens[-1]      )
      _lowerCAmelCase       :   Union[str, Any]  =    [
          self.convert_to_tensors(
              [input_ids + [artists_id[i]] + genres_ids[i] + full_tokens[i]]    ,tensor_type=_A      )
          for i in range(len(self.version      )      )
      ]
      return BatchEncoding({'input_ids': input_ids, 'attention_masks': attention_masks}      )

  def __lowerCamelCase		(		self    ,_A    ,_A = None      ):
      '''simple docstring'''

      if not os.path.isdir(_A      ):
          logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""      )
          return

      _lowerCAmelCase       :   int  =    os.path.join(
          _A    ,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['artists_file']      )
      with open(_A    ,'w'    ,encoding='utf-8'      ) as f:
          f.write(json.dumps(self.artists_encoder    ,ensure_ascii=_A      )      )

      _lowerCAmelCase       :   str  =    os.path.join(
          _A    ,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['genres_file']      )
      with open(_A    ,'w'    ,encoding='utf-8'      ) as f:
          f.write(json.dumps(self.genres_encoder    ,ensure_ascii=_A      )      )

      _lowerCAmelCase       :   str  =    os.path.join(
          _A    ,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['lyrics_file']      )
      with open(_A    ,'w'    ,encoding='utf-8'      ) as f:
          f.write(json.dumps(self.lyrics_encoder    ,ensure_ascii=_A      )      )

      return (artists_file, genres_file, lyrics_file)







  def __lowerCamelCase		(		self    ,_A    ,_A    ,_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   int  =    self.artists_decoder.get(_A      )
      _lowerCAmelCase       :   Any  =    [self.genres_decoder.get(_A      ) for genre in genres_index]
      _lowerCAmelCase       :   Any  =    [self.lyrics_decoder.get(_A      ) for character in lyric_index]
      return artist, genres, lyrics








"""simple docstring"""






import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest







class 	__UpperCamelCase    (    a__						):
  _UpperCAmelCase          =		(UniPCMultistepScheduler,)
  _UpperCAmelCase          =		(("num_inference_steps", 25),)

  def __lowerCamelCase		(		self    ,**_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Optional[Any]  =    {
          'num_train_timesteps': 1000,
          'beta_start': 0.0_0_0_1,
          'beta_end': 0.0_2,
          'beta_schedule': 'linear',
          'solver_order': 2,
          'solver_type': 'bh2',
      }

      config.update(**_A      )
      return config

  def __lowerCamelCase		(		self    ,_A=0    ,**_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Tuple  =    dict(self.forward_default_kwargs      )
      _lowerCAmelCase       :   int  =    kwargs.pop('num_inference_steps'    ,_A      )
      _lowerCAmelCase       :   Optional[Any]  =    self.dummy_sample
      _lowerCAmelCase       :   Union[str, Any]  =    0.1 * sample
      _lowerCAmelCase       :   Tuple  =    [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

      for scheduler_class in self.scheduler_classes:
          _lowerCAmelCase       :   Optional[int]  =    self.get_scheduler_config(**_A      )
          _lowerCAmelCase       :   Union[str, Any]  =    scheduler_class(**_A      )
          scheduler.set_timesteps(_A      )
          # copy over dummy past residuals
          _lowerCAmelCase       :   Tuple  =    dummy_past_residuals[: scheduler.config.solver_order]

          with tempfile.TemporaryDirectory() as tmpdirname:
              scheduler.save_config(_A      )
              _lowerCAmelCase       :   Union[str, Any]  =    scheduler_class.from_pretrained(_A      )
              new_scheduler.set_timesteps(_A      )
              # copy over dummy past residuals
              _lowerCAmelCase       :   Union[str, Any]  =    dummy_past_residuals[: new_scheduler.config.solver_order]

          _lowerCAmelCase,					_lowerCAmelCase       :   str  =    sample, sample
          for t in range(_A    ,time_step + scheduler.config.solver_order + 1      ):
              _lowerCAmelCase       :   Dict  =    scheduler.step(_A    ,_A    ,_A    ,**_A      ).prev_sample
              _lowerCAmelCase       :   Union[str, Any]  =    new_scheduler.step(_A    ,_A    ,_A    ,**_A      ).prev_sample

              assert torch.sum(torch.abs(output - new_output      )      ) < 1E-5, "Scheduler outputs are not identical"

  def __lowerCamelCase		(		self    ,_A=0    ,**_A      ):
      '''simple docstring'''

      _lowerCAmelCase       :   List[Any]  =    dict(self.forward_default_kwargs      )
      _lowerCAmelCase       :   List[str]  =    kwargs.pop('num_inference_steps'    ,_A      )
      _lowerCAmelCase       :   Union[str, Any]  =    self.dummy_sample
      _lowerCAmelCase       :   Dict  =    0.1 * sample
      _lowerCAmelCase       :   str  =    [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

      for scheduler_class in self.scheduler_classes:
          _lowerCAmelCase       :   Any  =    self.get_scheduler_config()
          _lowerCAmelCase       :   Union[str, Any]  =    scheduler_class(**_A      )
          scheduler.set_timesteps(_A      )

          # copy over dummy past residuals (must be after setting timesteps)
          _lowerCAmelCase       :   List[str]  =    dummy_past_residuals[: scheduler.config.solver_order]

          with tempfile.TemporaryDirectory() as tmpdirname:
              scheduler.save_config(_A      )
              _lowerCAmelCase       :   int  =    scheduler_class.from_pretrained(_A      )
              # copy over dummy past residuals
              new_scheduler.set_timesteps(_A      )

              # copy over dummy past residual (must be after setting timesteps)
              _lowerCAmelCase       :   str  =    dummy_past_residuals[: new_scheduler.config.solver_order]

          _lowerCAmelCase       :   Optional[int]  =    scheduler.step(_A    ,_A    ,_A    ,**_A      ).prev_sample
          _lowerCAmelCase       :   Union[str, Any]  =    new_scheduler.step(_A    ,_A    ,_A    ,**_A      ).prev_sample

          assert torch.sum(torch.abs(output - new_output      )      ) < 1E-5, "Scheduler outputs are not identical"

  def __lowerCamelCase		(		self    ,_A=None    ,**_A      ):
      '''simple docstring'''

      if scheduler is None:
          _lowerCAmelCase       :   int  =    self.scheduler_classes[0]
          _lowerCAmelCase       :   List[str]  =    self.get_scheduler_config(**_A      )
          _lowerCAmelCase       :   Union[str, Any]  =    scheduler_class(**_A      )

      _lowerCAmelCase       :   Union[str, Any]  =    self.scheduler_classes[0]
      _lowerCAmelCase       :   Dict  =    self.get_scheduler_config(**_A      )
      _lowerCAmelCase       :   int  =    scheduler_class(**_A      )

      _lowerCAmelCase       :   List[str]  =    10
      _lowerCAmelCase       :   str  =    self.dummy_model()
      _lowerCAmelCase       :   str  =    self.dummy_sample_deter
      scheduler.set_timesteps(_A      )

      for i, t in enumerate(scheduler.timesteps      ):
          _lowerCAmelCase       :   Any  =    model(_A    ,_A      )
          _lowerCAmelCase       :   Union[str, Any]  =    scheduler.step(_A    ,_A    ,_A      ).prev_sample

      return sample

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Optional[Any]  =    dict(self.forward_default_kwargs      )

      _lowerCAmelCase       :   Any  =    kwargs.pop('num_inference_steps'    ,_A      )

      for scheduler_class in self.scheduler_classes:
          _lowerCAmelCase       :   str  =    self.get_scheduler_config()
          _lowerCAmelCase       :   List[str]  =    scheduler_class(**_A      )

          _lowerCAmelCase       :   Any  =    self.dummy_sample
          _lowerCAmelCase       :   Tuple  =    0.1 * sample

          if num_inference_steps is not None and hasattr(_A    ,'set_timesteps'      ):
              scheduler.set_timesteps(_A      )
          elif num_inference_steps is not None and not hasattr(_A    ,'set_timesteps'      ):
              _lowerCAmelCase       :   Optional[Any]  =    num_inference_steps

          # copy over dummy past residuals (must be done after set_timesteps)
          _lowerCAmelCase       :   Optional[Any]  =    [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
          _lowerCAmelCase       :   Optional[Any]  =    dummy_past_residuals[: scheduler.config.solver_order]

          _lowerCAmelCase       :   Any  =    scheduler.timesteps[5]
          _lowerCAmelCase       :   List[str]  =    scheduler.timesteps[6]

          _lowerCAmelCase       :   List[str]  =    scheduler.step(_A    ,_A    ,_A    ,**_A      ).prev_sample
          _lowerCAmelCase       :   Optional[int]  =    scheduler.step(_A    ,_A    ,_A    ,**_A      ).prev_sample

          self.assertEqual(output_a.shape    ,sample.shape      )
          self.assertEqual(output_a.shape    ,output_a.shape      )

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   int  =    UniPCMultistepScheduler(**self.get_scheduler_config()      )
      _lowerCAmelCase       :   Optional[Any]  =    self.full_loop(scheduler=_A      )
      _lowerCAmelCase       :   Tuple  =    torch.mean(torch.abs(_A      )      )

      assert abs(result_mean.item() - 0.2_4_6_4      ) < 1E-3

      _lowerCAmelCase       :   int  =    DPMSolverSinglestepScheduler.from_config(scheduler.config      )
      _lowerCAmelCase       :   List[str]  =    DEISMultistepScheduler.from_config(scheduler.config      )
      _lowerCAmelCase       :   Tuple  =    DPMSolverMultistepScheduler.from_config(scheduler.config      )
      _lowerCAmelCase       :   Any  =    UniPCMultistepScheduler.from_config(scheduler.config      )

      _lowerCAmelCase       :   Union[str, Any]  =    self.full_loop(scheduler=_A      )
      _lowerCAmelCase       :   List[str]  =    torch.mean(torch.abs(_A      )      )

      assert abs(result_mean.item() - 0.2_4_6_4      ) < 1E-3

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      for timesteps in [25, 50, 100, 999, 1000]:
          self.check_over_configs(num_train_timesteps=_A      )

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      self.check_over_configs(thresholding=_A      )
      for order in [1, 2, 3]:
          for solver_type in ["bh1", "bh2"]:
              for threshold in [0.5, 1.0, 2.0]:
                  for prediction_type in ["epsilon", "sample"]:
                      self.check_over_configs(
                          thresholding=_A    ,prediction_type=_A    ,sample_max_value=_A    ,solver_order=_A    ,solver_type=_A    ,)

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      for prediction_type in ["epsilon", "v_prediction"]:
          self.check_over_configs(prediction_type=_A      )

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      for solver_type in ["bh1", "bh2"]:
          for order in [1, 2, 3]:
              for prediction_type in ["epsilon", "sample"]:
                  self.check_over_configs(
                      solver_order=_A    ,solver_type=_A    ,prediction_type=_A    ,)
                  _lowerCAmelCase       :   List[Any]  =    self.full_loop(
                      solver_order=_A    ,solver_type=_A    ,prediction_type=_A    ,)
                  assert not torch.isnan(_A      ).any(), "Samples have nan numbers"

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      self.check_over_configs(lower_order_final=_A      )
      self.check_over_configs(lower_order_final=_A      )

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
          self.check_over_forward(num_inference_steps=_A    ,time_step=0      )

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Tuple  =    self.full_loop()
      _lowerCAmelCase       :   Tuple  =    torch.mean(torch.abs(_A      )      )

      assert abs(result_mean.item() - 0.2_4_6_4      ) < 1E-3

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   str  =    self.full_loop(prediction_type='v_prediction'      )
      _lowerCAmelCase       :   Union[str, Any]  =    torch.mean(torch.abs(_A      )      )

      assert abs(result_mean.item() - 0.1_0_1_4      ) < 1E-3

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Optional[Any]  =    self.scheduler_classes[0]
      _lowerCAmelCase       :   int  =    self.get_scheduler_config(thresholding=_A    ,dynamic_thresholding_ratio=0      )
      _lowerCAmelCase       :   Tuple  =    scheduler_class(**_A      )

      _lowerCAmelCase       :   Optional[Any]  =    10
      _lowerCAmelCase       :   Union[str, Any]  =    self.dummy_model()
      _lowerCAmelCase       :   Dict  =    self.dummy_sample_deter.half()
      scheduler.set_timesteps(_A      )

      for i, t in enumerate(scheduler.timesteps      ):
          _lowerCAmelCase       :   Tuple  =    model(_A    ,_A      )
          _lowerCAmelCase       :   Dict  =    scheduler.step(_A    ,_A    ,_A      ).prev_sample

      assert sample.dtype == torch.floataa







  def __lowerCamelCase		(		self    ,**_A      ):
      '''simple docstring'''

      for scheduler_class in self.scheduler_classes:
          _lowerCAmelCase       :   Dict  =    self.get_scheduler_config(**_A      )
          _lowerCAmelCase       :   str  =    scheduler_class(**_A      )

          scheduler.set_timesteps(scheduler.config.num_train_timesteps      )
          assert len(scheduler.timesteps.unique()      ) == scheduler.num_inference_steps





