


import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


A_		:     Any        =     logging.get_logger(__name__)


A_		:     Dict        =     {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt'}

# See all BART models at https://huggingface.co/models?filter=bart
A_		:     Any        =     {
    'vocab_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/vocab.json',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/vocab.json',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json',
    },
    'merges_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/merges.txt',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/merges.txt',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt',
    },
}

A_		:     List[str]        =     {
    'facebook/bart-base': 10_24,
    'facebook/bart-large': 10_24,
    'facebook/bart-large-mnli': 10_24,
    'facebook/bart-large-cnn': 10_24,
    'facebook/bart-large-xsum': 10_24,
    'yjernite/bart_eli5': 10_24,
}


@lru_cache()
def 	UpperCAmelCase__      (      ):






 '''simple docstring'''

 a						=		(
     list(range(ord("!"					) ,					ord("~"					) + 1					)					) + list(range(ord("¡"					) ,					ord("¬"					) + 1					)					) + list(range(ord("®"					) ,					ord("ÿ"					) + 1					)					)
 )
 a						=		bs[:]
 a						=		0
 for b in range(2**8					):
  if b not in bs:
   bs.append(_lowercase					)
   cs.append(2**8 + n					)
   n += 1
 a						=		[chr(_lowercase					) for n in cs]
 return dict(zip(_lowercase ,					_lowercase					)					)


def 	UpperCAmelCase__      (      UpperCAmelCase__ :str					):






 '''simple docstring'''

 a						=		set()
 a						=		word[0]
 for char in word[1:]:
  pairs.add((prev_char, char)					)
  a						=		char
 return pairs




class 		_lowercase		(       UpperCamelCase_	):
   _UpperCAmelCase     	=			VOCAB_FILES_NAMES
   _UpperCAmelCase     	=			PRETRAINED_VOCAB_FILES_MAP
   _UpperCAmelCase     	=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _UpperCAmelCase     	=			['''input_ids''', '''attention_mask''']

   def __init__(      self			:       List[str]							,   __lowerCAmelCase			:       int							,   __lowerCAmelCase			:       Dict							,   __lowerCAmelCase			:       Union[str, Any]="replace"							,   __lowerCAmelCase			:       Any="<s>"							,   __lowerCAmelCase			:       List[str]="</s>"							,   __lowerCAmelCase			:       Optional[Any]="</s>"							,   __lowerCAmelCase			:       Union[str, Any]="<s>"							,   __lowerCAmelCase			:       Dict="<unk>"							,   __lowerCAmelCase			:       Union[str, Any]="<pad>"							,   __lowerCAmelCase			:       List[str]="<mask>"							,   __lowerCAmelCase			:       int=False							,   **__lowerCAmelCase			:       Union[str, Any]							,   )       ->				int:


    """simple docstring"""






    a						=		AddedToken(UpperCamelCase__							,   lstrip=UpperCamelCase__							,   rstrip=UpperCamelCase__							) if isinstance(UpperCamelCase__							,   UpperCamelCase__							) else bos_token
    a						=		AddedToken(UpperCamelCase__							,   lstrip=UpperCamelCase__							,   rstrip=UpperCamelCase__							) if isinstance(UpperCamelCase__							,   UpperCamelCase__							) else eos_token
    a						=		AddedToken(UpperCamelCase__							,   lstrip=UpperCamelCase__							,   rstrip=UpperCamelCase__							) if isinstance(UpperCamelCase__							,   UpperCamelCase__							) else sep_token
    a						=		AddedToken(UpperCamelCase__							,   lstrip=UpperCamelCase__							,   rstrip=UpperCamelCase__							) if isinstance(UpperCamelCase__							,   UpperCamelCase__							) else cls_token
    a						=		AddedToken(UpperCamelCase__							,   lstrip=UpperCamelCase__							,   rstrip=UpperCamelCase__							) if isinstance(UpperCamelCase__							,   UpperCamelCase__							) else unk_token
    a						=		AddedToken(UpperCamelCase__							,   lstrip=UpperCamelCase__							,   rstrip=UpperCamelCase__							) if isinstance(UpperCamelCase__							,   UpperCamelCase__							) else pad_token

    # Mask token behave like a normal word, i.e. include the space before it
    a						=		AddedToken(UpperCamelCase__							,   lstrip=UpperCamelCase__							,   rstrip=UpperCamelCase__							) if isinstance(UpperCamelCase__							,   UpperCamelCase__							) else mask_token

    super().__init__(
        errors=UpperCamelCase__							,   bos_token=UpperCamelCase__							,   eos_token=UpperCamelCase__							,   unk_token=UpperCamelCase__							,   sep_token=UpperCamelCase__							,   cls_token=UpperCamelCase__							,   pad_token=UpperCamelCase__							,   mask_token=UpperCamelCase__							,   add_prefix_space=UpperCamelCase__							,   **UpperCamelCase__							,   )

    with open(UpperCamelCase__							,   encoding="utf-8"							) as vocab_handle:
     a						=		json.load(UpperCamelCase__							)
    a						=		{v: k for k, v in self.encoder.items()}
    a						=		errors  # how to handle errors in decoding
    a						=		bytes_to_unicode()
    a						=		{v: k for k, v in self.byte_encoder.items()}
    with open(UpperCamelCase__							,   encoding="utf-8"							) as merges_handle:
     a						=		merges_handle.read().split("\n"							)[1:-1]
    a						=		[tuple(merge.split()							) for merge in bpe_merges]
    a						=		dict(zip(UpperCamelCase__							,   range(len(UpperCamelCase__							)							)							)							)
    a						=		{}
    a						=		add_prefix_space

    # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
    a						=		re.compile(R"\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"							)

   @property
   def 		A							(      self			:       Dict							)       ->				Dict:


    """simple docstring"""






    return len(self.encoder							)

   def 		A							(      self			:       str							)       ->				Tuple:


    """simple docstring"""






    return dict(self.encoder							,   **self.added_tokens_encoder							)

   def 		A							(      self			:       str							,   __lowerCAmelCase			:       Optional[Any]							)       ->				Union[str, Any]:


    """simple docstring"""






    if token in self.cache:
     return self.cache[token]
    a						=		tuple(UpperCamelCase__							)
    a						=		get_pairs(UpperCamelCase__							)

    if not pairs:
     return token

    while True:
     a						=		min(UpperCamelCase__							,   key=lambda __lowerCAmelCase							: self.bpe_ranks.get(UpperCamelCase__							,   float("inf"							)							)							)
     if bigram not in self.bpe_ranks:
      break
     a						=		bigram
     a						=		[]
     a						=		0
     while i < len(UpperCamelCase__							):
      try:
       a						=		word.index(UpperCamelCase__							,   UpperCamelCase__							)
      except ValueError:
       new_word.extend(word[i:]							)
       break
      else:
       new_word.extend(word[i:j]							)
       a						=		j

      if word[i] == first and i < len(UpperCamelCase__							) - 1 and word[i + 1] == second:
       new_word.append(first + second							)
       i += 2
      else:
       new_word.append(word[i]							)
       i += 1
     a						=		tuple(UpperCamelCase__							)
     a						=		new_word
     if len(UpperCamelCase__							) == 1:
      break
     else:
      a						=		get_pairs(UpperCamelCase__							)
    a						=		''' '''.join(UpperCamelCase__							)
    a						=		word
    return word

   def 		A							(      self			:       Tuple							,   __lowerCAmelCase			:       Any							)       ->				Any:


    """simple docstring"""






    a						=		[]
    for token in re.findall(self.pat							,   UpperCamelCase__							):
     a						=		''''''.join(
         self.byte_encoder[b] for b in token.encode("utf-8"							)							)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
     bpe_tokens.extend(bpe_token for bpe_token in self.bpe(UpperCamelCase__							).split(" "							)							)
    return bpe_tokens

   def 		A							(      self			:       List[Any]							,   __lowerCAmelCase			:       Union[str, Any]							)       ->				List[Any]:


    """simple docstring"""






    return self.encoder.get(UpperCamelCase__							,   self.encoder.get(self.unk_token							)							)

   def 		A							(      self			:       Dict							,   __lowerCAmelCase			:       List[Any]							)       ->				int:


    """simple docstring"""






    return self.decoder.get(UpperCamelCase__							)

   def 		A							(      self			:       str							,   __lowerCAmelCase			:       str							)       ->				Any:


    """simple docstring"""






    a						=		''''''.join(UpperCamelCase__							)
    a						=		bytearray([self.byte_decoder[c] for c in text]							).decode("utf-8"							,   errors=self.errors							)
    return text

   def 		A							(      self			:       Optional[Any]							,   __lowerCAmelCase			:       str							,   __lowerCAmelCase			:       Optional[str] = None							)       ->				List[str]:


    """simple docstring"""






    if not os.path.isdir(UpperCamelCase__							):
     logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""							)
     return
    a						=		os.path.join(
        UpperCamelCase__							,   (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]							)
    a						=		os.path.join(
        UpperCamelCase__							,   (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]							)

    with open(UpperCamelCase__							,   "w"							,   encoding="utf-8"							) as f:
     f.write(json.dumps(self.encoder							,   indent=2							,   sort_keys=UpperCamelCase__							,   ensure_ascii=UpperCamelCase__							) + "\n"							)

    a						=		0
    with open(UpperCamelCase__							,   "w"							,   encoding="utf-8"							) as writer:
     writer.write("#version: 0.2\n"							)
     for bpe_tokens, token_index in sorted(self.bpe_ranks.items()							,   key=lambda __lowerCAmelCase							: kv[1]							):
      if index != token_index:
       logger.warning(
           f"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
           " Please check that the tokenizer is not corrupted!"							)
       a						=		token_index
      writer.write(" ".join(UpperCamelCase__							) + "\n"							)
      index += 1

    return vocab_file, merge_file

   def 		A							(      self			:       Any							,   __lowerCAmelCase			:       List[int]							,   __lowerCAmelCase			:       Optional[List[int]] = None							)       ->				Optional[int]:


    """simple docstring"""






    if token_ids_a is None:
     return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
    a						=		[self.cls_token_id]
    a						=		[self.sep_token_id]
    return cls + token_ids_a + sep + sep + token_ids_a + sep

   def 		A							(      self			:       str							,   __lowerCAmelCase			:       List[int]							,   __lowerCAmelCase			:       Optional[List[int]] = None							,   __lowerCAmelCase			:       bool = False							)       ->				Union[str, Any]:


    """simple docstring"""






    if already_has_special_tokens:
     return super().get_special_tokens_mask(
         token_ids_a=UpperCamelCase__							,   token_ids_a=UpperCamelCase__							,   already_has_special_tokens=UpperCamelCase__							)

    if token_ids_a is None:
     return [1] + ([0] * len(UpperCamelCase__							)) + [1]
    return [1] + ([0] * len(UpperCamelCase__							)) + [1, 1] + ([0] * len(UpperCamelCase__							)) + [1]

   def 		A							(      self			:       Optional[int]							,   __lowerCAmelCase			:       List[int]							,   __lowerCAmelCase			:       Optional[List[int]] = None							)       ->				str:


    """simple docstring"""






    a						=		[self.sep_token_id]
    a						=		[self.cls_token_id]

    if token_ids_a is None:
     return len(cls + token_ids_a + sep							) * [0]
    return len(cls + token_ids_a + sep + sep + token_ids_a + sep							) * [0]




   def 		A							(      self			:       str							,   __lowerCAmelCase			:       Any							,   __lowerCAmelCase			:       int=False							,   **__lowerCAmelCase			:       int							)       ->				Dict:


    """simple docstring"""






    a						=		kwargs.pop("add_prefix_space"							,   self.add_prefix_space							)
    if (is_split_into_words or add_prefix_space) and (len(UpperCamelCase__							) > 0 and not text[0].isspace()):
     a						=		''' ''' + text
    return (text, kwargs)






import tempfile

import torch

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

from .test_schedulers import SchedulerCommonTest



class 		_lowercase		(       UpperCAmelCase__	):
   _UpperCAmelCase     	=			(UniPCMultistepScheduler,)
   _UpperCAmelCase     	=			(('''num_inference_steps''', 25),)

   def 		A							(      self			:       List[Any]							,   **__lowerCAmelCase			:       Optional[int]							)       ->				int:


    """simple docstring"""






    a						=		{
        "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(**__lowerCAmelCase							)
    return config

   def 		A							(      self			:       List[Any]							,   __lowerCAmelCase			:       Optional[int]=0							,   **__lowerCAmelCase			:       Optional[Any]							)       ->				int:


    """simple docstring"""






    a						=		dict(self.forward_default_kwargs							)
    a						=		kwargs.pop("num_inference_steps"							,   __lowerCAmelCase							)
    a						=		self.dummy_sample
    a						=		0.1 * sample
    a						=		[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

    for scheduler_class in self.scheduler_classes:
     a						=		self.get_scheduler_config(**__lowerCAmelCase							)
     a						=		scheduler_class(**__lowerCAmelCase							)
     scheduler.set_timesteps(__lowerCAmelCase							)
     # copy over dummy past residuals
     a						=		dummy_past_residuals[: scheduler.config.solver_order]

     with tempfile.TemporaryDirectory() as tmpdirname:
      scheduler.save_config(__lowerCAmelCase							)
      a						=		scheduler_class.from_pretrained(__lowerCAmelCase							)
      new_scheduler.set_timesteps(__lowerCAmelCase							)
      # copy over dummy past residuals
      a						=		dummy_past_residuals[: new_scheduler.config.solver_order]

     a		,  a						=		sample, sample
     for t in range(__lowerCAmelCase							,   time_step + scheduler.config.solver_order + 1							):
      a						=		scheduler.step(__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							,   **__lowerCAmelCase							).prev_sample
      a						=		new_scheduler.step(__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							,   **__lowerCAmelCase							).prev_sample

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

   def 		A							(      self			:       List[Any]							,   __lowerCAmelCase			:       Optional[Any]=0							,   **__lowerCAmelCase			:       List[Any]							)       ->				List[str]:


    """simple docstring"""






    a						=		dict(self.forward_default_kwargs							)
    a						=		kwargs.pop("num_inference_steps"							,   __lowerCAmelCase							)
    a						=		self.dummy_sample
    a						=		0.1 * sample
    a						=		[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

    for scheduler_class in self.scheduler_classes:
     a						=		self.get_scheduler_config()
     a						=		scheduler_class(**__lowerCAmelCase							)
     scheduler.set_timesteps(__lowerCAmelCase							)

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

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

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

     a						=		scheduler.step(__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							,   **__lowerCAmelCase							).prev_sample
     a						=		new_scheduler.step(__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							,   **__lowerCAmelCase							).prev_sample

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

   def 		A							(      self			:       str							,   __lowerCAmelCase			:       Any=None							,   **__lowerCAmelCase			:       List[str]							)       ->				Any:


    """simple docstring"""






    if scheduler is None:
     a						=		self.scheduler_classes[0]
     a						=		self.get_scheduler_config(**__lowerCAmelCase							)
     a						=		scheduler_class(**__lowerCAmelCase							)

    a						=		self.scheduler_classes[0]
    a						=		self.get_scheduler_config(**__lowerCAmelCase							)
    a						=		scheduler_class(**__lowerCAmelCase							)

    a						=		10
    a						=		self.dummy_model()
    a						=		self.dummy_sample_deter
    scheduler.set_timesteps(__lowerCAmelCase							)

    for i, t in enumerate(scheduler.timesteps							):
     a						=		model(__lowerCAmelCase							,   __lowerCAmelCase							)
     a						=		scheduler.step(__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							).prev_sample

    return sample

   def 		A							(      self			:       Any							)       ->				int:


    """simple docstring"""






    a						=		dict(self.forward_default_kwargs							)

    a						=		kwargs.pop("num_inference_steps"							,   __lowerCAmelCase							)

    for scheduler_class in self.scheduler_classes:
     a						=		self.get_scheduler_config()
     a						=		scheduler_class(**__lowerCAmelCase							)

     a						=		self.dummy_sample
     a						=		0.1 * sample

     if num_inference_steps is not None and hasattr(__lowerCAmelCase							,   "set_timesteps"							):
      scheduler.set_timesteps(__lowerCAmelCase							)
     elif num_inference_steps is not None and not hasattr(__lowerCAmelCase							,   "set_timesteps"							):
      a						=		num_inference_steps

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

     a						=		scheduler.timesteps[5]
     a						=		scheduler.timesteps[6]

     a						=		scheduler.step(__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							,   **__lowerCAmelCase							).prev_sample
     a						=		scheduler.step(__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							,   **__lowerCAmelCase							).prev_sample

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

   def 		A							(      self			:       List[str]							)       ->				Dict:


    """simple docstring"""






    a						=		UniPCMultistepScheduler(**self.get_scheduler_config()							)
    a						=		self.full_loop(scheduler=__lowerCAmelCase							)
    a						=		torch.mean(torch.abs(__lowerCAmelCase							)							)

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

    a						=		DPMSolverSinglestepScheduler.from_config(scheduler.config							)
    a						=		DEISMultistepScheduler.from_config(scheduler.config							)
    a						=		DPMSolverMultistepScheduler.from_config(scheduler.config							)
    a						=		UniPCMultistepScheduler.from_config(scheduler.config							)

    a						=		self.full_loop(scheduler=__lowerCAmelCase							)
    a						=		torch.mean(torch.abs(__lowerCAmelCase							)							)

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

   def 		A							(      self			:       List[Any]							)       ->				Dict:


    """simple docstring"""






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

   def 		A							(      self			:       Optional[Any]							)       ->				Tuple:


    """simple docstring"""






    self.check_over_configs(thresholding=__lowerCAmelCase							)
    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=__lowerCAmelCase							,   prediction_type=__lowerCAmelCase							,   sample_max_value=__lowerCAmelCase							,   solver_order=__lowerCAmelCase							,   solver_type=__lowerCAmelCase							,   )

   def 		A							(      self			:       Optional[Any]							)       ->				Any:


    """simple docstring"""






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

   def 		A							(      self			:       Optional[Any]							)       ->				Any:


    """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=__lowerCAmelCase							,   solver_type=__lowerCAmelCase							,   prediction_type=__lowerCAmelCase							,   )
       a						=		self.full_loop(
           solver_order=__lowerCAmelCase							,   solver_type=__lowerCAmelCase							,   prediction_type=__lowerCAmelCase							,   )
       assert not torch.isnan(__lowerCAmelCase							).any(), "Samples have nan numbers"

   def 		A							(      self			:       Optional[int]							)       ->				Any:


    """simple docstring"""






    self.check_over_configs(lower_order_final=__lowerCAmelCase							)
    self.check_over_configs(lower_order_final=__lowerCAmelCase							)

   def 		A							(      self			:       Dict							)       ->				str:


    """simple docstring"""






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

   def 		A							(      self			:       Dict							)       ->				int:


    """simple docstring"""






    a						=		self.full_loop()
    a						=		torch.mean(torch.abs(__lowerCAmelCase							)							)

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

   def 		A							(      self			:       Optional[int]							)       ->				int:


    """simple docstring"""






    a						=		self.full_loop(prediction_type="v_prediction"							)
    a						=		torch.mean(torch.abs(__lowerCAmelCase							)							)

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

   def 		A							(      self			:       Union[str, Any]							)       ->				str:


    """simple docstring"""






    a						=		self.scheduler_classes[0]
    a						=		self.get_scheduler_config(thresholding=__lowerCAmelCase							,   dynamic_thresholding_ratio=0							)
    a						=		scheduler_class(**__lowerCAmelCase							)

    a						=		10
    a						=		self.dummy_model()
    a						=		self.dummy_sample_deter.half()
    scheduler.set_timesteps(__lowerCAmelCase							)

    for i, t in enumerate(scheduler.timesteps							):
     a						=		model(__lowerCAmelCase							,   __lowerCAmelCase							)
     a						=		scheduler.step(__lowerCAmelCase							,   __lowerCAmelCase							,   __lowerCAmelCase							).prev_sample

    assert sample.dtype == torch.floataa




   def 		A							(      self			:       List[str]							,   **__lowerCAmelCase			:       int							)       ->				Dict:


    """simple docstring"""






    for scheduler_class in self.scheduler_classes:
     a						=		self.get_scheduler_config(**__lowerCAmelCase							)
     a						=		scheduler_class(**__lowerCAmelCase							)

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

