





"""simple docstring"""
import os
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError

from transformers import (
    AlbertTokenizer,
    AutoTokenizer,
    BertTokenizer,
    BertTokenizerFast,
    GPTaTokenizerFast,
    is_tokenizers_available,
)
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_tokenizers
from transformers.tokenization_utils import Trie


sys.path.append(str(Path(__file__).parent.parent / '''utils'''))

from test_module.custom_tokenization import CustomTokenizer  # noqa E402


if is_tokenizers_available():
       from test_module.custom_tokenization_fast import CustomTokenizerFast







class       a							(   unittest.TestCase  ):



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= mock.Mock()
  lowerCAmelCase    				= 5_00
  lowerCAmelCase    				= {}
  lowerCAmelCase    				= HTTPError
  lowerCAmelCase    				= {}

  # Download this model to make sure it's in the cache.
  lowerCAmelCase    				= BertTokenizer.from_pretrained('hf-internal-testing/tiny-random-bert'     )

  # Under the mock environment we get a 500 error when trying to reach the tokenizer.
  with mock.patch('requests.Session.request'				,       return_value=_SCREAMING_SNAKE_CASE     ) as mock_head:
   lowerCAmelCase    				= BertTokenizer.from_pretrained('hf-internal-testing/tiny-random-bert'     )
   # This check we did call the fake head request
   mock_head.assert_called()



 @require_tokenizers
 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= mock.Mock()
  lowerCAmelCase    				= 5_00
  lowerCAmelCase    				= {}
  lowerCAmelCase    				= HTTPError
  lowerCAmelCase    				= {}

  # Download this model to make sure it's in the cache.
  lowerCAmelCase    				= GPTaTokenizerFast.from_pretrained('gpt2'     )

  # Under the mock environment we get a 500 error when trying to reach the tokenizer.
  with mock.patch('requests.Session.request'				,       return_value=_SCREAMING_SNAKE_CASE     ) as mock_head:
   lowerCAmelCase    				= GPTaTokenizerFast.from_pretrained('gpt2'     )
   # This check we did call the fake head request
   mock_head.assert_called()



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  try:
   lowerCAmelCase    				= tempfile.mktemp()
   with open(_SCREAMING_SNAKE_CASE				,       'wb'     ) as f:
    http_get('https://huggingface.co/albert-base-v1/resolve/main/spiece.model'				,       _SCREAMING_SNAKE_CASE     )

   lowerCAmelCase    				= AlbertTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE     )
  finally:
   os.remove(_SCREAMING_SNAKE_CASE     )

  # Supporting this legacy load introduced a weird bug where the tokenizer would load local files if they are in
  # the current folder and have the right name.
  if os.path.isfile('tokenizer.json'     ):
   # We skip the test if the user has a `tokenizer.json` in this folder to avoid deleting it.
   return
  try:
   with open('tokenizer.json'				,       'wb'     ) as f:
    http_get('https://huggingface.co/hf-internal-testing/tiny-random-bert/blob/main/tokenizer.json'				,       _SCREAMING_SNAKE_CASE     )
   lowerCAmelCase    				= AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-gpt2'     )
   # The tiny random BERT has a vocab size of 1024, tiny gpt2 as a vocab size of 1000
   self.assertEqual(tokenizer.vocab_size				,       10_00     )
   # Tokenizer should depend on the remote checkpoint, not the local tokenizer.json file.

  finally:
   os.remove('tokenizer.json'     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= AlbertTokenizer.from_pretrained('https://huggingface.co/albert-base-v1/resolve/main/spiece.model'     )







@is_staging_test
class       a							(   unittest.TestCase  ):
 snake_case__             =       ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''bla''', '''blou''']



 @classmethod
 def    UpperCamelCase__  (     cls     ):







  """simple docstring"""






  lowerCAmelCase    				= TOKEN
  HfFolder.save_token(_SCREAMING_SNAKE_CASE     )



 @classmethod
 def    UpperCamelCase__  (     cls     ):







  """simple docstring"""






  try:
   delete_repo(token=cls._token				,       repo_id='test-tokenizer'     )
  except HTTPError:
   pass

  try:
   delete_repo(token=cls._token				,       repo_id='valid_org/test-tokenizer-org'     )
  except HTTPError:
   pass

  try:
   delete_repo(token=cls._token				,       repo_id='test-dynamic-tokenizer'     )
  except HTTPError:
   pass



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  with tempfile.TemporaryDirectory() as tmp_dir:
   lowerCAmelCase    				= os.path.join(_SCREAMING_SNAKE_CASE				,       'vocab.txt'     )
   with open(_SCREAMING_SNAKE_CASE				,       'w'				,       encoding='utf-8'     ) as vocab_writer:
    vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens]     )     )
   lowerCAmelCase    				= BertTokenizer(_SCREAMING_SNAKE_CASE     )

  tokenizer.push_to_hub('test-tokenizer'				,       use_auth_token=self._token     )
  lowerCAmelCase    				= BertTokenizer.from_pretrained(F'{USER}/test-tokenizer'     )
  self.assertDictEqual(new_tokenizer.vocab				,       tokenizer.vocab     )

  # Reset repo
  delete_repo(token=self._token				,       repo_id='test-tokenizer'     )

  # Push to hub via save_pretrained
  with tempfile.TemporaryDirectory() as tmp_dir:
   tokenizer.save_pretrained(_SCREAMING_SNAKE_CASE				,       repo_id='test-tokenizer'				,       push_to_hub=_SCREAMING_SNAKE_CASE				,       use_auth_token=self._token     )

  lowerCAmelCase    				= BertTokenizer.from_pretrained(F'{USER}/test-tokenizer'     )
  self.assertDictEqual(new_tokenizer.vocab				,       tokenizer.vocab     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  with tempfile.TemporaryDirectory() as tmp_dir:
   lowerCAmelCase    				= os.path.join(_SCREAMING_SNAKE_CASE				,       'vocab.txt'     )
   with open(_SCREAMING_SNAKE_CASE				,       'w'				,       encoding='utf-8'     ) as vocab_writer:
    vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens]     )     )
   lowerCAmelCase    				= BertTokenizer(_SCREAMING_SNAKE_CASE     )

  tokenizer.push_to_hub('valid_org/test-tokenizer-org'				,       use_auth_token=self._token     )
  lowerCAmelCase    				= BertTokenizer.from_pretrained('valid_org/test-tokenizer-org'     )
  self.assertDictEqual(new_tokenizer.vocab				,       tokenizer.vocab     )

  # Reset repo
  delete_repo(token=self._token				,       repo_id='valid_org/test-tokenizer-org'     )

  # Push to hub via save_pretrained
  with tempfile.TemporaryDirectory() as tmp_dir:
   tokenizer.save_pretrained(
       _SCREAMING_SNAKE_CASE				,       repo_id='valid_org/test-tokenizer-org'				,       push_to_hub=_SCREAMING_SNAKE_CASE				,       use_auth_token=self._token     )

  lowerCAmelCase    				= BertTokenizer.from_pretrained('valid_org/test-tokenizer-org'     )
  self.assertDictEqual(new_tokenizer.vocab				,       tokenizer.vocab     )



 @require_tokenizers
 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  CustomTokenizer.register_for_auto_class()
  with tempfile.TemporaryDirectory() as tmp_dir:
   lowerCAmelCase    				= os.path.join(_SCREAMING_SNAKE_CASE				,       'vocab.txt'     )
   with open(_SCREAMING_SNAKE_CASE				,       'w'				,       encoding='utf-8'     ) as vocab_writer:
    vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens]     )     )
   lowerCAmelCase    				= CustomTokenizer(_SCREAMING_SNAKE_CASE     )

  # No fast custom tokenizer
  tokenizer.push_to_hub('test-dynamic-tokenizer'				,       use_auth_token=self._token     )

  lowerCAmelCase    				= AutoTokenizer.from_pretrained(F'{USER}/test-dynamic-tokenizer'				,       trust_remote_code=_SCREAMING_SNAKE_CASE     )
  # Can't make an isinstance check because the new_model.config is from the CustomTokenizer class of a dynamic module
  self.assertEqual(tokenizer.__class__.__name__				,       'CustomTokenizer'     )

  # Fast and slow custom tokenizer
  CustomTokenizerFast.register_for_auto_class()
  with tempfile.TemporaryDirectory() as tmp_dir:
   lowerCAmelCase    				= os.path.join(_SCREAMING_SNAKE_CASE				,       'vocab.txt'     )
   with open(_SCREAMING_SNAKE_CASE				,       'w'				,       encoding='utf-8'     ) as vocab_writer:
    vocab_writer.write(''.join([x + '\n' for x in self.vocab_tokens]     )     )

   lowerCAmelCase    				= BertTokenizerFast.from_pretrained(_SCREAMING_SNAKE_CASE     )
   bert_tokenizer.save_pretrained(_SCREAMING_SNAKE_CASE     )
   lowerCAmelCase    				= CustomTokenizerFast.from_pretrained(_SCREAMING_SNAKE_CASE     )

  tokenizer.push_to_hub('test-dynamic-tokenizer'				,       use_auth_token=self._token     )

  lowerCAmelCase    				= AutoTokenizer.from_pretrained(F'{USER}/test-dynamic-tokenizer'				,       trust_remote_code=_SCREAMING_SNAKE_CASE     )
  # Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
  self.assertEqual(tokenizer.__class__.__name__				,       'CustomTokenizerFast'     )
  lowerCAmelCase    				= AutoTokenizer.from_pretrained(
      F'{USER}/test-dynamic-tokenizer'				,       use_fast=_SCREAMING_SNAKE_CASE				,       trust_remote_code=_SCREAMING_SNAKE_CASE     )
  # Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
  self.assertEqual(tokenizer.__class__.__name__				,       'CustomTokenizer'     )







class       a							(   unittest.TestCase  ):



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= Trie()
  trie.add('Hello 友達'     )
  self.assertEqual(trie.data				,       {'H': {'e': {'l': {'l': {'o': {' ': {'友': {'達': {'': 1}}}}}}}}}     )
  trie.add('Hello'     )
  trie.data
  self.assertEqual(trie.data				,       {'H': {'e': {'l': {'l': {'o': {'': 1, ' ': {'友': {'達': {'': 1}}}}}}}}}     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= Trie()
  self.assertEqual(trie.split('[CLS] This is a extra_id_100'     )				,       ['[CLS] This is a extra_id_100']     )
  trie.add('[CLS]'     )
  trie.add('extra_id_1'     )
  trie.add('extra_id_100'     )
  self.assertEqual(trie.split('[CLS] This is a extra_id_100'     )				,       ['[CLS]', ' This is a ', 'extra_id_100']     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= Trie()
  trie.add('A'     )
  self.assertEqual(trie.split('ABC'     )				,       ['A', 'BC']     )
  self.assertEqual(trie.split('BCA'     )				,       ['BC', 'A']     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= Trie()
  trie.add('TOKEN]'     )
  trie.add('[SPECIAL_TOKEN]'     )
  self.assertEqual(trie.split('This is something [SPECIAL_TOKEN]'     )				,       ['This is something ', '[SPECIAL_TOKEN]']     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= Trie()
  trie.add('A'     )
  trie.add('P'     )
  trie.add('[SPECIAL_TOKEN]'     )
  self.assertEqual(trie.split('This is something [SPECIAL_TOKEN]'     )				,       ['This is something ', '[SPECIAL_TOKEN]']     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= Trie()
  trie.add('AB'     )
  trie.add('B'     )
  trie.add('C'     )
  self.assertEqual(trie.split('ABC'     )				,       ['AB', 'C']     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= Trie()
  trie.add('ABC'     )
  trie.add('B'     )
  trie.add('CD'     )
  self.assertEqual(trie.split('ABCD'     )				,       ['ABC', 'D']     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  lowerCAmelCase    				= Trie()
  lowerCAmelCase    				= trie.cut_text('ABC'				,       [0, 0, 2, 1, 2, 3]     )
  self.assertEqual(_SCREAMING_SNAKE_CASE				,       ['AB', 'C']     )








'''simple docstring'''



import torch

from diffusers import DDPMParallelScheduler

from .test_schedulers import SchedulerCommonTest



class 				_snake_case					(   a_			):
  SCREAMING_SNAKE_CASE			:       Union[str, Any]				=     (DDPMParallelScheduler,)

  def   _SCREAMING_SNAKE_CASE				(				self      , **_SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      lowerCAmelCase 						=							{
          'num_train_timesteps': 10_00,
          'beta_start': 0.0_001,
          'beta_end': 0.02,
          'beta_schedule': 'linear',
          'variance_type': 'fixed_small',
          'clip_sample': True,
      }

      config.update(**_SCREAMING_SNAKE_CASE	)
      return config

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      for timesteps in [1, 5, 1_00, 10_00]:
          self.check_over_configs(num_train_timesteps=_SCREAMING_SNAKE_CASE	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1]      , [0.002, 0.02, 0.2, 2]	):
          self.check_over_configs(beta_start=_SCREAMING_SNAKE_CASE      , beta_end=_SCREAMING_SNAKE_CASE	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      for schedule in ["linear", "squaredcos_cap_v2"]:
          self.check_over_configs(beta_schedule=_SCREAMING_SNAKE_CASE	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      for variance in ["fixed_small", "fixed_large", "other"]:
          self.check_over_configs(variance_type=_SCREAMING_SNAKE_CASE	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      for clip_sample in [True, False]:
          self.check_over_configs(clip_sample=_SCREAMING_SNAKE_CASE	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      self.check_over_configs(thresholding=_SCREAMING_SNAKE_CASE	)
      for threshold in [0.5, 1.0, 2.0]:
          for prediction_type in ["epsilon", "sample", "v_prediction"]:
              self.check_over_configs(
                  thresholding=_SCREAMING_SNAKE_CASE      , prediction_type=_SCREAMING_SNAKE_CASE      , sample_max_value=_SCREAMING_SNAKE_CASE      , )

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

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

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      for t in [0, 5_00, 9_99]:
          self.check_over_forward(time_step=_SCREAMING_SNAKE_CASE	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							self.scheduler_classes[0]
      lowerCAmelCase 						=							self.get_scheduler_config()
      lowerCAmelCase 						=							scheduler_class(**_SCREAMING_SNAKE_CASE	)

      assert torch.sum(torch.abs(scheduler._get_variance(0	) - 0.0	)	) < 1e-5
      assert torch.sum(torch.abs(scheduler._get_variance(4_87	) - 0.00_979	)	) < 1e-5
      assert torch.sum(torch.abs(scheduler._get_variance(9_99	) - 0.02	)	) < 1e-5

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							self.scheduler_classes[0]
      lowerCAmelCase 						=							self.get_scheduler_config()
      lowerCAmelCase 						=							scheduler_class(**_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							len(_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							self.dummy_model()
      lowerCAmelCase 						=							self.dummy_sample_deter
      lowerCAmelCase 						=							self.dummy_sample_deter + 0.1
      lowerCAmelCase 						=							self.dummy_sample_deter - 0.1

      lowerCAmelCase 						=							samplea.shape[0]
      lowerCAmelCase 						=							torch.stack([samplea, samplea, samplea]      , dim=0	)
      lowerCAmelCase 						=							torch.arange(_SCREAMING_SNAKE_CASE	)[0:3, None].repeat(1      , _SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							model(samples.flatten(0      , 1	)      , timesteps.flatten(0      , 1	)	)
      lowerCAmelCase 						=							scheduler.batch_step_no_noise(_SCREAMING_SNAKE_CASE      , timesteps.flatten(0      , 1	)      , samples.flatten(0      , 1	)	)

      lowerCAmelCase 						=							torch.sum(torch.abs(_SCREAMING_SNAKE_CASE	)	)
      lowerCAmelCase 						=							torch.mean(torch.abs(_SCREAMING_SNAKE_CASE	)	)

      assert abs(result_sum.item() - 1_153.1_833	) < 1e-2
      assert abs(result_mean.item() - 0.5_005	) < 1e-3

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							self.scheduler_classes[0]
      lowerCAmelCase 						=							self.get_scheduler_config()
      lowerCAmelCase 						=							scheduler_class(**_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							len(_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							self.dummy_model()
      lowerCAmelCase 						=							self.dummy_sample_deter
      lowerCAmelCase 						=							torch.manual_seed(0	)

      for t in reversed(range(_SCREAMING_SNAKE_CASE	)	):
          # 1. predict noise residual
          lowerCAmelCase 						=							model(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	)

          # 2. predict previous mean of sample x_t-1
          lowerCAmelCase 						=							scheduler.step(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , generator=_SCREAMING_SNAKE_CASE	).prev_sample

          lowerCAmelCase 						=							pred_prev_sample

      lowerCAmelCase 						=							torch.sum(torch.abs(_SCREAMING_SNAKE_CASE	)	)
      lowerCAmelCase 						=							torch.mean(torch.abs(_SCREAMING_SNAKE_CASE	)	)

      assert abs(result_sum.item() - 258.9_606	) < 1e-2
      assert abs(result_mean.item() - 0.3_372	) < 1e-3

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							self.scheduler_classes[0]
      lowerCAmelCase 						=							self.get_scheduler_config(prediction_type='v_prediction'	)
      lowerCAmelCase 						=							scheduler_class(**_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							len(_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							self.dummy_model()
      lowerCAmelCase 						=							self.dummy_sample_deter
      lowerCAmelCase 						=							torch.manual_seed(0	)

      for t in reversed(range(_SCREAMING_SNAKE_CASE	)	):
          # 1. predict noise residual
          lowerCAmelCase 						=							model(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	)

          # 2. predict previous mean of sample x_t-1
          lowerCAmelCase 						=							scheduler.step(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE      , generator=_SCREAMING_SNAKE_CASE	).prev_sample

          lowerCAmelCase 						=							pred_prev_sample

      lowerCAmelCase 						=							torch.sum(torch.abs(_SCREAMING_SNAKE_CASE	)	)
      lowerCAmelCase 						=							torch.mean(torch.abs(_SCREAMING_SNAKE_CASE	)	)

      assert abs(result_sum.item() - 202.0_296	) < 1e-2
      assert abs(result_mean.item() - 0.2_631	) < 1e-3

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							self.scheduler_classes[0]
      lowerCAmelCase 						=							self.get_scheduler_config()
      lowerCAmelCase 						=							scheduler_class(**_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							[1_00, 87, 50, 1, 0]

      scheduler.set_timesteps(timesteps=_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							scheduler.timesteps

      for i, timestep in enumerate(_SCREAMING_SNAKE_CASE	):
          if i == len(_SCREAMING_SNAKE_CASE	) - 1:
              lowerCAmelCase 						=							-1
          else:
              lowerCAmelCase 						=							timesteps[i + 1]

          lowerCAmelCase 						=							scheduler.previous_timestep(_SCREAMING_SNAKE_CASE	)
          lowerCAmelCase 						=							prev_t.item()

          self.assertEqual(_SCREAMING_SNAKE_CASE      , _SCREAMING_SNAKE_CASE	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							self.scheduler_classes[0]
      lowerCAmelCase 						=							self.get_scheduler_config()
      lowerCAmelCase 						=							scheduler_class(**_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							[1_00, 87, 50, 51, 0]

      with self.assertRaises(_SCREAMING_SNAKE_CASE      , msg='`custom_timesteps` must be in descending order.'	):
          scheduler.set_timesteps(timesteps=_SCREAMING_SNAKE_CASE	)

  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							self.scheduler_classes[0]
      lowerCAmelCase 						=							self.get_scheduler_config()
      lowerCAmelCase 						=							scheduler_class(**_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							[1_00, 87, 50, 1, 0]
      lowerCAmelCase 						=							len(_SCREAMING_SNAKE_CASE	)

      with self.assertRaises(_SCREAMING_SNAKE_CASE      , msg='Can only pass one of `num_inference_steps` or `custom_timesteps`.'	):
          scheduler.set_timesteps(num_inference_steps=_SCREAMING_SNAKE_CASE      , timesteps=_SCREAMING_SNAKE_CASE	)


  def   _SCREAMING_SNAKE_CASE				(				self	):





      '''simple docstring'''

      lowerCAmelCase 						=							self.scheduler_classes[0]
      lowerCAmelCase 						=							self.get_scheduler_config()
      lowerCAmelCase 						=							scheduler_class(**_SCREAMING_SNAKE_CASE	)

      lowerCAmelCase 						=							[scheduler.config.num_train_timesteps]

      with self.assertRaises(
          _SCREAMING_SNAKE_CASE      , msg='`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'      , ):
          scheduler.set_timesteps(timesteps=_SCREAMING_SNAKE_CASE	)





