






"""simple docstring"""






import os
import re
import shutil
import sys
import tempfile
import unittest

import black


UpperCAmelCase_   : Optional[Any]   			= os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If BertLMPredictionHead is changed in modeling_bert.py, this code needs to be manually updated.
UpperCAmelCase_   : List[Any]   			= """    def __init__(self, config):
        super().__init__()
        self.transform = BertPredictionHeadTransform(config)

        # The output weights are the same as the input embeddings, but there is
        # an output-only bias for each token.
        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

        self.bias = nn.Parameter(torch.zeros(config.vocab_size))

        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
        self.decoder.bias = self.bias

    def forward(self, hidden_states):
        hidden_states = self.transform(hidden_states)
        hidden_states = self.decoder(hidden_states)
        return hidden_states
"""




class 							lowerCAmelCase__				(			unittest.TestCase ):






      '''simple docstring'''

      def _SCREAMING_SNAKE_CASE (						self     :  int):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  List[str]    						=		tempfile.mkdtemp()
          os.makedirs(os.path.join(self.transformer_dir						,	'''models/bert/'''))
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		self.transformer_dir
          shutil.copy(
              os.path.join(lowercase_						,	'''src/transformers/models/bert/modeling_bert.py''')						,	os.path.join(self.transformer_dir						,	'''models/bert/modeling_bert.py''')						,	)
      def _SCREAMING_SNAKE_CASE (						self     :  Dict):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		'''src/transformers'''
          shutil.rmtree(self.transformer_dir)
      def _SCREAMING_SNAKE_CASE (						self     :  Dict						,	lowercase_     :  Dict						,	lowercase_     :  str						,	lowercase_     :  int						,	lowercase_     :  Tuple=None):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		comment + F'\nclass {class_name}(nn.Module):\n' + class_code
          if overwrite_result is not None:
              SCREAMING_SNAKE_CASE_      :  List[Any]    						=		comment + F'\nclass {class_name}(nn.Module):\n' + overwrite_result
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		black.Mode(target_versions={black.TargetVersion.PYaa}						,	line_length=119)
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		black.format_str(lowercase_						,	mode=lowercase_)
          SCREAMING_SNAKE_CASE_      :  Any    						=		os.path.join(self.transformer_dir						,	'''new_code.py''')
          with open(lowercase_						,	'''w'''						,	newline='''\n''') as f:
              f.write(lowercase_)
          if overwrite_result is None:
              self.assertTrue(len(check_copies.is_copy_consistent(lowercase_)) == 0)
          else:
              check_copies.is_copy_consistent(f.name						,	overwrite=lowercase_)
              with open(lowercase_						,	'''r''') as f:
                  self.assertTrue(f.read()						,	lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  str    						=		check_copies.find_code_in_transformers('''models.bert.modeling_bert.BertLMPredictionHead''')
          self.assertEqual(lowercase_						,	lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  Tuple):




          '''simple docstring'''


          self.check_copy_consistency(
              '''# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead'''						,	'''BertLMPredictionHead'''						,	REFERENCE_CODE + '''\n'''						,	)

          # With no empty line at the end
          self.check_copy_consistency(
              '''# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead'''						,	'''BertLMPredictionHead'''						,	lowercase_						,	)

          # Copy consistency with rename
          self.check_copy_consistency(
              '''# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel'''						,	'''TestModelLMPredictionHead'''						,	re.sub('''Bert'''						,	'''TestModel'''						,	lowercase_)						,	)

          # Copy consistency with a really long name
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		'''TestModelWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason'''
          self.check_copy_consistency(
              F'# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->{long_class_name}'						,	F'{long_class_name}LMPredictionHead'						,	re.sub('''Bert'''						,	lowercase_						,	lowercase_)						,	)

          # Copy consistency with overwrite
          self.check_copy_consistency(
              '''# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel'''						,	'''TestModelLMPredictionHead'''						,	lowercase_						,	overwrite_result=re.sub('''Bert'''						,	'''TestModel'''						,	lowercase_)						,	)



      def _SCREAMING_SNAKE_CASE (						self     :  List[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Any    						=		check_copies.LOCALIZED_READMES['''README_zh-hans.md''']

          SCREAMING_SNAKE_CASE_      :  Any    						=		(
              '''1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the'''
              ''' Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for'''
              ''' Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong'''
              ''' Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.\n1.'''
              ''' **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (from HuggingFace),'''
              ''' released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and'''
              ''' lighter](https://arxiv.org/abs/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same'''
              ''' method has been applied to compress GPT2 into'''
              ''' [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into'''
              ''' [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),'''
              ''' Multilingual BERT into'''
              ''' [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German'''
              ''' version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)**'''
              ''' (from Google Research/Stanford University) released with the paper [ELECTRA: Pre-training text encoders'''
              ''' as discriminators rather than generators](https://arxiv.org/abs/2003.10555) by Kevin Clark, Minh-Thang'''
              ''' Luong, Quoc V. Le, Christopher D. Manning.'''
          )
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		(
              '''1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the'''
              ''' Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'''
              ''' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'''
              ''' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n'''
          )
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		(
              '''1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the'''
              ''' Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'''
              ''' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'''
              ''' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n1.'''
              ''' **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (来自 HuggingFace) 伴随论文'''
              ''' [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and'''
              ''' lighter](https://arxiv.org/abs/1910.01108) 由 Victor Sanh, Lysandre Debut and Thomas Wolf 发布。 The same'''
              ''' method has been applied to compress GPT2 into'''
              ''' [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into'''
              ''' [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),'''
              ''' Multilingual BERT into'''
              ''' [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German'''
              ''' version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)** (来自'''
              ''' Google Research/Stanford University) 伴随论文 [ELECTRA: Pre-training text encoders as discriminators rather'''
              ''' than generators](https://arxiv.org/abs/2003.10555) 由 Kevin Clark, Minh-Thang Luong, Quoc V. Le,'''
              ''' Christopher D. Manning 发布。\n'''
          )

          SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  List[str]    						=		check_copies.convert_to_localized_md(
              lowercase_						,	lowercase_						,	localized_readme['''format_model_list'''])

          self.assertFalse(lowercase_)
          self.assertEqual(lowercase_						,	lowercase_)

          SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		check_copies.convert_to_localized_md(
              lowercase_						,	lowercase_						,	localized_readme['''format_model_list'''])

          # Check whether the number of models is equal to README.md after conversion.
          self.assertTrue(lowercase_)

          SCREAMING_SNAKE_CASE_      :  Dict    						=		(
              '''1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the'''
              ''' Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for'''
              ''' Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong'''
              ''' Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.'''
          )
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		(
              '''1. **[ALBERT](https://huggingface.co/transformers/main/model_doc/albert.html)** (来自 Google Research and'''
              ''' the Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'''
              ''' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'''
              ''' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n'''
          )
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		(
              '''1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the'''
              ''' Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of'''
              ''' Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian'''
              ''' Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n'''
          )

          SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  List[str]    						=		check_copies.convert_to_localized_md(
              lowercase_						,	lowercase_						,	localized_readme['''format_model_list'''])

          # Check if the model link is synchronized.
          self.assertEqual(lowercase_						,	lowercase_)










"""simple docstring"""






import tempfile

import torch

from diffusers import PNDMScheduler

from .test_schedulers import SchedulerCommonTest




class 							lowerCAmelCase__				(			UpperCAmelCase__ ):






      '''simple docstring'''

      __UpperCamelCase	 =  (PNDMScheduler,)
      __UpperCamelCase	 =  (("num_inference_steps", 5_0),)
      def _SCREAMING_SNAKE_CASE (						self     :  Any						,	**lowercase_     :  Optional[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Any    						=		{
              '''num_train_timesteps''': 1000,
              '''beta_start''': 0.00_01,
              '''beta_end''': 0.02,
              '''beta_schedule''': '''linear''',
          }

          config.update(**lowercase_)
          return config
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[Any]						,	lowercase_     :  List[str]=0						,	**lowercase_     :  Union[str, Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		dict(self.forward_default_kwargs)
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		kwargs.pop('''num_inference_steps'''						,	lowercase_)
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		self.dummy_sample
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		0.1 * sample
          SCREAMING_SNAKE_CASE_      :  Dict    						=		[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

          for scheduler_class in self.scheduler_classes:
              SCREAMING_SNAKE_CASE_      :  Tuple    						=		self.get_scheduler_config(**lowercase_)
              SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		scheduler_class(**lowercase_)
              scheduler.set_timesteps(lowercase_)
              # copy over dummy past residuals
              SCREAMING_SNAKE_CASE_      :  Dict    						=		dummy_past_residuals[:]

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

              SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		scheduler.step_prk(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_).prev_sample
              SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		new_scheduler.step_prk(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_).prev_sample

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

              SCREAMING_SNAKE_CASE_      :  List[str]    						=		scheduler.step_plms(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_).prev_sample
              SCREAMING_SNAKE_CASE_      :  Dict    						=		new_scheduler.step_plms(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_).prev_sample

              assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"
      def _SCREAMING_SNAKE_CASE (						self     :  Any):




          '''simple docstring'''


          pass
      def _SCREAMING_SNAKE_CASE (						self     :  List[Any]						,	lowercase_     :  List[str]=0						,	**lowercase_     :  List[str]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		dict(self.forward_default_kwargs)
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		kwargs.pop('''num_inference_steps'''						,	lowercase_)
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		self.dummy_sample
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		0.1 * sample
          SCREAMING_SNAKE_CASE_      :  int    						=		[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

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

              # copy over dummy past residuals (must be after setting timesteps)
              SCREAMING_SNAKE_CASE_      :  Dict    						=		dummy_past_residuals[:]

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

                  # copy over dummy past residual (must be after setting timesteps)
                  SCREAMING_SNAKE_CASE_      :  Any    						=		dummy_past_residuals[:]

              SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		scheduler.step_prk(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_).prev_sample
              SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		new_scheduler.step_prk(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_).prev_sample

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

              SCREAMING_SNAKE_CASE_      :  List[str]    						=		scheduler.step_plms(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_).prev_sample
              SCREAMING_SNAKE_CASE_      :  Tuple    						=		new_scheduler.step_plms(lowercase_						,	lowercase_						,	lowercase_						,	**lowercase_).prev_sample

              assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"
      def _SCREAMING_SNAKE_CASE (						self     :  str						,	**lowercase_     :  str):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Tuple    						=		self.scheduler_classes[0]
          SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		self.get_scheduler_config(**lowercase_)
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		scheduler_class(**lowercase_)

          SCREAMING_SNAKE_CASE_      :  Dict    						=		10
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		self.dummy_model()
          SCREAMING_SNAKE_CASE_      :  str    						=		self.dummy_sample_deter
          scheduler.set_timesteps(lowercase_)

          for i, t in enumerate(scheduler.prk_timesteps):
              SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		model(lowercase_						,	lowercase_)
              SCREAMING_SNAKE_CASE_      :  str    						=		scheduler.step_prk(lowercase_						,	lowercase_						,	lowercase_).prev_sample

          for i, t in enumerate(scheduler.plms_timesteps):
              SCREAMING_SNAKE_CASE_      :  List[Any]    						=		model(lowercase_						,	lowercase_)
              SCREAMING_SNAKE_CASE_      :  List[str]    						=		scheduler.step_plms(lowercase_						,	lowercase_						,	lowercase_).prev_sample

          return sample
      def _SCREAMING_SNAKE_CASE (						self     :  Dict):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Any    						=		dict(self.forward_default_kwargs)

          SCREAMING_SNAKE_CASE_      :  Dict    						=		kwargs.pop('''num_inference_steps'''						,	lowercase_)

          for scheduler_class in self.scheduler_classes:
              SCREAMING_SNAKE_CASE_      :  Tuple    						=		self.get_scheduler_config()
              SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		scheduler_class(**lowercase_)

              SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		self.dummy_sample
              SCREAMING_SNAKE_CASE_      :  Any    						=		0.1 * sample

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

              # copy over dummy past residuals (must be done after set_timesteps)
              SCREAMING_SNAKE_CASE_      :  str    						=		[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
              SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		dummy_past_residuals[:]

              SCREAMING_SNAKE_CASE_      :  Dict    						=		scheduler.step_prk(lowercase_						,	0						,	lowercase_						,	**lowercase_).prev_sample
              SCREAMING_SNAKE_CASE_      :  List[Any]    						=		scheduler.step_prk(lowercase_						,	1						,	lowercase_						,	**lowercase_).prev_sample

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

              SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		scheduler.step_plms(lowercase_						,	0						,	lowercase_						,	**lowercase_).prev_sample
              SCREAMING_SNAKE_CASE_      :  Any    						=		scheduler.step_plms(lowercase_						,	1						,	lowercase_						,	**lowercase_).prev_sample

              self.assertEqual(output_a.shape						,	sample.shape)
              self.assertEqual(output_a.shape						,	output_a.shape)
      def _SCREAMING_SNAKE_CASE (						self     :  str):




          '''simple docstring'''


          for timesteps in [100, 1000]:
              self.check_over_configs(num_train_timesteps=lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  Dict):




          '''simple docstring'''


          for steps_offset in [0, 1]:
              self.check_over_configs(steps_offset=lowercase_)

          SCREAMING_SNAKE_CASE_      :  Dict    						=		self.scheduler_classes[0]
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		self.get_scheduler_config(steps_offset=1)
          SCREAMING_SNAKE_CASE_      :  Tuple    						=		scheduler_class(**lowercase_)
          scheduler.set_timesteps(10)
          assert torch.equal(
              scheduler.timesteps						,	torch.LongTensor(
                  [901, 851, 851, 801, 801, 751, 751, 701, 701, 651, 651, 601, 601, 501, 401, 301, 201, 101, 1])						,	)
      def _SCREAMING_SNAKE_CASE (						self     :  Any):




          '''simple docstring'''


          for beta_start, beta_end in zip([0.00_01, 0.0_01]						,	[0.0_02, 0.02]):
              self.check_over_configs(beta_start=lowercase_						,	beta_end=lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  List[Any]):




          '''simple docstring'''


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




          '''simple docstring'''


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




          '''simple docstring'''


          for t in [1, 5, 10]:
              self.check_over_forward(time_step=lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[Any]):




          '''simple docstring'''


          for t, num_inference_steps in zip([1, 5, 10]						,	[10, 50, 100]):
              self.check_over_forward(num_inference_steps=lowercase_)
      def _SCREAMING_SNAKE_CASE (						self     :  Any):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		27

          for scheduler_class in self.scheduler_classes:
              SCREAMING_SNAKE_CASE_      :  List[Any]    						=		self.dummy_sample
              SCREAMING_SNAKE_CASE_      :  str    						=		0.1 * sample

              SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		self.get_scheduler_config()
              SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		scheduler_class(**lowercase_)

              scheduler.set_timesteps(lowercase_)

              # before power of 3 fix, would error on first step, so we only need to do two
              for i, t in enumerate(scheduler.prk_timesteps[:2]):
                  SCREAMING_SNAKE_CASE_      :  int    						=		scheduler.step_prk(lowercase_						,	lowercase_						,	lowercase_).prev_sample
      def _SCREAMING_SNAKE_CASE (						self     :  List[Any]):




          '''simple docstring'''


          with self.assertRaises(lowercase_):
              SCREAMING_SNAKE_CASE_      :  int    						=		self.scheduler_classes[0]
              SCREAMING_SNAKE_CASE_      :  List[str]    						=		self.get_scheduler_config()
              SCREAMING_SNAKE_CASE_      :  Dict    						=		scheduler_class(**lowercase_)

              scheduler.step_plms(self.dummy_sample						,	1						,	self.dummy_sample).prev_sample
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[int]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Any    						=		self.full_loop()
          SCREAMING_SNAKE_CASE_      :  List[Any]    						=		torch.sum(torch.abs(lowercase_))
          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		torch.mean(torch.abs(lowercase_))

          assert abs(result_sum.item() - 1_98.13_18) < 1e-2
          assert abs(result_mean.item() - 0.25_80) < 1e-3
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[int]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  str    						=		self.full_loop(prediction_type='''v_prediction''')
          SCREAMING_SNAKE_CASE_      :  str    						=		torch.sum(torch.abs(lowercase_))
          SCREAMING_SNAKE_CASE_      :  Any    						=		torch.mean(torch.abs(lowercase_))

          assert abs(result_sum.item() - 67.39_86) < 1e-2
          assert abs(result_mean.item() - 0.08_78) < 1e-3
      def _SCREAMING_SNAKE_CASE (						self     :  Optional[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Tuple    						=		self.full_loop(set_alpha_to_one=lowercase_						,	beta_start=0.01)
          SCREAMING_SNAKE_CASE_      :  Optional[Any]    						=		torch.sum(torch.abs(lowercase_))
          SCREAMING_SNAKE_CASE_      :  Any    						=		torch.mean(torch.abs(lowercase_))

          assert abs(result_sum.item() - 2_30.03_99) < 1e-2
          assert abs(result_mean.item() - 0.29_95) < 1e-3



      def _SCREAMING_SNAKE_CASE (						self     :  Optional[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		self.full_loop(set_alpha_to_one=lowercase_						,	beta_start=0.01)
          SCREAMING_SNAKE_CASE_      :  int    						=		torch.sum(torch.abs(lowercase_))
          SCREAMING_SNAKE_CASE_      :  List[str]    						=		torch.mean(torch.abs(lowercase_))

          assert abs(result_sum.item() - 1_86.94_82) < 1e-2
          assert abs(result_mean.item() - 0.24_34) < 1e-3


