

"""simple docstring"""






import argparse
import json
import math
import os
import time
import traceback
import zipfile
from collections import Counter

import requests

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase=None   ):

    '''simple docstring'''



    _lowerCAmelCase       :   Optional[Any]  =    None
    if token is not None:
        _lowerCAmelCase       :   Union[str, Any]  =    {'Accept': 'application/vnd.github+json', 'Authorization': f"""Bearer {token}"""}

    _lowerCAmelCase       :   List[str]  =    f"""https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100"""
    _lowerCAmelCase       :   List[Any]  =    requests.get(_lowerCamelCase ,   headers=_lowerCamelCase   ).json()
    _lowerCAmelCase       :   int  =    {}

    try:
        job_links.update({job['name']: job['html_url'] for job in result['jobs']}   )
        _lowerCAmelCase       :   Union[str, Any]  =    math.ceil((result['total_count'] - 100) / 100   )

        for i in range(_lowerCamelCase   ):
            _lowerCAmelCase       :   List[Any]  =    requests.get(url + f"""&page={i + 2}""" ,   headers=_lowerCamelCase   ).json()
            job_links.update({job['name']: job['html_url'] for job in result['jobs']}   )

        return job_links
    except Exception:
        print(f"""Unknown error, could not fetch links:\n{traceback.format_exc()}"""   )

    return {}

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase=None   ):

    '''simple docstring'''



    _lowerCAmelCase       :   Dict  =    None
    if token is not None:
        _lowerCAmelCase       :   List[str]  =    {'Accept': 'application/vnd.github+json', 'Authorization': f"""Bearer {token}"""}

    _lowerCAmelCase       :   Optional[Any]  =    f"""https://api.github.com/repos/huggingface/transformers/actions/runs/{worflow_run_id}/artifacts?per_page=100"""
    _lowerCAmelCase       :   List[Any]  =    requests.get(_lowerCamelCase ,   headers=_lowerCamelCase   ).json()
    _lowerCAmelCase       :   Union[str, Any]  =    {}

    try:
        artifacts.update({artifact['name']: artifact['archive_download_url'] for artifact in result['artifacts']}   )
        _lowerCAmelCase       :   int  =    math.ceil((result['total_count'] - 100) / 100   )

        for i in range(_lowerCamelCase   ):
            _lowerCAmelCase       :   str  =    requests.get(url + f"""&page={i + 2}""" ,   headers=_lowerCamelCase   ).json()
            artifacts.update({artifact['name']: artifact['archive_download_url'] for artifact in result['artifacts']}   )

        return artifacts
    except Exception:
        print(f"""Unknown error, could not fetch links:\n{traceback.format_exc()}"""   )

    return {}

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase   ):

    '''simple docstring'''



    _lowerCAmelCase       :   Dict  =    None
    if token is not None:
        _lowerCAmelCase       :   List[Any]  =    {'Accept': 'application/vnd.github+json', 'Authorization': f"""Bearer {token}"""}

    _lowerCAmelCase       :   Union[str, Any]  =    requests.get(_lowerCamelCase ,   headers=_lowerCamelCase ,   allow_redirects=_lowerCamelCase   )
    _lowerCAmelCase       :   Union[str, Any]  =    result.headers['Location']
    _lowerCAmelCase       :   Optional[Any]  =    requests.get(_lowerCamelCase ,   allow_redirects=_lowerCamelCase   )
    _lowerCAmelCase       :   Any  =    os.path.join(_lowerCamelCase ,   f"""{artifact_name}.zip"""   )
    with open(_lowerCamelCase ,   'wb'   ) as fp:
        fp.write(response.content   )

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase=None   ):

    '''simple docstring'''



    _lowerCAmelCase       :   Any  =    []
    _lowerCAmelCase       :   Dict  =    []
    _lowerCAmelCase       :   Union[str, Any]  =    None

    with zipfile.ZipFile(_lowerCamelCase   ) as z:
        for filename in z.namelist():
            if not os.path.isdir(_lowerCamelCase   ):
                # read the file
                if filename in ["failures_line.txt", "summary_short.txt", "job_name.txt"]:
                    with z.open(_lowerCamelCase   ) as f:
                        for line in f:
                            _lowerCAmelCase       :   Any  =    line.decode('UTF-8'   ).strip()
                            if filename == "failures_line.txt":
                                try:
                                    # `error_line` is the place where `error` occurs
                                    _lowerCAmelCase       :   Any  =    line[: line.index(': '   )]
                                    _lowerCAmelCase       :   List[str]  =    line[line.index(': '   ) + len(': '   ) :]
                                    errors.append([error_line, error]   )
                                except Exception:
                                    # skip un-related lines
                                    pass
                            elif filename == "summary_short.txt" and line.startswith('FAILED '   ):
                                # `test` is the test method that failed
                                _lowerCAmelCase       :   List[Any]  =    line[len('FAILED '   ) :]
                                failed_tests.append(_lowerCamelCase   )
                            elif filename == "job_name.txt":
                                _lowerCAmelCase       :   List[str]  =    line

    if len(_lowerCamelCase   ) != len(_lowerCamelCase   ):
        raise ValueError(
            f"""`errors` and `failed_tests` should have the same number of elements. Got {len(_lowerCamelCase   )} for `errors` """
            f"""and {len(_lowerCamelCase   )} for `failed_tests` instead. The test reports in {artifact_zip_path} have some"""
            ' problem.'   )

    _lowerCAmelCase       :   Dict  =    None
    if job_name and job_links:
        _lowerCAmelCase       :   Tuple  =    job_links.get(_lowerCamelCase ,   _lowerCamelCase   )

    # A list with elements of the form (line of error, error, failed test)
    _lowerCAmelCase       :   List[str]  =    [x + [y] + [job_link] for x, y in zip(_lowerCamelCase ,   _lowerCamelCase   )]

    return result

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase=None   ):

    '''simple docstring'''



    _lowerCAmelCase       :   Dict  =    []

    _lowerCAmelCase       :   int  =    [os.path.join(_lowerCamelCase ,   _lowerCamelCase   ) for p in os.listdir(_lowerCamelCase   ) if p.endswith('.zip'   )]
    for p in paths:
        errors.extend(get_errors_from_single_artifact(_lowerCamelCase ,   job_links=_lowerCamelCase   )   )

    return errors

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase=None   ):

    '''simple docstring'''



    _lowerCAmelCase       :   Tuple  =    Counter()
    counter.update([x[1] for x in logs]   )
    _lowerCAmelCase       :   Optional[Any]  =    counter.most_common()
    _lowerCAmelCase       :   Optional[int]  =    {}
    for error, count in counts:
        if error_filter is None or error not in error_filter:
            _lowerCAmelCase       :   Optional[int]  =    {'count': count, 'failed_tests': [(x[2], x[0]) for x in logs if x[1] == error]}

    _lowerCAmelCase       :   List[Any]  =    dict(sorted(r.items() ,   key=lambda _lowerCamelCase   : item[1]["count"] ,   reverse=_lowerCamelCase   )   )
    return r

def 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



    _lowerCAmelCase       :   Any  =    test.split('::'   )[0]
    if test.startswith('tests/models/'   ):
        _lowerCAmelCase       :   List[str]  =    test.split('/'   )[2]
    else:
        _lowerCAmelCase       :   Tuple  =    None

    return test

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase=None   ):

    '''simple docstring'''



    _lowerCAmelCase       :   Optional[Any]  =    [(x[0], x[1], get_model(x[2]   )) for x in logs]
    _lowerCAmelCase       :   List[Any]  =    [x for x in logs if x[2] is not None]
    _lowerCAmelCase       :   Optional[Any]  =    {x[2] for x in logs}

    _lowerCAmelCase       :   Optional[Any]  =    {}
    for test in tests:
        _lowerCAmelCase       :   Any  =    Counter()
        # count by errors in `test`
        counter.update([x[1] for x in logs if x[2] == test]   )
        _lowerCAmelCase       :   List[str]  =    counter.most_common()
        _lowerCAmelCase       :   str  =    {error: count for error, count in counts if (error_filter is None or error not in error_filter)}
        _lowerCAmelCase       :   Dict  =    sum(error_counts.values()   )
        if n_errors > 0:
            _lowerCAmelCase       :   Union[str, Any]  =    {'count': n_errors, 'errors': error_counts}

    _lowerCAmelCase       :   Optional[Any]  =    dict(sorted(r.items() ,   key=lambda _lowerCamelCase   : item[1]["count"] ,   reverse=_lowerCamelCase   )   )
    return r

def 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



    _lowerCAmelCase       :   List[str]  =    '| no. | error | status |'
    _lowerCAmelCase       :   Any  =    '|-:|:-|:-|'
    _lowerCAmelCase       :   Dict  =    [header, sep]
    for error in reduced_by_error:
        _lowerCAmelCase       :   Optional[Any]  =    reduced_by_error[error]['count']
        _lowerCAmelCase       :   Any  =    f"""| {count} | {error[:100]} |  |"""
        lines.append(_lowerCamelCase   )

    return "\n".join(_lowerCamelCase   )

def 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



    _lowerCAmelCase       :   Tuple  =    '| model | no. of errors | major error | count |'
    _lowerCAmelCase       :   Dict  =    '|-:|-:|-:|-:|'
    _lowerCAmelCase       :   str  =    [header, sep]
    for model in reduced_by_model:
        _lowerCAmelCase       :   Dict  =    reduced_by_model[model]['count']
        _lowerCAmelCase       :   Tuple  =    list(reduced_by_model[model]['errors'].items()   )[0]
        _lowerCAmelCase       :   Union[str, Any]  =    f"""| {model} | {count} | {error[:60]} | {_count} |"""
        lines.append(_lowerCamelCase   )

    return "\n".join(_lowerCamelCase   )


if __name__ == "__main__":
    _lowerCAmelCase						=		argparse.ArgumentParser()
    # Required parameters
    parser.add_argument("""--workflow_run_id""", type=str, required=True, help="""A GitHub Actions workflow run id.""")
    parser.add_argument(
        """--output_dir""",
        type=str,
        required=True,
        help="""Where to store the downloaded artifacts and other result files.""",
    )
    parser.add_argument("""--token""", default=None, type=str, help="""A token that has actions:read permission.""")
    _lowerCAmelCase						=		parser.parse_args()

    os.makedirs(args.output_dir, exist_ok=True)

    _lowerCAmelCase						=		get_job_links(args.workflow_run_id, token=args.token)
    _lowerCAmelCase						=		{}
    # To deal with `workflow_call` event, where a job name is the combination of the job names in the caller and callee.
    # For example, `PyTorch 1.11 / Model tests (models/albert, single-gpu)`.
    if _job_links:
        for k, v in _job_links.items():
            # This is how GitHub actions combine job names.
            if " / " in k:
                _lowerCAmelCase						=		k.find(""" / """)
                _lowerCAmelCase						=		k[index + len(""" / """) :]
            _lowerCAmelCase						=		v
    with open(os.path.join(args.output_dir, """job_links.json"""), """w""", encoding="""UTF-8""") as fp:
        json.dump(job_links, fp, ensure_ascii=False, indent=4)

    _lowerCAmelCase						=		get_artifacts_links(args.workflow_run_id, token=args.token)
    with open(os.path.join(args.output_dir, """artifacts.json"""), """w""", encoding="""UTF-8""") as fp:
        json.dump(artifacts, fp, ensure_ascii=False, indent=4)

    for idx, (name, url) in enumerate(artifacts.items()):
        download_artifact(name, url, args.output_dir, args.token)
        # Be gentle to GitHub
        time.sleep(1)

    _lowerCAmelCase						=		get_all_errors(args.output_dir, job_links=job_links)

    # `e[1]` is the error
    _lowerCAmelCase						=		Counter()
    counter.update([e[1] for e in errors])

    # print the top 30 most common test errors
    _lowerCAmelCase						=		counter.most_common(3_0)
    for item in most_common:
        print(item)

    with open(os.path.join(args.output_dir, """errors.json"""), """w""", encoding="""UTF-8""") as fp:
        json.dump(errors, fp, ensure_ascii=False, indent=4)

    _lowerCAmelCase						=		reduce_by_error(errors)
    _lowerCAmelCase						=		reduce_by_model(errors)

    _lowerCAmelCase						=		make_github_table(reduced_by_error)
    _lowerCAmelCase						=		make_github_table_per_model(reduced_by_model)

    with open(os.path.join(args.output_dir, """reduced_by_error.txt"""), """w""", encoding="""UTF-8""") as fp:
        fp.write(sa)
    with open(os.path.join(args.output_dir, """reduced_by_model.txt"""), """w""", encoding="""UTF-8""") as fp:
        fp.write(sa)








"""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





