




"""simple docstring"""

import torch

from diffusers import UnCLIPScheduler

from .test_schedulers import SchedulerCommonTest





class     lowercase_	(					__lowerCAmelCase							):



	'''simple docstring'''
	UpperCAmelCase   :					Optional[int]				  =     (UnCLIPScheduler,)

	def       lowerCAmelCase_							(			self	:	Any   ,							**_UpperCAmelCase	:	Union[str, Any]		):
							_A        =       {
							    'num_train_timesteps': 1_000,
							    'variance_type': 'fixed_small_log',
							    'clip_sample': True,
							    'clip_sample_range': 1.0,
							    'prediction_type': 'epsilon',
							}

							config.update(**_UpperCAmelCase		)
							return config

	def       lowerCAmelCase_							(			self	:	int		):
							for timesteps in [1, 5, 100, 1_000]:
													self.check_over_configs(num_train_timesteps=_UpperCAmelCase		)

	def       lowerCAmelCase_							(			self	:	int		):
							for variance in ["fixed_small_log", "learned_range"]:
													self.check_over_configs(variance_type=_UpperCAmelCase		)

	def       lowerCAmelCase_							(			self	:	Optional[int]		):
							for clip_sample in [True, False]:
													self.check_over_configs(clip_sample=_UpperCAmelCase		)

	def       lowerCAmelCase_							(			self	:	str		):
							for clip_sample_range in [1, 5, 10, 20]:
													self.check_over_configs(clip_sample_range=_UpperCAmelCase		)

	def       lowerCAmelCase_							(			self	:	Optional[Any]		):
							for prediction_type in ["epsilon", "sample"]:
													self.check_over_configs(prediction_type=_UpperCAmelCase		)

	def       lowerCAmelCase_							(			self	:	str		):
							for time_step in [0, 500, 999]:
													for prev_timestep in [None, 5, 100, 250, 500, 750]:
																			if prev_timestep is not None and prev_timestep >= time_step:
																									continue

																			self.check_over_forward(time_step=_UpperCAmelCase   ,							prev_timestep=_UpperCAmelCase		)

	def       lowerCAmelCase_							(			self	:	Union[str, Any]		):
							_A        =       self.scheduler_classes[0]
							_A        =       self.get_scheduler_config(variance_type='fixed_small_log'		)
							_A        =       scheduler_class(**_UpperCAmelCase		)

							assert torch.sum(torch.abs(scheduler._get_variance(0		) - 1.0_0_0_0E-1_0		)		) < 1E-5
							assert torch.sum(torch.abs(scheduler._get_variance(487		) - 0.054_9625		)		) < 1E-5
							assert torch.sum(torch.abs(scheduler._get_variance(999		) - 0.999_4987		)		) < 1E-5

	def       lowerCAmelCase_							(			self	:	List[Any]		):
							_A        =       self.scheduler_classes[0]
							_A        =       self.get_scheduler_config(variance_type='learned_range'		)
							_A        =       scheduler_class(**_UpperCAmelCase		)

							_A        =       0.5

							assert scheduler._get_variance(1   ,							predicted_variance=_UpperCAmelCase		) - -10.171_2790 < 1E-5
							assert scheduler._get_variance(487   ,							predicted_variance=_UpperCAmelCase		) - -5.799_8052 < 1E-5
							assert scheduler._get_variance(999   ,							predicted_variance=_UpperCAmelCase		) - -0.001_0011 < 1E-5

	def       lowerCAmelCase_							(			self	:	Union[str, Any]		):
							_A        =       self.scheduler_classes[0]
							_A        =       self.get_scheduler_config()
							_A        =       scheduler_class(**_UpperCAmelCase		)

							_A        =       scheduler.timesteps

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

							for i, t in enumerate(_UpperCAmelCase		):
													# 1. predict noise residual
													_A        =       model(_UpperCAmelCase   ,							_UpperCAmelCase		)

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

													_A        =       pred_prev_sample

							_A        =       torch.sum(torch.abs(_UpperCAmelCase		)		)
							_A        =       torch.mean(torch.abs(_UpperCAmelCase		)		)

							assert abs(result_sum.item() - 252.268_2495		) < 1E-2
							assert abs(result_mean.item() - 0.328_4743		) < 1E-3

	def       lowerCAmelCase_							(			self	:	Dict		):
							_A        =       self.scheduler_classes[0]
							_A        =       self.get_scheduler_config()
							_A        =       scheduler_class(**_UpperCAmelCase		)

							scheduler.set_timesteps(25		)

							_A        =       scheduler.timesteps

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

							for i, t in enumerate(_UpperCAmelCase		):
													# 1. predict noise residual
													_A        =       model(_UpperCAmelCase   ,							_UpperCAmelCase		)

													if i + 1 == timesteps.shape[0]:
																			_A        =       None
													else:
																			_A        =       timesteps[i + 1]

													# 2. predict previous mean of sample x_t-1
													_A        =       scheduler.step(
													    _UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							prev_timestep=_UpperCAmelCase   ,							generator=_UpperCAmelCase		).prev_sample

													_A        =       pred_prev_sample

							_A        =       torch.sum(torch.abs(_UpperCAmelCase		)		)
							_A        =       torch.mean(torch.abs(_UpperCAmelCase		)		)

							assert abs(result_sum.item() - 258.204_4983		) < 1E-2
							assert abs(result_mean.item() - 0.336_2038		) < 1E-3

	def       lowerCAmelCase_							(			self	:	int		):
							pass



	def       lowerCAmelCase_							(			self	:	List[str]		):
							pass







"""simple docstring"""

import argparse
from collections import defaultdict

import yaml


a         =      '''docs/source/en/_toctree.yml'''






def 						_snake_case					( _snake_case							: List[Any]							)   -> Optional[Any]:






      '''simple docstring'''


      _A        =       defaultdict(_snake_case							)
      _A        =       []
      _A        =       []
      for doc in doc_list:
            if "local" in doc:
                  counts[doc["local"]] += 1

            if doc["title"].lower() == "overview":
                  overview_doc.append({'local': doc['local'], 'title': doc['title']}							)
            else:
                  new_doc_list.append(_snake_case							)

      _A        =       new_doc_list
      _A        =       [key for key, value in counts.items() if value > 1]

      _A        =       []
      for duplicate_key in duplicates:
            _A        =       list({doc['title'] for doc in doc_list if doc['local'] == duplicate_key}							)
            if len(_snake_case							) > 1:
                  raise ValueError(
                      F'''{duplicate_key} is present several times in the documentation table of content at '''
                      '`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '
                      'others.'							)
            # Only add this once
            new_doc.append({'local': duplicate_key, 'title': titles[0]}							)

      # Add none duplicate-keys
      new_doc.extend([doc for doc in doc_list if 'local' not in counts or counts[doc['local']] == 1]							)
      _A        =       sorted(_snake_case    ,  key=lambda _snake_case							: s["title"].lower()							)

      # "overview" gets special treatment and is always first
      if len(_snake_case							) > 1:
            raise ValueError('{doc_list} has two \'overview\' docs which is not allowed.'							)

      overview_doc.extend(_snake_case							)

      # Sort
      return overview_doc






def 						_snake_case					( _snake_case							: Tuple=False							)   -> List[Any]:






      '''simple docstring'''


      with open(_snake_case    ,  encoding='utf-8'							) as f:
            _A        =       yaml.safe_load(f.read()							)

      # Get to the API doc
      _A        =       0
      while content[api_idx]["title"] != "API":
            api_idx += 1
      _A        =       content[api_idx]['sections']

      # Then to the model doc
      _A        =       0
      while api_doc[scheduler_idx]["title"] != "Schedulers":
            scheduler_idx += 1

      _A        =       api_doc[scheduler_idx]['sections']
      _A        =       clean_doc_toc(_snake_case							)

      _A        =       False
      if new_scheduler_doc != scheduler_doc:
            _A        =       True
            if overwrite:
                  _A        =       new_scheduler_doc

      if diff:
            if overwrite:
                  _A        =       api_doc
                  with open(_snake_case    ,  'w'    ,  encoding='utf-8'							) as f:
                        f.write(yaml.dump(_snake_case    ,  allow_unicode=_snake_case							)							)
            else:
                  raise ValueError(
                      'The model doc part of the table of content is not properly sorted, run `make style` to fix this.'							)






def 						_snake_case					( _snake_case							: str=False							)   -> Union[str, Any]:






      '''simple docstring'''


      with open(_snake_case    ,  encoding='utf-8'							) as f:
            _A        =       yaml.safe_load(f.read()							)

      # Get to the API doc
      _A        =       0
      while content[api_idx]["title"] != "API":
            api_idx += 1
      _A        =       content[api_idx]['sections']

      # Then to the model doc
      _A        =       0
      while api_doc[pipeline_idx]["title"] != "Pipelines":
            pipeline_idx += 1

      _A        =       False
      _A        =       api_doc[pipeline_idx]['sections']
      _A        =       []

      # sort sub pipeline docs
      for pipeline_doc in pipeline_docs:
            if "section" in pipeline_doc:
                  _A        =       pipeline_doc['section']
                  _A        =       clean_doc_toc(_snake_case							)
                  if overwrite:
                        _A        =       new_sub_pipeline_doc
            new_pipeline_docs.append(_snake_case							)

      # sort overall pipeline doc
      _A        =       clean_doc_toc(_snake_case							)

      if new_pipeline_docs != pipeline_docs:
            _A        =       True
            if overwrite:
                  _A        =       new_pipeline_docs

      if diff:
            if overwrite:
                  _A        =       api_doc
                  with open(_snake_case    ,  'w'    ,  encoding='utf-8'							) as f:
                        f.write(yaml.dump(_snake_case    ,  allow_unicode=_snake_case							)							)
            else:
                  raise ValueError(
                      'The model doc part of the table of content is not properly sorted, run `make style` to fix this.'							)


if __name__ == "__main__":
       a         =      argparse.ArgumentParser()
       parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
       a         =      parser.parse_args()

       check_scheduler_doc(args.fix_and_overwrite)
       check_pipeline_doc(args.fix_and_overwrite)

