




"""simple docstring"""

import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union

import numpy as np
import torch

from .utils import deprecate, is_transformers_available


if is_transformers_available():
							import transformers






def 						_snake_case					( _snake_case							: int							)   -> Any:






						'''simple docstring'''


						random.seed(_snake_case							)
						np.random.seed(_snake_case							)
						torch.manual_seed(_snake_case							)
						torch.cuda.manual_seed_all(_snake_case							)
						# ^^ safe to call this function even if cuda is not available







class     lowercase_	:



	'''simple docstring'''

	def __init__(			self	:	Tuple   ,							_UpperCAmelCase	:	Iterable[torch.nn.Parameter]   ,							_UpperCAmelCase	:	float = 0.9999   ,							_UpperCAmelCase	:	float = 0.0   ,							_UpperCAmelCase	:	int = 0   ,							_UpperCAmelCase	:	bool = False   ,							_UpperCAmelCase	:	Union[float, int] = 1.0   ,							_UpperCAmelCase	:	Union[float, int] = 2 / 3   ,							_UpperCAmelCase	:	Optional[Any] = None   ,							_UpperCAmelCase	:	Dict[str, Any] = None   ,							**_UpperCAmelCase	:	Optional[int]   ,							):

							if isinstance(_UpperCAmelCase   ,							torch.nn.Module		):
													_A        =       (
													    'Passing a `torch.nn.Module` to `ExponentialMovingAverage` is deprecated. '
													    'Please pass the parameters of the module instead.'
													)
													deprecate(
													    'passing a `torch.nn.Module` to `ExponentialMovingAverage`'   ,							'1.0.0'   ,							_UpperCAmelCase   ,							standard_warn=_UpperCAmelCase   ,							)
													_A        =       parameters.parameters()

													# set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
													_A        =       True

							if kwargs.get('max_value'   ,							_UpperCAmelCase		) is not None:
													_A        =       'The `max_value` argument is deprecated. Please use `decay` instead.'
													deprecate('max_value'   ,							'1.0.0'   ,							_UpperCAmelCase   ,							standard_warn=_UpperCAmelCase		)
													_A        =       kwargs['max_value']

							if kwargs.get('min_value'   ,							_UpperCAmelCase		) is not None:
													_A        =       'The `min_value` argument is deprecated. Please use `min_decay` instead.'
													deprecate('min_value'   ,							'1.0.0'   ,							_UpperCAmelCase   ,							standard_warn=_UpperCAmelCase		)
													_A        =       kwargs['min_value']

							_A        =       list(_UpperCAmelCase		)
							_A        =       [p.clone().detach() for p in parameters]

							if kwargs.get('device'   ,							_UpperCAmelCase		) is not None:
													_A        =       'The `device` argument is deprecated. Please use `to` instead.'
													deprecate('device'   ,							'1.0.0'   ,							_UpperCAmelCase   ,							standard_warn=_UpperCAmelCase		)
													self.to(device=kwargs['device']		)

							_A        =       None

							_A        =       decay
							_A        =       min_decay
							_A        =       update_after_step
							_A        =       use_ema_warmup
							_A        =       inv_gamma
							_A        =       power
							_A        =       0
							_A        =       None  # set in `step()`

							_A        =       model_cls
							_A        =       model_config

	@classmethod
	def       lowerCAmelCase_							(			cls	:	Any   ,							_UpperCAmelCase	:	Optional[Any]   ,							_UpperCAmelCase	:	Optional[int]		):
							_A       ,   _A        =       model_cls.load_config(_UpperCAmelCase   ,							return_unused_kwargs=_UpperCAmelCase		)
							_A        =       model_cls.from_pretrained(_UpperCAmelCase		)

							_A        =       cls(model.parameters()   ,							model_cls=_UpperCAmelCase   ,							model_config=model.config		)

							ema_model.load_state_dict(_UpperCAmelCase		)
							return ema_model

	def       lowerCAmelCase_							(			self	:	Optional[Any]   ,							_UpperCAmelCase	:	Tuple		):
							if self.model_cls is None:
													raise ValueError('`save_pretrained` can only be used if `model_cls` was defined at __init__.'		)

							if self.model_config is None:
													raise ValueError('`save_pretrained` can only be used if `model_config` was defined at __init__.'		)

							_A        =       self.model_cls.from_config(self.model_config		)
							_A        =       self.state_dict()
							state_dict.pop('shadow_params'   ,							_UpperCAmelCase		)

							model.register_to_config(**_UpperCAmelCase		)
							self.copy_to(model.parameters()		)
							model.save_pretrained(_UpperCAmelCase		)

	def       lowerCAmelCase_							(			self	:	Optional[Any]   ,							_UpperCAmelCase	:	int		):
							_A        =       max(0   ,							optimization_step - self.update_after_step - 1		)

							if step <= 0:
													return 0.0

							if self.use_ema_warmup:
													_A        =       1 - (1 + step / self.inv_gamma) ** -self.power
							else:
													_A        =       (1 + step) / (10 + step)

							_A        =       min(_UpperCAmelCase   ,							self.decay		)
							# make sure decay is not smaller than min_decay
							_A        =       max(_UpperCAmelCase   ,							self.min_decay		)
							return cur_decay_value

	@torch.no_grad()
	def       lowerCAmelCase_							(			self	:	Dict   ,							_UpperCAmelCase	:	Iterable[torch.nn.Parameter]		):
							if isinstance(_UpperCAmelCase   ,							torch.nn.Module		):
													_A        =       (
													    'Passing a `torch.nn.Module` to `ExponentialMovingAverage.step` is deprecated. '
													    'Please pass the parameters of the module instead.'
													)
													deprecate(
													    'passing a `torch.nn.Module` to `ExponentialMovingAverage.step`'   ,							'1.0.0'   ,							_UpperCAmelCase   ,							standard_warn=_UpperCAmelCase   ,							)
													_A        =       parameters.parameters()

							_A        =       list(_UpperCAmelCase		)

							self.optimization_step += 1

							# Compute the decay factor for the exponential moving average.
							_A        =       self.get_decay(self.optimization_step		)
							_A        =       decay
							_A        =       1 - decay

							_A        =       contextlib.nullcontext
							if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
													import deepspeed

							for s_param, param in zip(self.shadow_params   ,							_UpperCAmelCase		):
													if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
																			_A        =       deepspeed.zero.GatheredParameters(_UpperCAmelCase   ,							modifier_rank=_UpperCAmelCase		)

													with context_manager():
																			if param.requires_grad:
																									s_param.sub_(one_minus_decay * (s_param - param)		)
																			else:
																									s_param.copy_(_UpperCAmelCase		)

	def       lowerCAmelCase_							(			self	:	str   ,							_UpperCAmelCase	:	Iterable[torch.nn.Parameter]		):
							_A        =       list(_UpperCAmelCase		)
							for s_param, param in zip(self.shadow_params   ,							_UpperCAmelCase		):
													param.data.copy_(s_param.to(param.device		).data		)

	def       lowerCAmelCase_							(			self	:	Dict   ,							_UpperCAmelCase	:	Optional[int]=None   ,							_UpperCAmelCase	:	Dict=None		):
							_A        =       [
							    p.to(device=_UpperCAmelCase   ,							dtype=_UpperCAmelCase		) if p.is_floating_point() else p.to(device=_UpperCAmelCase		)
							    for p in self.shadow_params
							]

	def       lowerCAmelCase_							(			self	:	Dict		):
							return {
							    "decay": self.decay,
							    "min_decay": self.min_decay,
							    "optimization_step": self.optimization_step,
							    "update_after_step": self.update_after_step,
							    "use_ema_warmup": self.use_ema_warmup,
							    "inv_gamma": self.inv_gamma,
							    "power": self.power,
							    "shadow_params": self.shadow_params,
							}

	def       lowerCAmelCase_							(			self	:	Any   ,							_UpperCAmelCase	:	Iterable[torch.nn.Parameter]		):
							_A        =       [param.detach().cpu().clone() for param in parameters]

	def       lowerCAmelCase_							(			self	:	List[Any]   ,							_UpperCAmelCase	:	Iterable[torch.nn.Parameter]		):
							if self.temp_stored_params is None:
													raise RuntimeError('This ExponentialMovingAverage has no `store()`ed weights ' 'to `restore()`'		)
							for c_param, param in zip(self.temp_stored_params   ,							_UpperCAmelCase		):
													param.data.copy_(c_param.data		)

							# Better memory-wise.
							_A        =       None



	def       lowerCAmelCase_							(			self	:	int   ,							_UpperCAmelCase	:	dict		):
							_A        =       copy.deepcopy(_UpperCAmelCase		)

							_A        =       state_dict.get('decay'   ,							self.decay		)
							if self.decay < 0.0 or self.decay > 1.0:
													raise ValueError('Decay must be between 0 and 1'		)

							_A        =       state_dict.get('min_decay'   ,							self.min_decay		)
							if not isinstance(self.min_decay   ,							_UpperCAmelCase		):
													raise ValueError('Invalid min_decay'		)

							_A        =       state_dict.get('optimization_step'   ,							self.optimization_step		)
							if not isinstance(self.optimization_step   ,							_UpperCAmelCase		):
													raise ValueError('Invalid optimization_step'		)

							_A        =       state_dict.get('update_after_step'   ,							self.update_after_step		)
							if not isinstance(self.update_after_step   ,							_UpperCAmelCase		):
													raise ValueError('Invalid update_after_step'		)

							_A        =       state_dict.get('use_ema_warmup'   ,							self.use_ema_warmup		)
							if not isinstance(self.use_ema_warmup   ,							_UpperCAmelCase		):
													raise ValueError('Invalid use_ema_warmup'		)

							_A        =       state_dict.get('inv_gamma'   ,							self.inv_gamma		)
							if not isinstance(self.inv_gamma   ,							(float, int)		):
													raise ValueError('Invalid inv_gamma'		)

							_A        =       state_dict.get('power'   ,							self.power		)
							if not isinstance(self.power   ,							(float, int)		):
													raise ValueError('Invalid power'		)

							_A        =       state_dict.get('shadow_params'   ,							_UpperCAmelCase		)
							if shadow_params is not None:
													_A        =       shadow_params
													if not isinstance(self.shadow_params   ,							_UpperCAmelCase		):
																			raise ValueError('shadow_params must be a list'		)
													if not all(isinstance(_UpperCAmelCase   ,							torch.Tensor		) for p in self.shadow_params		):
																			raise ValueError('shadow_params must all be Tensors'		)







"""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)

