



'''simple docstring'''





import argparse
from collections import defaultdict

import yaml


A__    :   List[Any]           =					'docs/source/en/_toctree.yml'





def   a_   (		_UpperCAmelCase    :      Optional[int]				)    ->  str:
      __snake_case :   List[Any]													=    defaultdict(__UpperCAmelCase				)
      for doc in model_doc:
            counts[doc["local"]] += 1
      __snake_case :   Dict													=    [key for key, value in counts.items() if value > 1]

      __snake_case :   Tuple													=    []
      for duplicate_key in duplicates:
            __snake_case :   List[str]													=    list({doc['title'] for doc in model_doc if doc['local'] == duplicate_key}				)
            if len(__UpperCAmelCase				) > 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 model_doc if counts[doc['local']] == 1]				)

      # Sort
      return sorted(__UpperCAmelCase ,key=lambda _UpperCAmelCase				: s["title"].lower()				)





def   a_   (		_UpperCAmelCase    :      Dict=False				)    ->  List[Any]:

      with open(__UpperCAmelCase ,encoding='utf-8'				) as f:
            __snake_case :   int													=    yaml.safe_load(f.read()				)

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

      # Then to the model doc
      __snake_case :   Optional[int]													=    0
      while api_doc[model_idx]["title"] != "Models":
            model_idx += 1

      __snake_case :   Union[str, Any]													=    api_doc[model_idx]['sections']

      __snake_case :   Dict													=    [(idx, section) for idx, section in enumerate(__UpperCAmelCase				) if 'sections' in section]
      __snake_case :   Dict													=    False
      for idx, modality_doc in modalities_docs:
            __snake_case :   int													=    modality_doc['sections']
            __snake_case :   List[Any]													=    clean_model_doc_toc(__UpperCAmelCase				)

            if old_modality_doc != new_modality_doc:
                  __snake_case :   str													=    True
                  if overwrite:
                        __snake_case :   List[Any]													=    new_modality_doc

      if diff:
            if overwrite:
                  __snake_case :   Tuple													=    model_doc
                  __snake_case :   int													=    api_doc
                  with open(__UpperCAmelCase ,'w' ,encoding='utf-8'				) as f:
                        f.write(yaml.dump(__UpperCAmelCase ,allow_unicode=__UpperCAmelCase				)				)
            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__    :   Dict           =					argparse.ArgumentParser()
    parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
    A__    :   Union[str, Any]           =					parser.parse_args()

    check_model_doc(args.fix_and_overwrite)









'''simple docstring'''

import math
from enum import Enum
from typing import Optional, Union

from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR

from .utils import logging


a    :							Any										=    logging.get_logger(__name__)






class 		a							(  _lowerCamelCase	):
				snake_case_           =						"linear"
				snake_case_           =						"cosine"
				snake_case_           =						"cosine_with_restarts"
				snake_case_           =						"polynomial"
				snake_case_           =						"constant"
				snake_case_           =						"constant_with_warmup"
				snake_case_           =						"piecewise_constant"







def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase = -1      )						->   Dict:

					'''simple docstring'''

					return LambdaLR(__UpperCAmelCase, lambda __UpperCAmelCase      : 1, last_epoch=__UpperCAmelCase      )


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase = -1      )						->   Dict:

					'''simple docstring'''

					def lr_lambda(__UpperCAmelCase      ):
										if current_step < num_warmup_steps:
															return float(__UpperCAmelCase      ) / float(max(1.0, __UpperCAmelCase      )      )
										return 1.0

					return LambdaLR(__UpperCAmelCase, __UpperCAmelCase, last_epoch=__UpperCAmelCase      )


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase = -1      )						->   Any:

					'''simple docstring'''

					snake_case_		     =							{}
					snake_case_		     =							step_rules.split(''','''      )
					for rule_str in rule_list[:-1]:
										snake_case_     ,snake_case_		     =							rule_str.split(''':'''      )
										snake_case_		     =							int(__UpperCAmelCase      )
										snake_case_		     =							float(__UpperCAmelCase      )
										snake_case_		     =							value
					snake_case_		     =							float(rule_list[-1]      )

					def create_rules_function(__UpperCAmelCase, __UpperCAmelCase      ):
										def rule_func(__UpperCAmelCase      ) -> float:
															snake_case_		     =							sorted(rules_dict.keys()      )
															for i, sorted_step in enumerate(__UpperCAmelCase      ):
																				if steps < sorted_step:
																									return rules_dict[sorted_steps[i]]
															return last_lr_multiple

										return rule_func

					snake_case_		     =							create_rules_function(__UpperCAmelCase, __UpperCAmelCase      )

					return LambdaLR(__UpperCAmelCase, __UpperCAmelCase, last_epoch=__UpperCAmelCase      )


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase=-1      )						->   List[Any]:

					'''simple docstring'''

					def lr_lambda(__UpperCAmelCase      ):
										if current_step < num_warmup_steps:
															return float(__UpperCAmelCase      ) / float(max(1, __UpperCAmelCase      )      )
										return max(
										    0.0, float(num_training_steps - current_step      ) / float(max(1, num_training_steps - num_warmup_steps      )      )      )

					return LambdaLR(__UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      )


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase = 0.5, __UpperCAmelCase = -1      )						->   Optional[Any]:

					'''simple docstring'''

					def lr_lambda(__UpperCAmelCase      ):
										if current_step < num_warmup_steps:
															return float(__UpperCAmelCase      ) / float(max(1, __UpperCAmelCase      )      )
										snake_case_		     =							float(current_step - num_warmup_steps      ) / float(max(1, num_training_steps - num_warmup_steps      )      )
										return max(0.0, 0.5 * (1.0 + math.cos(math.pi * float(__UpperCAmelCase      ) * 2.0 * progress      ))      )

					return LambdaLR(__UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      )


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase = 1, __UpperCAmelCase = -1      )						->   Dict:

					'''simple docstring'''

					def lr_lambda(__UpperCAmelCase      ):
										if current_step < num_warmup_steps:
															return float(__UpperCAmelCase      ) / float(max(1, __UpperCAmelCase      )      )
										snake_case_		     =							float(current_step - num_warmup_steps      ) / float(max(1, num_training_steps - num_warmup_steps      )      )
										if progress >= 1.0:
															return 0.0
										return max(0.0, 0.5 * (1.0 + math.cos(math.pi * ((float(__UpperCAmelCase      ) * progress) % 1.0)      ))      )

					return LambdaLR(__UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      )


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase=1e-7, __UpperCAmelCase=1.0, __UpperCAmelCase=-1      )						->   str:

					'''simple docstring'''

					snake_case_		     =							optimizer.defaults['''lr''']
					if not (lr_init > lr_end):
										raise ValueError(F"lr_end ({lr_end}) must be be smaller than initial lr ({lr_init})"      )

					def lr_lambda(__UpperCAmelCase      ):
										if current_step < num_warmup_steps:
															return float(__UpperCAmelCase      ) / float(max(1, __UpperCAmelCase      )      )
										elif current_step > num_training_steps:
															return lr_end / lr_init  # as LambdaLR multiplies by lr_init
										else:
															snake_case_		     =							lr_init - lr_end
															snake_case_		     =							num_training_steps - num_warmup_steps
															snake_case_		     =							1 - (current_step - num_warmup_steps) / decay_steps
															snake_case_		     =							lr_range * pct_remaining**power + lr_end
															return decay / lr_init  # as LambdaLR multiplies by lr_init

					return LambdaLR(__UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase      )


a    :							Tuple										=    {
    SchedulerType.LINEAR: get_linear_schedule_with_warmup,
    SchedulerType.COSINE: get_cosine_schedule_with_warmup,
    SchedulerType.COSINE_WITH_RESTARTS: get_cosine_with_hard_restarts_schedule_with_warmup,
    SchedulerType.POLYNOMIAL: get_polynomial_decay_schedule_with_warmup,
    SchedulerType.CONSTANT: get_constant_schedule,
    SchedulerType.CONSTANT_WITH_WARMUP: get_constant_schedule_with_warmup,
    SchedulerType.PIECEWISE_CONSTANT: get_piecewise_constant_schedule,
}


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase = None, __UpperCAmelCase = None, __UpperCAmelCase = None, __UpperCAmelCase = 1, __UpperCAmelCase = 1.0, __UpperCAmelCase = -1, )						->   int:

					'''simple docstring'''

					snake_case_		     =							SchedulerType(__UpperCAmelCase      )
					snake_case_		     =							TYPE_TO_SCHEDULER_FUNCTION[name]
					if name == SchedulerType.CONSTANT:
										return schedule_func(__UpperCAmelCase, last_epoch=__UpperCAmelCase      )

					if name == SchedulerType.PIECEWISE_CONSTANT:
										return schedule_func(__UpperCAmelCase, step_rules=__UpperCAmelCase, last_epoch=__UpperCAmelCase      )

					# All other schedulers require `num_warmup_steps`
					if num_warmup_steps is None:
										raise ValueError(F"{name} requires `num_warmup_steps`, please provide that argument."      )

					if name == SchedulerType.CONSTANT_WITH_WARMUP:
										return schedule_func(__UpperCAmelCase, num_warmup_steps=__UpperCAmelCase, last_epoch=__UpperCAmelCase      )

					# All other schedulers require `num_training_steps`
					if num_training_steps is None:
										raise ValueError(F"{name} requires `num_training_steps`, please provide that argument."      )

					if name == SchedulerType.COSINE_WITH_RESTARTS:
										return schedule_func(
										    __UpperCAmelCase, num_warmup_steps=__UpperCAmelCase, num_training_steps=__UpperCAmelCase, num_cycles=__UpperCAmelCase, last_epoch=__UpperCAmelCase, )

					if name == SchedulerType.POLYNOMIAL:
										return schedule_func(
										    __UpperCAmelCase, num_warmup_steps=__UpperCAmelCase, num_training_steps=__UpperCAmelCase, power=__UpperCAmelCase, last_epoch=__UpperCAmelCase, )

					return schedule_func(
					    __UpperCAmelCase, num_warmup_steps=__UpperCAmelCase, num_training_steps=__UpperCAmelCase, last_epoch=__UpperCAmelCase      )

