




"""simple docstring"""

import dataclasses
import json
import sys
import types
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser, ArgumentTypeError
from copy import copy
from enum import Enum
from inspect import isclass
from pathlib import Path
from typing import Any, Callable, Dict, Iterable, List, Literal, NewType, Optional, Tuple, Union, get_type_hints

import yaml


a         =      NewType('''DataClass''', Any)
a         =      NewType('''DataClassType''', Any)





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






      '''simple docstring'''


      if isinstance(__snake_case    ,  __snake_case							):
            return v
      if v.lower() in ("yes", "true", "t", "y", "1"):
            return True
      elif v.lower() in ("no", "false", "f", "n", "0"):
            return False
      else:
            raise ArgumentTypeError(
                F'''Truthy value expected: got {v} but expected one of yes/no, true/false, t/f, y/n, 1/0 (case insensitive).'''							)





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






      '''simple docstring'''


      _A        =       {str(__snake_case							): choice for choice in choices}
      return lambda _snake_case							: str_to_choice.get(__snake_case    ,  __snake_case							)





def 						_snake_case					( *,
    _snake_case							: Union[str, Any] = None    ,  _snake_case							: Any = None    ,  _snake_case							: int = dataclasses.MISSING    ,  _snake_case							: str = dataclasses.MISSING    ,  _snake_case							: Dict = None    ,  **_snake_case							: int    ,  )   -> dataclasses.Field:






      '''simple docstring'''


      if metadata is None:
            # Important, don't use as default param in function signature because dict is mutable and shared across function calls
            _A        =       {}
      if aliases is not None:
            _A        =       aliases
      if help is not None:
            _A        =       help

      return dataclasses.field(metadata=__snake_case    ,  default=__snake_case    ,  default_factory=__snake_case    ,  **__snake_case							)







class     lowercase_	(					__SCREAMING_SNAKE_CASE							):



 '''simple docstring'''
 UpperCAmelCase   :					str				  =     42

 def __init__(			self	:	Union[str, Any]   ,							_UpperCAmelCase	:	Union[DataClassType, Iterable[DataClassType]]   ,							**_UpperCAmelCase	:	Tuple		):

       if "formatter_class" not in kwargs:
             _A        =       ArgumentDefaultsHelpFormatter
       super().__init__(**_SCREAMING_SNAKE_CASE		)
       if dataclasses.is_dataclass(_SCREAMING_SNAKE_CASE		):
             _A        =       [dataclass_types]
       _A        =       list(_SCREAMING_SNAKE_CASE		)
       for dtype in self.dataclass_types:
             self._add_dataclass_arguments(_SCREAMING_SNAKE_CASE		)

 @staticmethod
 def       lowerCAmelCase_							(			_UpperCAmelCase	:	ArgumentParser   ,							_UpperCAmelCase	:	dataclasses.Field		):
       _A        =       F'''--{field.name}'''
       _A        =       field.metadata.copy()
       # field.metadata is not used at all by Data Classes,
       # it is provided as a third-party extension mechanism.
       if isinstance(field.type   ,							_SCREAMING_SNAKE_CASE		):
             raise RuntimeError(
                 'Unresolved type detected, which should have been done with the help of '
                 '`typing.get_type_hints` method by default'		)

       _A        =       kwargs.pop('aliases'   ,							[]		)
       if isinstance(_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE		):
             _A        =       [aliases]

       _A        =       getattr(field.type   ,							'__origin__'   ,							field.type		)
       if origin_type is Union or (hasattr(_SCREAMING_SNAKE_CASE   ,							'UnionType'		) and isinstance(_SCREAMING_SNAKE_CASE   ,							types.UnionType		)):
             if str not in field.type.__args__ and (
                 len(field.type.__args__		) != 2 or type(_SCREAMING_SNAKE_CASE		) not in field.type.__args__
             ):
                   raise ValueError(
                       'Only `Union[X, NoneType]` (i.e., `Optional[X]`) is allowed for `Union` because'
                       ' the argument parser only supports one type per argument.'
                       F''' Problem encountered in field \'{field.name}\'.'''		)
             if type(_SCREAMING_SNAKE_CASE		) not in field.type.__args__:
                   # filter `str` in Union
                   _A        =       field.type.__args__[0] if field.type.__args__[1] == str else field.type.__args__[1]
                   _A        =       getattr(field.type   ,							'__origin__'   ,							field.type		)
             elif bool not in field.type.__args__:
                   # filter `NoneType` in Union (except for `Union[bool, NoneType]`)
                   _A        =       (
                       field.type.__args__[0] if isinstance(_SCREAMING_SNAKE_CASE   ,							field.type.__args__[1]		) else field.type.__args__[1]
                   )
                   _A        =       getattr(field.type   ,							'__origin__'   ,							field.type		)

        # A variable to store kwargs for a boolean field, if needed
        # so that we can init a `no_*` complement argument (see below)
       _A        =       {}
       if origin_type is Literal or (isinstance(field.type   ,							_SCREAMING_SNAKE_CASE		) and issubclass(field.type   ,							_SCREAMING_SNAKE_CASE		)):
             if origin_type is Literal:
                   _A        =       field.type.__args__
             else:
                   _A        =       [x.value for x in field.type]

             _A        =       make_choice_type_function(kwargs['choices']		)

             if field.default is not dataclasses.MISSING:
                   _A        =       field.default
             else:
                   _A        =       True
       elif field.type is bool or field.type == Optional[bool]:
             # Copy the currect kwargs to use to instantiate a `no_*` complement argument below.
             # We do not initialize it here because the `no_*` alternative must be instantiated after the real argument
             _A        =       copy(_SCREAMING_SNAKE_CASE		)

             # Hack because type=bool in argparse does not behave as we want.
             _A        =       string_to_bool
             if field.type is bool or (field.default is not None and field.default is not dataclasses.MISSING):
                   # Default value is False if we have no default when of type bool.
                   _A        =       False if field.default is dataclasses.MISSING else field.default
                   # This is the value that will get picked if we don't include --field_name in any way
                   _A        =       default
                   # This tells argparse we accept 0 or 1 value after --field_name
                   _A        =       "?"
                   # This is the value that will get picked if we do --field_name (without value)
                   _A        =       True
       elif isclass(_SCREAMING_SNAKE_CASE		) and issubclass(_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE		):
             _A        =       field.type.__args__[0]
             _A        =       "+"
             if field.default_factory is not dataclasses.MISSING:
                   _A        =       field.default_factory()
             elif field.default is dataclasses.MISSING:
                   _A        =       True
       else:
             _A        =       field.type
             if field.default is not dataclasses.MISSING:
                   _A        =       field.default
             elif field.default_factory is not dataclasses.MISSING:
                   _A        =       field.default_factory()
             else:
                   _A        =       True
       parser.add_argument(_SCREAMING_SNAKE_CASE   ,							*_SCREAMING_SNAKE_CASE   ,							**_SCREAMING_SNAKE_CASE		)

       # Add a complement `no_*` argument for a boolean field AFTER the initial field has already been added.
       # Order is important for arguments with the same destination!
       # We use a copy of earlier kwargs because the original kwargs have changed a lot before reaching down
       # here and we do not need those changes/additional keys.
       if field.default is True and (field.type is bool or field.type == Optional[bool]):
             _A        =       False
             parser.add_argument(F'''--no_{field.name}'''   ,							action='store_false'   ,							dest=field.name   ,							**_SCREAMING_SNAKE_CASE		)

 def       lowerCAmelCase_							(			self	:	int   ,							_UpperCAmelCase	:	DataClassType		):

       if hasattr(_SCREAMING_SNAKE_CASE   ,							'_argument_group_name'		):
             _A        =       self.add_argument_group(dtype._argument_group_name		)
       else:
             _A        =       self

       try:
             _A        =       get_type_hints(_SCREAMING_SNAKE_CASE		)
       except NameError:
             raise RuntimeError(
                 F'''Type resolution failed for {dtype}. Try declaring the class in global scope or '''
                 'removing line of `from __future__ import annotations` which opts in Postponed '
                 'Evaluation of Annotations (PEP 563)'		)
       except TypeError as ex:
             # Remove this block when we drop Python 3.9 support
             if sys.version_info[:2] < (3, 10) and "unsupported operand type(s) for |" in str(_SCREAMING_SNAKE_CASE		):
                   _A        =       ".".join(map(_SCREAMING_SNAKE_CASE   ,							sys.version_info[:3]		)		)
                   raise RuntimeError(
                       F'''Type resolution failed for {dtype} on Python {python_version}. Try removing '''
                       'line of `from __future__ import annotations` which opts in union types as '
                       '`X | Y` (PEP 604) via Postponed Evaluation of Annotations (PEP 563). To '
                       'support Python versions that lower than 3.10, you need to use '
                       '`typing.Union[X, Y]` instead of `X | Y` and `typing.Optional[X]` instead of '
                       '`X | None`.'		) from ex
             raise

       for field in dataclasses.fields(_SCREAMING_SNAKE_CASE		):
             if not field.init:
                   continue
             _A        =       type_hints[field.name]
             self._parse_dataclass_field(_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE		)

 def       lowerCAmelCase_							(			self	:	List[str]   ,							_UpperCAmelCase	:	int=None   ,							_UpperCAmelCase	:	List[Any]=False   ,							_UpperCAmelCase	:	Any=True   ,							_UpperCAmelCase	:	Any=None   ,							_UpperCAmelCase	:	str=None   ,							):

       if args_file_flag or args_filename or (look_for_args_file and len(sys.argv		)):
             _A        =       []

             if args_filename:
                   args_files.append(Path(_SCREAMING_SNAKE_CASE		)		)
             elif look_for_args_file and len(sys.argv		):
                   args_files.append(Path(sys.argv[0]		).with_suffix('.args'		)		)

             # args files specified via command line flag should overwrite default args files so we add them last
             if args_file_flag:
                   # Create special parser just to extract the args_file_flag values
                   _A        =       ArgumentParser()
                   args_file_parser.add_argument(_SCREAMING_SNAKE_CASE   ,							type=_SCREAMING_SNAKE_CASE   ,							action='append'		)

                   # Use only remaining args for further parsing (remove the args_file_flag)
                   _A        =       args_file_parser.parse_known_args(args=_SCREAMING_SNAKE_CASE		)
                   _A        =       vars(_SCREAMING_SNAKE_CASE		).get(args_file_flag.lstrip('-'		)   ,							_SCREAMING_SNAKE_CASE		)

                   if cmd_args_file_paths:
                         args_files.extend([Path(_SCREAMING_SNAKE_CASE		) for p in cmd_args_file_paths]		)

             _A        =       []
             for args_file in args_files:
                   if args_file.exists():
                         file_args += args_file.read_text().split()

            # in case of duplicate arguments the last one has precedence
            # args specified via the command line should overwrite args from files, so we add them last
             _A        =       file_args + args if args is not None else file_args + sys.argv[1:]
       _A        =       self.parse_known_args(args=_SCREAMING_SNAKE_CASE		)
       _A        =       []
       for dtype in self.dataclass_types:
             _A        =       {f.name for f in dataclasses.fields(_SCREAMING_SNAKE_CASE		) if f.init}
             _A        =       {k: v for k, v in vars(_SCREAMING_SNAKE_CASE		).items() if k in keys}
             for k in keys:
                   delattr(_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE		)
             _A        =       dtype(**_SCREAMING_SNAKE_CASE		)
             outputs.append(_SCREAMING_SNAKE_CASE		)
       if len(namespace.__dict__		) > 0:
             # additional namespace.
             outputs.append(_SCREAMING_SNAKE_CASE		)
       if return_remaining_strings:
             return (*outputs, remaining_args)
       else:
             if remaining_args:
                   raise ValueError(F'''Some specified arguments are not used by the HfArgumentParser: {remaining_args}'''		)

             return (*outputs,)

 def       lowerCAmelCase_							(			self	:	List[str]   ,							_UpperCAmelCase	:	Dict[str, Any]   ,							_UpperCAmelCase	:	bool = False		):
       _A        =       set(args.keys()		)
       _A        =       []
       for dtype in self.dataclass_types:
             _A        =       {f.name for f in dataclasses.fields(_SCREAMING_SNAKE_CASE		) if f.init}
             _A        =       {k: v for k, v in args.items() if k in keys}
             unused_keys.difference_update(inputs.keys()		)
             _A        =       dtype(**_SCREAMING_SNAKE_CASE		)
             outputs.append(_SCREAMING_SNAKE_CASE		)
       if not allow_extra_keys and unused_keys:
             raise ValueError(F'''Some keys are not used by the HfArgumentParser: {sorted(_SCREAMING_SNAKE_CASE		)}'''		)
       return tuple(_SCREAMING_SNAKE_CASE		)

 def       lowerCAmelCase_							(			self	:	Any   ,							_UpperCAmelCase	:	str   ,							_UpperCAmelCase	:	bool = False		):

       with open(Path(_SCREAMING_SNAKE_CASE		)   ,							encoding='utf-8'		) as open_json_file:
             _A        =       json.loads(open_json_file.read()		)
       _A        =       self.parse_dict(_SCREAMING_SNAKE_CASE   ,							allow_extra_keys=_SCREAMING_SNAKE_CASE		)
       return tuple(_SCREAMING_SNAKE_CASE		)



 def       lowerCAmelCase_							(			self	:	Union[str, Any]   ,							_UpperCAmelCase	:	str   ,							_UpperCAmelCase	:	bool = False		):
       _A        =       self.parse_dict(yaml.safe_load(Path(_SCREAMING_SNAKE_CASE		).read_text()		)   ,							allow_extra_keys=_SCREAMING_SNAKE_CASE		)
       return tuple(_SCREAMING_SNAKE_CASE		)







"""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'		)

