






"""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      _lowerCAmelCase		(							UpperCAmelCase__       :					int      )		->Tuple:
						random.seed(UpperCAmelCase__      )
						np.random.seed(UpperCAmelCase__      )
						torch.manual_seed(UpperCAmelCase__      )
						torch.cuda.manual_seed_all(UpperCAmelCase__      )
						# ^^ safe to call this function even if cuda is not available






class 							__SCREAMING_SNAKE_CASE						:


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





													if isinstance(snake_case			,    torch.nn.Module							):
																			A__						:  int		  =  (
																			    """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"""			,    snake_case			,    standard_warn=snake_case			,    )
																			A__						:  Union[str, Any]		  =  parameters.parameters()

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

													if kwargs.get("""max_value"""			,    snake_case							) is not None:
																			A__						:  int		  =  """The `max_value` argument is deprecated. Please use `decay` instead."""
																			deprecate("""max_value"""			,    """1.0.0"""			,    snake_case			,    standard_warn=snake_case							)
																			A__						:  List[str]		  =  kwargs["""max_value"""]

													if kwargs.get("""min_value"""			,    snake_case							) is not None:
																			A__						:  Optional[int]		  =  """The `min_value` argument is deprecated. Please use `min_decay` instead."""
																			deprecate("""min_value"""			,    """1.0.0"""			,    snake_case			,    standard_warn=snake_case							)
																			A__						:  Any		  =  kwargs["""min_value"""]

													A__						:  str		  =  list(snake_case							)
													A__						:  Optional[int]		  =  [p.clone().detach() for p in parameters]

													if kwargs.get("""device"""			,    snake_case							) is not None:
																			A__						:  int		  =  """The `device` argument is deprecated. Please use `to` instead."""
																			deprecate("""device"""			,    """1.0.0"""			,    snake_case			,    standard_warn=snake_case							)
																			self.to(device=kwargs["""device"""]							)

													A__						:  Any		  =  None

													A__						:  Optional[int]		  =  decay
													A__						:  Any		  =  min_decay
													A__						:  Optional[Any]		  =  update_after_step
													A__						:  List[str]		  =  use_ema_warmup
													A__						:  Tuple		  =  inv_gamma
													A__						:  Any		  =  power
													A__						:  Tuple		  =  0
													A__						:  int		  =  None  # set in `step()`

													A__						:  List[str]		  =  model_cls
													A__						:  Any		  =  model_config


							@classmethod
							def 						_UpperCamelCase       (			cls      :						Any			,    snake_case      :						Tuple			,    snake_case      :						Optional[Any]							):
													'''simple docstring'''





													A__  ,  A__						:  Dict		  =  model_cls.load_config(snake_case			,    return_unused_kwargs=snake_case							)
													A__						:  str		  =  model_cls.from_pretrained(snake_case							)

													A__						:  List[Any]		  =  cls(model.parameters()			,    model_cls=snake_case			,    model_config=model.config							)

													ema_model.load_state_dict(snake_case							)
													return ema_model


							def 						_UpperCamelCase       (			self      :						Union[str, Any]			,    snake_case      :						Dict							):
													'''simple docstring'''





													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__						:  List[str]		  =  self.model_cls.from_config(self.model_config							)
													A__						:  Union[str, Any]		  =  self.state_dict()
													state_dict.pop("""shadow_params"""			,    snake_case							)

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


							def 						_UpperCamelCase       (			self      :						List[str]			,    snake_case      :						int							):
													'''simple docstring'''





													A__						:  Dict		  =  max(0			,    optimization_step - self.update_after_step - 1							)

													if step <= 0:
																			return 0.0

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

													A__						:  Any		  =  min(snake_case			,    self.decay							)
													# make sure decay is not smaller than min_decay
													A__						:  Union[str, Any]		  =  max(snake_case			,    self.min_decay							)
													return cur_decay_value


							@torch.no_grad()
							def 						_UpperCamelCase       (			self      :						Optional[int]			,    snake_case      :						Iterable[torch.nn.Parameter]							):
													'''simple docstring'''





													if isinstance(snake_case			,    torch.nn.Module							):
																			A__						:  Any		  =  (
																			    """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"""			,    snake_case			,    standard_warn=snake_case			,    )
																			A__						:  int		  =  parameters.parameters()

													A__						:  Dict		  =  list(snake_case							)

													self.optimization_step += 1

													# Compute the decay factor for the exponential moving average.
													A__						:  List[Any]		  =  self.get_decay(self.optimization_step							)
													A__						:  Any		  =  decay
													A__						:  str		  =  1 - decay

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

													for s_param, param in zip(self.shadow_params			,    snake_case							):
																			if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
																									A__						:  Optional[int]		  =  deepspeed.zero.GatheredParameters(snake_case			,    modifier_rank=snake_case							)

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


							def 						_UpperCamelCase       (			self      :						List[Any]			,    snake_case      :						Iterable[torch.nn.Parameter]							):
													'''simple docstring'''





													A__						:  Dict		  =  list(snake_case							)
													for s_param, param in zip(self.shadow_params			,    snake_case							):
																			param.data.copy_(s_param.to(param.device							).data							)


							def 						_UpperCamelCase       (			self      :						Dict			,    snake_case      :						Union[str, Any]=None			,    snake_case      :						int=None							):
													'''simple docstring'''





													A__						:  Optional[int]		  =  [
													    p.to(device=snake_case			,    dtype=snake_case							) if p.is_floating_point() else p.to(device=snake_case							)
													    for p in self.shadow_params
													]


							def 						_UpperCamelCase       (			self      :						Optional[Any]							):
													'''simple docstring'''





													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 						_UpperCamelCase       (			self      :						Any			,    snake_case      :						Iterable[torch.nn.Parameter]							):
													'''simple docstring'''





													A__						:  Any		  =  [param.detach().cpu().clone() for param in parameters]


							def 						_UpperCamelCase       (			self      :						List[str]			,    snake_case      :						Iterable[torch.nn.Parameter]							):
													'''simple docstring'''





													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			,    snake_case							):
																			param.data.copy_(c_param.data							)

													# Better memory-wise.
													A__						:  Dict		  =  None


							def 						_UpperCamelCase       (			self      :						List[str]			,    snake_case      :						dict							):
													'''simple docstring'''





													A__						:  List[str]		  =  copy.deepcopy(snake_case							)

													A__						:  Any		  =  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__						:  Union[str, Any]		  =  state_dict.get("""min_decay"""			,    self.min_decay							)
													if not isinstance(self.min_decay			,    snake_case							):
																			raise ValueError("""Invalid min_decay"""							)

													A__						:  Any		  =  state_dict.get("""optimization_step"""			,    self.optimization_step							)
													if not isinstance(self.optimization_step			,    snake_case							):
																			raise ValueError("""Invalid optimization_step"""							)

													A__						:  str		  =  state_dict.get("""update_after_step"""			,    self.update_after_step							)
													if not isinstance(self.update_after_step			,    snake_case							):
																			raise ValueError("""Invalid update_after_step"""							)

													A__						:  List[str]		  =  state_dict.get("""use_ema_warmup"""			,    self.use_ema_warmup							)
													if not isinstance(self.use_ema_warmup			,    snake_case							):
																			raise ValueError("""Invalid use_ema_warmup"""							)

													A__						:  Any		  =  state_dict.get("""inv_gamma"""			,    self.inv_gamma							)
													if not isinstance(self.inv_gamma			,    (float, int)							):
																			raise ValueError("""Invalid inv_gamma"""							)

													A__						:  Any		  =  state_dict.get("""power"""			,    self.power							)
													if not isinstance(self.power			,    (float, int)							):
																			raise ValueError("""Invalid power"""							)

													A__						:  Optional[int]		  =  state_dict.get("""shadow_params"""			,    snake_case							)
													if shadow_params is not None:
																			A__						:  Optional[int]		  =  shadow_params
																			if not isinstance(self.shadow_params			,    snake_case							):
																									raise ValueError("""shadow_params must be a list"""							)
																			if not all(isinstance(snake_case			,    torch.Tensor							) for p in self.shadow_params							):
																									raise ValueError("""shadow_params must all be Tensors"""							)














"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A_    =							{'''configuration_sew''': ['''SEW_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SEWConfig''']}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_    =							[
						    '''SEW_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''SEWForCTC''',
						    '''SEWForSequenceClassification''',
						    '''SEWModel''',
						    '''SEWPreTrainedModel''',
						]

if TYPE_CHECKING:
						from .configuration_sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_sew import (
												    SEW_PRETRAINED_MODEL_ARCHIVE_LIST,
												    SEWForCTC,
												    SEWForSequenceClassification,
												    SEWModel,
												    SEWPreTrainedModel,
												)


else:
						import sys

						A_    =							_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






