


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 	SCREAMING_SNAKE_CASE_						(      __A			:      int    )     ->		List[str]:

       """simple docstring"""

       random.seed(__A    )
       np.random.seed(__A    )
       torch.manual_seed(__A    )
       torch.cuda.manual_seed_all(__A    )
       # ^^ safe to call this function even if cuda is not available
class 			SCREAMING_SNAKE_CASE__  :
      def __init__(	self    :	str					,							SCREAMING_SNAKE_CASE__    :	Iterable[torch.nn.Parameter]					,							SCREAMING_SNAKE_CASE__    :	float = 0.9999					,							SCREAMING_SNAKE_CASE__    :	float = 0.0					,							SCREAMING_SNAKE_CASE__    :	int = 0					,							SCREAMING_SNAKE_CASE__    :	bool = False					,							SCREAMING_SNAKE_CASE__    :	Union[float, int] = 1.0					,							SCREAMING_SNAKE_CASE__    :	Union[float, int] = 2 / 3					,							SCREAMING_SNAKE_CASE__    :	Optional[Any] = None					,							SCREAMING_SNAKE_CASE__    :	Dict[str, Any] = None					,							**SCREAMING_SNAKE_CASE__    :	str					,							)		->      Optional[Any]:

             if isinstance(SCREAMING_SNAKE_CASE__					,							torch.nn.Module					):
                    a_ :		Tuple							=  (
                        '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'					,							SCREAMING_SNAKE_CASE__					,							standard_warn=SCREAMING_SNAKE_CASE__					,							)
                    a_ :		Optional[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'					,							SCREAMING_SNAKE_CASE__					) is not None:
                    a_ :		str							=  'The `max_value` argument is deprecated. Please use `decay` instead.'
                    deprecate('max_value'					,							'1.0.0'					,							SCREAMING_SNAKE_CASE__					,							standard_warn=SCREAMING_SNAKE_CASE__					)
                    a_ :		Optional[int]							=  kwargs['max_value']

             if kwargs.get('min_value'					,							SCREAMING_SNAKE_CASE__					) is not None:
                    a_ :		Optional[Any]							=  'The `min_value` argument is deprecated. Please use `min_decay` instead.'
                    deprecate('min_value'					,							'1.0.0'					,							SCREAMING_SNAKE_CASE__					,							standard_warn=SCREAMING_SNAKE_CASE__					)
                    a_ :		Dict							=  kwargs['min_value']

             a_ :		List[Any]							=  list(SCREAMING_SNAKE_CASE__					)
             a_ :		Union[str, Any]							=  [p.clone().detach() for p in parameters]

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

             a_ :		int							=  None

             a_ :		List[Any]							=  decay
             a_ :		Dict							=  min_decay
             a_ :		Dict							=  update_after_step
             a_ :		Union[str, Any]							=  use_ema_warmup
             a_ :		List[Any]							=  inv_gamma
             a_ :		Dict							=  power
             a_ :		str							=  0
             a_ :		Any							=  None  # set in `step()`

             a_ :		str							=  model_cls
             a_ :		List[str]							=  model_config
      @classmethod
      def 	SCREAMING_SNAKE_CASE							(	cls    :	List[str]					,							SCREAMING_SNAKE_CASE__    :	int					,							SCREAMING_SNAKE_CASE__    :	Any					)		->      "EMAModel":
             a_ :		Tuple							=  model_cls.load_config(SCREAMING_SNAKE_CASE__					,							return_unused_kwargs=SCREAMING_SNAKE_CASE__					)
             a_ :		Union[str, Any]							=  model_cls.from_pretrained(SCREAMING_SNAKE_CASE__					)

             a_ :		Any							=  cls(model.parameters()					,							model_cls=SCREAMING_SNAKE_CASE__					,							model_config=model.config					)

             ema_model.load_state_dict(SCREAMING_SNAKE_CASE__					)
             return ema_model
      def 	SCREAMING_SNAKE_CASE							(	self    :	Any					,							SCREAMING_SNAKE_CASE__    :	int					)		->      List[str]:
             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_ :		Dict							=  self.model_cls.from_config(self.model_config					)
             a_ :		Optional[Any]							=  self.state_dict()
             state_dict.pop('shadow_params'					,							SCREAMING_SNAKE_CASE__					)

             model.register_to_config(**SCREAMING_SNAKE_CASE__					)
             self.copy_to(model.parameters()					)
             model.save_pretrained(SCREAMING_SNAKE_CASE__					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Any					,							SCREAMING_SNAKE_CASE__    :	int					)		->      float:
             a_ :		Tuple							=  max(0					,							optimization_step - self.update_after_step - 1					)

             if step <= 0:
                    return 0.0

             if self.use_ema_warmup:
                    a_ :		Dict							=  1 - (1 + step / self.inv_gamma) ** -self.power
             else:
                    a_ :		Any							=  (1 + step) / (1_0 + step)

             a_ :		List[Any]							=  min(SCREAMING_SNAKE_CASE__					,							self.decay					)
             # make sure decay is not smaller than min_decay
             a_ :		Optional[Any]							=  max(SCREAMING_SNAKE_CASE__					,							self.min_decay					)
             return cur_decay_value
      @torch.no_grad()
      def 	SCREAMING_SNAKE_CASE							(	self    :	List[Any]					,							SCREAMING_SNAKE_CASE__    :	Iterable[torch.nn.Parameter]					)		->      Optional[Any]:
             if isinstance(SCREAMING_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'					,							SCREAMING_SNAKE_CASE__					,							standard_warn=SCREAMING_SNAKE_CASE__					,							)
                    a_ :		Optional[Any]							=  parameters.parameters()

             a_ :		Tuple							=  list(SCREAMING_SNAKE_CASE__					)

             self.optimization_step += 1

             # Compute the decay factor for the exponential moving average.
             a_ :		Any							=  self.get_decay(self.optimization_step					)
             a_ :		Any							=  decay
             a_ :		Optional[Any]							=  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					,							SCREAMING_SNAKE_CASE__					):
                    if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
                           a_ :		Union[str, Any]							=  deepspeed.zero.GatheredParameters(SCREAMING_SNAKE_CASE__					,							modifier_rank=SCREAMING_SNAKE_CASE__					)

                    with context_manager():
                           if param.requires_grad:
                                  s_param.sub_(one_minus_decay * (s_param - param)					)
                           else:
                                  s_param.copy_(SCREAMING_SNAKE_CASE__					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	Dict					,							SCREAMING_SNAKE_CASE__    :	Iterable[torch.nn.Parameter]					)		->      None:
             a_ :		Any							=  list(SCREAMING_SNAKE_CASE__					)
             for s_param, param in zip(self.shadow_params					,							SCREAMING_SNAKE_CASE__					):
                    param.data.copy_(s_param.to(param.device					).data					)
      def 	SCREAMING_SNAKE_CASE							(	self    :	str					,							SCREAMING_SNAKE_CASE__    :	int=None					,							SCREAMING_SNAKE_CASE__    :	Dict=None					)		->      None:
             a_ :		str							=  [
                 p.to(device=SCREAMING_SNAKE_CASE__					,							dtype=SCREAMING_SNAKE_CASE__					) if p.is_floating_point() else p.to(device=SCREAMING_SNAKE_CASE__					)
                 for p in self.shadow_params
             ]
      def 	SCREAMING_SNAKE_CASE							(	self    :	Union[str, Any]					)		->      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 	SCREAMING_SNAKE_CASE							(	self    :	Any					,							SCREAMING_SNAKE_CASE__    :	Iterable[torch.nn.Parameter]					)		->      None:
             a_ :		List[str]							=  [param.detach().cpu().clone() for param in parameters]
      def 	SCREAMING_SNAKE_CASE							(	self    :	List[Any]					,							SCREAMING_SNAKE_CASE__    :	Iterable[torch.nn.Parameter]					)		->      None:
             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					,							SCREAMING_SNAKE_CASE__					):
                    param.data.copy_(c_param.data					)

             # Better memory-wise.
             a_ :		Dict							=  None

      def 	SCREAMING_SNAKE_CASE							(	self    :	Any					,							SCREAMING_SNAKE_CASE__    :	dict					)		->      None:
             a_ :		str							=  copy.deepcopy(SCREAMING_SNAKE_CASE__					)

             a_ :		Dict							=  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_ :		Optional[int]							=  state_dict.get('min_decay'					,							self.min_decay					)
             if not isinstance(self.min_decay					,							SCREAMING_SNAKE_CASE__					):
                    raise ValueError('Invalid min_decay'					)

             a_ :		int							=  state_dict.get('optimization_step'					,							self.optimization_step					)
             if not isinstance(self.optimization_step					,							SCREAMING_SNAKE_CASE__					):
                    raise ValueError('Invalid optimization_step'					)

             a_ :		Union[str, Any]							=  state_dict.get('update_after_step'					,							self.update_after_step					)
             if not isinstance(self.update_after_step					,							SCREAMING_SNAKE_CASE__					):
                    raise ValueError('Invalid update_after_step'					)

             a_ :		Tuple							=  state_dict.get('use_ema_warmup'					,							self.use_ema_warmup					)
             if not isinstance(self.use_ema_warmup					,							SCREAMING_SNAKE_CASE__					):
                    raise ValueError('Invalid use_ema_warmup'					)

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

             a_ :		Optional[int]							=  state_dict.get('power'					,							self.power					)
             if not isinstance(self.power					,							(float, int)					):
                    raise ValueError('Invalid power'					)

             a_ :		Optional[Any]							=  state_dict.get('shadow_params'					,							SCREAMING_SNAKE_CASE__					)
             if shadow_params is not None:
                    a_ :		Optional[Any]							=  shadow_params
                    if not isinstance(self.shadow_params					,							SCREAMING_SNAKE_CASE__					):
                           raise ValueError('shadow_params must be a list'					)
                    if not all(isinstance(SCREAMING_SNAKE_CASE__					,							torch.Tensor					) for p in self.shadow_params					):
                           raise ValueError('shadow_params must all be Tensors'					)






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase_				:			Optional[Any]   =	{'configuration_plbart': ['PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PLBartConfig']}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase_				:			Union[str, Any]   =	['PLBartTokenizer']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase_				:			List[str]   =	[
       'PLBART_PRETRAINED_MODEL_ARCHIVE_LIST',
       'PLBartForCausalLM',
       'PLBartForConditionalGeneration',
       'PLBartForSequenceClassification',
       'PLBartModel',
       'PLBartPreTrainedModel',
   ]


if TYPE_CHECKING:
   from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_plbart import PLBartTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_plbart import (
          PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
          PLBartForCausalLM,
          PLBartForConditionalGeneration,
          PLBartForSequenceClassification,
          PLBartModel,
          PLBartPreTrainedModel,
      )


else:
   import sys

   UpperCAmelCase_				:			Union[str, Any]   =	_LazyModule(__name__, globals()['__file__'], _import_structure)


