
"""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 				A_  (__a					):

					'''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 __lowerCAmelCase					:




			"""simple docstring"""







			def __init__(					self    :       Union[str, Any]							, _snake_case    :       Iterable[torch.nn.Parameter]							, _snake_case    :       float = 0.9_9_9_9							, _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    :       str							, )   ->			Optional[int]:







								"""simple docstring"""

								if isinstance(_snake_case							, 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"							, _snake_case							, standard_warn=_snake_case							, )
													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"							, _snake_case						) is not None:
													A_								=    "The `max_value` argument is deprecated. Please use `decay` instead."
													deprecate("max_value"							, "1.0.0"							, _snake_case							, standard_warn=_snake_case						)
													A_								=    kwargs["max_value"]

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

								A_								=    list(_snake_case						)
								A_								=    [p.clone().detach() for p in parameters]

								if kwargs.get("device"							, _snake_case						) is not None:
													A_								=    "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_								=    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    :       Union[str, Any]							, _snake_case    :       Dict							, _snake_case    :       str						)   ->			"EMAModel":







								"""simple docstring"""
								A_			,     A_								=    model_cls.load_config(_snake_case							, return_unused_kwargs=_snake_case						)
								A_								=    model_cls.from_pretrained(_snake_case						)

								A_								=    cls(model.parameters()							, model_cls=_snake_case							, model_config=model.config						)

								ema_model.load_state_dict(_snake_case						)
								return ema_model




			def   lowerCamelCase__  (					self    :       Dict							, _snake_case    :       Tuple						)   ->			List[str]:







								"""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_								=    self.model_cls.from_config(self.model_config						)
								A_								=    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   lowerCamelCase__  (					self    :       List[str]							, _snake_case    :       int						)   ->			float:







								"""simple docstring"""
								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(_snake_case							, self.decay						)
								# make sure decay is not smaller than min_decay
								A_								=    max(_snake_case							, self.min_decay						)
								return cur_decay_value




			@torch.no_grad()
			def   lowerCamelCase__  (					self    :       Optional[Any]							, _snake_case    :       Iterable[torch.nn.Parameter]						)   ->			Optional[int]:







								"""simple docstring"""
								if isinstance(_snake_case							, 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"							, _snake_case							, standard_warn=_snake_case							, )
													A_								=    parameters.parameters()

								A_								=    list(_snake_case						)

								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							, _snake_case						):
													if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
																		A_								=    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   lowerCamelCase__  (					self    :       List[Any]							, _snake_case    :       Iterable[torch.nn.Parameter]						)   ->			None:







								"""simple docstring"""
								A_								=    list(_snake_case						)
								for s_param, param in zip(self.shadow_params							, _snake_case						):
													param.data.copy_(s_param.to(param.device						).data						)




			def   lowerCamelCase__  (					self    :       List[Any]							, _snake_case    :       int=None							, _snake_case    :       str=None						)   ->			None:







								"""simple docstring"""
								A_								=    [
								    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   lowerCamelCase__  (					self    :       Dict						)   ->			dict:







								"""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   lowerCamelCase__  (					self    :       Union[str, Any]							, _snake_case    :       Iterable[torch.nn.Parameter]						)   ->			None:







								"""simple docstring"""
								A_								=    [param.detach().cpu().clone() for param in parameters]




			def   lowerCamelCase__  (					self    :       List[Any]							, _snake_case    :       Iterable[torch.nn.Parameter]						)   ->			None:







								"""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_								=    None






			def   lowerCamelCase__  (					self    :       str							, _snake_case    :       dict						)   ->			None:







								"""simple docstring"""
								A_								=    copy.deepcopy(_snake_case						)

								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							, _snake_case						):
													raise ValueError("Invalid min_decay"						)

								A_								=    state_dict.get("optimization_step"							, self.optimization_step						)
								if not isinstance(self.optimization_step							, _snake_case						):
													raise ValueError("Invalid optimization_step"						)

								A_								=    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_								=    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_								=    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"							, _snake_case						)
								if shadow_params is not None:
													A_								=    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_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase_       :    Dict						      =			{'''configuration_plbart''': ['''PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PLBartConfig''']}

try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCamelCase_       :    int						      =			['''PLBartTokenizer''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCamelCase_       :    Optional[int]						      =			[
		    '''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_       :    Optional[int]						      =			_LazyModule(__name__, globals()['''__file__'''], _import_structure)




