


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			(						__UpperCamelCase ):
					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   a__	:

							def __init__(   self      :     List[Any]      ,							lowerCamelCase_      :     Iterable[torch.nn.Parameter]      ,							lowerCamelCase_      :     float = 0.9_9_9_9      ,							lowerCamelCase_      :     float = 0.0      ,							lowerCamelCase_      :     int = 0      ,							lowerCamelCase_      :     bool = False      ,							lowerCamelCase_      :     Union[float, int] = 1.0      ,							lowerCamelCase_      :     Union[float, int] = 2 / 3      ,							lowerCamelCase_      :     Optional[Any] = None      ,							lowerCamelCase_      :     Dict[str, Any] = None      ,							**lowerCamelCase_      :     str      ,							):

												if isinstance(lowerCamelCase_      ,							torch.nn.Module    ):
																	a_    :     Dict										=    (
																	    """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"""      ,							lowerCamelCase_      ,							standard_warn=lowerCamelCase_      ,							)
																	a_    :     Optional[Any]										=    parameters.parameters()

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

												if kwargs.get("""max_value"""      ,							lowerCamelCase_    ) is not None:
																	a_    :     Union[str, Any]										=    """The `max_value` argument is deprecated. Please use `decay` instead."""
																	deprecate("""max_value"""      ,							"""1.0.0"""      ,							lowerCamelCase_      ,							standard_warn=lowerCamelCase_    )
																	a_    :     Tuple										=    kwargs["""max_value"""]

												if kwargs.get("""min_value"""      ,							lowerCamelCase_    ) is not None:
																	a_    :     Any										=    """The `min_value` argument is deprecated. Please use `min_decay` instead."""
																	deprecate("""min_value"""      ,							"""1.0.0"""      ,							lowerCamelCase_      ,							standard_warn=lowerCamelCase_    )
																	a_    :     List[str]										=    kwargs["""min_value"""]

												a_    :     int										=    list(lowerCamelCase_    )
												a_    :     Any										=    [p.clone().detach() for p in parameters]

												if kwargs.get("""device"""      ,							lowerCamelCase_    ) is not None:
																	a_    :     Union[str, Any]										=    """The `device` argument is deprecated. Please use `to` instead."""
																	deprecate("""device"""      ,							"""1.0.0"""      ,							lowerCamelCase_      ,							standard_warn=lowerCamelCase_    )
																	self.to(device=kwargs["""device"""]    )

												a_    :     str										=    None

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

												a_    :     Dict										=    model_cls
												a_    :     Dict										=    model_config

							@classmethod
							def 						UpperCAmelCase(   cls      :     Optional[Any]      ,							lowerCamelCase_      :     str      ,							lowerCamelCase_      :     List[Any]    ):
												a_    :     Union[str, Any]										=    model_cls.load_config(lowerCamelCase_      ,							return_unused_kwargs=lowerCamelCase_    )
												a_    :     List[str]										=    model_cls.from_pretrained(lowerCamelCase_    )

												a_    :     Tuple										=    cls(model.parameters()      ,							model_cls=lowerCamelCase_      ,							model_config=model.config    )

												ema_model.load_state_dict(lowerCamelCase_    )
												return ema_model

							def 						UpperCAmelCase(   self      :     int      ,							lowerCamelCase_      :     Optional[int]    ):
												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_    :     str										=    self.model_cls.from_config(self.model_config    )
												a_    :     Tuple										=    self.state_dict()
												state_dict.pop("""shadow_params"""      ,							lowerCamelCase_    )

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

							def 						UpperCAmelCase(   self      :     List[Any]      ,							lowerCamelCase_      :     int    ):
												a_    :     Union[str, Any]										=    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_    :     Union[str, Any]										=    (1 + step) / (1_0 + step)

												a_    :     List[Any]										=    min(lowerCamelCase_      ,							self.decay    )
												# make sure decay is not smaller than min_decay
												a_    :     Any										=    max(lowerCamelCase_      ,							self.min_decay    )
												return cur_decay_value

							@torch.no_grad()
							def 						UpperCAmelCase(   self      :     Dict      ,							lowerCamelCase_      :     Iterable[torch.nn.Parameter]    ):
												if isinstance(lowerCamelCase_      ,							torch.nn.Module    ):
																	a_    :     Dict										=    (
																	    """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"""      ,							lowerCamelCase_      ,							standard_warn=lowerCamelCase_      ,							)
																	a_    :     Optional[Any]										=    parameters.parameters()

												a_    :     Optional[Any]										=    list(lowerCamelCase_    )

												self.optimization_step += 1

												# Compute the decay factor for the exponential moving average.
												a_    :     Tuple										=    self.get_decay(self.optimization_step    )
												a_    :     Tuple										=    decay
												a_    :     Optional[Any]										=    1 - decay

												a_    :     Tuple										=    contextlib.nullcontext
												if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
																	import deepspeed

												for s_param, param in zip(self.shadow_params      ,							lowerCamelCase_    ):
																	if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
																						a_    :     List[str]										=    deepspeed.zero.GatheredParameters(lowerCamelCase_      ,							modifier_rank=lowerCamelCase_    )

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

							def 						UpperCAmelCase(   self      :     Dict      ,							lowerCamelCase_      :     Iterable[torch.nn.Parameter]    ):
												a_    :     List[str]										=    list(lowerCamelCase_    )
												for s_param, param in zip(self.shadow_params      ,							lowerCamelCase_    ):
																	param.data.copy_(s_param.to(param.device    ).data    )

							def 						UpperCAmelCase(   self      :     Dict      ,							lowerCamelCase_      :     Union[str, Any]=None      ,							lowerCamelCase_      :     Union[str, Any]=None    ):
												a_    :     List[str]										=    [
												    p.to(device=lowerCamelCase_      ,							dtype=lowerCamelCase_    ) if p.is_floating_point() else p.to(device=lowerCamelCase_    )
												    for p in self.shadow_params
												]

							def 						UpperCAmelCase(   self      :     List[str]    ):
												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      :     List[str]      ,							lowerCamelCase_      :     Iterable[torch.nn.Parameter]    ):
												a_    :     List[Any]										=    [param.detach().cpu().clone() for param in parameters]

							def 						UpperCAmelCase(   self      :     List[str]      ,							lowerCamelCase_      :     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      ,							lowerCamelCase_    ):
																	param.data.copy_(c_param.data    )

												# Better memory-wise.
												a_    :     Union[str, Any]										=    None

							def 						UpperCAmelCase(   self      :     Tuple      ,							lowerCamelCase_      :     dict    ):
												a_    :     Optional[Any]										=    copy.deepcopy(lowerCamelCase_    )

												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[Any]										=    state_dict.get("""min_decay"""      ,							self.min_decay    )
												if not isinstance(self.min_decay      ,							lowerCamelCase_    ):
																	raise ValueError("""Invalid min_decay"""    )

												a_    :     List[Any]										=    state_dict.get("""optimization_step"""      ,							self.optimization_step    )
												if not isinstance(self.optimization_step      ,							lowerCamelCase_    ):
																	raise ValueError("""Invalid optimization_step"""    )

												a_    :     Any										=    state_dict.get("""update_after_step"""      ,							self.update_after_step    )
												if not isinstance(self.update_after_step      ,							lowerCamelCase_    ):
																	raise ValueError("""Invalid update_after_step"""    )

												a_    :     Any										=    state_dict.get("""use_ema_warmup"""      ,							self.use_ema_warmup    )
												if not isinstance(self.use_ema_warmup      ,							lowerCamelCase_    ):
																	raise ValueError("""Invalid use_ema_warmup"""    )

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

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

												a_    :     List[Any]										=    state_dict.get("""shadow_params"""      ,							lowerCamelCase_    )
												if shadow_params is not None:
																	a_    :     Tuple										=    shadow_params
																	if not isinstance(self.shadow_params      ,							lowerCamelCase_    ):
																						raise ValueError("""shadow_params must be a list"""    )
																	if not all(isinstance(lowerCamelCase_      ,							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_torch_available


__lowerCamelCase        =     {
    '''configuration_pegasus_x''': ['''PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PegasusXConfig'''],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowerCamelCase        =     [
           '''PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST''',
           '''PegasusXForConditionalGeneration''',
           '''PegasusXModel''',
           '''PegasusXPreTrainedModel''',
       ]


if TYPE_CHECKING:
       from .configuration_pegasus_x import PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusXConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_pegasus_x import (
                  PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST,
                  PegasusXForConditionalGeneration,
                  PegasusXModel,
                  PegasusXPreTrainedModel,
              )


else:
       import sys

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



