






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 _UpperCAmelCase   (	a						:	int ):
							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    :    List[str]			,		UpperCamelCase__    :    Iterable[torch.nn.Parameter]			,		UpperCamelCase__    :    float = 0.99_99			,		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__    :    List[str]			,		):






											'''simple docstring'''







											if isinstance(UpperCamelCase__			,		torch.nn.Module):
																		snake_case__	  =       (
																		    """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__			,		)
																		snake_case__	  =       parameters.parameters()

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

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

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

											snake_case__	  =       list(UpperCamelCase__)
											snake_case__	  =       [p.clone().detach() for p in parameters]

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

											snake_case__	  =       None

											snake_case__	  =       decay
											snake_case__	  =       min_decay
											snake_case__	  =       update_after_step
											snake_case__	  =       use_ema_warmup
											snake_case__	  =       inv_gamma
											snake_case__	  =       power
											snake_case__	  =       0
											snake_case__	  =       None  # set in `step()`

											snake_case__	  =       model_cls
											snake_case__	  =       model_config



				@classmethod
				def 					__magic_name__							(      cls    :    List[Any]			,		UpperCamelCase__    :    List[str]			,		UpperCamelCase__    :    Optional[Any]):






											'''simple docstring'''







											snake_case__  , snake_case__	  =       model_cls.load_config(UpperCamelCase__			,		return_unused_kwargs=UpperCamelCase__)
											snake_case__	  =       model_cls.from_pretrained(UpperCamelCase__)

											snake_case__	  =       cls(model.parameters()			,		model_cls=UpperCamelCase__			,		model_config=model.config)

											ema_model.load_state_dict(UpperCamelCase__)
											return ema_model



				def 					__magic_name__							(      self    :    List[Any]			,		UpperCamelCase__    :    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__.""")

											snake_case__	  =       self.model_cls.from_config(self.model_config)
											snake_case__	  =       self.state_dict()
											state_dict.pop("""shadow_params"""			,		UpperCamelCase__)

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



				def 					__magic_name__							(      self    :    Optional[Any]			,		UpperCamelCase__    :    int):






											'''simple docstring'''







											snake_case__	  =       max(0			,		optimization_step - self.update_after_step - 1)

											if step <= 0:
																		return 0.0

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

											snake_case__	  =       min(UpperCamelCase__			,		self.decay)
											# make sure decay is not smaller than min_decay
											snake_case__	  =       max(UpperCamelCase__			,		self.min_decay)
											return cur_decay_value



				@torch.no_grad()
				def 					__magic_name__							(      self    :    str			,		UpperCamelCase__    :    Iterable[torch.nn.Parameter]):






											'''simple docstring'''







											if isinstance(UpperCamelCase__			,		torch.nn.Module):
																		snake_case__	  =       (
																		    """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__			,		)
																		snake_case__	  =       parameters.parameters()

											snake_case__	  =       list(UpperCamelCase__)

											self.optimization_step += 1

											# Compute the decay factor for the exponential moving average.
											snake_case__	  =       self.get_decay(self.optimization_step)
											snake_case__	  =       decay
											snake_case__	  =       1 - decay

											snake_case__	  =       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():
																									snake_case__	  =       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 					__magic_name__							(      self    :    Dict			,		UpperCamelCase__    :    Iterable[torch.nn.Parameter]):






											'''simple docstring'''







											snake_case__	  =       list(UpperCamelCase__)
											for s_param, param in zip(self.shadow_params			,		UpperCamelCase__):
																		param.data.copy_(s_param.to(param.device).data)



				def 					__magic_name__							(      self    :    List[str]			,		UpperCamelCase__    :    Tuple=None			,		UpperCamelCase__    :    Optional[int]=None):






											'''simple docstring'''







											snake_case__	  =       [
											    p.to(device=UpperCamelCase__			,		dtype=UpperCamelCase__) if p.is_floating_point() else p.to(device=UpperCamelCase__)
											    for p in self.shadow_params
											]



				def 					__magic_name__							(      self    :    List[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 					__magic_name__							(      self    :    str			,		UpperCamelCase__    :    Iterable[torch.nn.Parameter]):






											'''simple docstring'''







											snake_case__	  =       [param.detach().cpu().clone() for param in parameters]



				def 					__magic_name__							(      self    :    str			,		UpperCamelCase__    :    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			,		UpperCamelCase__):
																		param.data.copy_(c_param.data)

											# Better memory-wise.
											snake_case__	  =       None



				def 					__magic_name__							(      self    :    List[Any]			,		UpperCamelCase__    :    dict):






											'''simple docstring'''







											snake_case__	  =       copy.deepcopy(UpperCamelCase__)

											snake_case__	  =       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""")

											snake_case__	  =       state_dict.get("""min_decay"""			,		self.min_decay)
											if not isinstance(self.min_decay			,		UpperCamelCase__):
																		raise ValueError("""Invalid min_decay""")

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

											snake_case__	  =       state_dict.get("""update_after_step"""			,		self.update_after_step)
											if not isinstance(self.update_after_step			,		UpperCamelCase__):
																		raise ValueError("""Invalid update_after_step""")

											snake_case__	  =       state_dict.get("""use_ema_warmup"""			,		self.use_ema_warmup)
											if not isinstance(self.use_ema_warmup			,		UpperCamelCase__):
																		raise ValueError("""Invalid use_ema_warmup""")

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

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

											snake_case__	  =       state_dict.get("""shadow_params"""			,		UpperCamelCase__)
											if shadow_params is not None:
																		snake_case__	  =       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""")








from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a__       =     {
    """configuration_autoformer""": [
        """AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """AutoformerConfig""",
    ],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a__       =     [
	    """AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """AutoformerForPrediction""",
	    """AutoformerModel""",
	    """AutoformerPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_autoformer import (
	    AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    AutoformerConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_autoformer import (
		    AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    AutoformerForPrediction,
		    AutoformerModel,
		    AutoformerPreTrainedModel,
		)

else:
	import sys

	a__       =     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
