



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      (    _lowerCAmelCase						)			->    Union[str, Any]:



    """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__	:




    '''simple docstring'''






    def __init__(			self,						lowerCamelCase__,						lowerCamelCase__ = 0.9999,						lowerCamelCase__ = 0.0,						lowerCamelCase__ = 0,						lowerCamelCase__ = False,						lowerCamelCase__ = 1.0,						lowerCamelCase__ = 2 / 3,						lowerCamelCase__ = None,						lowerCamelCase__ = None,						**lowerCamelCase__,						):

        if isinstance(__A,						torch.nn.Module						):
            A					:			Optional[Any]		=  (
                """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""",						__A,						standard_warn=__A,						)
            A					:			List[Any]		=  parameters.parameters()

            # set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
            A					:			List[Any]		=  True

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

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

        A					:			Optional[int]		=  list(__A						)
        A					:			Optional[int]		=  [p.clone().detach() for p in parameters]

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

        A					:			Tuple		=  None

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

        A					:			Optional[int]		=  model_cls
        A					:			str		=  model_config






    @classmethod
    def      _lowerCAmelCase   (			cls,						lowerCamelCase__,						lowerCamelCase__						):
        A					:			str		=  model_cls.load_config(__A,						return_unused_kwargs=__A						)
        A					:			Optional[Any]		=  model_cls.from_pretrained(__A						)

        A					:			Optional[Any]		=  cls(model.parameters(),						model_cls=__A,						model_config=model.config						)

        ema_model.load_state_dict(__A						)
        return ema_model






    def      _lowerCAmelCase   (			self,						lowerCamelCase__						):
        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					:			List[str]		=  self.state_dict()
        state_dict.pop("""shadow_params""",						__A						)

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






    def      _lowerCAmelCase   (			self,						lowerCamelCase__						):
        A					:			Tuple		=  max(0,						optimization_step - self.update_after_step - 1						)

        if step <= 0:
            return 0.0

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

        A					:			Dict		=  min(__A,						self.decay						)
        # make sure decay is not smaller than min_decay
        A					:			Dict		=  max(__A,						self.min_decay						)
        return cur_decay_value






    @torch.no_grad()
    def      _lowerCAmelCase   (			self,						lowerCamelCase__						):
        if isinstance(__A,						torch.nn.Module						):
            A					:			Tuple		=  (
                """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""",						__A,						standard_warn=__A,						)
            A					:			Union[str, Any]		=  parameters.parameters()

        A					:			Any		=  list(__A						)

        self.optimization_step += 1

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

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






    def      _lowerCAmelCase   (			self,						lowerCamelCase__						):
        A					:			Union[str, Any]		=  list(__A						)
        for s_param, param in zip(self.shadow_params,						__A						):
            param.data.copy_(s_param.to(param.device						).data						)






    def      _lowerCAmelCase   (			self,						lowerCamelCase__=None,						lowerCamelCase__=None						):
        A					:			Tuple		=  [
            p.to(device=__A,						dtype=__A						) if p.is_floating_point() else p.to(device=__A						)
            for p in self.shadow_params
        ]






    def      _lowerCAmelCase   (			self						):
        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,						lowerCamelCase__						):
        A					:			List[str]		=  [param.detach().cpu().clone() for param in parameters]






    def      _lowerCAmelCase   (			self,						lowerCamelCase__						):
        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,						__A						):
            param.data.copy_(c_param.data						)

        # Better memory-wise.
        A					:			Any		=  None






    def      _lowerCAmelCase   (			self,						lowerCamelCase__						):
        A					:			Optional[Any]		=  copy.deepcopy(__A						)

        A					:			Optional[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					:			int		=  state_dict.get("""min_decay""",						self.min_decay						)
        if not isinstance(self.min_decay,						__A						):
            raise ValueError("""Invalid min_decay"""						)

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

        A					:			List[Any]		=  state_dict.get("""update_after_step""",						self.update_after_step						)
        if not isinstance(self.update_after_step,						__A						):
            raise ValueError("""Invalid update_after_step"""						)

        A					:			int		=  state_dict.get("""use_ema_warmup""",						self.use_ema_warmup						)
        if not isinstance(self.use_ema_warmup,						__A						):
            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					:			List[Any]		=  state_dict.get("""power""",						self.power						)
        if not isinstance(self.power,						(float, int)						):
            raise ValueError("""Invalid power"""						)

        A					:			Dict		=  state_dict.get("""shadow_params""",						__A						)
        if shadow_params is not None:
            A					:			Union[str, Any]		=  shadow_params
            if not isinstance(self.shadow_params,						__A						):
                raise ValueError("""shadow_params must be a list"""						)
            if not all(isinstance(__A,						torch.Tensor						) for p in self.shadow_params						):
                raise ValueError("""shadow_params must all be Tensors"""						)





from collections.abc import Sequence
def 							__SCREAMING_SNAKE_CASE     (   a__    :   Sequence[float]			,a__    :   float       )					->				float:
	return sum(c * (x**i) for i, c in enumerate(a__       )       )
def 							__SCREAMING_SNAKE_CASE     (   a__    :   Sequence[float]			,a__    :   float       )					->				float:
	__A :			Any		    =			0.0
	for coeff in reversed(a__       ):
		__A :			List[str]		    =			result * x + coeff
	return result


if __name__ == "__main__":
			UpperCAmelCase_							:					List[str]		 =   (0.0, 0.0, 5.0, 9.3, 7.0)
			UpperCAmelCase_							:					str		 =   10.0
			print(evaluate_poly(poly, x))
			print(horner(poly, x))





