


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__						(UpperCamelCase_    ):



     """simple docstring"""






     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__ :







    """simple docstring"""

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

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

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

         snake_case					=    list(__snake_case   )
         snake_case					=    [p.clone().detach() for p in parameters]

         if kwargs.get('''device'''      , __snake_case   ) is not None:
              snake_case					=    '''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''']   )

         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 		a_      (	cls      , __snake_case      , __snake_case   ):
         snake_case			,  snake_case					=    model_cls.load_config(__snake_case      , return_unused_kwargs=__snake_case   )
         snake_case					=    model_cls.from_pretrained(__snake_case   )

         snake_case					=    cls(model.parameters()      , model_cls=__snake_case      , model_config=model.config   )

         ema_model.load_state_dict(__snake_case   )
         return ema_model
    def 		a_      (	self      , __snake_case   ):
         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'''      , __snake_case   )

         model.register_to_config(**__snake_case   )
         self.copy_to(model.parameters()   )
         model.save_pretrained(__snake_case   )
    def 		a_      (	self      , __snake_case   ):
         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(__snake_case      , self.decay   )
         # make sure decay is not smaller than min_decay
         snake_case					=    max(__snake_case      , self.min_decay   )
         return cur_decay_value
    @torch.no_grad()
    def 		a_      (	self      , __snake_case   ):
         if isinstance(__snake_case      , 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'''      , __snake_case      , standard_warn=__snake_case      , )
              snake_case					=    parameters.parameters()

         snake_case					=    list(__snake_case   )

         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      , __snake_case   ):
              if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
                   snake_case					=    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 		a_      (	self      , __snake_case   ):
         snake_case					=    list(__snake_case   )
         for s_param, param in zip(self.shadow_params      , __snake_case   ):
              param.data.copy_(s_param.to(param.device   ).data   )
    def 		a_      (	self      , __snake_case=None      , __snake_case=None   ):
         snake_case					=    [
             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 		a_      (	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 		a_      (	self      , __snake_case   ):
         snake_case					=    [param.detach().cpu().clone() for param in parameters]
    def 		a_      (	self      , __snake_case   ):
         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.
         snake_case					=    None




    def 		a_      (	self      , __snake_case   ):
         snake_case					=    copy.deepcopy(__snake_case   )

         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      , __snake_case   ):
              raise ValueError('''Invalid min_decay'''   )

         snake_case					=    state_dict.get('''optimization_step'''      , self.optimization_step   )
         if not isinstance(self.optimization_step      , __snake_case   ):
              raise ValueError('''Invalid optimization_step'''   )

         snake_case					=    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'''   )

         snake_case					=    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'''   )

         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'''      , __snake_case   )
         if shadow_params is not None:
              snake_case					=    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'''   )








def       UpperCAmelCase__						(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_    ):



     """simple docstring"""






     snake_case					=    len(UpperCamelCase_    )
     snake_case					=    [[0] * n for i in range(UpperCamelCase_    )]
     for i in range(UpperCamelCase_    ):
          snake_case					=    y_points[i]

     for i in range(2							,UpperCamelCase_    ):
          for j in range(UpperCamelCase_							,UpperCamelCase_    ):
               snake_case					=    (
                   (xa - x_points[j - i + 1]) * q[j][i - 1]
                   - (xa - x_points[j]) * q[j - 1][i - 1]
               ) / (x_points[j] - x_points[j - i + 1])

     return [q[n - 1][n - 1], q]


if __name__ == "__main__":
 import doctest

 doctest.testmod()




