



'''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 lowerCAmelCase    (__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 __A     :



 '''simple docstring'''







 def __init__(self					,  A					,  A = 0.9999					,  A = 0.0					,  A = 0					,  A = False					,  A = 1.0					,  A = 2 / 3					,  A = None					,  A = None					,  **A					,  )       ->	List[str]:


      """simple docstring"""



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

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

      _a           =    list(A			)
      _a           =    [p.clone().detach() for p in parameters]

      if kwargs.get('''device'''					,  A			) is not None:
           _a           =    '''The `device` argument is deprecated. Please use `to` instead.'''
           deprecate('''device'''					,  '''1.0.0'''					,  A					,  standard_warn=A			)
           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    a__    (cls					,  A					,  A			)       ->	"EMAModel":


      """simple docstring"""



      _a    ,       _a           =    model_cls.load_config(A					,  return_unused_kwargs=A			)
      _a           =    model_cls.from_pretrained(A			)

      _a           =    cls(model.parameters()					,  model_cls=A					,  model_config=model.config			)

      ema_model.load_state_dict(A			)
      return ema_model



 def    a__    (self					,  A			)       ->	Tuple:


      """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'''					,  A			)

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



 def    a__    (self					,  A			)       ->	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(A					,  self.decay			)
      # make sure decay is not smaller than min_decay
      _a           =    max(A					,  self.min_decay			)
      return cur_decay_value



 @torch.no_grad()
 def    a__    (self					,  A			)       ->	Optional[Any]:


      """simple docstring"""



      if isinstance(A					,  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'''					,  A					,  standard_warn=A					,  )
           _a           =    parameters.parameters()

      _a           =    list(A			)

      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					,  A			):
           if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
                _a           =    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    a__    (self					,  A			)       ->	None:


      """simple docstring"""



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



 def    a__    (self					,  A=None					,  A=None			)       ->	None:


      """simple docstring"""



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



 def    a__    (self			)       ->	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    a__    (self					,  A			)       ->	None:


      """simple docstring"""



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



 def    a__    (self					,  A			)       ->	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					,  A			):
           param.data.copy_(c_param.data			)

      # Better memory-wise.
      _a           =    None



 def    a__    (self					,  A			)       ->	None:


      """simple docstring"""



      _a           =    copy.deepcopy(A			)

      _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					,  A			):
           raise ValueError('''Invalid min_decay'''			)

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

      _a           =    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           =    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           =    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'''					,  A			)
      if shadow_params is not None:
           _a           =    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'''			)








'''simple docstring'''





from binascii import hexlify
from hashlib import shaaaa
from os import urandom

# RFC 3526 - More Modular Exponential (MODP) Diffie-Hellman groups for
# Internet Key Exchange (IKE) https://tools.ietf.org/html/rfc3526

lowercase_									=    {
    # 1536-bit
    5: {
        "prime": int(
            "FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
            + "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
            + "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
            + "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
            + "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
            + "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
            + "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
            + "670C354E4ABC9804F1746C08CA237327FFFFFFFFFFFFFFFF",
            base=16,
        ),
        "generator": 2,
    },
    # 2048-bit
    14: {
        "prime": int(
            "FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
            + "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
            + "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
            + "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
            + "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
            + "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
            + "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
            + "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"
            + "E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"
            + "DE2BCBF6955817183995497CEA956AE515D2261898FA0510"
            + "15728E5A8AACAA68FFFFFFFFFFFFFFFF",
            base=16,
        ),
        "generator": 2,
    },
    # 3072-bit
    15: {
        "prime": int(
            "FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
            + "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
            + "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
            + "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
            + "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
            + "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
            + "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
            + "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"
            + "E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"
            + "DE2BCBF6955817183995497CEA956AE515D2261898FA0510"
            + "15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64"
            + "ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7"
            + "ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B"
            + "F12FFA06D98A0864D87602733EC86A64521F2B18177B200C"
            + "BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31"
            + "43DB5BFCE0FD108E4B82D120A93AD2CAFFFFFFFFFFFFFFFF",
            base=16,
        ),
        "generator": 2,
    },
    # 4096-bit
    16: {
        "prime": int(
            "FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
            + "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
            + "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
            + "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
            + "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
            + "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
            + "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
            + "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"
            + "E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"
            + "DE2BCBF6955817183995497CEA956AE515D2261898FA0510"
            + "15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64"
            + "ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7"
            + "ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B"
            + "F12FFA06D98A0864D87602733EC86A64521F2B18177B200C"
            + "BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31"
            + "43DB5BFCE0FD108E4B82D120A92108011A723C12A787E6D7"
            + "88719A10BDBA5B2699C327186AF4E23C1A946834B6150BDA"
            + "2583E9CA2AD44CE8DBBBC2DB04DE8EF92E8EFC141FBECAA6"
            + "287C59474E6BC05D99B2964FA090C3A2233BA186515BE7ED"
            + "1F612970CEE2D7AFB81BDD762170481CD0069127D5B05AA9"
            + "93B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934063199"
            + "FFFFFFFFFFFFFFFF",
            base=16,
        ),
        "generator": 2,
    },
    # 6144-bit
    17: {
        "prime": int(
            "FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD129024E08"
            + "8A67CC74020BBEA63B139B22514A08798E3404DDEF9519B3CD3A431B"
            + "302B0A6DF25F14374FE1356D6D51C245E485B576625E7EC6F44C42E9"
            + "A637ED6B0BFF5CB6F406B7EDEE386BFB5A899FA5AE9F24117C4B1FE6"
            + "49286651ECE45B3DC2007CB8A163BF0598DA48361C55D39A69163FA8"
            + "FD24CF5F83655D23DCA3AD961C62F356208552BB9ED529077096966D"
            + "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3BE39E772C"
            + "180E86039B2783A2EC07A28FB5C55DF06F4C52C9DE2BCBF695581718"
            + "3995497CEA956AE515D2261898FA051015728E5A8AAAC42DAD33170D"
            + "04507A33A85521ABDF1CBA64ECFB850458DBEF0A8AEA71575D060C7D"
            + "B3970F85A6E1E4C7ABF5AE8CDB0933D71E8C94E04A25619DCEE3D226"
            + "1AD2EE6BF12FFA06D98A0864D87602733EC86A64521F2B18177B200C"
            + "BBE117577A615D6C770988C0BAD946E208E24FA074E5AB3143DB5BFC"
            + "E0FD108E4B82D120A92108011A723C12A787E6D788719A10BDBA5B26"
            + "99C327186AF4E23C1A946834B6150BDA2583E9CA2AD44CE8DBBBC2DB"
            + "04DE8EF92E8EFC141FBECAA6287C59474E6BC05D99B2964FA090C3A2"
            + "233BA186515BE7ED1F612970CEE2D7AFB81BDD762170481CD0069127"
            + "D5B05AA993B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934028492"
            + "36C3FAB4D27C7026C1D4DCB2602646DEC9751E763DBA37BDF8FF9406"
            + "AD9E530EE5DB382F413001AEB06A53ED9027D831179727B0865A8918"
            + "DA3EDBEBCF9B14ED44CE6CBACED4BB1BDB7F1447E6CC254B33205151"
            + "2BD7AF426FB8F401378CD2BF5983CA01C64B92ECF032EA15D1721D03"
            + "F482D7CE6E74FEF6D55E702F46980C82B5A84031900B1C9E59E7C97F"
            + "BEC7E8F323A97A7E36CC88BE0F1D45B7FF585AC54BD407B22B4154AA"
            + "CC8F6D7EBF48E1D814CC5ED20F8037E0A79715EEF29BE32806A1D58B"
            + "B7C5DA76F550AA3D8A1FBFF0EB19CCB1A313D55CDA56C9EC2EF29632"
            + "387FE8D76E3C0468043E8F663F4860EE12BF2D5B0B7474D6E694F91E"
            + "6DCC4024FFFFFFFFFFFFFFFF",
            base=16,
        ),
        "generator": 2,
    },
    # 8192-bit
    18: {
        "prime": int(
            "FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"
            + "29024E088A67CC74020BBEA63B139B22514A08798E3404DD"
            + "EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"
            + "E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"
            + "EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"
            + "C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"
            + "83655D23DCA3AD961C62F356208552BB9ED529077096966D"
            + "670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"
            + "E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"
            + "DE2BCBF6955817183995497CEA956AE515D2261898FA0510"
            + "15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64"
            + "ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7"
            + "ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B"
            + "F12FFA06D98A0864D87602733EC86A64521F2B18177B200C"
            + "BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31"
            + "43DB5BFCE0FD108E4B82D120A92108011A723C12A787E6D7"
            + "88719A10BDBA5B2699C327186AF4E23C1A946834B6150BDA"
            + "2583E9CA2AD44CE8DBBBC2DB04DE8EF92E8EFC141FBECAA6"
            + "287C59474E6BC05D99B2964FA090C3A2233BA186515BE7ED"
            + "1F612970CEE2D7AFB81BDD762170481CD0069127D5B05AA9"
            + "93B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934028492"
            + "36C3FAB4D27C7026C1D4DCB2602646DEC9751E763DBA37BD"
            + "F8FF9406AD9E530EE5DB382F413001AEB06A53ED9027D831"
            + "179727B0865A8918DA3EDBEBCF9B14ED44CE6CBACED4BB1B"
            + "DB7F1447E6CC254B332051512BD7AF426FB8F401378CD2BF"
            + "5983CA01C64B92ECF032EA15D1721D03F482D7CE6E74FEF6"
            + "D55E702F46980C82B5A84031900B1C9E59E7C97FBEC7E8F3"
            + "23A97A7E36CC88BE0F1D45B7FF585AC54BD407B22B4154AA"
            + "CC8F6D7EBF48E1D814CC5ED20F8037E0A79715EEF29BE328"
            + "06A1D58BB7C5DA76F550AA3D8A1FBFF0EB19CCB1A313D55C"
            + "DA56C9EC2EF29632387FE8D76E3C0468043E8F663F4860EE"
            + "12BF2D5B0B7474D6E694F91E6DBE115974A3926F12FEE5E4"
            + "38777CB6A932DF8CD8BEC4D073B931BA3BC832B68D9DD300"
            + "741FA7BF8AFC47ED2576F6936BA424663AAB639C5AE4F568"
            + "3423B4742BF1C978238F16CBE39D652DE3FDB8BEFC848AD9"
            + "22222E04A4037C0713EB57A81A23F0C73473FC646CEA306B"
            + "4BCBC8862F8385DDFA9D4B7FA2C087E879683303ED5BDD3A"
            + "062B3CF5B3A278A66D2A13F83F44F82DDF310EE074AB6A36"
            + "4597E899A0255DC164F31CC50846851DF9AB48195DED7EA1"
            + "B1D510BD7EE74D73FAF36BC31ECFA268359046F4EB879F92"
            + "4009438B481C6CD7889A002ED5EE382BC9190DA6FC026E47"
            + "9558E4475677E9AA9E3050E2765694DFC81F56E880B96E71"
            + "60C980DD98EDD3DFFFFFFFFFFFFFFFFF",
            base=16,
        ),
        "generator": 2,
    },
}






class __A     :



 '''simple docstring'''







 def __init__(self					,  A = 14			)       ->	None:


      """simple docstring"""



      if group not in primes:
           raise ValueError('''Unsupported Group'''			)
      _a           =    primes[group]['''prime''']
      _a           =    primes[group]['''generator''']

      _a           =    int(hexlify(urandom(32			)			)					,  base=16			)



 def    a__    (self			)       ->	str:


      """simple docstring"""



      return hex(self.__private_key			)[2:]



 def    a__    (self			)       ->	str:


      """simple docstring"""



      _a           =    pow(self.generator					,  self.__private_key					,  self.prime			)
      return hex(A			)[2:]



 def    a__    (self					,  A			)       ->	bool:


      """simple docstring"""



      return (
          2 <= key <= self.prime - 2
          and pow(A					,  (self.prime - 1) // 2					,  self.prime			) == 1
      )



 def    a__    (self					,  A			)       ->	str:


      """simple docstring"""



      _a           =    int(A					,  base=16			)
      if not self.is_valid_public_key(A			):
           raise ValueError('''Invalid public key'''			)
      _a           =    pow(A					,  self.__private_key					,  self.prime			)
      return shaaaa(str(A			).encode()			).hexdigest()



 @staticmethod
 def    a__    (A					,  A			)       ->	bool:


      """simple docstring"""



      return (
          2 <= remote_public_key_str <= prime - 2
          and pow(A					,  (prime - 1) // 2					,  A			) == 1
      )



 @staticmethod
 def    a__    (A					,  A					,  A = 14			)       ->	str:


      """simple docstring"""



      _a           =    int(A					,  base=16			)
      _a           =    int(A					,  base=16			)
      _a           =    primes[group]['''prime''']
      if not DiffieHellman.is_valid_public_key_static(A					,  A			):
           raise ValueError('''Invalid public key'''			)
      _a           =    pow(A					,  A					,  A			)
      return shaaaa(str(A			).encode()			).hexdigest()


if __name__ == "__main__":
  import doctest

  doctest.testmod()



