






"""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       snake_case  (A_				:int				):







  '''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 						snake_case  :





    def __init__(							self     : List[str]    ,		A     : Iterable[torch.nn.Parameter]    ,		A     : float = 0.99_99    ,		A     : float = 0.0    ,		A     : int = 0    ,		A     : bool = False    ,		A     : Union[float, int] = 1.0    ,		A     : Union[float, int] = 2 / 3    ,		A     : Optional[Any] = None    ,		A     : Dict[str, Any] = None    ,		**A     : int    ,		):


      '''simple docstring'''




      if isinstance(A    ,		torch.nn.Module      ):
        a					:      Tuple    =       (
            '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					:      Any    =       parameters.parameters()

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

      if kwargs.get('max_value'    ,		A      ) is not None:
        a					:      Optional[int]    =       'The `max_value` argument is deprecated. Please use `decay` instead.'
        deprecate('max_value'    ,		'1.0.0'    ,		A    ,		standard_warn=A      )
        a					:      int    =       kwargs['max_value']

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

      a					:      Optional[Any]    =       list(A      )
      a					:      Dict    =       [p.clone().detach() for p in parameters]

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

      a					:      str    =       None

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

      a					:      Optional[Any]    =       model_cls
      a					:      Tuple    =       model_config





    @classmethod
    def     lowerCamelCase__				(							cls     : Union[str, Any]    ,		A     : List[Any]    ,		A     : List[str]      ):


      '''simple docstring'''




      a,					a					:      Optional[int]    =       model_cls.load_config(A    ,		return_unused_kwargs=A      )
      a					:      str    =       model_cls.from_pretrained(A      )

      a					:      int    =       cls(model.parameters()    ,		model_cls=A    ,		model_config=model.config      )

      ema_model.load_state_dict(A      )
      return ema_model





    def     lowerCamelCase__				(							self     : Any    ,		A     : Optional[int]      ):


      '''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					:      Union[str, Any]    =       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     : Dict    ,		A     : int      ):


      '''simple docstring'''




      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					:      Any    =       1 - (1 + step / self.inv_gamma) ** -self.power
      else:
        a					:      Dict    =       (1 + step) / (1_0 + step)

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





    @torch.no_grad()
    def     lowerCamelCase__				(							self     : Optional[int]    ,		A     : Iterable[torch.nn.Parameter]      ):


      '''simple docstring'''




      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					:      int    =       parameters.parameters()

      a					:      str    =       list(A      )

      self.optimization_step += 1

      # Compute the decay factor for the exponential moving average.
      a					:      Dict    =       self.get_decay(self.optimization_step      )
      a					:      List[Any]    =       decay
      a					:      Optional[int]    =       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					:      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     : Optional[int]    ,		A     : Iterable[torch.nn.Parameter]      ):


      '''simple docstring'''




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





    def     lowerCamelCase__				(							self     : str    ,		A     : str=None    ,		A     : Dict=None      ):


      '''simple docstring'''




      a					:      Optional[int]    =       [
          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     : int      ):


      '''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     lowerCamelCase__				(							self     : Union[str, Any]    ,		A     : Iterable[torch.nn.Parameter]      ):


      '''simple docstring'''




      a					:      Optional[Any]    =       [param.detach().cpu().clone() for param in parameters]





    def     lowerCamelCase__				(							self     : List[Any]    ,		A     : 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    ,		A      ):
        param.data.copy_(c_param.data      )

      # Better memory-wise.
      a					:      Optional[Any]    =       None





    def     lowerCamelCase__				(							self     : int    ,		A     : dict      ):


      '''simple docstring'''




      a					:      Tuple    =       copy.deepcopy(A      )

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

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

      a					:      Optional[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					:      Dict    =       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					:      List[Any]    =       state_dict.get('inv_gamma'    ,		self.inv_gamma      )
      if not isinstance(self.inv_gamma    ,		(float, int)      ):
        raise ValueError('Invalid inv_gamma'      )

      a					:      Optional[int]    =       state_dict.get('power'    ,		self.power      )
      if not isinstance(self.power    ,		(float, int)      ):
        raise ValueError('Invalid power'      )

      a					:      Tuple    =       state_dict.get('shadow_params'    ,		A      )
      if shadow_params is not None:
        a					:      Optional[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'      )












"""simple docstring"""


import unittest

from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow


if is_flax_available():
   import jax.numpy as jnp

   from transformers import FlaxXLMRobertaModel






@require_sentencepiece
@require_tokenizers
@require_flax
class 						snake_case  (  unittest.TestCase	):





    @slow
    def     lowerCamelCase__				(							self     : Optional[Any]      ):


      '''simple docstring'''




      a					:      Optional[int]    =       FlaxXLMRobertaModel.from_pretrained('xlm-roberta-base'      )
      a					:      int    =       AutoTokenizer.from_pretrained('xlm-roberta-base'      )
      a					:      int    =       'The dog is cute and lives in the garden house'
      a					:      List[Any]    =       jnp.array([tokenizer.encode(A      )]      )

      a					:      int    =       (1, 1_2, 7_6_8)  # batch_size, sequence_length, embedding_vector_dim
      a					:      Dict    =       jnp.array(
          [[-0.01_01, 0.12_18, -0.08_03, 0.08_01, 0.13_27, 0.07_76, -0.12_15, 0.23_83, 0.33_38, 0.31_06, 0.03_00, 0.02_52]]      )

      a					:      Any    =       model(A      )['last_hidden_state']
      self.assertEqual(output.shape    ,		A      )
      # compare the actual values for a slice of last dim
      self.assertTrue(jnp.allclose(output[:, :, -1]    ,		A    ,		atol=1E-3      )      )




