
'''simple docstring'''






import unittest

from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import (
     MODEL_FOR_PRETRAINING_MAPPING,
     AlbertForMaskedLM,
     AlbertForMultipleChoice,
     AlbertForPreTraining,
     AlbertForQuestionAnswering,
     AlbertForSequenceClassification,
     AlbertForTokenClassification,
     AlbertModel,
 )
 from transformers.models.albert.modeling_albert import ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST

class  UpperCAmelCase__						:



    def __init__(	self      :       List[Any],__A      :       int,__A      :       List[str]=1_3,__A      :       Any=7,__A      :       Dict=True,__A      :       Dict=True,__A      :       int=True,__A      :       Optional[Any]=True,__A      :       Dict=9_9,__A      :       List[str]=1_6,__A      :       Dict=3_6,__A      :       List[Any]=6,__A      :       Optional[int]=6,__A      :       str=6,__A      :       Any=3_7,__A      :       List[str]="gelu",__A      :       Union[str, Any]=0.1,__A      :       Union[str, Any]=0.1,__A      :       Optional[Any]=5_1_2,__A      :       int=1_6,__A      :       List[str]=2,__A      :       List[Any]=0.02,__A      :       Optional[int]=3,__A      :       str=4,__A      :       Dict=None,):
          _lowerCamelCase				:   Tuple								=   parent
          _lowerCamelCase				:   Union[str, Any]								=   batch_size
          _lowerCamelCase				:   Any								=   seq_length
          _lowerCamelCase				:   str								=   is_training
          _lowerCamelCase				:   int								=   use_input_mask
          _lowerCamelCase				:   List[Any]								=   use_token_type_ids
          _lowerCamelCase				:   Dict								=   use_labels
          _lowerCamelCase				:   List[Any]								=   vocab_size
          _lowerCamelCase				:   Union[str, Any]								=   embedding_size
          _lowerCamelCase				:   Any								=   hidden_size
          _lowerCamelCase				:   Dict								=   num_hidden_layers
          _lowerCamelCase				:   Any								=   num_hidden_groups
          _lowerCamelCase				:   List[str]								=   num_attention_heads
          _lowerCamelCase				:   List[str]								=   intermediate_size
          _lowerCamelCase				:   Union[str, Any]								=   hidden_act
          _lowerCamelCase				:   Dict								=   hidden_dropout_prob
          _lowerCamelCase				:   List[Any]								=   attention_probs_dropout_prob
          _lowerCamelCase				:   Optional[int]								=   max_position_embeddings
          _lowerCamelCase				:   Optional[int]								=   type_vocab_size
          _lowerCamelCase				:   str								=   type_sequence_label_size
          _lowerCamelCase				:   Dict								=   initializer_range
          _lowerCamelCase				:   List[str]								=   num_labels
          _lowerCamelCase				:   List[Any]								=   num_choices
          _lowerCamelCase				:   Any								=   scope



    def   lowerCamelCase_  (	self      :       Optional[int]     ):
          _lowerCamelCase				:   Optional[Any]								=   ids_tensor([self.batch_size, self.seq_length],self.vocab_size     )

          _lowerCamelCase				:   Union[str, Any]								=   None
          if self.use_input_mask:
                _lowerCamelCase				:   Any								=   random_attention_mask([self.batch_size, self.seq_length]     )

          _lowerCamelCase				:   Optional[int]								=   None
          if self.use_token_type_ids:
                _lowerCamelCase				:   List[str]								=   ids_tensor([self.batch_size, self.seq_length],self.type_vocab_size     )

          _lowerCamelCase				:   Optional[int]								=   None
          _lowerCamelCase				:   Tuple								=   None
          _lowerCamelCase				:   int								=   None
          if self.use_labels:
                _lowerCamelCase				:   Tuple								=   ids_tensor([self.batch_size],self.type_sequence_label_size     )
                _lowerCamelCase				:   Dict								=   ids_tensor([self.batch_size, self.seq_length],self.num_labels     )
                _lowerCamelCase				:   List[Any]								=   ids_tensor([self.batch_size],self.num_choices     )

          _lowerCamelCase				:   Any								=   self.get_config()

          return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels



    def   lowerCamelCase_  (	self      :       Optional[int]     ):
          return AlbertConfig(
              vocab_size=self.vocab_size,hidden_size=self.hidden_size,num_hidden_layers=self.num_hidden_layers,num_attention_heads=self.num_attention_heads,intermediate_size=self.intermediate_size,hidden_act=self.hidden_act,hidden_dropout_prob=self.hidden_dropout_prob,attention_probs_dropout_prob=self.attention_probs_dropout_prob,max_position_embeddings=self.max_position_embeddings,type_vocab_size=self.type_vocab_size,initializer_range=self.initializer_range,num_hidden_groups=self.num_hidden_groups,)



    def   lowerCamelCase_  (	self      :       Any,__A      :       Tuple,__A      :       List[str],__A      :       Union[str, Any],__A      :       int,__A      :       Union[str, Any],__A      :       Tuple,__A      :       Optional[Any]     ):
          _lowerCamelCase				:   Optional[Any]								=   AlbertModel(config=__A     )
          model.to(__A     )
          model.eval()
          _lowerCamelCase				:   Dict								=   model(__A,attention_mask=__A,token_type_ids=__A     )
          _lowerCamelCase				:   List[Any]								=   model(__A,token_type_ids=__A     )
          _lowerCamelCase				:   Tuple								=   model(__A     )
          self.parent.assertEqual(result.last_hidden_state.shape,(self.batch_size, self.seq_length, self.hidden_size)     )
          self.parent.assertEqual(result.pooler_output.shape,(self.batch_size, self.hidden_size)     )



    def   lowerCamelCase_  (	self      :       str,__A      :       str,__A      :       str,__A      :       int,__A      :       Tuple,__A      :       Any,__A      :       Optional[int],__A      :       Tuple     ):
          _lowerCamelCase				:   Optional[Any]								=   AlbertForPreTraining(config=__A     )
          model.to(__A     )
          model.eval()
          _lowerCamelCase				:   Optional[Any]								=   model(
              __A,attention_mask=__A,token_type_ids=__A,labels=__A,sentence_order_label=__A,)
          self.parent.assertEqual(result.prediction_logits.shape,(self.batch_size, self.seq_length, self.vocab_size)     )
          self.parent.assertEqual(result.sop_logits.shape,(self.batch_size, config.num_labels)     )



    def   lowerCamelCase_  (	self      :       Optional[Any],__A      :       List[str],__A      :       Union[str, Any],__A      :       str,__A      :       Optional[Any],__A      :       str,__A      :       Optional[int],__A      :       Dict     ):
          _lowerCamelCase				:   Tuple								=   AlbertForMaskedLM(config=__A     )
          model.to(__A     )
          model.eval()
          _lowerCamelCase				:   int								=   model(__A,attention_mask=__A,token_type_ids=__A,labels=__A     )
          self.parent.assertEqual(result.logits.shape,(self.batch_size, self.seq_length, self.vocab_size)     )



    def   lowerCamelCase_  (	self      :       Any,__A      :       Any,__A      :       Tuple,__A      :       Optional[Any],__A      :       Any,__A      :       Optional[Any],__A      :       Tuple,__A      :       Optional[Any]     ):
          _lowerCamelCase				:   Optional[int]								=   AlbertForQuestionAnswering(config=__A     )
          model.to(__A     )
          model.eval()
          _lowerCamelCase				:   Dict								=   model(
              __A,attention_mask=__A,token_type_ids=__A,start_positions=__A,end_positions=__A,)
          self.parent.assertEqual(result.start_logits.shape,(self.batch_size, self.seq_length)     )
          self.parent.assertEqual(result.end_logits.shape,(self.batch_size, self.seq_length)     )



    def   lowerCamelCase_  (	self      :       List[str],__A      :       Union[str, Any],__A      :       int,__A      :       Dict,__A      :       Tuple,__A      :       Union[str, Any],__A      :       List[Any],__A      :       str     ):
          _lowerCamelCase				:   Union[str, Any]								=   self.num_labels
          _lowerCamelCase				:   List[Any]								=   AlbertForSequenceClassification(__A     )
          model.to(__A     )
          model.eval()
          _lowerCamelCase				:   Dict								=   model(__A,attention_mask=__A,token_type_ids=__A,labels=__A     )
          self.parent.assertEqual(result.logits.shape,(self.batch_size, self.num_labels)     )



    def   lowerCamelCase_  (	self      :       str,__A      :       str,__A      :       Union[str, Any],__A      :       Optional[int],__A      :       Tuple,__A      :       Tuple,__A      :       List[str],__A      :       Optional[int]     ):
          _lowerCamelCase				:   Union[str, Any]								=   self.num_labels
          _lowerCamelCase				:   Dict								=   AlbertForTokenClassification(config=__A     )
          model.to(__A     )
          model.eval()
          _lowerCamelCase				:   List[Any]								=   model(__A,attention_mask=__A,token_type_ids=__A,labels=__A     )
          self.parent.assertEqual(result.logits.shape,(self.batch_size, self.seq_length, self.num_labels)     )



    def   lowerCamelCase_  (	self      :       Dict,__A      :       Union[str, Any],__A      :       List[str],__A      :       Tuple,__A      :       Dict,__A      :       Optional[int],__A      :       Dict,__A      :       Optional[int]     ):
          _lowerCamelCase				:   Any								=   self.num_choices
          _lowerCamelCase				:   str								=   AlbertForMultipleChoice(config=__A     )
          model.to(__A     )
          model.eval()
          _lowerCamelCase				:   Union[str, Any]								=   input_ids.unsqueeze(1     ).expand(-1,self.num_choices,-1     ).contiguous()
          _lowerCamelCase				:   Dict								=   token_type_ids.unsqueeze(1     ).expand(-1,self.num_choices,-1     ).contiguous()
          _lowerCamelCase				:   Optional[Any]								=   input_mask.unsqueeze(1     ).expand(-1,self.num_choices,-1     ).contiguous()
          _lowerCamelCase				:   Any								=   model(
              __A,attention_mask=__A,token_type_ids=__A,labels=__A,)
          self.parent.assertEqual(result.logits.shape,(self.batch_size, self.num_choices)     )



    def   lowerCamelCase_  (	self      :       List[str]     ):
          _lowerCamelCase				:   List[Any]								=   self.prepare_config_and_inputs()
          (
              (
              _lowerCamelCase
          )						,		(
              _lowerCamelCase
          )						,		(
              _lowerCamelCase
          )						,		(
              _lowerCamelCase
          )						,		(
              _lowerCamelCase
          )						,		(
              _lowerCamelCase
          )						,		(
              _lowerCamelCase
          )						,		
          )				:   str								=   config_and_inputs
          _lowerCamelCase				:   Optional[Any]								=   {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
          return config, inputs_dict


@require_torch
class  UpperCAmelCase__						(       A						,	A						,	unittest.TestCase ):
    lowerCAmelCase_				    =		(
        (
            AlbertModel,
            AlbertForPreTraining,
            AlbertForMaskedLM,
            AlbertForMultipleChoice,
            AlbertForSequenceClassification,
            AlbertForTokenClassification,
            AlbertForQuestionAnswering,
        )
        if is_torch_available()
        else ()
    )
    lowerCAmelCase_				    =		(
        {
            'feature-extraction': AlbertModel,
            'fill-mask': AlbertForMaskedLM,
            'question-answering': AlbertForQuestionAnswering,
            'text-classification': AlbertForSequenceClassification,
            'token-classification': AlbertForTokenClassification,
            'zero-shot': AlbertForSequenceClassification,
        }
        if is_torch_available()
        else {}
    )
    lowerCAmelCase_				    =		True



    def   lowerCamelCase_  (	self      :       Any,__A      :       List[Any],__A      :       List[str],__A      :       Tuple=False     ):
          _lowerCamelCase				:   int								=   super()._prepare_for_class(__A,__A,return_labels=__A     )

          if return_labels:
                if model_class in get_values(__A     ):
                      _lowerCamelCase				:   str								=   torch.zeros(
                          (self.model_tester.batch_size, self.model_tester.seq_length),dtype=torch.long,device=__A     )
                      _lowerCamelCase				:   List[Any]								=   torch.zeros(
                          self.model_tester.batch_size,dtype=torch.long,device=__A     )
          return inputs_dict



    def   lowerCamelCase_  (	self      :       Union[str, Any]     ):
          _lowerCamelCase				:   Dict								=   AlbertModelTester(self     )
          _lowerCamelCase				:   Dict								=   ConfigTester(self,config_class=__A,hidden_size=3_7     )



    def   lowerCamelCase_  (	self      :       Dict     ):
          self.config_tester.run_common_tests()



    def   lowerCamelCase_  (	self      :       int     ):
          _lowerCamelCase				:   Dict								=   self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*__A     )



    def   lowerCamelCase_  (	self      :       int     ):
          _lowerCamelCase				:   List[Any]								=   self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_pretraining(*__A     )



    def   lowerCamelCase_  (	self      :       List[Any]     ):
          _lowerCamelCase				:   Union[str, Any]								=   self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_masked_lm(*__A     )



    def   lowerCamelCase_  (	self      :       Union[str, Any]     ):
          _lowerCamelCase				:   List[str]								=   self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_multiple_choice(*__A     )



    def   lowerCamelCase_  (	self      :       int     ):
          _lowerCamelCase				:   List[Any]								=   self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_question_answering(*__A     )



    def   lowerCamelCase_  (	self      :       int     ):
          _lowerCamelCase				:   Tuple								=   self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_sequence_classification(*__A     )



    def   lowerCamelCase_  (	self      :       List[Any]     ):
          _lowerCamelCase				:   Dict								=   self.model_tester.prepare_config_and_inputs()
          for type in ["absolute", "relative_key", "relative_key_query"]:
                _lowerCamelCase				:   List[str]								=   type
                self.model_tester.create_and_check_model(*__A     )



    @slow
    def   lowerCamelCase_  (	self      :       Optional[int]     ):
          for model_name in ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                _lowerCamelCase				:   Dict								=   AlbertModel.from_pretrained(__A     )
                self.assertIsNotNone(__A     )


@require_torch
class  UpperCAmelCase__						(       unittest.TestCase ):



    @slow
    def   lowerCamelCase_  (	self      :       Any     ):
          _lowerCamelCase				:   str								=   AlbertModel.from_pretrained("albert-base-v2"     )
          _lowerCamelCase				:   Any								=   torch.tensor([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]]     )
          _lowerCamelCase				:   Union[str, Any]								=   torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]     )
          with torch.no_grad():
                _lowerCamelCase				:   Union[str, Any]								=   model(__A,attention_mask=__A     )[0]
          _lowerCamelCase				:   Optional[int]								=   torch.Size((1, 1_1, 7_6_8)     )
          self.assertEqual(output.shape,__A     )
          _lowerCamelCase				:   Dict								=   torch.tensor(
              [[[-0.6513, 1.5035, -0.2766], [-0.6515, 1.5046, -0.2780], [-0.6512, 1.5049, -0.2784]]]     )

          self.assertTrue(torch.allclose(output[:, 1:4, 1:4],__A,atol=1e-4     )     )

'''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 		A_       (	_lowerCAmelCase   :    int							):



      """simple docstring"""




      random.seed(_lowerCAmelCase							)
      np.random.seed(_lowerCAmelCase							)
      torch.manual_seed(_lowerCAmelCase							)
      torch.cuda.manual_seed_all(_lowerCAmelCase							)
      # ^^ safe to call this function even if cuda is not available



class  UpperCAmelCase__						:



    def __init__(	self      :       List[str],__A      :       Iterable[torch.nn.Parameter],__A      :       float = 0.9999,__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      :       Optional[Any],):

          if isinstance(__A,torch.nn.Module     ):
                _lowerCamelCase				:   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,)
                _lowerCamelCase				:   Dict								=   parameters.parameters()

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

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

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

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

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

          _lowerCamelCase				:   Tuple								=   None

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

          _lowerCamelCase				:   List[str]								=   model_cls
          _lowerCamelCase				:   Dict								=   model_config



    @classmethod
    def   lowerCamelCase_  (	cls      :       Union[str, Any],__A      :       List[str],__A      :       Optional[int]     ):
          _lowerCamelCase						,		_lowerCamelCase				:   Optional[int]								=   model_cls.load_config(__A,return_unused_kwargs=__A     )
          _lowerCamelCase				:   Optional[Any]								=   model_cls.from_pretrained(__A     )

          _lowerCamelCase				:   Union[str, Any]								=   cls(model.parameters(),model_cls=__A,model_config=model.config     )

          ema_model.load_state_dict(__A     )
          return ema_model



    def   lowerCamelCase_  (	self      :       str,__A      :       int     ):
          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__."     )

          _lowerCamelCase				:   Tuple								=   self.model_cls.from_config(self.model_config     )
          _lowerCamelCase				:   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      :       Optional[int],__A      :       int     ):
          _lowerCamelCase				:   str								=   max(0,optimization_step - self.update_after_step - 1     )

          if step <= 0:
                return 0.0

          if self.use_ema_warmup:
                _lowerCamelCase				:   Tuple								=   1 - (1 + step / self.inv_gamma) ** -self.power
          else:
                _lowerCamelCase				:   List[str]								=   (1 + step) / (1_0 + step)

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



    @torch.no_grad()
    def   lowerCamelCase_  (	self      :       Any,__A      :       Iterable[torch.nn.Parameter]     ):
          if isinstance(__A,torch.nn.Module     ):
                _lowerCamelCase				:   Dict								=   (
                    "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,)
                _lowerCamelCase				:   Any								=   parameters.parameters()

          _lowerCamelCase				:   str								=   list(__A     )

          self.optimization_step += 1

          # Compute the decay factor for the exponential moving average.
          _lowerCamelCase				:   Dict								=   self.get_decay(self.optimization_step     )
          _lowerCamelCase				:   Optional[Any]								=   decay
          _lowerCamelCase				:   List[Any]								=   1 - decay

          _lowerCamelCase				:   List[Any]								=   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():
                      _lowerCamelCase				:   List[Any]								=   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      :       Dict,__A      :       Iterable[torch.nn.Parameter]     ):
          _lowerCamelCase				:   Tuple								=   list(__A     )
          for s_param, param in zip(self.shadow_params,__A     ):
                param.data.copy_(s_param.to(param.device     ).data     )



    def   lowerCamelCase_  (	self      :       List[str],__A      :       Dict=None,__A      :       Any=None     ):
          _lowerCamelCase				:   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      :       List[Any]     ):
          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      :       Tuple,__A      :       Iterable[torch.nn.Parameter]     ):
          _lowerCamelCase				:   Tuple								=   [param.detach().cpu().clone() for param in parameters]



    def   lowerCamelCase_  (	self      :       int,__A      :       Iterable[torch.nn.Parameter]     ):
          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.
          _lowerCamelCase				:   List[str]								=   None



    def   lowerCamelCase_  (	self      :       Dict,__A      :       dict     ):
          _lowerCamelCase				:   List[str]								=   copy.deepcopy(__A     )

          _lowerCamelCase				:   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"     )

          _lowerCamelCase				:   Dict								=   state_dict.get("min_decay",self.min_decay     )
          if not isinstance(self.min_decay,__A     ):
                raise ValueError("Invalid min_decay"     )

          _lowerCamelCase				:   Tuple								=   state_dict.get("optimization_step",self.optimization_step     )
          if not isinstance(self.optimization_step,__A     ):
                raise ValueError("Invalid optimization_step"     )

          _lowerCamelCase				:   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"     )

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

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

          _lowerCamelCase				:   Union[str, Any]								=   state_dict.get("power",self.power     )
          if not isinstance(self.power,(float, int)     ):
                raise ValueError("Invalid power"     )

          _lowerCamelCase				:   Optional[Any]								=   state_dict.get("shadow_params",__A     )
          if shadow_params is not None:
                _lowerCamelCase				:   str								=   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"     )