





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 							SCREAMING_SNAKE_CASE__       (  snake_case__  :int     )      ->						int:
  random.seed(snake_case__     )
  np.random.seed(snake_case__     )
  torch.manual_seed(snake_case__     )
  torch.cuda.manual_seed_all(snake_case__     )
  # ^^ safe to call this function even if cuda is not available







class 				A_				:







       """simple docstring"""





       def __init__(    self	:	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[int]						,)						->					List[Any]:

         if isinstance(__A						,torch.nn.Module  ):
           _lowercase							= (
               '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						,)
           _lowercase							= parameters.parameters()

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

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

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

         _lowercase							= list(__A  )
         _lowercase							= [p.clone().detach() for p in parameters]

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

         _lowercase							= None

         _lowercase							= decay
         _lowercase							= min_decay
         _lowercase							= update_after_step
         _lowercase							= use_ema_warmup
         _lowercase							= inv_gamma
         _lowercase							= power
         _lowercase							= 0
         _lowercase							= None  # set in `step()`

         _lowercase							= model_cls
         _lowercase							= model_config







       @classmethod
       def    __UpperCAmelCase (    cls	:	int						,__A	:	Any						,__A	:	Optional[Any]  )						->					"EMAModel":
         _lowercase						,		_lowercase							= model_cls.load_config(__A						,return_unused_kwargs=__A  )
         _lowercase							= model_cls.from_pretrained(__A  )

         _lowercase							= cls(model.parameters()						,model_cls=__A						,model_config=model.config  )

         ema_model.load_state_dict(__A  )
         return ema_model







       def    __UpperCAmelCase (    self	:	Tuple						,__A	:	Union[str, Any]  )						->					Optional[Any]:
         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__.'  )

         _lowercase							= self.model_cls.from_config(self.model_config  )
         _lowercase							= self.state_dict()
         state_dict.pop('shadow_params'						,__A  )

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







       def    __UpperCAmelCase (    self	:	Any						,__A	:	int  )						->					float:
         _lowercase							= max(0						,optimization_step - self.update_after_step - 1  )

         if step <= 0:
           return 0.0

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

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







       @torch.no_grad()
       def    __UpperCAmelCase (    self	:	Tuple						,__A	:	Iterable[torch.nn.Parameter]  )						->					List[Any]:
         if isinstance(__A						,torch.nn.Module  ):
           _lowercase							= (
               '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						,)
           _lowercase							= parameters.parameters()

         _lowercase							= list(__A  )

         self.optimization_step += 1

         # Compute the decay factor for the exponential moving average.
         _lowercase							= self.get_decay(self.optimization_step  )
         _lowercase							= decay
         _lowercase							= 1 - decay

         _lowercase							= 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():
             _lowercase							= 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    __UpperCAmelCase (    self	:	Tuple						,__A	:	Iterable[torch.nn.Parameter]  )						->					None:
         _lowercase							= list(__A  )
         for s_param, param in zip(self.shadow_params						,__A  ):
           param.data.copy_(s_param.to(param.device  ).data  )







       def    __UpperCAmelCase (    self	:	int						,__A	:	int=None						,__A	:	Optional[int]=None  )						->					None:
         _lowercase							= [
             p.to(device=__A						,dtype=__A  ) if p.is_floating_point() else p.to(device=__A  )
             for p in self.shadow_params
         ]







       def    __UpperCAmelCase (    self	:	str  )						->					dict:
         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    __UpperCAmelCase (    self	:	List[Any]						,__A	:	Iterable[torch.nn.Parameter]  )						->					None:
         _lowercase							= [param.detach().cpu().clone() for param in parameters]







       def    __UpperCAmelCase (    self	:	Any						,__A	:	Iterable[torch.nn.Parameter]  )						->					None:
         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.
         _lowercase							= None







       def    __UpperCAmelCase (    self	:	List[Any]						,__A	:	dict  )						->					None:
         _lowercase							= copy.deepcopy(__A  )

         _lowercase							= 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'  )

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

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

         _lowercase							= state_dict.get('update_after_step'						,self.update_after_step  )
         if not isinstance(self.update_after_step						,__A  ):
           raise ValueError('Invalid update_after_step'  )

         _lowercase							= state_dict.get('use_ema_warmup'						,self.use_ema_warmup  )
         if not isinstance(self.use_ema_warmup						,__A  ):
           raise ValueError('Invalid use_ema_warmup'  )

         _lowercase							= state_dict.get('inv_gamma'						,self.inv_gamma  )
         if not isinstance(self.inv_gamma						,(float, int)  ):
           raise ValueError('Invalid inv_gamma'  )

         _lowercase							= state_dict.get('power'						,self.power  )
         if not isinstance(self.power						,(float, int)  ):
           raise ValueError('Invalid power'  )

         _lowercase							= state_dict.get('shadow_params'						,__A  )
         if shadow_params is not None:
           _lowercase							= 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 typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tensorflow_text_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


snake_case 				=       {
    """configuration_bert""": ["""BERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BertConfig""", """BertOnnxConfig"""],
    """tokenization_bert""": ["""BasicTokenizer""", """BertTokenizer""", """WordpieceTokenizer"""],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      snake_case 				=       ["""BertTokenizerFast"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      snake_case 				=       [
          """BERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """BertForMaskedLM""",
          """BertForMultipleChoice""",
          """BertForNextSentencePrediction""",
          """BertForPreTraining""",
          """BertForQuestionAnswering""",
          """BertForSequenceClassification""",
          """BertForTokenClassification""",
          """BertLayer""",
          """BertLMHeadModel""",
          """BertModel""",
          """BertPreTrainedModel""",
          """load_tf_weights_in_bert""",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      snake_case 				=       [
          """TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """TFBertEmbeddings""",
          """TFBertForMaskedLM""",
          """TFBertForMultipleChoice""",
          """TFBertForNextSentencePrediction""",
          """TFBertForPreTraining""",
          """TFBertForQuestionAnswering""",
          """TFBertForSequenceClassification""",
          """TFBertForTokenClassification""",
          """TFBertLMHeadModel""",
          """TFBertMainLayer""",
          """TFBertModel""",
          """TFBertPreTrainedModel""",
      ]
try:
      if not is_tensorflow_text_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      snake_case 				=       ["""TFBertTokenizer"""]

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      snake_case 				=       [
          """FlaxBertForCausalLM""",
          """FlaxBertForMaskedLM""",
          """FlaxBertForMultipleChoice""",
          """FlaxBertForNextSentencePrediction""",
          """FlaxBertForPreTraining""",
          """FlaxBertForQuestionAnswering""",
          """FlaxBertForSequenceClassification""",
          """FlaxBertForTokenClassification""",
          """FlaxBertModel""",
          """FlaxBertPreTrainedModel""",
      ]

if TYPE_CHECKING:
      from .configuration_bert import BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, BertConfig, BertOnnxConfig
      from .tokenization_bert import BasicTokenizer, BertTokenizer, WordpieceTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_bert_fast import BertTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_bert import (
                BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                BertForMaskedLM,
                BertForMultipleChoice,
                BertForNextSentencePrediction,
                BertForPreTraining,
                BertForQuestionAnswering,
                BertForSequenceClassification,
                BertForTokenClassification,
                BertLayer,
                BertLMHeadModel,
                BertModel,
                BertPreTrainedModel,
                load_tf_weights_in_bert,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_bert import (
                TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFBertEmbeddings,
                TFBertForMaskedLM,
                TFBertForMultipleChoice,
                TFBertForNextSentencePrediction,
                TFBertForPreTraining,
                TFBertForQuestionAnswering,
                TFBertForSequenceClassification,
                TFBertForTokenClassification,
                TFBertLMHeadModel,
                TFBertMainLayer,
                TFBertModel,
                TFBertPreTrainedModel,
            )

      try:
            if not is_tensorflow_text_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_bert_tf import TFBertTokenizer

      try:
            if not is_flax_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flax_bert import (
                FlaxBertForCausalLM,
                FlaxBertForMaskedLM,
                FlaxBertForMultipleChoice,
                FlaxBertForNextSentencePrediction,
                FlaxBertForPreTraining,
                FlaxBertForQuestionAnswering,
                FlaxBertForSequenceClassification,
                FlaxBertForTokenClassification,
                FlaxBertModel,
                FlaxBertPreTrainedModel,
            )

else:
      import sys

      snake_case 				=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)