"""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   (		A      ):






     '''simple docstring'''



     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__    :


     def __init__(     self,				_UpperCAmelCase,				_UpperCAmelCase = 0.9_999,				_UpperCAmelCase = 0.0,				_UpperCAmelCase = 0,				_UpperCAmelCase = False,				_UpperCAmelCase = 1.0,				_UpperCAmelCase = 2 / 3,				_UpperCAmelCase = None,				_UpperCAmelCase = None,				**_UpperCAmelCase,				):
          '''simple docstring'''



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

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

          lowercase__				 =							list(lowerCAmelCase_  )
          lowercase__				 =							[p.clone().detach() for p in parameters]

          if kwargs.get("device",				lowerCAmelCase_  ) is not None:
               lowercase__				 =							"The `device` argument is deprecated. Please use `to` instead."
               deprecate("device",				"1.0.0",				lowerCAmelCase_,				standard_warn=lowerCAmelCase_  )
               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 	snake_case__	(     cls,				_UpperCAmelCase,				_UpperCAmelCase  ):
          '''simple docstring'''



          lowercase__ ,						lowercase__				 =							model_cls.load_config(lowerCAmelCase_,				return_unused_kwargs=lowerCAmelCase_  )
          lowercase__				 =							model_cls.from_pretrained(lowerCAmelCase_  )

          lowercase__				 =							cls(model.parameters(),				model_cls=lowerCAmelCase_,				model_config=model.config  )

          ema_model.load_state_dict(lowerCAmelCase_  )
          return ema_model


     def 	snake_case__	(     self,				_UpperCAmelCase  ):
          '''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__."  )

          lowercase__				 =							self.model_cls.from_config(self.model_config  )
          lowercase__				 =							self.state_dict()
          state_dict.pop("shadow_params",				lowerCAmelCase_  )

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


     def 	snake_case__	(     self,				_UpperCAmelCase  ):
          '''simple docstring'''



          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(lowerCAmelCase_,				self.decay  )
          # make sure decay is not smaller than min_decay
          lowercase__				 =							max(lowerCAmelCase_,				self.min_decay  )
          return cur_decay_value


     @torch.no_grad()
     def 	snake_case__	(     self,				_UpperCAmelCase  ):
          '''simple docstring'''



          if isinstance(lowerCAmelCase_,				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",				lowerCAmelCase_,				standard_warn=lowerCAmelCase_,				)
               lowercase__				 =							parameters.parameters()

          lowercase__				 =							list(lowerCAmelCase_  )

          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,				lowerCAmelCase_  ):
               if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
                    lowercase__				 =							deepspeed.zero.GatheredParameters(lowerCAmelCase_,				modifier_rank=lowerCAmelCase_  )

               with context_manager():
                    if param.requires_grad:
                         s_param.sub_(one_minus_decay * (s_param - param)  )
                    else:
                         s_param.copy_(lowerCAmelCase_  )


     def 	snake_case__	(     self,				_UpperCAmelCase  ):
          '''simple docstring'''



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


     def 	snake_case__	(     self,				_UpperCAmelCase=None,				_UpperCAmelCase=None  ):
          '''simple docstring'''



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


     def 	snake_case__	(     self  ):
          '''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 	snake_case__	(     self,				_UpperCAmelCase  ):
          '''simple docstring'''



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


     def 	snake_case__	(     self,				_UpperCAmelCase  ):
          '''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,				lowerCAmelCase_  ):
               param.data.copy_(c_param.data  )

          # Better memory-wise.
          lowercase__				 =							None


     def 	snake_case__	(     self,				_UpperCAmelCase  ):
          '''simple docstring'''



          lowercase__				 =							copy.deepcopy(lowerCAmelCase_  )

          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,				lowerCAmelCase_  ):
               raise ValueError("Invalid min_decay"  )

          lowercase__				 =							state_dict.get("optimization_step",				self.optimization_step  )
          if not isinstance(self.optimization_step,				lowerCAmelCase_  ):
               raise ValueError("Invalid optimization_step"  )

          lowercase__				 =							state_dict.get("update_after_step",				self.update_after_step  )
          if not isinstance(self.update_after_step,				lowerCAmelCase_  ):
               raise ValueError("Invalid update_after_step"  )

          lowercase__				 =							state_dict.get("use_ema_warmup",				self.use_ema_warmup  )
          if not isinstance(self.use_ema_warmup,				lowerCAmelCase_  ):
               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",				lowerCAmelCase_  )
          if shadow_params is not None:
               lowercase__				 =							shadow_params
               if not isinstance(self.shadow_params,				lowerCAmelCase_  ):
                    raise ValueError("shadow_params must be a list"  )
               if not all(isinstance(lowerCAmelCase_,				torch.Tensor  ) for p in self.shadow_params  ):
                    raise ValueError("shadow_params must all be Tensors"  )





"""simple docstring"""


from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase_:					List[Any]     =					logging.get_logger(__name__)

lowerCAmelCase_:					int     =					{
    "microsoft/markuplm-base": "https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json",
    "microsoft/markuplm-large": "https://huggingface.co/microsoft/markuplm-large/resolve/main/config.json",
}






class    a__    (   _a	):
   snake_case_							=      "markuplm"


   def __init__(     self,				_UpperCAmelCase=3_0522,				_UpperCAmelCase=768,				_UpperCAmelCase=12,				_UpperCAmelCase=12,				_UpperCAmelCase=3072,				_UpperCAmelCase="gelu",				_UpperCAmelCase=0.1,				_UpperCAmelCase=0.1,				_UpperCAmelCase=512,				_UpperCAmelCase=2,				_UpperCAmelCase=0.02,				_UpperCAmelCase=1E-12,				_UpperCAmelCase=0,				_UpperCAmelCase=0,				_UpperCAmelCase=2,				_UpperCAmelCase=256,				_UpperCAmelCase=1024,				_UpperCAmelCase=216,				_UpperCAmelCase=1001,				_UpperCAmelCase=32,				_UpperCAmelCase=50,				_UpperCAmelCase="absolute",				_UpperCAmelCase=True,				_UpperCAmelCase=None,				**_UpperCAmelCase,				):
        '''simple docstring'''



        super().__init__(
            pad_token_id=_UpperCAmelCase,				bos_token_id=_UpperCAmelCase,				eos_token_id=_UpperCAmelCase,				**_UpperCAmelCase,				)
        lowercase__				 =							vocab_size
        lowercase__				 =							hidden_size
        lowercase__				 =							num_hidden_layers
        lowercase__				 =							num_attention_heads
        lowercase__				 =							hidden_act
        lowercase__				 =							intermediate_size
        lowercase__				 =							hidden_dropout_prob
        lowercase__				 =							attention_probs_dropout_prob
        lowercase__				 =							max_position_embeddings
        lowercase__				 =							type_vocab_size
        lowercase__				 =							initializer_range
        lowercase__				 =							layer_norm_eps
        lowercase__				 =							position_embedding_type
        lowercase__				 =							use_cache
        lowercase__				 =							classifier_dropout
        # additional properties
        lowercase__				 =							max_depth
        lowercase__				 =							max_xpath_tag_unit_embeddings
        lowercase__				 =							max_xpath_subs_unit_embeddings
        lowercase__				 =							tag_pad_id
        lowercase__				 =							subs_pad_id
        lowercase__				 =							xpath_unit_hidden_size




