



'''simple docstring'''






import os
import re
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


UpperCAmelCase_       :   str   =       logging.get_logger(__name__)

UpperCAmelCase_       :   Dict   =       {
    'vocab_file': 'vocab.txt',
    'merges_file': 'bpe.codes',
}

UpperCAmelCase_       :   Tuple   =       {
    'vocab_file': {
        'vinai/phobert-base': 'https://huggingface.co/vinai/phobert-base/resolve/main/vocab.txt',
        'vinai/phobert-large': 'https://huggingface.co/vinai/phobert-large/resolve/main/vocab.txt',
    },
    'merges_file': {
        'vinai/phobert-base': 'https://huggingface.co/vinai/phobert-base/resolve/main/bpe.codes',
        'vinai/phobert-large': 'https://huggingface.co/vinai/phobert-large/resolve/main/bpe.codes',
    },
}

UpperCAmelCase_       :   str   =       {
    'vinai/phobert-base': 256,
    'vinai/phobert-large': 256,
}







def 		A_       (	_lowerCAmelCase   :    Dict							):



      """simple docstring"""




      _lowerCamelCase				:   Tuple								=   set()
      _lowerCamelCase				:   Tuple								=   word[0]
      for char in word[1:]:
            pairs.add((prev_char, char)							)
            _lowerCamelCase				:   Optional[Any]								=   char

      _lowerCamelCase				:   Optional[Any]								=   set(lowerCAmelCase_							)
      return pairs



class  UpperCAmelCase__						(       __lowerCamelCase ):
    lowerCAmelCase_				    =		VOCAB_FILES_NAMES
    lowerCAmelCase_				    =		PRETRAINED_VOCAB_FILES_MAP
    lowerCAmelCase_				    =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES



    def __init__(	self      :       Optional[Any],__A      :       Optional[Any],__A      :       List[str],__A      :       Any="<s>",__A      :       Dict="</s>",__A      :       int="</s>",__A      :       List[Any]="<s>",__A      :       Any="<unk>",__A      :       str="<pad>",__A      :       Any="<mask>",**__A      :       Optional[Any],):
          super().__init__(
              bos_token=a_,eos_token=a_,unk_token=a_,sep_token=a_,cls_token=a_,pad_token=a_,mask_token=a_,**a_,)

          _lowerCamelCase				:   Optional[int]								=   vocab_file
          _lowerCamelCase				:   Any								=   merges_file

          _lowerCamelCase				:   Any								=   {}
          _lowerCamelCase				:   Any								=   0
          _lowerCamelCase				:   Union[str, Any]								=   1
          _lowerCamelCase				:   Tuple								=   2
          _lowerCamelCase				:   List[str]								=   3

          self.add_from_file(a_     )

          _lowerCamelCase				:   Optional[int]								=   {v: k for k, v in self.encoder.items()}

          with open(a_,encoding="utf-8"     ) as merges_handle:
                _lowerCamelCase				:   Optional[Any]								=   merges_handle.read().split("\n"     )[:-1]
          _lowerCamelCase				:   Tuple								=   [tuple(merge.split()[:-1]     ) for merge in merges]
          _lowerCamelCase				:   Tuple								=   dict(zip(a_,range(len(a_     )     )     )     )
          _lowerCamelCase				:   List[str]								=   {}



    def   lowerCamelCase_  (	self      :       Tuple,__A      :       List[str],__A      :       Union[str, Any] = None     ):

          if token_ids_a is None:
                return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
          _lowerCamelCase				:   Any								=   [self.cls_token_id]
          _lowerCamelCase				:   Union[str, Any]								=   [self.sep_token_id]
          return cls + token_ids_a + sep + sep + token_ids_a + sep



    def   lowerCamelCase_  (	self      :       Union[str, Any],__A      :       int,__A      :       int = None,__A      :       Dict = False     ):

          if already_has_special_tokens:
                return super().get_special_tokens_mask(
                    token_ids_a=a_,token_ids_a=a_,already_has_special_tokens=a_     )

          if token_ids_a is None:
                return [1] + ([0] * len(a_     )) + [1]
          return [1] + ([0] * len(a_     )) + [1, 1] + ([0] * len(a_     )) + [1]



    def   lowerCamelCase_  (	self      :       Any,__A      :       int,__A      :       str = None     ):
          _lowerCamelCase				:   Optional[int]								=   [self.sep_token_id]
          _lowerCamelCase				:   List[Any]								=   [self.cls_token_id]

          if token_ids_a is None:
                return len(cls + token_ids_a + sep     ) * [0]
          return len(cls + token_ids_a + sep + sep + token_ids_a + sep     ) * [0]



    @property
    def   lowerCamelCase_  (	self      :       Optional[Any]     ):
          return len(self.encoder     )



    def   lowerCamelCase_  (	self      :       str     ):
          return dict(self.encoder,**self.added_tokens_encoder     )



    def   lowerCamelCase_  (	self      :       Tuple,__A      :       List[str]     ):
          if token in self.cache:
                return self.cache[token]
          _lowerCamelCase				:   Tuple								=   tuple(a_     )
          _lowerCamelCase				:   int								=   tuple(list(word[:-1]     ) + [word[-1] + "</w>"]     )
          _lowerCamelCase				:   List[Any]								=   get_pairs(a_     )

          if not pairs:
                return token

          while True:
                _lowerCamelCase				:   Tuple								=   min(a_,key=lambda __A     : self.bpe_ranks.get(a_,float("inf"     )     )     )
                if bigram not in self.bpe_ranks:
                      break
                _lowerCamelCase				:   Any								=   bigram
                _lowerCamelCase				:   int								=   []
                _lowerCamelCase				:   Optional[Any]								=   0
                while i < len(a_     ):
                      try:
                            _lowerCamelCase				:   Tuple								=   word.index(a_,a_     )
                      except ValueError:
                            new_word.extend(word[i:]     )
                            break
                      else:
                            new_word.extend(word[i:j]     )
                            _lowerCamelCase				:   Dict								=   j

                      if word[i] == first and i < len(a_     ) - 1 and word[i + 1] == second:
                            new_word.append(first + second     )
                            i += 2
                      else:
                            new_word.append(word[i]     )
                            i += 1
                _lowerCamelCase				:   Optional[Any]								=   tuple(a_     )
                _lowerCamelCase				:   List[Any]								=   new_word
                if len(a_     ) == 1:
                      break
                else:
                      _lowerCamelCase				:   Union[str, Any]								=   get_pairs(a_     )
          _lowerCamelCase				:   Tuple								=   "@@ ".join(a_     )
          _lowerCamelCase				:   Union[str, Any]								=   word[:-4]
          _lowerCamelCase				:   Any								=   word
          return word



    def   lowerCamelCase_  (	self      :       Tuple,__A      :       Optional[Any]     ):
          _lowerCamelCase				:   str								=   []

          _lowerCamelCase				:   Dict								=   re.findall(r"\S+\n?",a_     )

          for token in words:
                split_tokens.extend(list(self.bpe(a_     ).split(" "     )     )     )
          return split_tokens



    def   lowerCamelCase_  (	self      :       Optional[int],__A      :       Optional[Any]     ):
          return self.encoder.get(a_,self.encoder.get(self.unk_token     )     )



    def   lowerCamelCase_  (	self      :       List[Any],__A      :       Dict     ):
          return self.decoder.get(a_,self.unk_token     )



    def   lowerCamelCase_  (	self      :       List[Any],__A      :       List[str]     ):
          _lowerCamelCase				:   Any								=   " ".join(a_     ).replace("@@ ",""     ).strip()
          return out_string



    def   lowerCamelCase_  (	self      :       Tuple,__A      :       List[Any],__A      :       str = None     ):
          if not os.path.isdir(a_     ):
                logger.error(f'Vocabulary path ({save_directory}) should be a directory'     )
                return
          _lowerCamelCase				:   Optional[int]								=   os.path.join(
              a_,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]     )
          _lowerCamelCase				:   Optional[Any]								=   os.path.join(
              a_,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]     )

          if os.path.abspath(self.vocab_file     ) != os.path.abspath(a_     ):
                copyfile(self.vocab_file,a_     )

          if os.path.abspath(self.merges_file     ) != os.path.abspath(a_     ):
                copyfile(self.merges_file,a_     )

          return out_vocab_file, out_merge_file



    def   lowerCamelCase_  (	self      :       Optional[int],__A      :       Any     ):
          if isinstance(a_,a_     ):
                try:
                      with open(a_,"r",encoding="utf-8"     ) as fd:
                            self.add_from_file(a_     )
                except FileNotFoundError as fnfe:
                      raise fnfe
                except UnicodeError:
                      raise Exception(f'Incorrect encoding detected in {f}, please rebuild the dataset'     )
                return

          _lowerCamelCase				:   Tuple								=   f.readlines()
          for lineTmp in lines:
                _lowerCamelCase				:   Dict								=   lineTmp.strip()
                _lowerCamelCase				:   Optional[Any]								=   line.rfind(" "     )
                if idx == -1:
                      raise ValueError("Incorrect dictionary format, expected '<token> <cnt>'"     )
                _lowerCamelCase				:   Any								=   line[:idx]
                _lowerCamelCase				:   Optional[Any]								=   len(self.encoder     )

'''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"     )