


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 				__lowerCamelCase						( UpperCAmelCase_						:		int						):




      """simple docstring"""





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







class 		_snake_case  :




 def __init__(		self					,  _lowerCamelCase					,  _lowerCamelCase = 0.9999					,  _lowerCamelCase = 0.0					,  _lowerCamelCase = 0					,  _lowerCamelCase = False					,  _lowerCamelCase = 1.0					,  _lowerCamelCase = 2 / 3					,  _lowerCamelCase = None					,  _lowerCamelCase = None					,  **_lowerCamelCase					,  ):

       if isinstance(_lowerCamelCase					,  torch.nn.Module    ):
             a						:Dict 							=       (
                 '''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					,  )
             a						:Optional[int] 							=       parameters.parameters()

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

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

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

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

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

       a						:Optional[Any] 							=       None

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

       a						:int 							=       model_cls
       a						:Union[str, Any] 							=       model_config




 @classmethod
 def        SCREAMING_SNAKE_CASE__				(		cls					,  _lowerCamelCase					,  _lowerCamelCase    ):
       a							,				a						:int 							=       model_cls.load_config(_lowerCamelCase					,  return_unused_kwargs=_lowerCamelCase    )
       a						:List[str] 							=       model_cls.from_pretrained(_lowerCamelCase    )

       a						:Dict 							=       cls(model.parameters()					,  model_cls=_lowerCamelCase					,  model_config=model.config    )

       ema_model.load_state_dict(_lowerCamelCase    )
       return ema_model




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase    ):
       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						:List[Any] 							=       self.model_cls.from_config(self.model_config    )
       a						:Optional[int] 							=       self.state_dict()
       state_dict.pop('''shadow_params'''					,  _lowerCamelCase    )

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




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase    ):
       a						:List[Any] 							=       max(0					,  optimization_step - self.update_after_step - 1    )

       if step <= 0:
             return 0.0

       if self.use_ema_warmup:
             a						:Optional[Any] 							=       1 - (1 + step / self.inv_gamma) ** -self.power
       else:
             a						:Optional[Any] 							=       (1 + step) / (10 + step)

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




 @torch.no_grad()
 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase    ):
       if isinstance(_lowerCamelCase					,  torch.nn.Module    ):
             a						:int 							=       (
                 '''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					,  )
             a						:Tuple 							=       parameters.parameters()

       a						:Union[str, Any] 							=       list(_lowerCamelCase    )

       self.optimization_step += 1

       # Compute the decay factor for the exponential moving average.
       a						:Dict 							=       self.get_decay(self.optimization_step    )
       a						:List[str] 							=       decay
       a						:Tuple 							=       1 - decay

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




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase=None					,  _lowerCamelCase=None    ):
       a						:List[Any] 							=       [
           p.to(device=_lowerCamelCase					,  dtype=_lowerCamelCase    ) if p.is_floating_point() else p.to(device=_lowerCamelCase    )
           for p in self.shadow_params
       ]




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       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        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase    ):
       a						:List[Any] 							=       [param.detach().cpu().clone() for param in parameters]




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase    ):
       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.
       a						:Union[str, Any] 							=       None




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase    ):
       a						:str 							=       copy.deepcopy(_lowerCamelCase    )

       a						:str 							=       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						:str 							=       state_dict.get('''min_decay'''					,  self.min_decay    )
       if not isinstance(self.min_decay					,  _lowerCamelCase    ):
             raise ValueError('''Invalid min_decay'''    )

       a						:Optional[Any] 							=       state_dict.get('''optimization_step'''					,  self.optimization_step    )
       if not isinstance(self.optimization_step					,  _lowerCamelCase    ):
             raise ValueError('''Invalid optimization_step'''    )

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

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

       a						:List[str] 							=       state_dict.get('''shadow_params'''					,  _lowerCamelCase    )
       if shadow_params is not None:
             a						:Optional[Any] 							=       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'''    )













import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer


lowercase  							=					logging.get_logger(__name__)

lowercase  							=					{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

lowercase  							=					{
    "vocab_file": {
        "squeezebert/squeezebert-uncased": (
            "https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/vocab.txt"
        ),
        "squeezebert/squeezebert-mnli": "https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/vocab.txt",
        "squeezebert/squeezebert-mnli-headless": (
            "https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "squeezebert/squeezebert-uncased": (
            "https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/tokenizer.json"
        ),
        "squeezebert/squeezebert-mnli": (
            "https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/tokenizer.json"
        ),
        "squeezebert/squeezebert-mnli-headless": (
            "https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/tokenizer.json"
        ),
    },
}

lowercase  							=					{
    "squeezebert/squeezebert-uncased": 512,
    "squeezebert/squeezebert-mnli": 512,
    "squeezebert/squeezebert-mnli-headless": 512,
}


lowercase  							=					{
    "squeezebert/squeezebert-uncased": {"do_lower_case": True},
    "squeezebert/squeezebert-mnli": {"do_lower_case": True},
    "squeezebert/squeezebert-mnli-headless": {"do_lower_case": True},
}




class  UpperCamelCase_      (			snake_case_				):



	'''simple docstring'''




	lowerCAmelCase													= VOCAB_FILES_NAMES
	lowerCAmelCase													= PRETRAINED_VOCAB_FILES_MAP
	lowerCAmelCase													= PRETRAINED_INIT_CONFIGURATION
	lowerCAmelCase													= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	lowerCAmelCase													= SqueezeBertTokenizer




	def __init__(       self       ,			a=None       ,			a=None       ,			a=True       ,			a="[UNK]"       ,			a="[SEP]"       ,			a="[PAD]"       ,			a="[CLS]"       ,			a="[MASK]"       ,			a=True       ,			a=None       ,			**a       ,			)       ->    Tuple:
								super().__init__(
								    a       ,			tokenizer_file=a       ,			do_lower_case=a       ,			unk_token=a       ,			sep_token=a       ,			pad_token=a       ,			cls_token=a       ,			mask_token=a       ,			tokenize_chinese_chars=a       ,			strip_accents=a       ,			**a       ,			)

								snake_case_				=							json.loads(self.backend_tokenizer.normalizer.__getstate__()					)
								if (
								    normalizer_state.get('lowercase'       ,			a					) != do_lower_case
								    or normalizer_state.get('strip_accents'       ,			a					) != strip_accents
								    or normalizer_state.get('handle_chinese_chars'       ,			a					) != tokenize_chinese_chars
								):
															snake_case_				=							getattr(a       ,			normalizer_state.pop('type'					)					)
															snake_case_				=							do_lower_case
															snake_case_				=							strip_accents
															snake_case_				=							tokenize_chinese_chars
															snake_case_				=							normalizer_class(**a					)

								snake_case_				=							do_lower_case




	def 							_UpperCamelCase			(       self       ,			a       ,			a=None					)       ->    Tuple:
								snake_case_				=							[self.cls_token_id] + token_ids_a + [self.sep_token_id]

								if token_ids_a:
															output += token_ids_a + [self.sep_token_id]

								return output




	def 							_UpperCamelCase			(       self       ,			a       ,			a = None					)       ->    List[int]:
								snake_case_				=							[self.sep_token_id]
								snake_case_				=							[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					) * [0] + len(token_ids_a + sep					) * [1]






	def 							_UpperCamelCase			(       self       ,			a       ,			a = None					)       ->    Tuple[str]:
								snake_case_				=							self._tokenizer.model.save(a       ,			name=a					)
								return tuple(a					)





