




"""simple docstring"""

import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

from filelock import FileLock

from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available


a         =      logging.getLogger(__name__)





@dataclass
class     lowercase_	:



 '''simple docstring'''
 UpperCAmelCase   :					str
 UpperCAmelCase   :					List[str]
 UpperCAmelCase   :					Optional[List[str]]





@dataclass
class     lowercase_	:



 '''simple docstring'''
 UpperCAmelCase   :					List[int]
 UpperCAmelCase   :					List[int]
 UpperCAmelCase   :					Optional[List[int]]				  =     None
 UpperCAmelCase   :					Optional[List[int]]				  =     None





class     lowercase_	(					__lowerCAmelCase							):



 '''simple docstring'''
 UpperCAmelCase   :					Any				  =     '''train'''
 UpperCAmelCase   :					Tuple				  =     '''dev'''
 UpperCAmelCase   :					int				  =     '''test'''





class     lowercase_	:



 '''simple docstring'''

 @staticmethod
 def       lowerCAmelCase_							(			_UpperCAmelCase	:	int   ,							_UpperCAmelCase	:	Union[Split, str]		):
       raise NotImplementedError

 @staticmethod
 def       lowerCAmelCase_							(			_UpperCAmelCase	:	str		):
       raise NotImplementedError



 @staticmethod
 def       lowerCAmelCase_							(			_UpperCAmelCase	:	List[InputExample]   ,							_UpperCAmelCase	:	List[str]   ,							_UpperCAmelCase	:	int   ,							_UpperCAmelCase	:	PreTrainedTokenizer   ,							_UpperCAmelCase	:	List[str]=False   ,							_UpperCAmelCase	:	List[str]="[CLS]"   ,							_UpperCAmelCase	:	List[Any]=1   ,							_UpperCAmelCase	:	Tuple="[SEP]"   ,							_UpperCAmelCase	:	List[str]=False   ,							_UpperCAmelCase	:	Optional[Any]=False   ,							_UpperCAmelCase	:	str=0   ,							_UpperCAmelCase	:	Optional[int]=0   ,							_UpperCAmelCase	:	Any=-100   ,							_UpperCAmelCase	:	Union[str, Any]=0   ,							_UpperCAmelCase	:	List[Any]=True   ,							):
       _A        =       {label: i for i, label in enumerate(_UpperCAmelCase		)}

       _A        =       []
       for ex_index, example in enumerate(_UpperCAmelCase		):
             if ex_index % 10_000 == 0:
                   logger.info('Writing example %d of %d'   ,							_UpperCAmelCase   ,							len(_UpperCAmelCase		)		)

             _A        =       []
             _A        =       []
             for word, label in zip(example.words   ,							example.labels		):
                   _A        =       tokenizer.tokenize(_UpperCAmelCase		)

                   # bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
                   if len(_UpperCAmelCase		) > 0:
                         tokens.extend(_UpperCAmelCase		)
                         # Use the real label id for the first token of the word, and padding ids for the remaining tokens
                         label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(_UpperCAmelCase		) - 1)		)

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
             _A        =       tokenizer.num_special_tokens_to_add()
             if len(_UpperCAmelCase		) > max_seq_length - special_tokens_count:
                   _A        =       tokens[: (max_seq_length - special_tokens_count)]
                   _A        =       label_ids[: (max_seq_length - special_tokens_count)]

             # The convention in BERT is:
             # (a) For sequence pairs:
             #  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
             #  type_ids:   0   0  0    0    0     0       0   0   1  1  1  1   1   1
             # (b) For single sequences:
             #  tokens:   [CLS] the dog is hairy . [SEP]
             #  type_ids:   0   0   0   0  0     0   0
             #
             # Where "type_ids" are used to indicate whether this is the first
             # sequence or the second sequence. The embedding vectors for `type=0` and
             # `type=1` were learned during pre-training and are added to the wordpiece
             # embedding vector (and position vector). This is not *strictly* necessary
             # since the [SEP] token unambiguously separates the sequences, but it makes
             # it easier for the model to learn the concept of sequences.
             #
             # For classification tasks, the first vector (corresponding to [CLS]) is
             # used as the "sentence vector". Note that this only makes sense because
             # the entire model is fine-tuned.
             tokens += [sep_token]
             label_ids += [pad_token_label_id]
             if sep_token_extra:
                   # roberta uses an extra separator b/w pairs of sentences
                   tokens += [sep_token]
                   label_ids += [pad_token_label_id]
             _A        =       [sequence_a_segment_id] * len(_UpperCAmelCase		)

             if cls_token_at_end:
                   tokens += [cls_token]
                   label_ids += [pad_token_label_id]
                   segment_ids += [cls_token_segment_id]
             else:
                   _A        =       [cls_token] + tokens
                   _A        =       [pad_token_label_id] + label_ids
                   _A        =       [cls_token_segment_id] + segment_ids

             _A        =       tokenizer.convert_tokens_to_ids(_UpperCAmelCase		)

             # The mask has 1 for real tokens and 0 for padding tokens. Only real
             # tokens are attended to.
             _A        =       [1 if mask_padding_with_zero else 0] * len(_UpperCAmelCase		)

             # Zero-pad up to the sequence length.
             _A        =       max_seq_length - len(_UpperCAmelCase		)
             if pad_on_left:
                   _A        =       ([pad_token] * padding_length) + input_ids
                   _A        =       ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
                   _A        =       ([pad_token_segment_id] * padding_length) + segment_ids
                   _A        =       ([pad_token_label_id] * padding_length) + label_ids
             else:
                   input_ids += [pad_token] * padding_length
                   input_mask += [0 if mask_padding_with_zero else 1] * padding_length
                   segment_ids += [pad_token_segment_id] * padding_length
                   label_ids += [pad_token_label_id] * padding_length

             assert len(_UpperCAmelCase		) == max_seq_length
             assert len(_UpperCAmelCase		) == max_seq_length
             assert len(_UpperCAmelCase		) == max_seq_length
             assert len(_UpperCAmelCase		) == max_seq_length

             if ex_index < 5:
                   logger.info('*** Example ***'		)
                   logger.info('guid: %s'   ,							example.guid		)
                   logger.info('tokens: %s'   ,							' '.join([str(_UpperCAmelCase		) for x in tokens]		)		)
                   logger.info('input_ids: %s'   ,							' '.join([str(_UpperCAmelCase		) for x in input_ids]		)		)
                   logger.info('input_mask: %s'   ,							' '.join([str(_UpperCAmelCase		) for x in input_mask]		)		)
                   logger.info('segment_ids: %s'   ,							' '.join([str(_UpperCAmelCase		) for x in segment_ids]		)		)
                   logger.info('label_ids: %s'   ,							' '.join([str(_UpperCAmelCase		) for x in label_ids]		)		)

             if "token_type_ids" not in tokenizer.model_input_names:
                   _A        =       None

             features.append(
                 InputFeatures(
                     input_ids=_UpperCAmelCase   ,							attention_mask=_UpperCAmelCase   ,							token_type_ids=_UpperCAmelCase   ,							label_ids=_UpperCAmelCase		)		)
       return features


if is_torch_available():
       import torch
       from torch import nn
       from torch.utils.data import Dataset





       class     lowercase_	(					__lowerCAmelCase							):



        '''simple docstring'''
        UpperCAmelCase   :					List[InputFeatures]
        UpperCAmelCase   :					int				  =     nn.CrossEntropyLoss().ignore_index

        def __init__(			self	:	int   ,							_UpperCAmelCase	:	TokenClassificationTask   ,							_UpperCAmelCase	:	str   ,							_UpperCAmelCase	:	PreTrainedTokenizer   ,							_UpperCAmelCase	:	List[str]   ,							_UpperCAmelCase	:	str   ,							_UpperCAmelCase	:	Optional[int] = None   ,							_UpperCAmelCase	:	Optional[Any]=False   ,							_UpperCAmelCase	:	Split = Split.train   ,							):
              # Load data features from cache or dataset file
              _A        =       os.path.join(
                  _UpperCAmelCase   ,							'cached_{}_{}_{}'.format(mode.value   ,							tokenizer.__class__.__name__   ,							str(_UpperCAmelCase		)		)   ,							)

              # Make sure only the first process in distributed training processes the dataset,
              # and the others will use the cache.
              _A        =       cached_features_file + '.lock'
              with FileLock(_UpperCAmelCase		):
                    if os.path.exists(_UpperCAmelCase		) and not overwrite_cache:
                          logger.info(F'''Loading features from cached file {cached_features_file}'''		)
                          _A        =       torch.load(_UpperCAmelCase		)
                    else:
                          logger.info(F'''Creating features from dataset file at {data_dir}'''		)
                          _A        =       token_classification_task.read_examples_from_file(_UpperCAmelCase   ,							_UpperCAmelCase		)
                          # TODO clean up all this to leverage built-in features of tokenizers
                          _A        =       token_classification_task.convert_examples_to_features(
                              _UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							cls_token_at_end=bool(model_type in ['xlnet']		)   ,							cls_token=tokenizer.cls_token   ,							cls_token_segment_id=2 if model_type in ['xlnet'] else 0   ,							sep_token=tokenizer.sep_token   ,							sep_token_extra=_UpperCAmelCase   ,							pad_on_left=bool(tokenizer.padding_side == 'left'		)   ,							pad_token=tokenizer.pad_token_id   ,							pad_token_segment_id=tokenizer.pad_token_type_id   ,							pad_token_label_id=self.pad_token_label_id   ,							)
                          logger.info(F'''Saving features into cached file {cached_features_file}'''		)
                          torch.save(self.features   ,							_UpperCAmelCase		)

        def __len__(			self	:	Dict		):
              return len(self.features		)



        def __getitem__(			self	:	int   ,							_UpperCAmelCase	:	Union[str, Any]		):
              return self.features[i]


if is_tf_available():
       import tensorflow as tf





       class     lowercase_	:



        '''simple docstring'''
        UpperCAmelCase   :					List[InputFeatures]
        UpperCAmelCase   :					int				  =     -100

        def __init__(			self	:	int   ,							_UpperCAmelCase	:	TokenClassificationTask   ,							_UpperCAmelCase	:	str   ,							_UpperCAmelCase	:	PreTrainedTokenizer   ,							_UpperCAmelCase	:	List[str]   ,							_UpperCAmelCase	:	str   ,							_UpperCAmelCase	:	Optional[int] = None   ,							_UpperCAmelCase	:	Optional[Any]=False   ,							_UpperCAmelCase	:	Split = Split.train   ,							):
              _A        =       token_classification_task.read_examples_from_file(_UpperCAmelCase   ,							_UpperCAmelCase		)
              # TODO clean up all this to leverage built-in features of tokenizers
              _A        =       token_classification_task.convert_examples_to_features(
                  _UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							_UpperCAmelCase   ,							cls_token_at_end=bool(model_type in ['xlnet']		)   ,							cls_token=tokenizer.cls_token   ,							cls_token_segment_id=2 if model_type in ['xlnet'] else 0   ,							sep_token=tokenizer.sep_token   ,							sep_token_extra=_UpperCAmelCase   ,							pad_on_left=bool(tokenizer.padding_side == 'left'		)   ,							pad_token=tokenizer.pad_token_id   ,							pad_token_segment_id=tokenizer.pad_token_type_id   ,							pad_token_label_id=self.pad_token_label_id   ,							)

              def gen():
                    for ex in self.features:
                          if ex.token_type_ids is None:
                                yield (
                                    {"input_ids": ex.input_ids, "attention_mask": ex.attention_mask},
                                    ex.label_ids,
                                )
                          else:
                                yield (
                                    {
                                        "input_ids": ex.input_ids,
                                        "attention_mask": ex.attention_mask,
                                        "token_type_ids": ex.token_type_ids,
                                    },
                                    ex.label_ids,
                                )

              if "token_type_ids" not in tokenizer.model_input_names:
                    _A        =       tf.data.Dataset.from_generator(
                        _UpperCAmelCase   ,							({'input_ids': tf.intaa, 'attention_mask': tf.intaa}, tf.intaa)   ,							(
                            {'input_ids': tf.TensorShape([None]		), 'attention_mask': tf.TensorShape([None]		)},
                            tf.TensorShape([None]		),
                        )   ,							)
              else:
                    _A        =       tf.data.Dataset.from_generator(
                        _UpperCAmelCase   ,							({'input_ids': tf.intaa, 'attention_mask': tf.intaa, 'token_type_ids': tf.intaa}, tf.intaa)   ,							(
                            {
                                'input_ids': tf.TensorShape([None]		),
                                'attention_mask': tf.TensorShape([None]		),
                                'token_type_ids': tf.TensorShape([None]		),
                            },
                            tf.TensorShape([None]		),
                        )   ,							)

        def       lowerCAmelCase_							(			self	:	Dict		):
              _A        =       self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features		)		)		)

              return self.dataset

        def __len__(			self	:	Tuple		):
              return len(self.features		)



        def __getitem__(			self	:	Dict   ,							_UpperCAmelCase	:	Optional[int]		):
              return self.features[i]







"""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 						_snake_case					( _snake_case							: int							)   -> Any:






      '''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     lowercase_	:



 '''simple docstring'''

 def __init__(			self	:	Tuple   ,							_UpperCAmelCase	:	Iterable[torch.nn.Parameter]   ,							_UpperCAmelCase	:	float = 0.9999   ,							_UpperCAmelCase	:	float = 0.0   ,							_UpperCAmelCase	:	int = 0   ,							_UpperCAmelCase	:	bool = False   ,							_UpperCAmelCase	:	Union[float, int] = 1.0   ,							_UpperCAmelCase	:	Union[float, int] = 2 / 3   ,							_UpperCAmelCase	:	Optional[Any] = None   ,							_UpperCAmelCase	:	Dict[str, Any] = None   ,							**_UpperCAmelCase	:	Optional[int]   ,							):

       if isinstance(_UpperCAmelCase   ,							torch.nn.Module		):
             _A        =       (
                 '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'   ,							_UpperCAmelCase   ,							standard_warn=_UpperCAmelCase   ,							)
             _A        =       parameters.parameters()

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

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

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

       _A        =       list(_UpperCAmelCase		)
       _A        =       [p.clone().detach() for p in parameters]

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

       _A        =       None

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

       _A        =       model_cls
       _A        =       model_config

 @classmethod
 def       lowerCAmelCase_							(			cls	:	Any   ,							_UpperCAmelCase	:	Optional[Any]   ,							_UpperCAmelCase	:	Optional[int]		):
       _A       ,   _A        =       model_cls.load_config(_UpperCAmelCase   ,							return_unused_kwargs=_UpperCAmelCase		)
       _A        =       model_cls.from_pretrained(_UpperCAmelCase		)

       _A        =       cls(model.parameters()   ,							model_cls=_UpperCAmelCase   ,							model_config=model.config		)

       ema_model.load_state_dict(_UpperCAmelCase		)
       return ema_model

 def       lowerCAmelCase_							(			self	:	Optional[Any]   ,							_UpperCAmelCase	:	Tuple		):
       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        =       self.model_cls.from_config(self.model_config		)
       _A        =       self.state_dict()
       state_dict.pop('shadow_params'   ,							_UpperCAmelCase		)

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

 def       lowerCAmelCase_							(			self	:	Optional[Any]   ,							_UpperCAmelCase	:	int		):
       _A        =       max(0   ,							optimization_step - self.update_after_step - 1		)

       if step <= 0:
             return 0.0

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

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

 @torch.no_grad()
 def       lowerCAmelCase_							(			self	:	Dict   ,							_UpperCAmelCase	:	Iterable[torch.nn.Parameter]		):
       if isinstance(_UpperCAmelCase   ,							torch.nn.Module		):
             _A        =       (
                 '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'   ,							_UpperCAmelCase   ,							standard_warn=_UpperCAmelCase   ,							)
             _A        =       parameters.parameters()

       _A        =       list(_UpperCAmelCase		)

       self.optimization_step += 1

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

       _A        =       contextlib.nullcontext
       if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
             import deepspeed

       for s_param, param in zip(self.shadow_params   ,							_UpperCAmelCase		):
             if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
                   _A        =       deepspeed.zero.GatheredParameters(_UpperCAmelCase   ,							modifier_rank=_UpperCAmelCase		)

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

 def       lowerCAmelCase_							(			self	:	str   ,							_UpperCAmelCase	:	Iterable[torch.nn.Parameter]		):
       _A        =       list(_UpperCAmelCase		)
       for s_param, param in zip(self.shadow_params   ,							_UpperCAmelCase		):
             param.data.copy_(s_param.to(param.device		).data		)

 def       lowerCAmelCase_							(			self	:	Dict   ,							_UpperCAmelCase	:	Optional[int]=None   ,							_UpperCAmelCase	:	Dict=None		):
       _A        =       [
           p.to(device=_UpperCAmelCase   ,							dtype=_UpperCAmelCase		) if p.is_floating_point() else p.to(device=_UpperCAmelCase		)
           for p in self.shadow_params
       ]

 def       lowerCAmelCase_							(			self	:	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       lowerCAmelCase_							(			self	:	Any   ,							_UpperCAmelCase	:	Iterable[torch.nn.Parameter]		):
       _A        =       [param.detach().cpu().clone() for param in parameters]

 def       lowerCAmelCase_							(			self	:	List[Any]   ,							_UpperCAmelCase	:	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   ,							_UpperCAmelCase		):
             param.data.copy_(c_param.data		)

       # Better memory-wise.
       _A        =       None



 def       lowerCAmelCase_							(			self	:	int   ,							_UpperCAmelCase	:	dict		):
       _A        =       copy.deepcopy(_UpperCAmelCase		)

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

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

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

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

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

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

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

