



'''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


snake_case_			:     Optional[Any]  	=  logging.getLogger(__name__)






@dataclass
class 							lowercase__     :
	lowercase__      =      42
	lowercase__      =      42
	lowercase__      =      42






@dataclass
class 							lowercase__     :
	lowercase__      =      42
	lowercase__      =      42
	lowercase__      =      None
	lowercase__      =      None






class 							lowercase__     (							lowercase       ):
	lowercase__      =      """train"""
	lowercase__      =      """dev"""
	lowercase__      =      """test"""






class 							lowercase__     :





	@staticmethod
	def      UpperCamelCase_  (			lowerCamelCase__ :    Tuple			,lowerCamelCase__ :    Union[Split, str]			):







							'''simple docstring'''
							raise NotImplementedError





	@staticmethod
	def      UpperCamelCase_  (			lowerCamelCase__ :    str			):







							'''simple docstring'''
							raise NotImplementedError





	@staticmethod
	def      UpperCamelCase_  (			lowerCamelCase__ :    List[InputExample]			,lowerCamelCase__ :    List[str]			,lowerCamelCase__ :    int			,lowerCamelCase__ :    PreTrainedTokenizer			,lowerCamelCase__ :    str=False			,lowerCamelCase__ :    Dict="[CLS]"			,lowerCamelCase__ :    int=1			,lowerCamelCase__ :    List[str]="[SEP]"			,lowerCamelCase__ :    Tuple=False			,lowerCamelCase__ :    Tuple=False			,lowerCamelCase__ :    int=0			,lowerCamelCase__ :    List[Any]=0			,lowerCamelCase__ :    int=-100			,lowerCamelCase__ :    List[str]=0			,lowerCamelCase__ :    Union[str, Any]=True			,):







							'''simple docstring'''
							_UpperCamelCase :		str			    =       {label: i for i, label in enumerate(lowerCamelCase__			)}

							_UpperCamelCase :		int			    =       []
							for ex_index, example in enumerate(lowerCamelCase__			):
													if ex_index % 10000 == 0:
																			logger.info('Writing example %d of %d'			,lowerCamelCase__			,len(lowerCamelCase__			)			)

													_UpperCamelCase :		Optional[int]			    =       []
													_UpperCamelCase :		int			    =       []
													for word, label in zip(example.words			,example.labels			):
																			_UpperCamelCase :		Union[str, Any]			    =       tokenizer.tokenize(lowerCamelCase__			)

																			# bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
																			if len(lowerCamelCase__			) > 0:
																									tokens.extend(lowerCamelCase__			)
																									# 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(lowerCamelCase__			) - 1)			)

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
													_UpperCamelCase :		Union[str, Any]			    =       tokenizer.num_special_tokens_to_add()
													if len(lowerCamelCase__			) > max_seq_length - special_tokens_count:
																			_UpperCamelCase :		List[str]			    =       tokens[: (max_seq_length - special_tokens_count)]
																			_UpperCamelCase :		str			    =       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]
													_UpperCamelCase :		str			    =       [sequence_a_segment_id] * len(lowerCamelCase__			)

													if cls_token_at_end:
																			tokens += [cls_token]
																			label_ids += [pad_token_label_id]
																			segment_ids += [cls_token_segment_id]
													else:
																			_UpperCamelCase :		int			    =       [cls_token] + tokens
																			_UpperCamelCase :		Optional[Any]			    =       [pad_token_label_id] + label_ids
																			_UpperCamelCase :		Tuple			    =       [cls_token_segment_id] + segment_ids

													_UpperCamelCase :		int			    =       tokenizer.convert_tokens_to_ids(lowerCamelCase__			)

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

													# Zero-pad up to the sequence length.
													_UpperCamelCase :		int			    =       max_seq_length - len(lowerCamelCase__			)
													if pad_on_left:
																			_UpperCamelCase :		Dict			    =       ([pad_token] * padding_length) + input_ids
																			_UpperCamelCase :		Tuple			    =       ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
																			_UpperCamelCase :		List[str]			    =       ([pad_token_segment_id] * padding_length) + segment_ids
																			_UpperCamelCase :		List[Any]			    =       ([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(lowerCamelCase__			) == max_seq_length
													assert len(lowerCamelCase__			) == max_seq_length
													assert len(lowerCamelCase__			) == max_seq_length
													assert len(lowerCamelCase__			) == max_seq_length

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

													if "token_type_ids" not in tokenizer.model_input_names:
																			_UpperCamelCase :		List[Any]			    =       None

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


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






					class 							lowercase__     (							lowercase       ):
						lowercase__      =      42
						lowercase__      =      nn.CrossEntropyLoss().ignore_index





						def __init__(			self :    Union[str, Any]			,lowerCamelCase__ :    TokenClassificationTask			,lowerCamelCase__ :    str			,lowerCamelCase__ :    PreTrainedTokenizer			,lowerCamelCase__ :    List[str]			,lowerCamelCase__ :    str			,lowerCamelCase__ :    Optional[int] = None			,lowerCamelCase__ :    Dict=False			,lowerCamelCase__ :    Split = Split.train			,):







												'''simple docstring'''
												_UpperCamelCase :		Tuple			    =       os.path.join(
												    lowerCamelCase__			,'cached_{}_{}_{}'.format(mode.value			,tokenizer.__class__.__name__			,str(lowerCamelCase__			)			)			,)

												# Make sure only the first process in distributed training processes the dataset,
												# and the others will use the cache.
												_UpperCamelCase :		Any			    =       cached_features_file + '.lock'
												with FileLock(lowerCamelCase__			):
																		if os.path.exists(lowerCamelCase__			) and not overwrite_cache:
																								logger.info(F'Loading features from cached file {cached_features_file}'			)
																								_UpperCamelCase :		Union[str, Any]			    =       torch.load(lowerCamelCase__			)
																		else:
																								logger.info(F'Creating features from dataset file at {data_dir}'			)
																								_UpperCamelCase :		Optional[int]			    =       token_classification_task.read_examples_from_file(lowerCamelCase__			,lowerCamelCase__			)
																								# TODO clean up all this to leverage built-in features of tokenizers
																								_UpperCamelCase :		List[Any]			    =       token_classification_task.convert_examples_to_features(
																								    lowerCamelCase__			,lowerCamelCase__			,lowerCamelCase__			,lowerCamelCase__			,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=lowerCamelCase__			,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			,lowerCamelCase__			)





						def __len__(			self :    Optional[Any]			):







												'''simple docstring'''
												return len(self.features			)





						def __getitem__(			self :    Dict			,lowerCamelCase__ :    Union[str, Any]			):







												'''simple docstring'''
												return self.features[i]


if is_tf_available():
					import tensorflow as tf






					class 							lowercase__     :
						lowercase__      =      42
						lowercase__      =      -1_00





						def __init__(			self :    Tuple			,lowerCamelCase__ :    TokenClassificationTask			,lowerCamelCase__ :    str			,lowerCamelCase__ :    PreTrainedTokenizer			,lowerCamelCase__ :    List[str]			,lowerCamelCase__ :    str			,lowerCamelCase__ :    Optional[int] = None			,lowerCamelCase__ :    Optional[Any]=False			,lowerCamelCase__ :    Split = Split.train			,):







												'''simple docstring'''
												_UpperCamelCase :		Tuple			    =       token_classification_task.read_examples_from_file(lowerCamelCase__			,lowerCamelCase__			)
												# TODO clean up all this to leverage built-in features of tokenizers
												_UpperCamelCase :		Union[str, Any]			    =       token_classification_task.convert_examples_to_features(
												    lowerCamelCase__			,lowerCamelCase__			,lowerCamelCase__			,lowerCamelCase__			,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=lowerCamelCase__			,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:
																		_UpperCamelCase :		Any			    =       tf.data.Dataset.from_generator(
																		    lowerCamelCase__			,({'input_ids': tf.intaa, 'attention_mask': tf.intaa}, tf.intaa)			,(
																		        {'input_ids': tf.TensorShape([None]			), 'attention_mask': tf.TensorShape([None]			)},
																		        tf.TensorShape([None]			),
																		    )			,)
												else:
																		_UpperCamelCase :		List[Any]			    =       tf.data.Dataset.from_generator(
																		    lowerCamelCase__			,({'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      UpperCamelCase_  (			self :    Tuple			):







												'''simple docstring'''
												_UpperCamelCase :		Tuple			    =       self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features			)			)			)

												return self.dataset





						def __len__(			self :    str			):







												'''simple docstring'''
												return len(self.features			)





						def __getitem__(			self :    int			,lowerCamelCase__ :    List[str]			):







												'''simple docstring'''
												return self.features[i]







'''simple docstring'''
from torch import nn






def A__   (					UpperCAmelCase_							):
						if act_fn in ["swish", "silu"]:
												return nn.SiLU()
						elif act_fn == "mish":
												return nn.Mish()
						elif act_fn == "gelu":
												return nn.GELU()
						else:
												raise ValueError(f'Unsupported activation function: {act_fn}'							)


