



"""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


lowerCAmelCase__		:					List[str]							=logging.getLogger(__name__)

@dataclass
class        UpperCAmelCase_  :

						'''simple docstring'''




						UpperCamelCase__      :			List[Any]      =  42
						UpperCamelCase__      :			int      =  42
						UpperCamelCase__      :			List[Any]      =  42







@dataclass
class        UpperCAmelCase_  :

						'''simple docstring'''




						UpperCamelCase__      :			int      =  42
						UpperCamelCase__      :			Union[str, Any]      =  42
						UpperCamelCase__      :			str      =  None
						UpperCamelCase__      :			Any      =  None







class        UpperCAmelCase_  (       _SCREAMING_SNAKE_CASE	):

						'''simple docstring'''




						UpperCamelCase__      :			Union[str, Any]      =  '''train'''
						UpperCamelCase__      :			int      =  '''dev'''
						UpperCamelCase__      :			Optional[int]      =  '''test'''







class        UpperCAmelCase_  :

						'''simple docstring'''




						@staticmethod
						def 	_A     (   _A				,							_A		):







													'''simple docstring'''

													raise NotImplementedError






						@staticmethod
						def 	_A     (   _A		):







													'''simple docstring'''

													raise NotImplementedError






						@staticmethod
						def 	_A     (   _A				,							_A				,							_A				,							_A				,							_A=False				,							_A="[CLS]"				,							_A=1				,							_A="[SEP]"				,							_A=False				,							_A=False				,							_A=0				,							_A=0				,							_A=-100				,							_A=0				,							_A=True				,							):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= {label: i for i, label in enumerate(_A		)}

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

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

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

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

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

																				__SCREAMING_SNAKE_CASE									= tokenizer.convert_tokens_to_ids(_A		)

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

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

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

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

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


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

			class        UpperCAmelCase_  (       _SCREAMING_SNAKE_CASE	):

									'''simple docstring'''




									UpperCamelCase__      :			Optional[int]      =  42
									UpperCamelCase__      :			Union[str, Any]      =  nn.CrossEntropyLoss().ignore_index






									def __init__(   self				,							_A				,							_A				,							_A				,							_A				,							_A				,							_A = None				,							_A=False				,							_A = Split.train				,							):







																'''simple docstring'''

																__SCREAMING_SNAKE_CASE									= os.path.join(
																    _A				,							'cached_{}_{}_{}'.format(mode.value				,							tokenizer.__class__.__name__				,							str(_A		)		)				,							)

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






									def __len__(   self		):







																'''simple docstring'''

																return len(self.features		)






									def __getitem__(   self				,							_A		):







																'''simple docstring'''

																return self.features[i]


if is_tf_available():
			import tensorflow as tf

			class        UpperCAmelCase_  :

									'''simple docstring'''




									UpperCamelCase__      :			List[Any]      =  42
									UpperCamelCase__      :			List[Any]      =  -100






									def __init__(   self				,							_A				,							_A				,							_A				,							_A				,							_A				,							_A = None				,							_A=False				,							_A = Split.train				,							):







																'''simple docstring'''

																__SCREAMING_SNAKE_CASE									= token_classification_task.read_examples_from_file(_A				,							_A		)
																# TODO clean up all this to leverage built-in features of tokenizers
																__SCREAMING_SNAKE_CASE									= token_classification_task.convert_examples_to_features(
																    _A				,							_A				,							_A				,							_A				,							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=_A				,							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:
																							__SCREAMING_SNAKE_CASE									= tf.data.Dataset.from_generator(
																							    _A				,							({'input_ids': tf.intaa, 'attention_mask': tf.intaa}, tf.intaa)				,							(
																							        {'input_ids': tf.TensorShape([None]		), 'attention_mask': tf.TensorShape([None]		)},
																							        tf.TensorShape([None]		),
																							    )				,							)
																else:
																							__SCREAMING_SNAKE_CASE									= tf.data.Dataset.from_generator(
																							    _A				,							({'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 	_A     (   self		):







																'''simple docstring'''

																__SCREAMING_SNAKE_CASE									= self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features		)		)		)

																return self.dataset






									def __len__(   self		):







																'''simple docstring'''

																return len(self.features		)






									def __getitem__(   self				,							_A		):







																'''simple docstring'''

																return self.features[i]











from collections import deque

from .hash_table import HashTable

class        UpperCAmelCase_  (       UpperCamelCase_	):

						'''simple docstring'''




						def __init__(   self				,							*_A				,							**_A		):







													'''simple docstring'''

													super().__init__(*_A				,							**_A		)






						def 	_A     (   self				,							_A				,							_A		):







													'''simple docstring'''

													__SCREAMING_SNAKE_CASE									= deque([]		) if self.values[key] is None else self.values[key]
													self.values[key].appendleft(_A		)
													__SCREAMING_SNAKE_CASE									= self.values[key]






						def 	_A     (   self		):







													'''simple docstring'''

													return (
													    sum(self.charge_factor - len(_A		) for slot in self.values		)
													    / self.size_table
													    * self.charge_factor
													)






						def 	_A     (   self				,							_A				,							_A=None		):







													'''simple docstring'''

													if not (
													    len(self.values[key]		) == self.charge_factor and self.values.count(_A		) == 0
													):
																				return key
													return super()._collision_resolution(_A				,							_A		)






