





'''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


_SCREAMING_SNAKE_CASE   =      logging.getLogger(__name__)






@dataclass
class 							__lowercase    :







	'''simple docstring'''

	a      :			str
	a      :			List[str]
	a      :			Optional[List[str]]






@dataclass
class 							__lowercase    :







	'''simple docstring'''

	a      :			List[int]
	a      :			List[int]
	a      :			Optional[List[int]]     =    None
	a      :			Optional[List[int]]     =    None






class 							__lowercase    ( lowerCAmelCase__  ):







	'''simple docstring'''

	a      :			List[str]     =    "train"
	a      :			Optional[int]     =    "dev"
	a      :			Optional[Any]     =    "test"






class 							__lowercase    :







	'''simple docstring'''

	@staticmethod
	def   _UpperCAmelCase       (_lowerCamelCase       ,_lowerCamelCase	):







				'''simple docstring'''





				raise NotImplementedError


	@staticmethod
	def   _UpperCAmelCase       (_lowerCamelCase	):







				'''simple docstring'''





				raise NotImplementedError


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







				'''simple docstring'''





				__lowercase			  =							{label: i for i, label in enumerate(_lowerCamelCase	)}

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

							__lowercase			  =							[]
							__lowercase			  =							[]
							for word, label in zip(example.words       ,example.labels	):
										__lowercase			  =							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.
							__lowercase			  =							tokenizer.num_special_tokens_to_add()
							if len(_lowerCamelCase	) > max_seq_length - special_tokens_count:
										__lowercase			  =							tokens[: (max_seq_length - special_tokens_count)]
										__lowercase			  =							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]
							__lowercase			  =							[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:
										__lowercase			  =							[cls_token] + tokens
										__lowercase			  =							[pad_token_label_id] + label_ids
										__lowercase			  =							[cls_token_segment_id] + segment_ids

							__lowercase			  =							tokenizer.convert_tokens_to_ids(_lowerCamelCase	)

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

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







		'''simple docstring'''

		a      :			List[InputFeatures]
		a      :			int     =    nn.CrossEntropyLoss().ignore_index


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







					'''simple docstring'''





					__lowercase			  =							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.
					__lowercase			  =							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}"	)
											__lowercase			  =							torch.load(_lowerCamelCase	)
								else:
											logger.info(f"Creating features from dataset file at {data_dir}"	)
											__lowercase			  =							token_classification_task.read_examples_from_file(_lowerCamelCase       ,_lowerCamelCase	)
											# TODO clean up all this to leverage built-in features of tokenizers
											__lowercase			  =							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	):







					'''simple docstring'''





					return len(self.features	)


		def __getitem__(self       ,_lowerCamelCase	):







					'''simple docstring'''





					return self.features[i]


if is_tf_available():
	import tensorflow as tf






	class 							__lowercase    :







		'''simple docstring'''

		a      :			List[InputFeatures]
		a      :			int     =    -100


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







					'''simple docstring'''





					__lowercase			  =							token_classification_task.read_examples_from_file(_lowerCamelCase       ,_lowerCamelCase	)
					# TODO clean up all this to leverage built-in features of tokenizers
					__lowercase			  =							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:
								__lowercase			  =							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:
								__lowercase			  =							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	):







					'''simple docstring'''





					__lowercase			  =							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       ,_lowerCamelCase	):







					'''simple docstring'''





					return self.features[i]












'''simple docstring'''



import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_electra import ElectraTokenizer


_SCREAMING_SNAKE_CASE   =      {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

_SCREAMING_SNAKE_CASE   =      {
    '''vocab_file''': {
        '''google/electra-small-generator''': (
            '''https://huggingface.co/google/electra-small-generator/resolve/main/vocab.txt'''
        ),
        '''google/electra-base-generator''': '''https://huggingface.co/google/electra-base-generator/resolve/main/vocab.txt''',
        '''google/electra-large-generator''': (
            '''https://huggingface.co/google/electra-large-generator/resolve/main/vocab.txt'''
        ),
        '''google/electra-small-discriminator''': (
            '''https://huggingface.co/google/electra-small-discriminator/resolve/main/vocab.txt'''
        ),
        '''google/electra-base-discriminator''': (
            '''https://huggingface.co/google/electra-base-discriminator/resolve/main/vocab.txt'''
        ),
        '''google/electra-large-discriminator''': (
            '''https://huggingface.co/google/electra-large-discriminator/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''google/electra-small-generator''': (
            '''https://huggingface.co/google/electra-small-generator/resolve/main/tokenizer.json'''
        ),
        '''google/electra-base-generator''': (
            '''https://huggingface.co/google/electra-base-generator/resolve/main/tokenizer.json'''
        ),
        '''google/electra-large-generator''': (
            '''https://huggingface.co/google/electra-large-generator/resolve/main/tokenizer.json'''
        ),
        '''google/electra-small-discriminator''': (
            '''https://huggingface.co/google/electra-small-discriminator/resolve/main/tokenizer.json'''
        ),
        '''google/electra-base-discriminator''': (
            '''https://huggingface.co/google/electra-base-discriminator/resolve/main/tokenizer.json'''
        ),
        '''google/electra-large-discriminator''': (
            '''https://huggingface.co/google/electra-large-discriminator/resolve/main/tokenizer.json'''
        ),
    },
}

_SCREAMING_SNAKE_CASE   =      {
    '''google/electra-small-generator''': 5_1_2,
    '''google/electra-base-generator''': 5_1_2,
    '''google/electra-large-generator''': 5_1_2,
    '''google/electra-small-discriminator''': 5_1_2,
    '''google/electra-base-discriminator''': 5_1_2,
    '''google/electra-large-discriminator''': 5_1_2,
}

_SCREAMING_SNAKE_CASE   =      {
    '''google/electra-small-generator''': {'''do_lower_case''': True},
    '''google/electra-base-generator''': {'''do_lower_case''': True},
    '''google/electra-large-generator''': {'''do_lower_case''': True},
    '''google/electra-small-discriminator''': {'''do_lower_case''': True},
    '''google/electra-base-discriminator''': {'''do_lower_case''': True},
    '''google/electra-large-discriminator''': {'''do_lower_case''': True},
}






class 							__lowercase    ( lowerCAmelCase__  ):







	'''simple docstring'''

	a      :			str     =    VOCAB_FILES_NAMES
	a      :			Dict     =    PRETRAINED_VOCAB_FILES_MAP
	a      :			int     =    PRETRAINED_INIT_CONFIGURATION
	a      :			Tuple     =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	a      :			List[Any]     =    ElectraTokenizer


	def __init__(self       ,_lowerCamelCase=None       ,_lowerCamelCase=None       ,_lowerCamelCase=True       ,_lowerCamelCase="[UNK]"       ,_lowerCamelCase="[SEP]"       ,_lowerCamelCase="[PAD]"       ,_lowerCamelCase="[CLS]"       ,_lowerCamelCase="[MASK]"       ,_lowerCamelCase=True       ,_lowerCamelCase=None       ,**_lowerCamelCase       ,)							->							List[Any]:







				'''simple docstring'''





				super().__init__(
				    _lowerCamelCase       ,tokenizer_file=_lowerCamelCase       ,do_lower_case=_lowerCamelCase       ,unk_token=_lowerCamelCase       ,sep_token=_lowerCamelCase       ,pad_token=_lowerCamelCase       ,cls_token=_lowerCamelCase       ,mask_token=_lowerCamelCase       ,tokenize_chinese_chars=_lowerCamelCase       ,strip_accents=_lowerCamelCase       ,**_lowerCamelCase       ,)

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

				__lowercase			  =							do_lower_case


	def   _UpperCAmelCase       (self       ,_lowerCamelCase       ,_lowerCamelCase=None	)							->							str:







				'''simple docstring'''





				__lowercase			  =							[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       ,_lowerCamelCase       ,_lowerCamelCase = None	)							->							List[int]:







				'''simple docstring'''





				__lowercase			  =							[self.sep_token_id]
				__lowercase			  =							[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       ,_lowerCamelCase       ,_lowerCamelCase = None	)							->							Tuple[str]:







				'''simple docstring'''





				__lowercase			  =							self._tokenizer.model.save(_lowerCamelCase       ,name=_lowerCamelCase	)
				return tuple(_lowerCamelCase	)





