
import unittest

from transformers import BigBirdTokenizer, BigBirdTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


SCREAMING_SNAKE_CASE							:Optional[int]							 =				'▁'

SCREAMING_SNAKE_CASE							:List[Any]							 =				get_tests_dir('fixtures/test_sentencepiece.model')





@require_sentencepiece
@require_tokenizers
class 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE			,			unittest.TestCase  ):




	'''simple docstring'''





	snake_case_					    =     BigBirdTokenizer
	snake_case_					    =     BigBirdTokenizerFast
	snake_case_					    =     True
	snake_case_					    =     True






	def 				UpperCamelCase_  (       self					: Dict ):
					super().setUp()

					__A   			= self.tokenizer_class(A							,keep_accents=A )
					tokenizer.save_pretrained(self.tmpdirname )






	def 				UpperCamelCase_  (       self					: Optional[Any] ):
					__A   			= "<s>"
					__A   			= 1

					self.assertEqual(self.get_tokenizer()._convert_token_to_id(A )							,A )
					self.assertEqual(self.get_tokenizer()._convert_id_to_token(A )							,A )






	def 				UpperCamelCase_  (       self					: int ):
					__A   			= list(self.get_tokenizer().get_vocab().keys() )

					self.assertEqual(vocab_keys[0]							,"<unk>" )
					self.assertEqual(vocab_keys[1]							,"<s>" )
					self.assertEqual(vocab_keys[-1]							,"[MASK]" )
					self.assertEqual(len(A )							,10_04 )






	def 				UpperCamelCase_  (       self					: Dict ):
					self.assertEqual(self.get_tokenizer().vocab_size							,10_00 )






	def 				UpperCamelCase_  (       self					: str ):
					if not self.test_rust_tokenizer:
									return

					__A   			= self.get_tokenizer()
					__A   			= self.get_rust_tokenizer()

					__A   			= "I was born in 92000, and this is falsé."

					__A   			= tokenizer.tokenize(A )
					__A   			= rust_tokenizer.tokenize(A )
					self.assertListEqual(A							,A )

					__A   			= tokenizer.encode(A							,add_special_tokens=A )
					__A   			= rust_tokenizer.encode(A							,add_special_tokens=A )
					self.assertListEqual(A							,A )

					__A   			= self.get_rust_tokenizer()
					__A   			= tokenizer.encode(A )
					__A   			= rust_tokenizer.encode(A )
					self.assertListEqual(A							,A )






	def 				UpperCamelCase_  (       self					: Any ):
					__A   			= BigBirdTokenizer(A							,keep_accents=A )

					__A   			= tokenizer.tokenize("This is a test" )
					self.assertListEqual(A							,["▁This", "▁is", "▁a", "▁t", "est"] )

					self.assertListEqual(
					    tokenizer.convert_tokens_to_ids(A )							,[2_85, 46, 10, 1_70, 3_82]							,)

					__A   			= tokenizer.tokenize("I was born in 92000, and this is falsé." )
					self.assertListEqual(
					    A							,[
					        SPIECE_UNDERLINE + "I",
					        SPIECE_UNDERLINE + "was",
					        SPIECE_UNDERLINE + "b",
					        "or",
					        "n",
					        SPIECE_UNDERLINE + "in",
					        SPIECE_UNDERLINE + "",
					        "9",
					        "2",
					        "0",
					        "0",
					        "0",
					        ",",
					        SPIECE_UNDERLINE + "and",
					        SPIECE_UNDERLINE + "this",
					        SPIECE_UNDERLINE + "is",
					        SPIECE_UNDERLINE + "f",
					        "al",
					        "s",
					        "é",
					        ".",
					    ]							,)
					__A   			= tokenizer.convert_tokens_to_ids(A )
					self.assertListEqual(
					    A							,[8, 21, 84, 55, 24, 19, 7, 0, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 0, 4]							,)

					__A   			= tokenizer.convert_ids_to_tokens(A )
					self.assertListEqual(
					    A							,[
					        SPIECE_UNDERLINE + "I",
					        SPIECE_UNDERLINE + "was",
					        SPIECE_UNDERLINE + "b",
					        "or",
					        "n",
					        SPIECE_UNDERLINE + "in",
					        SPIECE_UNDERLINE + "",
					        "<unk>",
					        "2",
					        "0",
					        "0",
					        "0",
					        ",",
					        SPIECE_UNDERLINE + "and",
					        SPIECE_UNDERLINE + "this",
					        SPIECE_UNDERLINE + "is",
					        SPIECE_UNDERLINE + "f",
					        "al",
					        "s",
					        "<unk>",
					        ".",
					    ]							,)






	@cached_property
	def 				UpperCamelCase_  (       self					: str ):
					return BigBirdTokenizer.from_pretrained("google/bigbird-roberta-base" )






	@slow
	def 				UpperCamelCase_  (       self					: List[str] ):
					__A   			= "Hello World!"
					__A   			= [65, 1_85_36, 22_60, 1_01, 66]

					self.assertListEqual(A							,self.big_tokenizer.encode(A ) )






	@slow
	def 				UpperCamelCase_  (       self					: Dict ):
					__A   			= (
					    "This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"
					    " add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"
					)
					# fmt: off
					__A   			= [65, 8_71, 4_19, 3_58, 9_46, 9_91, 25_21, 4_52, 3_58, 13_57, 3_87, 77_51, 35_36, 1_12, 9_85, 4_56, 1_26, 8_65, 9_38, 54_00, 57_34, 4_58, 13_68, 4_67, 7_86, 24_62, 52_46, 11_59, 6_33, 8_65, 45_19, 4_57, 5_82, 8_52, 25_57, 4_27, 9_16, 5_08, 4_05, 3_43_24, 4_97, 3_91, 4_08, 1_13_42, 12_44, 3_85, 1_00, 9_38, 9_85, 4_56, 5_74, 3_62, 1_25_97, 32_00, 31_29, 11_72, 66]  # noqa: E231
					# fmt: on
					self.assertListEqual(A							,self.big_tokenizer.encode(A ) )






	@require_torch
	@slow
	def 				UpperCamelCase_  (       self					: Any ):
					import torch

					from transformers import BigBirdConfig, BigBirdModel

					# Build sequence
					__A   			= list(self.big_tokenizer.get_vocab().keys() )[:10]
					__A   			= " ".join(A )
					__A   			= self.big_tokenizer.encode_plus(A							,return_tensors="pt"							,return_token_type_ids=A )
					__A   			= self.big_tokenizer.batch_encode_plus(
					    [sequence + " " + sequence]							,return_tensors="pt"							,return_token_type_ids=A )

					__A   			= BigBirdConfig(attention_type="original_full" )
					__A   			= BigBirdModel(A )

					assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

					with torch.no_grad():
									model(**A )
									model(**A )






	@slow
	def 				UpperCamelCase_  (       self					: List[str] ):
					__A   			= BigBirdTokenizer.from_pretrained("google/bigbird-roberta-base" )
					__A   			= tokenizer.decode(tokenizer("Paris is the [MASK]." ).input_ids )

					self.assertTrue(decoded_text == "[CLS] Paris is the[MASK].[SEP]" )







	@slow
	def 				UpperCamelCase_  (       self					: str ):
					# fmt: off
					__A   			= {"input_ids": [[65, 3_92_86, 4_58, 3_63_35, 20_01, 4_56, 1_30_73, 1_32_66, 4_55, 1_13, 77_46, 17_41, 1_11_57, 3_91, 1_30_73, 1_32_66, 4_55, 1_13, 39_67, 3_54_12, 1_13, 49_36, 1_09, 38_70, 23_77, 1_13, 3_00_84, 4_57_20, 4_58, 1_34, 1_74_96, 1_12, 5_03, 1_16_72, 1_13, 1_18, 1_12, 56_65, 1_33_47, 3_86_87, 1_12, 14_96, 3_13_89, 1_12, 32_68, 4_72_64, 1_34, 9_62, 1_12, 1_63_77, 80_35, 2_31_30, 4_30, 1_21_69, 1_55_18, 2_85_92, 4_58, 1_46, 4_16_97, 1_09, 3_91, 1_21_69, 1_55_18, 1_66_89, 4_58, 1_46, 4_13_58, 1_09, 4_52, 7_26, 40_34, 1_11, 7_63, 3_54_12, 50_82, 3_88, 19_03, 1_11, 90_51, 3_91, 28_70, 4_89_18, 19_00, 11_23, 5_50, 9_98, 1_12, 95_86, 1_59_85, 4_55, 3_91, 4_10, 2_29_55, 3_76_36, 1_14, 66], [65, 4_48, 1_74_96, 4_19, 36_63, 3_85, 7_63, 1_13, 2_75_33, 28_70, 32_83, 1_30_43, 16_39, 2_47_13, 5_23, 6_56, 2_40_13, 1_85_50, 25_21, 5_17, 2_70_14, 2_12_44, 4_20, 12_12, 14_65, 3_91, 9_27, 48_33, 3_88, 5_78, 1_17_86, 1_14, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [65, 4_84, 21_69, 76_87, 2_19_32, 1_81_46, 7_26, 3_63, 1_70_32, 33_91, 1_14, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
					# fmt: on

					self.tokenizer_integration_test_util(
					    expected_encoding=A							,model_name="google/bigbird-roberta-base"							,revision="215c99f1600e06f83acce68422f2035b2b5c3510"							,)


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__   :			Optional[int]												=    logging.getLogger(__name__)

@dataclass
class    lowerCAmelCase__     :
		a__       :						str
		a__       :						List[str]
		a__       :						Optional[List[str]]





@dataclass
class    lowerCAmelCase__     :
		a__       :						List[int]
		a__       :						List[int]
		a__       :						Optional[List[int]]        =		None
		a__       :						Optional[List[int]]        =		None





class    lowerCAmelCase__     (	__lowercase			):
		a__       :						Optional[Any]        =		"""train"""
		a__       :						Optional[int]        =		"""dev"""
		a__       :						Dict        =		"""test"""





class    lowerCAmelCase__     :






		@staticmethod
		def  __A       (		SCREAMING_SNAKE_CASE__		:							Union[str, Any]							,			SCREAMING_SNAKE_CASE__		:							Union[Split, str]  )     ->							List[InputExample]:
						raise NotImplementedError






		@staticmethod
		def  __A       (		SCREAMING_SNAKE_CASE__		:							str  )     ->							List[str]:
						raise NotImplementedError







		@staticmethod
		def  __A       (		SCREAMING_SNAKE_CASE__		:							List[InputExample]							,			SCREAMING_SNAKE_CASE__		:							List[str]							,			SCREAMING_SNAKE_CASE__		:							int							,			SCREAMING_SNAKE_CASE__		:							PreTrainedTokenizer							,			SCREAMING_SNAKE_CASE__		:							Optional[Any]=False							,			SCREAMING_SNAKE_CASE__		:							List[str]="[CLS]"							,			SCREAMING_SNAKE_CASE__		:							Tuple=1							,			SCREAMING_SNAKE_CASE__		:							str="[SEP]"							,			SCREAMING_SNAKE_CASE__		:							List[Any]=False							,			SCREAMING_SNAKE_CASE__		:							Union[str, Any]=False							,			SCREAMING_SNAKE_CASE__		:							Tuple=0							,			SCREAMING_SNAKE_CASE__		:							int=0							,			SCREAMING_SNAKE_CASE__		:							str=-1_00							,			SCREAMING_SNAKE_CASE__		:							Union[str, Any]=0							,			SCREAMING_SNAKE_CASE__		:							List[Any]=True							,			)     ->							List[InputFeatures]:
						__lowerCamelCase    					=	{label: i for i, label in enumerate(SCREAMING_SNAKE_CASE__  )}

						__lowerCamelCase    					=	[]
						for ex_index, example in enumerate(SCREAMING_SNAKE_CASE__  ):
										if ex_index % 1_00_00 == 0:
														logger.info('''Writing example %d of %d'''							,			SCREAMING_SNAKE_CASE__							,			len(SCREAMING_SNAKE_CASE__  )  )

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

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

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

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

										__lowerCamelCase    					=	tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__  )

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

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

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

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

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


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

					class    lowerCAmelCase__     (	__lowercase			):
							a__       :						List[InputFeatures]
							a__       :						int        =		nn.CrossEntropyLoss().ignore_index






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

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






							def __len__(		self		:							Dict  )     ->							str:
											return len(self.features  )







							def __getitem__(		self		:							Any							,			SCREAMING_SNAKE_CASE__		:							Dict  )     ->							InputFeatures:
											return self.features[i]


if is_tf_available():
					import tensorflow as tf

					class    lowerCAmelCase__     :
							a__       :						List[InputFeatures]
							a__       :						int        =		-100






							def __init__(		self		:							Optional[Any]							,			SCREAMING_SNAKE_CASE__		:							TokenClassificationTask							,			SCREAMING_SNAKE_CASE__		:							str							,			SCREAMING_SNAKE_CASE__		:							PreTrainedTokenizer							,			SCREAMING_SNAKE_CASE__		:							List[str]							,			SCREAMING_SNAKE_CASE__		:							str							,			SCREAMING_SNAKE_CASE__		:							Optional[int] = None							,			SCREAMING_SNAKE_CASE__		:							Optional[Any]=False							,			SCREAMING_SNAKE_CASE__		:							Split = Split.train							,			)     ->							List[Any]:
											__lowerCamelCase    					=	token_classification_task.read_examples_from_file(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )
											# TODO clean up all this to leverage built-in features of tokenizers
											__lowerCamelCase    					=	token_classification_task.convert_examples_to_features(
											    SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__							,			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=SCREAMING_SNAKE_CASE__							,			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:
															__lowerCamelCase    					=	tf.data.Dataset.from_generator(
															    SCREAMING_SNAKE_CASE__							,			({'''input_ids''': tf.intaa, '''attention_mask''': tf.intaa}, tf.intaa)							,			(
															        {'''input_ids''': tf.TensorShape([None]  ), '''attention_mask''': tf.TensorShape([None]  )},
															        tf.TensorShape([None]  ),
															    )							,			)
											else:
															__lowerCamelCase    					=	tf.data.Dataset.from_generator(
															    SCREAMING_SNAKE_CASE__							,			({'''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		:							Union[str, Any]  )     ->							Union[str, Any]:
											__lowerCamelCase    					=	self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features  )  )  )

											return self.dataset






							def __len__(		self		:							List[Any]  )     ->							Any:
											return len(self.features  )







							def __getitem__(		self		:							List[str]							,			SCREAMING_SNAKE_CASE__		:							Optional[int]  )     ->							InputFeatures:
											return self.features[i]



