






import shutil
import tempfile
import unittest

from transformers import (
    SPIECE_UNDERLINE,
    AddedToken,
    BatchEncoding,
    NllbTokenizer,
    NllbTokenizerFast,
    is_torch_available,
)
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


lowercase			  =    get_tests_dir('''fixtures/test_sentencepiece.model''')


if is_torch_available():
							from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right

lowercase			  =    25_60_47
lowercase			  =    25_61_45




@require_sentencepiece
@require_tokenizers
class 		UpperCAmelCase_						(	lowercase_					,  unittest.TestCase					):


						'''simple docstring'''




						A     :   List[str]						=					NllbTokenizer
						A     :   List[str]						=					NllbTokenizerFast
						A     :   List[str]						=					True
						A     :   Any						=					True
						A     :   Dict						=					{}






						def       _lowerCAmelCase       (	self				) ->				int:
								super().setUp()

								# We have a SentencePiece fixture for testing
								snake_case_				:							Union[str, Any]						 =       NllbTokenizer(lowerCamelCase_		,				keep_accents=lowerCamelCase_				)
								tokenizer.save_pretrained(self.tmpdirname				)






						def       _lowerCAmelCase       (	self				) ->				Tuple:
								snake_case_				:							List[str]						 =       NllbTokenizer(lowerCamelCase_		,				keep_accents=lowerCamelCase_				)

								snake_case_				:							str						 =       tokenizer.tokenize("This is a test"				)
								self.assertListEqual(lowerCamelCase_		,				["▁This", "▁is", "▁a", "▁t", "est"]				)

								self.assertListEqual(
								    tokenizer.convert_tokens_to_ids(lowerCamelCase_				)		,				[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]		,				)

								snake_case_				:							Optional[int]						 =       tokenizer.tokenize("I was born in 92000, and this is falsé."				)
								self.assertListEqual(
								    lowerCamelCase_		,				[
								        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",
								        ".",
								    ]		,				)
								snake_case_				:							Union[str, Any]						 =       tokenizer.convert_tokens_to_ids(lowerCamelCase_				)
								self.assertListEqual(
								    lowerCamelCase_		,				[
								        value + tokenizer.fairseq_offset
								        for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
								    ]		,				)

								snake_case_				:							Optional[Any]						 =       tokenizer.convert_ids_to_tokens(lowerCamelCase_				)
								self.assertListEqual(
								    lowerCamelCase_		,				[
								        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>",
								        ".",
								    ]		,				)






						def       _lowerCAmelCase       (	self				) ->				Dict:
								snake_case_				:							Any						 =       (self.rust_tokenizer_class, """hf-internal-testing/tiny-random-nllb""", {})
								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
										with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''				):
												snake_case_				:							int						 =       self.rust_tokenizer_class.from_pretrained(lowerCamelCase_		,				**lowerCamelCase_				)
												snake_case_				:							Optional[Any]						 =       self.tokenizer_class.from_pretrained(lowerCamelCase_		,				**lowerCamelCase_				)

												snake_case_				:							Tuple						 =       tempfile.mkdtemp()

												snake_case_				:							List[str]						 =       tokenizer_r.save_pretrained(lowerCamelCase_				)
												snake_case_				:							Union[str, Any]						 =       tokenizer_p.save_pretrained(lowerCamelCase_				)

												# Checks it save with the same files + the tokenizer.json file for the fast one
												self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files				)				)
												snake_case_				:							Any						 =       tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f				)
												self.assertSequenceEqual(lowerCamelCase_		,				lowerCamelCase_				)

												# Checks everything loads correctly in the same way
												snake_case_				:							Dict						 =       tokenizer_r.from_pretrained(lowerCamelCase_				)
												snake_case_				:							Dict						 =       tokenizer_p.from_pretrained(lowerCamelCase_				)

												# Check special tokens are set accordingly on Rust and Python
												for key in tokenizer_pp.special_tokens_map:
														self.assertTrue(hasattr(lowerCamelCase_		,				lowerCamelCase_				)				)

												shutil.rmtree(lowerCamelCase_				)

												# Save tokenizer rust, legacy_format=True
												snake_case_				:							Tuple						 =       tempfile.mkdtemp()

												snake_case_				:							str						 =       tokenizer_r.save_pretrained(lowerCamelCase_		,				legacy_format=lowerCamelCase_				)
												snake_case_				:							int						 =       tokenizer_p.save_pretrained(lowerCamelCase_				)

												# Checks it save with the same files
												self.assertSequenceEqual(lowerCamelCase_		,				lowerCamelCase_				)

												# Checks everything loads correctly in the same way
												snake_case_				:							str						 =       tokenizer_r.from_pretrained(lowerCamelCase_				)
												snake_case_				:							Dict						 =       tokenizer_p.from_pretrained(lowerCamelCase_				)

												# Check special tokens are set accordingly on Rust and Python
												for key in tokenizer_pp.special_tokens_map:
														self.assertTrue(hasattr(lowerCamelCase_		,				lowerCamelCase_				)				)

												shutil.rmtree(lowerCamelCase_				)

												# Save tokenizer rust, legacy_format=False
												snake_case_				:							Tuple						 =       tempfile.mkdtemp()

												snake_case_				:							Dict						 =       tokenizer_r.save_pretrained(lowerCamelCase_		,				legacy_format=lowerCamelCase_				)
												snake_case_				:							Any						 =       tokenizer_p.save_pretrained(lowerCamelCase_				)

												# Checks it saved the tokenizer.json file
												self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files				)				)

												# Checks everything loads correctly in the same way
												snake_case_				:							int						 =       tokenizer_r.from_pretrained(lowerCamelCase_				)
												snake_case_				:							Optional[Any]						 =       tokenizer_p.from_pretrained(lowerCamelCase_				)

												# Check special tokens are set accordingly on Rust and Python
												for key in tokenizer_pp.special_tokens_map:
														self.assertTrue(hasattr(lowerCamelCase_		,				lowerCamelCase_				)				)

												shutil.rmtree(lowerCamelCase_				)






						@require_torch
						def       _lowerCAmelCase       (	self				) ->				Optional[Any]:



								if not self.test_seqaseq:
										return

								snake_case_				:							int						 =       self.get_tokenizers()
								for tokenizer in tokenizers:
										with self.subTest(f'''{tokenizer.__class__.__name__}'''				):
												# Longer text that will definitely require truncation.
												snake_case_				:							Optional[int]						 =       [
												    """ UN Chief Says There Is No Military Solution in Syria""",
												    """ Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for"""
												    """ Syria is that 'there is no military solution' to the nearly five-year conflict and more weapons"""
												    """ will only worsen the violence and misery for millions of people.""",
												]
												snake_case_				:							Tuple						 =       [
												    """Şeful ONU declară că nu există o soluţie militară în Siria""",
												    """Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al"""
												    """ Rusiei pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi"""
												    """ că noi arme nu vor face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.""",
												]
												try:
														snake_case_				:							Optional[Any]						 =       tokenizer.prepare_seqaseq_batch(
														    src_texts=lowerCamelCase_		,				tgt_texts=lowerCamelCase_		,				max_length=3		,				max_target_length=10		,				return_tensors="pt"		,				src_lang="eng_Latn"		,				tgt_lang="ron_Latn"		,				)
												except NotImplementedError:
														return
												self.assertEqual(batch.input_ids.shape[1]		,				3				)
												self.assertEqual(batch.labels.shape[1]		,				10				)
												# max_target_length will default to max_length if not specified
												snake_case_				:							List[str]						 =       tokenizer.prepare_seqaseq_batch(
												    lowerCamelCase_		,				tgt_texts=lowerCamelCase_		,				max_length=3		,				return_tensors="pt"				)
												self.assertEqual(batch.input_ids.shape[1]		,				3				)
												self.assertEqual(batch.labels.shape[1]		,				3				)

												snake_case_				:							Optional[Any]						 =       tokenizer.prepare_seqaseq_batch(
												    src_texts=lowerCamelCase_		,				max_length=3		,				max_target_length=10		,				return_tensors="pt"				)
												self.assertEqual(batch_encoder_only.input_ids.shape[1]		,				3				)
												self.assertEqual(batch_encoder_only.attention_mask.shape[1]		,				3				)
												self.assertNotIn("decoder_input_ids"		,				lowerCamelCase_				)






						@unittest.skip("Unfortunately way too slow to build a BPE with SentencePiece."				)
						def       _lowerCAmelCase       (	self				) ->				Optional[Any]:
								pass






						def       _lowerCAmelCase       (	self				) ->				Any:



								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
										with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''				):
												snake_case_				:							int						 =       [AddedToken("<special>"		,				lstrip=lowerCamelCase_				)]

												snake_case_				:							int						 =       self.rust_tokenizer_class.from_pretrained(
												    lowerCamelCase_		,				additional_special_tokens=lowerCamelCase_		,				**lowerCamelCase_				)
												snake_case_				:							List[Any]						 =       tokenizer_r.encode("Hey this is a <special> token"				)

												snake_case_				:							Tuple						 =       tokenizer_r.encode("<special>"		,				add_special_tokens=lowerCamelCase_				)[0]

												self.assertTrue(special_token_id in r_output				)

												if self.test_slow_tokenizer:
														snake_case_				:							Dict						 =       self.rust_tokenizer_class.from_pretrained(
														    lowerCamelCase_		,				additional_special_tokens=lowerCamelCase_		,				**lowerCamelCase_		,				)
														snake_case_				:							List[Any]						 =       self.tokenizer_class.from_pretrained(
														    lowerCamelCase_		,				additional_special_tokens=lowerCamelCase_		,				**lowerCamelCase_				)

														snake_case_				:							Union[str, Any]						 =       tokenizer_p.encode("Hey this is a <special> token"				)

														snake_case_				:							Any						 =       tokenizer_cr.encode("Hey this is a <special> token"				)

														self.assertEqual(lowerCamelCase_		,				lowerCamelCase_				)
														self.assertEqual(lowerCamelCase_		,				lowerCamelCase_				)
														self.assertTrue(special_token_id in p_output				)
														self.assertTrue(special_token_id in cr_output				)




@require_torch
@require_sentencepiece
@require_tokenizers
class 		UpperCAmelCase_						(	unittest.TestCase					):


						'''simple docstring'''




						A     :   Optional[int]						=					'facebook/nllb-200-distilled-600M'
						A     :   Any						=					[
						    ' UN Chief Says There Is No Military Solution in Syria',
						    ' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.',
						]
						A     :   List[str]						=					[
						    'Şeful ONU declară că nu există o soluţie militară în Siria',
						    'Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei'
						    ' pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor'
						    ' face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.',
						]
						A     :   int						=					[
						    25_60_47,
						    1_62_97,
						    13_44_08,
						    81_65,
						    24_80_66,
						    1_47_34,
						    9_50,
						    11_35,
						    10_57_21,
						    35_73,
						    83,
						    2_73_52,
						    1_08,
						    4_94_86,
						    2,
						]






						@classmethod
						def       _lowerCAmelCase       (	cls				) ->				List[str]:
								snake_case_				:							NllbTokenizer						 =       NllbTokenizer.from_pretrained(
								    cls.checkpoint_name		,				src_lang="eng_Latn"		,				tgt_lang="ron_Latn"				)
								snake_case_				:							List[Any]						 =       1
								return cls






						def       _lowerCAmelCase       (	self				) ->				List[str]:
								self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ace_Arab"]		,				25_6001				)
								self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ace_Latn"]		,				25_6002				)
								self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["fra_Latn"]		,				25_6057				)






						def       _lowerCAmelCase       (	self				) ->				Dict:
								snake_case_				:							List[Any]						 =       self.tokenizer.batch_encode_plus(self.src_text				).input_ids[0]
								self.assertListEqual(self.expected_src_tokens		,				lowerCamelCase_				)






						def       _lowerCAmelCase       (	self				) ->				Union[str, Any]:
								self.assertIn(lowerCamelCase_		,				self.tokenizer.all_special_ids				)
								# fmt: off
								snake_case_				:							Tuple						 =       [RO_CODE, 4254, 9_8068, 11_2923, 3_9072, 3909, 713, 10_2767, 26, 1_7314, 3_5642, 1_4683, 3_3118, 2022, 6_6987, 2, 25_6047]
								# fmt: on

								snake_case_				:							Dict						 =       self.tokenizer.decode(lowerCamelCase_		,				skip_special_tokens=lowerCamelCase_				)
								snake_case_				:							List[Any]						 =       self.tokenizer.decode(generated_ids[1:]		,				skip_special_tokens=lowerCamelCase_				)
								self.assertEqual(lowerCamelCase_		,				lowerCamelCase_				)
								self.assertNotIn(self.tokenizer.eos_token		,				lowerCamelCase_				)






						def       _lowerCAmelCase       (	self				) ->				Optional[int]:
								snake_case_				:							int						 =       ["""this is gunna be a long sentence """ * 20]
								assert isinstance(src_text[0]		,				lowerCamelCase_				)
								snake_case_				:							Dict						 =       10
								snake_case_				:							int						 =       self.tokenizer(lowerCamelCase_		,				max_length=lowerCamelCase_		,				truncation=lowerCamelCase_				).input_ids[0]
								self.assertEqual(ids[-1]		,				2				)
								self.assertEqual(ids[0]		,				lowerCamelCase_				)
								self.assertEqual(len(lowerCamelCase_				)		,				lowerCamelCase_				)






						def       _lowerCAmelCase       (	self				) ->				str:
								self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "ar_AR"]				)		,				[25_6203, 3]				)






						def       _lowerCAmelCase       (	self				) ->				str:
								snake_case_				:							Tuple						 =       tempfile.mkdtemp()
								snake_case_				:							str						 =       self.tokenizer.fairseq_tokens_to_ids
								self.tokenizer.save_pretrained(lowerCamelCase_				)
								snake_case_				:							List[str]						 =       NllbTokenizer.from_pretrained(lowerCamelCase_				)
								self.assertDictEqual(new_tok.fairseq_tokens_to_ids		,				lowerCamelCase_				)






						@require_torch
						def       _lowerCAmelCase       (	self				) ->				Dict:
								snake_case_				:							Dict						 =       self.tokenizer(
								    self.src_text		,				text_target=self.tgt_text		,				padding=lowerCamelCase_		,				truncation=lowerCamelCase_		,				max_length=len(self.expected_src_tokens				)		,				return_tensors="pt"		,				)
								snake_case_				:							List[str]						 =       shift_tokens_right(
								    batch["labels"]		,				self.tokenizer.pad_token_id		,				self.tokenizer.lang_code_to_id["ron_Latn"]				)

								self.assertIsInstance(lowerCamelCase_		,				lowerCamelCase_				)

								self.assertEqual((2, 15)		,				batch.input_ids.shape				)
								self.assertEqual((2, 15)		,				batch.attention_mask.shape				)
								snake_case_				:							Union[str, Any]						 =       batch.input_ids.tolist()[0]
								self.assertListEqual(self.expected_src_tokens		,				lowerCamelCase_				)
								self.assertEqual(lowerCamelCase_		,				batch.decoder_input_ids[0, 0]				)  # EOS
								# Test that special tokens are reset
								self.assertEqual(self.tokenizer.prefix_tokens		,				[EN_CODE]				)
								self.assertEqual(self.tokenizer.suffix_tokens		,				[self.tokenizer.eos_token_id]				)






						def       _lowerCAmelCase       (	self				) ->				Union[str, Any]:
								snake_case_				:							int						 =       self.tokenizer(self.src_text		,				padding=lowerCamelCase_		,				truncation=lowerCamelCase_		,				max_length=3		,				return_tensors="pt"				)
								snake_case_				:							Union[str, Any]						 =       self.tokenizer(
								    text_target=self.tgt_text		,				padding=lowerCamelCase_		,				truncation=lowerCamelCase_		,				max_length=10		,				return_tensors="pt"				)
								snake_case_				:							str						 =       targets["""input_ids"""]
								snake_case_				:							Optional[int]						 =       shift_tokens_right(
								    lowerCamelCase_		,				self.tokenizer.pad_token_id		,				decoder_start_token_id=self.tokenizer.lang_code_to_id[self.tokenizer.tgt_lang]		,				)

								self.assertEqual(batch.input_ids.shape[1]		,				3				)
								self.assertEqual(batch.decoder_input_ids.shape[1]		,				10				)






						@require_torch
						def       _lowerCAmelCase       (	self				) ->				List[Any]:
								snake_case_				:							int						 =       self.tokenizer._build_translation_inputs(
								    "A test"		,				return_tensors="pt"		,				src_lang="eng_Latn"		,				tgt_lang="fra_Latn"				)

								self.assertEqual(
								    nested_simplify(lowerCamelCase_				)		,				{
								        # A, test, EOS, en_XX
								        "input_ids": [[25_6047, 70, 7356, 2]],
								        "attention_mask": [[1, 1, 1, 1]],
								        # ar_AR
								        "forced_bos_token_id": 25_6057,
								    }		,				)






						@require_torch
						def       _lowerCAmelCase       (	self				) ->				str:
								snake_case_				:							List[Any]						 =       True
								snake_case_				:							Dict						 =       self.tokenizer(
								    "UN Chief says there is no military solution in Syria"		,				src_lang="eng_Latn"		,				tgt_lang="fra_Latn"				)
								self.assertEqual(
								    inputs.input_ids		,				[1_6297, 13_4408, 2_5653, 6370, 248, 254, 10_3929, 9_4995, 108, 4_9486, 2, 25_6047]				)

								snake_case_				:							List[str]						 =       False
								snake_case_				:							int						 =       self.tokenizer(
								    "UN Chief says there is no military solution in Syria"		,				src_lang="eng_Latn"		,				tgt_lang="fra_Latn"				)
								self.assertEqual(
								    inputs.input_ids		,				[25_6047, 1_6297, 13_4408, 2_5653, 6370, 248, 254, 10_3929, 9_4995, 108, 4_9486, 2]				)








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

import tqdm
from filelock import FileLock

from transformers import (
    BartTokenizer,
    BartTokenizerFast,
    DataProcessor,
    PreTrainedTokenizer,
    RobertaTokenizer,
    RobertaTokenizerFast,
    XLMRobertaTokenizer,
    is_tf_available,
    is_torch_available,
)


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




@dataclass(frozen=SCREAMING_SNAKE_CASE__					)
class 		UpperCAmelCase_						:


						'''simple docstring'''




						A     :   str
						A     :   str
						A     :   Optional[str]						=					None
						A     :   Optional[str]						=					None
						A     :   Optional[str]						=					None




@dataclass(frozen=SCREAMING_SNAKE_CASE__					)
class 		UpperCAmelCase_						:


						'''simple docstring'''




						A     :   List[int]
						A     :   Optional[List[int]]						=					None
						A     :   Optional[List[int]]						=					None
						A     :   Optional[Union[int, float]]						=					None
						A     :   Optional[int]						=					None


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




							class 		UpperCAmelCase_						(	SCREAMING_SNAKE_CASE__					):


													'''simple docstring'''




													A     :   List[InputFeatures]






													def __init__(	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE = None		,				_SCREAMING_SNAKE_CASE=False		,				_SCREAMING_SNAKE_CASE = False		,				) ->				Union[str, Any]:
															snake_case_				:							str						 =       hans_processors[task]()

															snake_case_				:							Tuple						 =       os.path.join(
															    _SCREAMING_SNAKE_CASE		,				"cached_{}_{}_{}_{}".format(
															        "dev" if evaluate else "train"		,				tokenizer.__class__.__name__		,				str(_SCREAMING_SNAKE_CASE				)		,				_SCREAMING_SNAKE_CASE		,				)		,				)
															snake_case_				:							Dict						 =       processor.get_labels()
															if tokenizer.__class__ in (
															    RobertaTokenizer,
															    RobertaTokenizerFast,
															    XLMRobertaTokenizer,
															    BartTokenizer,
															    BartTokenizerFast,
															):
																	# HACK(label indices are swapped in RoBERTa pretrained model)
																	snake_case_ ,       snake_case_				:							Any						 =       label_list[2], label_list[1]
															snake_case_				:							Tuple						 =       label_list

															# Make sure only the first process in distributed training processes the dataset,
															# and the others will use the cache.
															snake_case_				:							Dict						 =       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}'''				)
																			snake_case_				:							Dict						 =       torch.load(_SCREAMING_SNAKE_CASE				)
																	else:
																			logger.info(f'''Creating features from dataset file at {data_dir}'''				)

																			snake_case_				:							Any						 =       (
																			    processor.get_dev_examples(_SCREAMING_SNAKE_CASE				) if evaluate else processor.get_train_examples(_SCREAMING_SNAKE_CASE				)
																			)

																			logger.info("Training examples: %s"		,				len(_SCREAMING_SNAKE_CASE				)				)
																			snake_case_				:							Union[str, Any]						 =       hans_convert_examples_to_features(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)
																			logger.info("Saving features into cached file %s"		,				_SCREAMING_SNAKE_CASE				)
																			torch.save(self.features		,				_SCREAMING_SNAKE_CASE				)






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






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






													def       _lowerCAmelCase       (	self				) ->				Dict:
															return self.label_list


if is_tf_available():
							import tensorflow as tf




							class 		UpperCAmelCase_						:


													'''simple docstring'''




													A     :   List[InputFeatures]






													def __init__(	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE = 128		,				_SCREAMING_SNAKE_CASE=False		,				_SCREAMING_SNAKE_CASE = False		,				) ->				Optional[int]:
															snake_case_				:							Tuple						 =       hans_processors[task]()
															snake_case_				:							Dict						 =       processor.get_labels()
															if tokenizer.__class__ in (
															    RobertaTokenizer,
															    RobertaTokenizerFast,
															    XLMRobertaTokenizer,
															    BartTokenizer,
															    BartTokenizerFast,
															):
																	# HACK(label indices are swapped in RoBERTa pretrained model)
																	snake_case_ ,       snake_case_				:							List[str]						 =       label_list[2], label_list[1]
															snake_case_				:							Dict						 =       label_list

															snake_case_				:							int						 =       processor.get_dev_examples(_SCREAMING_SNAKE_CASE				) if evaluate else processor.get_train_examples(_SCREAMING_SNAKE_CASE				)
															snake_case_				:							str						 =       hans_convert_examples_to_features(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)

															def gen():
																	for ex_index, ex in tqdm.tqdm(enumerate(self.features				)		,				desc="convert examples to features"				):
																			if ex_index % 1_0000 == 0:
																					logger.info("Writing example %d of %d" % (ex_index, len(_SCREAMING_SNAKE_CASE				))				)

																			yield (
																			    {
																			        "example_id": 0,
																			        "input_ids": ex.input_ids,
																			        "attention_mask": ex.attention_mask,
																			        "token_type_ids": ex.token_type_ids,
																			    },
																			    ex.label,
																			)

															snake_case_				:							List[Any]						 =       tf.data.Dataset.from_generator(
															    _SCREAMING_SNAKE_CASE		,				(
															        {
															            "example_id": tf.intaa,
															            "input_ids": tf.intaa,
															            "attention_mask": tf.intaa,
															            "token_type_ids": tf.intaa,
															        },
															        tf.intaa,
															    )		,				(
															        {
															            "example_id": tf.TensorShape([]				),
															            "input_ids": tf.TensorShape([None, None]				),
															            "attention_mask": tf.TensorShape([None, None]				),
															            "token_type_ids": tf.TensorShape([None, None]				),
															        },
															        tf.TensorShape([]				),
															    )		,				)






													def       _lowerCAmelCase       (	self				) ->				int:
															return self.dataset






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






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






													def       _lowerCAmelCase       (	self				) ->				Dict:
															return self.label_list




class 		UpperCAmelCase_						(	SCREAMING_SNAKE_CASE__					):


						'''simple docstring'''




						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE				) ->				int:
								return self._create_examples(self._read_tsv(os.path.join(_SCREAMING_SNAKE_CASE		,				"heuristics_train_set.txt"				)				)		,				"train"				)






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE				) ->				List[Any]:
								return self._create_examples(self._read_tsv(os.path.join(_SCREAMING_SNAKE_CASE		,				"heuristics_evaluation_set.txt"				)				)		,				"dev"				)






						def       _lowerCAmelCase       (	self				) ->				Dict:
								return ["contradiction", "entailment", "neutral"]






						def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				) ->				Optional[int]:
								snake_case_				:							Optional[Any]						 =       []
								for i, line in enumerate(_SCREAMING_SNAKE_CASE				):
										if i == 0:
												continue
										snake_case_				:							List[Any]						 =       "%s-%s" % (set_type, line[0])
										snake_case_				:							int						 =       line[5]
										snake_case_				:							str						 =       line[6]
										snake_case_				:							Any						 =       line[7][2:] if line[7].startswith("ex"				) else line[7]
										snake_case_				:							Dict						 =       line[0]
										examples.append(InputExample(guid=_SCREAMING_SNAKE_CASE		,				text_a=_SCREAMING_SNAKE_CASE		,				text_b=_SCREAMING_SNAKE_CASE		,				label=_SCREAMING_SNAKE_CASE		,				pairID=_SCREAMING_SNAKE_CASE				)				)
								return examples




def      lowerCAmelCase__	(    _a       :			List[InputExample]  ,      _a       :			List[str]  ,      _a       :			int  ,      _a       :			PreTrainedTokenizer  ,      ):
		snake_case_				:							Optional[Any]						 =       {label: i for i, label in enumerate(_a    )}

		snake_case_				:							Optional[Any]						 =       []
		for ex_index, example in tqdm.tqdm(enumerate(_a    )  ,      desc="convert examples to features"    ):
				if ex_index % 1_00_00 == 0:
						logger.info("Writing example %d" % (ex_index)    )

				snake_case_				:							Optional[Any]						 =       tokenizer(
				    example.text_a  ,      example.text_b  ,      add_special_tokens=_a  ,      max_length=_a  ,      padding="max_length"  ,      truncation=_a  ,      return_overflowing_tokens=_a  ,      )

				snake_case_				:							Dict						 =       label_map[example.label] if example.label in label_map else 0

				snake_case_				:							Optional[Any]						 =       int(example.pairID    )

				features.append(InputFeatures(**_a  ,      label=_a  ,      pairID=_a    )    )

		for i, example in enumerate(examples[:5]    ):
				logger.info("*** Example ***"    )
				logger.info(F'''guid: {example}'''    )
				logger.info(F'''features: {features[i]}'''    )

		return features


lowercase  :							List[str]			  =    {
    '''hans''': 3,
}

lowercase  :							int			  =    {
    '''hans''': HansProcessor,
}
