






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,
)


__A      =				logging.getLogger(__name__)





@dataclass(frozen=snake_case					)
class 						SCREAMING_SNAKE_CASE				:





							"""simple docstring"""

							A_						  =					42
							A_						  =					42
							A_						  =					None
							A_						  =					None
							A_						  =					None





@dataclass(frozen=snake_case					)
class 						SCREAMING_SNAKE_CASE				:





							"""simple docstring"""

							A_						  =					42
							A_						  =					None
							A_						  =					None
							A_						  =					None
							A_						  =					None


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





				class 						SCREAMING_SNAKE_CASE				( snake_case					):





											"""simple docstring"""

											A_						  =					42

											def __init__(  self:   Optional[int]		,     __A:   str		,     __A:   PreTrainedTokenizer		,     __A:   str		,     __A:   Optional[int] = None		,     __A:   str=False		,     __A:   bool = False		,     )    ->				Dict:
																_A								= hans_processors[task]()

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

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

																										_A								= (
																										    processor.get_dev_examples(__A   ) if evaluate else processor.get_train_examples(__A   )
																										)

																										logger.info('''Training examples: %s'''		,     len(__A   )   )
																										_A								= hans_convert_examples_to_features(__A		,     __A		,     __A		,     __A   )
																										logger.info('''Saving features into cached file %s'''		,     __A   )
																										torch.save(self.features		,     __A   )

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

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



											def       __A (  self:   Tuple   )    ->				Optional[Any]:
																return self.label_list


if is_tf_available():
				import tensorflow as tf





				class 						SCREAMING_SNAKE_CASE				:





											"""simple docstring"""

											A_						  =					42

											def __init__(  self:   Tuple		,     __A:   str		,     __A:   PreTrainedTokenizer		,     __A:   str		,     __A:   Optional[int] = 1_28		,     __A:   Dict=False		,     __A:   bool = False		,     )    ->				Optional[int]:
																_A								= hans_processors[task]()
																_A								= processor.get_labels()
																if tokenizer.__class__ in (
																    RobertaTokenizer,
																    RobertaTokenizerFast,
																    XLMRobertaTokenizer,
																    BartTokenizer,
																    BartTokenizerFast,
																):
																					# HACK(label indices are swapped in RoBERTa pretrained model)
																					_A							,_A								= label_list[2], label_list[1]
																_A								= label_list

																_A								= processor.get_dev_examples(__A   ) if evaluate else processor.get_train_examples(__A   )
																_A								= hans_convert_examples_to_features(__A		,     __A		,     __A		,     __A   )

																def gen():
																					for ex_index, ex in tqdm.tqdm(enumerate(self.features   )		,     desc='''convert examples to features'''   ):
																										if ex_index % 1_00_00 == 0:
																															logger.info('''Writing example %d of %d''' % (ex_index, len(__A   ))   )

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

																_A								= tf.data.Dataset.from_generator(
																    __A		,     (
																        {
																            '''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       __A (  self:   int   )    ->				Dict:
																return self.dataset

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

											def __getitem__(  self:   int		,     __A:   Optional[Any]   )    ->				InputFeatures:
																return self.features[i]



											def       __A (  self:   List[str]   )    ->				int:
																return self.label_list





class 						SCREAMING_SNAKE_CASE				( snake_case					):





							"""simple docstring"""

							def       __A (  self:   Tuple		,     __A:   List[str]   )    ->				str:
												return self._create_examples(self._read_tsv(os.path.join(__A		,     '''heuristics_train_set.txt'''   )   )		,     '''train'''   )

							def       __A (  self:   Any		,     __A:   str   )    ->				int:
												return self._create_examples(self._read_tsv(os.path.join(__A		,     '''heuristics_evaluation_set.txt'''   )   )		,     '''dev'''   )

							def       __A (  self:   Tuple   )    ->				Any:
												return ["contradiction", "entailment", "neutral"]



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




def      __A					(     _lowercase					,			_lowercase					,			_lowercase					,			_lowercase					,			):

					'''simple docstring'''







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

					_A								= []
					for ex_index, example in tqdm.tqdm(enumerate(_lowercase )					,			desc='''convert examples to features''' ):
										if ex_index % 1_00_00 == 0:
															logger.info('''Writing example %d''' % (ex_index) )

										_A								= tokenizer(
										    example.text_a					,			example.text_b					,			add_special_tokens=_lowercase					,			max_length=_lowercase					,			padding='''max_length'''					,			truncation=_lowercase					,			return_overflowing_tokens=_lowercase					,			)

										_A								= label_map[example.label] if example.label in label_map else 0

										_A								= int(example.pairID )

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

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

					return features


__A      =				{
    'hans': 3,
}


__A      =				{
    'hans': HansProcessor,
}











import json
import os
import unittest

from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
    VOCAB_FILES_NAMES,
    BlenderbotSmallTokenizer,
)

from ...test_tokenization_common import TokenizerTesterMixin





class 						SCREAMING_SNAKE_CASE				( snake_case  ,	unittest.TestCase					):





							"""simple docstring"""

							A_						  =					BlenderbotSmallTokenizer
							A_						  =					False

							def       __A (  self:   List[str]   )    ->				int:
												super().setUp()

												_A								= ['''__start__''', '''adapt''', '''act''', '''ap@@''', '''te''', '''__end__''', '''__unk__''']
												_A								= dict(zip(__A		,     range(len(__A   )   )   )   )

												_A								= ['''#version: 0.2''', '''a p''', '''t e</w>''', '''ap t</w>''', '''a d''', '''ad apt</w>''', '''a c''', '''ac t</w>''', '''''']
												_A								= {'''unk_token''': '''__unk__''', '''bos_token''': '''__start__''', '''eos_token''': '''__end__'''}

												_A								= os.path.join(self.tmpdirname		,     VOCAB_FILES_NAMES['''vocab_file''']   )
												_A								= os.path.join(self.tmpdirname		,     VOCAB_FILES_NAMES['''merges_file''']   )
												with open(self.vocab_file		,     '''w'''		,     encoding='''utf-8'''   ) as fp:
																	fp.write(json.dumps(__A   ) + '''\n'''   )
												with open(self.merges_file		,     '''w'''		,     encoding='''utf-8'''   ) as fp:
																	fp.write('''\n'''.join(__A   )   )

							def       __A (  self:   str		,     **__A:   Optional[Any]   )    ->				Dict:
												kwargs.update(self.special_tokens_map   )
												return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname		,     **__A   )

							def       __A (  self:   str		,     __A:   List[str]   )    ->				int:
												_A								= '''adapt act apte'''
												_A								= '''adapt act apte'''
												return input_text, output_text

							def       __A (  self:   Union[str, Any]   )    ->				Any:
												_A								= BlenderbotSmallTokenizer(self.vocab_file		,     self.merges_file		,     **self.special_tokens_map   )
												_A								= '''adapt act apte'''
												_A								= ['''adapt''', '''act''', '''ap@@''', '''te''']
												_A								= tokenizer.tokenize(__A   )
												self.assertListEqual(__A		,     __A   )

												_A								= [tokenizer.bos_token] + tokens + [tokenizer.eos_token]

												_A								= [0, 1, 2, 3, 4, 5]
												self.assertListEqual(tokenizer.convert_tokens_to_ids(__A   )		,     __A   )

							def       __A (  self:   Any   )    ->				List[str]:
												_A								= BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''   )
												assert tok('''sam'''   ).input_ids == [13_84]
												_A								= '''I am a small frog.'''
												_A								= tok([src_text]		,     padding=__A		,     truncation=__A   )['''input_ids''']
												_A								= tok.batch_decode(__A		,     skip_special_tokens=__A		,     clean_up_tokenization_spaces=__A   )[0]
												assert src_text != decoded  # I wish it did!
												assert decoded == "i am a small frog ."



							def       __A (  self:   Any   )    ->				int:
												_A								= BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''   )
												_A								= '''I am a small frog .'''
												_A								= '''.'''
												_A								= tok(__A   )['''input_ids''']
												_A								= tok(__A   )['''input_ids''']

												assert encoded[-1] == encoded_dot[0]



