



import os
import shutil
import sys
import tempfile
import unittest
from pathlib import Path

import pytest

import transformers
from transformers import (
    BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    AutoTokenizer,
    BertConfig,
    BertTokenizer,
    BertTokenizerFast,
    CTRLTokenizer,
    GPTaTokenizer,
    GPTaTokenizerFast,
    PreTrainedTokenizerFast,
    RobertaTokenizer,
    RobertaTokenizerFast,
    is_tokenizers_available,
)
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.auto.tokenization_auto import (
    TOKENIZER_MAPPING,
    get_tokenizer_config,
    tokenizer_class_from_name,
)
from transformers.models.roberta.configuration_roberta import RobertaConfig
from transformers.testing_utils import (
    DUMMY_DIFF_TOKENIZER_IDENTIFIER,
    DUMMY_UNKNOWN_IDENTIFIER,
    SMALL_MODEL_IDENTIFIER,
    RequestCounter,
    require_tokenizers,
    slow,
)


sys.path.append(str(Path(__file__).parent.parent.parent.parent / """utils"""))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_tokenization import CustomTokenizer  # noqa E402


if is_tokenizers_available():
							from test_module.custom_tokenization_fast import CustomTokenizerFast

class 	lowerCamelCase_    ( unittest.TestCase						):






						'''simple docstring'''







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str] )      ->				Union[str, Any]:
									A							:					str              =						0







						@slow
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Tuple )      ->				List[Any]:
									for model_name in (x for x in BERT_PRETRAINED_CONFIG_ARCHIVE_MAP.keys() if "japanese" not in x):
												A							:					Any              =						AutoTokenizer.from_pretrained(__lowerCamelCase )
												self.assertIsNotNone(__lowerCamelCase )
												self.assertIsInstance(__lowerCamelCase	,	(BertTokenizer, BertTokenizerFast) )
												self.assertGreater(len(__lowerCamelCase )	,	0 )

									for model_name in GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP.keys():
												A							:					Any              =						AutoTokenizer.from_pretrained(__lowerCamelCase )
												self.assertIsNotNone(__lowerCamelCase )
												self.assertIsInstance(__lowerCamelCase	,	(GPTaTokenizer, GPTaTokenizerFast) )
												self.assertGreater(len(__lowerCamelCase )	,	0 )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str] )      ->				List[str]:
									A							:					str              =						AutoTokenizer.from_pretrained(__lowerCamelCase )
									self.assertIsInstance(__lowerCamelCase	,	(BertTokenizer, BertTokenizerFast) )
									self.assertEqual(tokenizer.vocab_size	,	12 )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str] )      ->				List[Any]:
									A							:					Optional[int]              =						AutoTokenizer.from_pretrained(__lowerCamelCase )
									self.assertIsInstance(__lowerCamelCase	,	(RobertaTokenizer, RobertaTokenizerFast) )
									self.assertEqual(tokenizer.vocab_size	,	20 )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Tuple )      ->				List[Any]:
									A							:					Optional[int]              =						AutoConfig.from_pretrained(__lowerCamelCase )
									self.assertIsInstance(__lowerCamelCase	,	__lowerCamelCase )
									# Check that tokenizer_type ≠ model_type
									A							:					Optional[Any]              =						AutoTokenizer.from_pretrained(__lowerCamelCase	,	config=__lowerCamelCase )
									self.assertIsInstance(__lowerCamelCase	,	(BertTokenizer, BertTokenizerFast) )
									self.assertEqual(tokenizer.vocab_size	,	12 )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Dict )      ->				int:
									with tempfile.TemporaryDirectory() as tmp_dir:
												shutil.copy("./tests/fixtures/vocab.txt"	,	os.path.join(__lowerCamelCase	,	"vocab.txt" ) )

												A							:					Optional[int]              =						AutoTokenizer.from_pretrained(__lowerCamelCase	,	tokenizer_type="bert"	,	use_fast=__lowerCamelCase )
												self.assertIsInstance(__lowerCamelCase	,	__lowerCamelCase )

									with tempfile.TemporaryDirectory() as tmp_dir:
												shutil.copy("./tests/fixtures/vocab.json"	,	os.path.join(__lowerCamelCase	,	"vocab.json" ) )
												shutil.copy("./tests/fixtures/merges.txt"	,	os.path.join(__lowerCamelCase	,	"merges.txt" ) )

												A							:					List[str]              =						AutoTokenizer.from_pretrained(__lowerCamelCase	,	tokenizer_type="gpt2"	,	use_fast=__lowerCamelCase )
												self.assertIsInstance(__lowerCamelCase	,	__lowerCamelCase )







						@require_tokenizers
						def 			SCREAMING_SNAKE_CASE__		(	self      :			str )      ->				List[Any]:
									with tempfile.TemporaryDirectory() as tmp_dir:
												shutil.copy("./tests/fixtures/vocab.txt"	,	os.path.join(__lowerCamelCase	,	"vocab.txt" ) )

												A							:					str              =						AutoTokenizer.from_pretrained(__lowerCamelCase	,	tokenizer_type="bert" )
												self.assertIsInstance(__lowerCamelCase	,	__lowerCamelCase )

									with tempfile.TemporaryDirectory() as tmp_dir:
												shutil.copy("./tests/fixtures/vocab.json"	,	os.path.join(__lowerCamelCase	,	"vocab.json" ) )
												shutil.copy("./tests/fixtures/merges.txt"	,	os.path.join(__lowerCamelCase	,	"merges.txt" ) )

												A							:					Optional[int]              =						AutoTokenizer.from_pretrained(__lowerCamelCase	,	tokenizer_type="gpt2" )
												self.assertIsInstance(__lowerCamelCase	,	__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			int )      ->				Dict:
									with pytest.raises(__lowerCamelCase ):
												AutoTokenizer.from_pretrained("./"	,	tokenizer_type="xxx" )







						@require_tokenizers
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[int] )      ->				Any:
									for tokenizer_class in [BertTokenizer, BertTokenizerFast, AutoTokenizer]:
												A							:					Optional[int]              =						tokenizer_class.from_pretrained("wietsedv/bert-base-dutch-cased" )
												self.assertIsInstance(__lowerCamelCase	,	(BertTokenizer, BertTokenizerFast) )

												if isinstance(__lowerCamelCase	,	__lowerCamelCase ):
															self.assertEqual(tokenizer.basic_tokenizer.do_lower_case	,	__lowerCamelCase )
												else:
															self.assertEqual(tokenizer.do_lower_case	,	__lowerCamelCase )

												self.assertEqual(tokenizer.model_max_length	,	5_12 )







						@require_tokenizers
						def 			SCREAMING_SNAKE_CASE__		(	self      :			int )      ->				str:
									for tokenizer_class in [BertTokenizer, BertTokenizerFast, AutoTokenizer]:
												with self.assertRaisesRegex(
												    __lowerCamelCase	,	"julien-c/herlolip-not-exists is not a local folder and is not a valid model identifier"	,	):
															A							:					Dict              =						tokenizer_class.from_pretrained("julien-c/herlolip-not-exists" )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Union[str, Any] )      ->				str:
									# tests: https://github.com/huggingface/transformers/pull/13251
									# 1. models with `-`, e.g. xlm-roberta -> xlm_roberta
									# 2. models that don't remap 1-1 from model-name to model file, e.g., openai-gpt -> openai
									A							:					List[Any]              =						TOKENIZER_MAPPING.values()
									A							:					List[Any]              =						[]

									for slow_tok, fast_tok in tokenizers:
												if slow_tok is not None:
															tokenizer_names.append(slow_tok.__name__ )

												if fast_tok is not None:
															tokenizer_names.append(fast_tok.__name__ )

									for tokenizer_name in tokenizer_names:
												# must find the right class
												tokenizer_class_from_name(__lowerCamelCase )







						@require_tokenizers
						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str] )      ->				int:
									self.assertIsInstance(AutoTokenizer.from_pretrained("bert-base-cased"	,	use_fast=__lowerCamelCase )	,	__lowerCamelCase )
									self.assertIsInstance(AutoTokenizer.from_pretrained("bert-base-cased" )	,	__lowerCamelCase )







						@require_tokenizers
						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str] )      ->				Tuple:
									A							:					int              =						AutoTokenizer.from_pretrained("distilbert-base-uncased"	,	do_lower_case=__lowerCamelCase )
									A							:					List[str]              =						"Hello, world. How are you?"
									A							:					Optional[Any]              =						tokenizer.tokenize(__lowerCamelCase )
									self.assertEqual("[UNK]"	,	tokens[0] )

									A							:					Dict              =						AutoTokenizer.from_pretrained("microsoft/mpnet-base"	,	do_lower_case=__lowerCamelCase )
									A							:					Any              =						tokenizer.tokenize(__lowerCamelCase )
									self.assertEqual("[UNK]"	,	tokens[0] )







						@require_tokenizers
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Any )      ->				Tuple:
									A							:					List[str]              =						AutoTokenizer.from_pretrained("robot-test/dummy-tokenizer-fast-with-model-config" )
									self.assertEqual(type(__lowerCamelCase )	,	__lowerCamelCase )
									self.assertEqual(tokenizer.model_max_length	,	5_12 )
									self.assertEqual(tokenizer.vocab_size	,	3_00_00 )
									self.assertEqual(tokenizer.unk_token	,	"[UNK]" )
									self.assertEqual(tokenizer.padding_side	,	"right" )
									self.assertEqual(tokenizer.truncation_side	,	"right" )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Any )      ->				List[str]:
									A							:					Tuple              =						AutoTokenizer.from_pretrained(__lowerCamelCase )
									self.assertIsInstance(__lowerCamelCase	,	(BertTokenizer, BertTokenizerFast) )
									with tempfile.TemporaryDirectory() as tmp_dir:
												tokenizer.save_pretrained(__lowerCamelCase )
												A							:					Union[str, Any]              =						AutoTokenizer.from_pretrained(__lowerCamelCase )

									self.assertIsInstance(__lowerCamelCase	,	tokenizer.__class__ )
									self.assertEqual(tokenizera.vocab_size	,	12 )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Dict )      ->				Any:
									A							:					int              =						AutoTokenizer.from_pretrained("ctrl" )
									# There is no fast CTRL so this always gives us a slow tokenizer.
									self.assertIsInstance(__lowerCamelCase	,	__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Dict )      ->				Optional[Any]:
									# Check we can load the tokenizer config of an online model.
									A							:					List[Any]              =						get_tokenizer_config("bert-base-cased" )
									A							:					str              =						config.pop("_commit_hash"	,	__lowerCamelCase )
									# If we ever update bert-base-cased tokenizer config, this dict here will need to be updated.
									self.assertEqual(__lowerCamelCase	,	{"do_lower_case": False} )

									# This model does not have a tokenizer_config so we get back an empty dict.
									A							:					Optional[Any]              =						get_tokenizer_config(__lowerCamelCase )
									self.assertDictEqual(__lowerCamelCase	,	{} )

									# A tokenizer saved with `save_pretrained` always creates a tokenizer config.
									A							:					Optional[int]              =						AutoTokenizer.from_pretrained(__lowerCamelCase )
									with tempfile.TemporaryDirectory() as tmp_dir:
												tokenizer.save_pretrained(__lowerCamelCase )
												A							:					Dict              =						get_tokenizer_config(__lowerCamelCase )

									# Check the class of the tokenizer was properly saved (note that it always saves the slow class).
									self.assertEqual(config["tokenizer_class"]	,	"BertTokenizer" )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Tuple )      ->				Optional[Any]:
									try:
												AutoConfig.register("custom"	,	__lowerCamelCase )

												AutoTokenizer.register(__lowerCamelCase	,	slow_tokenizer_class=__lowerCamelCase )
												# Trying to register something existing in the Transformers library will raise an error
												with self.assertRaises(__lowerCamelCase ):
															AutoTokenizer.register(__lowerCamelCase	,	slow_tokenizer_class=__lowerCamelCase )

												A							:					Dict              =						CustomTokenizer.from_pretrained(__lowerCamelCase )
												with tempfile.TemporaryDirectory() as tmp_dir:
															tokenizer.save_pretrained(__lowerCamelCase )

															A							:					List[Any]              =						AutoTokenizer.from_pretrained(__lowerCamelCase )
															self.assertIsInstance(__lowerCamelCase	,	__lowerCamelCase )

									finally:
												if "custom" in CONFIG_MAPPING._extra_content:
															del CONFIG_MAPPING._extra_content["custom"]
												if CustomConfig in TOKENIZER_MAPPING._extra_content:
															del TOKENIZER_MAPPING._extra_content[CustomConfig]







						@require_tokenizers
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Tuple )      ->				Tuple:
									try:
												AutoConfig.register("custom"	,	__lowerCamelCase )

												# Can register in two steps
												AutoTokenizer.register(__lowerCamelCase	,	slow_tokenizer_class=__lowerCamelCase )
												self.assertEqual(TOKENIZER_MAPPING[CustomConfig]	,	(CustomTokenizer, None) )
												AutoTokenizer.register(__lowerCamelCase	,	fast_tokenizer_class=__lowerCamelCase )
												self.assertEqual(TOKENIZER_MAPPING[CustomConfig]	,	(CustomTokenizer, CustomTokenizerFast) )

												del TOKENIZER_MAPPING._extra_content[CustomConfig]
												# Can register in one step
												AutoTokenizer.register(
												    __lowerCamelCase	,	slow_tokenizer_class=__lowerCamelCase	,	fast_tokenizer_class=__lowerCamelCase )
												self.assertEqual(TOKENIZER_MAPPING[CustomConfig]	,	(CustomTokenizer, CustomTokenizerFast) )

												# Trying to register something existing in the Transformers library will raise an error
												with self.assertRaises(__lowerCamelCase ):
															AutoTokenizer.register(__lowerCamelCase	,	fast_tokenizer_class=__lowerCamelCase )

												# We pass through a bert tokenizer fast cause there is no converter slow to fast for our new toknizer
												# and that model does not have a tokenizer.json
												with tempfile.TemporaryDirectory() as tmp_dir:
															A							:					Dict              =						BertTokenizerFast.from_pretrained(__lowerCamelCase )
															bert_tokenizer.save_pretrained(__lowerCamelCase )
															A							:					List[str]              =						CustomTokenizerFast.from_pretrained(__lowerCamelCase )

												with tempfile.TemporaryDirectory() as tmp_dir:
															tokenizer.save_pretrained(__lowerCamelCase )

															A							:					Union[str, Any]              =						AutoTokenizer.from_pretrained(__lowerCamelCase )
															self.assertIsInstance(__lowerCamelCase	,	__lowerCamelCase )

															A							:					Tuple              =						AutoTokenizer.from_pretrained(__lowerCamelCase	,	use_fast=__lowerCamelCase )
															self.assertIsInstance(__lowerCamelCase	,	__lowerCamelCase )

									finally:
												if "custom" in CONFIG_MAPPING._extra_content:
															del CONFIG_MAPPING._extra_content["custom"]
												if CustomConfig in TOKENIZER_MAPPING._extra_content:
															del TOKENIZER_MAPPING._extra_content[CustomConfig]







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[Any] )      ->				List[str]:
									# If remote code is not set, we will time out when asking whether to load the model.
									with self.assertRaises(__lowerCamelCase ):
												A							:					Optional[int]              =						AutoTokenizer.from_pretrained("hf-internal-testing/test_dynamic_tokenizer" )
									# If remote code is disabled, we can't load this config.
									with self.assertRaises(__lowerCamelCase ):
												A							:					Dict              =						AutoTokenizer.from_pretrained(
												    "hf-internal-testing/test_dynamic_tokenizer"	,	trust_remote_code=__lowerCamelCase )

									A							:					Optional[Any]              =						AutoTokenizer.from_pretrained("hf-internal-testing/test_dynamic_tokenizer"	,	trust_remote_code=__lowerCamelCase )
									self.assertTrue(tokenizer.special_attribute_present )
									# Test tokenizer can be reloaded.
									with tempfile.TemporaryDirectory() as tmp_dir:
												tokenizer.save_pretrained(__lowerCamelCase )
												A							:					str              =						AutoTokenizer.from_pretrained(__lowerCamelCase	,	trust_remote_code=__lowerCamelCase )
									self.assertTrue(reloaded_tokenizer.special_attribute_present )

									if is_tokenizers_available():
												self.assertEqual(tokenizer.__class__.__name__	,	"NewTokenizerFast" )
												self.assertEqual(reloaded_tokenizer.__class__.__name__	,	"NewTokenizerFast" )

												# Test we can also load the slow version
												A							:					int              =						AutoTokenizer.from_pretrained(
												    "hf-internal-testing/test_dynamic_tokenizer"	,	trust_remote_code=__lowerCamelCase	,	use_fast=__lowerCamelCase )
												self.assertTrue(tokenizer.special_attribute_present )
												self.assertEqual(tokenizer.__class__.__name__	,	"NewTokenizer" )
												# Test tokenizer can be reloaded.
												with tempfile.TemporaryDirectory() as tmp_dir:
															tokenizer.save_pretrained(__lowerCamelCase )
															A							:					List[str]              =						AutoTokenizer.from_pretrained(__lowerCamelCase	,	trust_remote_code=__lowerCamelCase	,	use_fast=__lowerCamelCase )
												self.assertEqual(reloaded_tokenizer.__class__.__name__	,	"NewTokenizer" )
												self.assertTrue(reloaded_tokenizer.special_attribute_present )
									else:
												self.assertEqual(tokenizer.__class__.__name__	,	"NewTokenizer" )
												self.assertEqual(reloaded_tokenizer.__class__.__name__	,	"NewTokenizer" )







						@require_tokenizers
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Dict )      ->				Any:

									class 	lowerCamelCase_    ( _A						):






															'''simple docstring'''
															a__   =  False







									class 	lowerCamelCase_    ( _A						):






															'''simple docstring'''
															a__   =  NewTokenizer
															a__   =  False

									try:
												AutoConfig.register("custom"	,	__lowerCamelCase )
												AutoTokenizer.register(__lowerCamelCase	,	slow_tokenizer_class=__lowerCamelCase )
												AutoTokenizer.register(__lowerCamelCase	,	fast_tokenizer_class=__lowerCamelCase )
												# If remote code is not set, the default is to use local
												A							:					List[str]              =						AutoTokenizer.from_pretrained("hf-internal-testing/test_dynamic_tokenizer" )
												self.assertEqual(tokenizer.__class__.__name__	,	"NewTokenizerFast" )
												self.assertFalse(tokenizer.special_attribute_present )
												A							:					List[Any]              =						AutoTokenizer.from_pretrained("hf-internal-testing/test_dynamic_tokenizer"	,	use_fast=__lowerCamelCase )
												self.assertEqual(tokenizer.__class__.__name__	,	"NewTokenizer" )
												self.assertFalse(tokenizer.special_attribute_present )

												# If remote code is disabled, we load the local one.
												A							:					Optional[int]              =						AutoTokenizer.from_pretrained(
												    "hf-internal-testing/test_dynamic_tokenizer"	,	trust_remote_code=__lowerCamelCase )
												self.assertEqual(tokenizer.__class__.__name__	,	"NewTokenizerFast" )
												self.assertFalse(tokenizer.special_attribute_present )
												A							:					List[str]              =						AutoTokenizer.from_pretrained(
												    "hf-internal-testing/test_dynamic_tokenizer"	,	trust_remote_code=__lowerCamelCase	,	use_fast=__lowerCamelCase )
												self.assertEqual(tokenizer.__class__.__name__	,	"NewTokenizer" )
												self.assertFalse(tokenizer.special_attribute_present )

												# If remote is enabled, we load from the Hub
												A							:					Any              =						AutoTokenizer.from_pretrained(
												    "hf-internal-testing/test_dynamic_tokenizer"	,	trust_remote_code=__lowerCamelCase )
												self.assertEqual(tokenizer.__class__.__name__	,	"NewTokenizerFast" )
												self.assertTrue(tokenizer.special_attribute_present )
												A							:					List[str]              =						AutoTokenizer.from_pretrained(
												    "hf-internal-testing/test_dynamic_tokenizer"	,	trust_remote_code=__lowerCamelCase	,	use_fast=__lowerCamelCase )
												self.assertEqual(tokenizer.__class__.__name__	,	"NewTokenizer" )
												self.assertTrue(tokenizer.special_attribute_present )

									finally:
												if "custom" in CONFIG_MAPPING._extra_content:
															del CONFIG_MAPPING._extra_content["custom"]
												if CustomConfig in TOKENIZER_MAPPING._extra_content:
															del TOKENIZER_MAPPING._extra_content[CustomConfig]







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Dict )      ->				int:
									A							:					Any              =						AutoTokenizer.from_pretrained(
									    "hf-internal-testing/test_dynamic_tokenizer_legacy"	,	trust_remote_code=__lowerCamelCase )
									self.assertTrue(tokenizer.special_attribute_present )
									if is_tokenizers_available():
												self.assertEqual(tokenizer.__class__.__name__	,	"NewTokenizerFast" )

												# Test we can also load the slow version
												A							:					Optional[Any]              =						AutoTokenizer.from_pretrained(
												    "hf-internal-testing/test_dynamic_tokenizer_legacy"	,	trust_remote_code=__lowerCamelCase	,	use_fast=__lowerCamelCase )
												self.assertTrue(tokenizer.special_attribute_present )
												self.assertEqual(tokenizer.__class__.__name__	,	"NewTokenizer" )
									else:
												self.assertEqual(tokenizer.__class__.__name__	,	"NewTokenizer" )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			str )      ->				Tuple:
									with self.assertRaisesRegex(
									    __lowerCamelCase	,	"bert-base is not a local folder and is not a valid model identifier" ):
												A							:					Optional[int]              =						AutoTokenizer.from_pretrained("bert-base" )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Dict )      ->				Optional[int]:
									with self.assertRaisesRegex(
									    __lowerCamelCase	,	r"aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)" ):
												A							:					List[str]              =						AutoTokenizer.from_pretrained(__lowerCamelCase	,	revision="aaaaaa" )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Any )      ->				Dict:
									# Make sure we have cached the tokenizer.
									A							:					List[Any]              =						AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-bert" )
									with RequestCounter() as counter:
												A							:					Dict              =						AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-bert" )
												self.assertEqual(counter.get_request_count	,	0 )
												self.assertEqual(counter.head_request_count	,	1 )
												self.assertEqual(counter.other_request_count	,	0 )




from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Features, Sequence, Value
from .base import TaskTemplate

@dataclass(frozen=_A						)
class 	lowerCamelCase_    ( _A						):






						'''simple docstring'''
						# `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
						a__   =  field(default="question-answering-extractive"						,metadata={"include_in_asdict_even_if_is_default": True}						)
						a__   =  Features({"question": Value("string"						), "context": Value("string"						)}						)
						a__   =  Features(
						    {
						        "answers": Sequence(
						            {
						                "text": Value("string"						),
						                "answer_start": Value("int32"						),
						            }						)
						    }						)
						a__   =  "question"
						a__   =  "context"
						a__   =  "answers"







						@property
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Union[str, Any] )      ->				Dict[str, str]:
									return {self.question_column: "question", self.context_column: "context", self.answers_column: "answers"}