


import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors
@require_tokenizers
class 						lowerCamelCase_							(					lowerCAmelCase__  ,  unittest.TestCase					):



					'''simple docstring'''







					__UpperCAmelCase							  =					MvpTokenizer
					__UpperCAmelCase							  =					MvpTokenizerFast
					__UpperCAmelCase							  =					True
					__UpperCAmelCase							  =					filter_roberta_detectors






					def A   (					self    ) ->      Any:







								'''simple docstring'''

								super().setUp()
								__lowercase			       = [
								    '''l''',
								    '''o''',
								    '''w''',
								    '''e''',
								    '''r''',
								    '''s''',
								    '''t''',
								    '''i''',
								    '''d''',
								    '''n''',
								    '''\u0120''',
								    '''\u0120l''',
								    '''\u0120n''',
								    '''\u0120lo''',
								    '''\u0120low''',
								    '''er''',
								    '''\u0120lowest''',
								    '''\u0120newer''',
								    '''\u0120wider''',
								    '''<unk>''',
								]
								__lowercase			       = dict(zip(snake_case_			,							range(len(snake_case_    )    )    )    )
								__lowercase			       = ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
								__lowercase			       = {'''unk_token''': '''<unk>'''}

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






					def A   (					self			,							**snake_case_    ) ->      List[Any]:







								'''simple docstring'''

								kwargs.update(self.special_tokens_map    )
								return self.tokenizer_class.from_pretrained(self.tmpdirname			,							**snake_case_    )






					def A   (					self			,							**snake_case_    ) ->      int:







								'''simple docstring'''

								kwargs.update(self.special_tokens_map    )
								return self.rust_tokenizer_class.from_pretrained(self.tmpdirname			,							**snake_case_    )






					def A   (					self			,							snake_case_    ) ->      Tuple:







								'''simple docstring'''

								return "lower newer", "lower newer"






					@cached_property
					def A   (					self    ) ->      List[Any]:







								'''simple docstring'''

								return MvpTokenizer.from_pretrained('''RUCAIBox/mvp'''    )






					@cached_property
					def A   (					self    ) ->      Tuple:







								'''simple docstring'''

								return MvpTokenizerFast.from_pretrained('''RUCAIBox/mvp'''    )






					@require_torch
					def A   (					self    ) ->      Tuple:







								'''simple docstring'''

								__lowercase			       = ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
								__lowercase			       = [0, 2_5_0, 2_5_1, 1_7_8_1_8, 1_3, 3_9_1_8_6, 1_9_3_8, 4, 2]

								for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											__lowercase			       = tokenizer(snake_case_			,							max_length=len(snake_case_    )			,							padding=snake_case_			,							return_tensors='''pt'''    )
											self.assertIsInstance(snake_case_			,							snake_case_    )

											self.assertEqual((2, 9)			,							batch.input_ids.shape    )
											self.assertEqual((2, 9)			,							batch.attention_mask.shape    )
											__lowercase			       = batch.input_ids.tolist()[0]
											self.assertListEqual(snake_case_			,							snake_case_    )
											# Test that special tokens are reset






					@require_torch
					def A   (					self    ) ->      str:







								'''simple docstring'''

								__lowercase			       = ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
								for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											__lowercase			       = tokenizer(snake_case_			,							padding=snake_case_			,							return_tensors='''pt'''    )
											# check if input_ids are returned and no labels
											self.assertIn('''input_ids'''			,							snake_case_    )
											self.assertIn('''attention_mask'''			,							snake_case_    )
											self.assertNotIn('''labels'''			,							snake_case_    )
											self.assertNotIn('''decoder_attention_mask'''			,							snake_case_    )






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







								'''simple docstring'''

								__lowercase			       = [
								    '''Summary of the text.''',
								    '''Another summary.''',
								]
								for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											__lowercase			       = tokenizer(text_target=snake_case_			,							max_length=3_2			,							padding='''max_length'''			,							return_tensors='''pt'''    )
											self.assertEqual(3_2			,							targets['''input_ids'''].shape[1]    )






					@require_torch
					def A   (					self    ) ->      Union[str, Any]:







								'''simple docstring'''

								for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											__lowercase			       = tokenizer(
											    ['''I am a small frog''' * 1_0_2_4, '''I am a small frog''']			,							padding=snake_case_			,							truncation=snake_case_			,							return_tensors='''pt'''    )
											self.assertIsInstance(snake_case_			,							snake_case_    )
											self.assertEqual(batch.input_ids.shape			,							(2, 1_0_2_4)    )






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







								'''simple docstring'''

								__lowercase			       = ['''A long paragraph for summarization.''']
								__lowercase			       = [
								    '''Summary of the text.''',
								]
								for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											__lowercase			       = tokenizer(snake_case_			,							text_target=snake_case_			,							return_tensors='''pt'''    )
											__lowercase			       = inputs['''input_ids''']
											__lowercase			       = inputs['''labels''']
											self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()    )
											self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()    )
											self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()    )
											self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()    )






					def A   (					self    ) ->      Optional[Any]:







								'''simple docstring'''

								pass






					def A   (					self    ) ->      str:







								'''simple docstring'''

								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
											with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'    ):
														__lowercase			       = self.rust_tokenizer_class.from_pretrained(snake_case_			,							**snake_case_    )
														__lowercase			       = self.tokenizer_class.from_pretrained(snake_case_			,							**snake_case_    )
														__lowercase			       = '''A, <mask> AllenNLP sentence.'''
														__lowercase			       = tokenizer_r.encode_plus(snake_case_			,							add_special_tokens=snake_case_			,							return_token_type_ids=snake_case_    )
														__lowercase			       = tokenizer_p.encode_plus(snake_case_			,							add_special_tokens=snake_case_			,							return_token_type_ids=snake_case_    )

														# token_type_ids should put 0 everywhere
														self.assertEqual(sum(tokens_r['''token_type_ids''']    )			,							sum(tokens_p['''token_type_ids''']    )    )

														# attention_mask should put 1 everywhere, so sum over length should be 1
														self.assertEqual(
														    sum(tokens_r['''attention_mask''']    ) / len(tokens_r['''attention_mask''']    )			,							sum(tokens_p['''attention_mask''']    ) / len(tokens_p['''attention_mask''']    )			,							)

														__lowercase			       = tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']    )
														__lowercase			       = tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids''']    )

														# Rust correctly handles the space before the mask while python doesnt
														self.assertSequenceEqual(tokens_p['''input_ids''']			,							[0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]    )
														self.assertSequenceEqual(tokens_r['''input_ids''']			,							[0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]    )

														self.assertSequenceEqual(
														    snake_case_			,							['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']    )
														self.assertSequenceEqual(
														    snake_case_			,							['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']    )






# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position

a			:			Dict												=					'''2.13.1'''

import platform

import pyarrow
from packaging import version


if version.parse(platform.python_version()) < version.parse('''3.7'''):
	raise ImportWarning(
	    '''To use `datasets`, Python>=3.7 is required, and the current version of Python doesn\'t match this condition.'''
	)

if version.parse(pyarrow.__version__).major < 8:
	raise ImportWarning(
	    '''To use `datasets`, the module `pyarrow>=8.0.0` is required, and the current version of `pyarrow` doesn\'t match this condition.\n'''
	    '''If you are running this in a Google Colab, you should probably just restart the runtime to use the right version of `pyarrow`.'''
	)

del platform
del pyarrow
del version

from .arrow_dataset import Dataset
from .arrow_reader import ReadInstruction
from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
from .combine import concatenate_datasets, interleave_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .download import *
from .features import *
from .fingerprint import disable_caching, enable_caching, is_caching_enabled, set_caching_enabled
from .info import DatasetInfo, MetricInfo
from .inspect import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
    list_datasets,
    list_metrics,
)
from .iterable_dataset import IterableDataset
from .load import load_dataset, load_dataset_builder, load_from_disk, load_metric
from .metric import Metric
from .splits import (
    NamedSplit,
    NamedSplitAll,
    Split,
    SplitBase,
    SplitDict,
    SplitGenerator,
    SplitInfo,
    SubSplitInfo,
    percent,
)
from .tasks import *
from .utils import *
from .utils import logging


# deprecated modules
from datasets import arrow_dataset as _arrow_dataset  # isort:skip
from datasets import utils as _utils  # isort:skip
from datasets.utils import download_manager as _deprecated_download_manager  # isort:skip

a			:			Optional[int]												=					concatenate_datasets
a			:			List[Any]												=					DownloadConfig
a			:			List[Any]												=					DownloadManager
a			:			str												=					DownloadMode
a			:			int												=					DownloadConfig
a			:			List[str]												=					DownloadMode
a			:			Optional[int]												=					DownloadManager

del _arrow_dataset, _utils, _deprecated_download_manager


