


import os
from argparse import ArgumentParser
from pathlib import Path
from shutil import copyfile, rmtree
from typing import Generator

import datasets.config
from datasets.builder import DatasetBuilder
from datasets.commands import BaseDatasetsCLICommand
from datasets.download.download_manager import DownloadMode
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.filelock import logger as fl_logger
from datasets.utils.info_utils import VerificationMode
from datasets.utils.logging import ERROR, get_logger


snake_case			:      int 							=   get_logger(__name__)


def 				SCREAMING_SNAKE_CASE (     UpperCAmelCase__							):

				"""simple docstring"""






				return TestCommand(
				    args.dataset   ,args.name   ,args.cache_dir   ,args.data_dir   ,args.all_configs   ,args.save_info or args.save_infos   ,args.ignore_verifications   ,args.force_redownload   ,args.clear_cache   ,)



class    __lowercase  (       UpperCamelCase							):

							"""simple docstring"""


							SCREAMING_SNAKE_CASE   :					List[Any]        =						False  # to tell pytest it's not a test class





							@staticmethod
							def 		__magic_name__			(    A_	)->	int:
											_SCREAMING_SNAKE_CASE      = parser.add_parser('test'				, help='Test dataset implementation.'	)
											test_parser.add_argument('--name'				, type=A_				, default=A_				, help='Dataset processing name'	)
											test_parser.add_argument(
											    '--cache_dir'				, type=A_				, default=A_				, help='Cache directory where the datasets are stored.'				, )
											test_parser.add_argument(
											    '--data_dir'				, type=A_				, default=A_				, help='Can be used to specify a manual directory to get the files from.'				, )
											test_parser.add_argument('--all_configs'				, action='store_true'				, help='Test all dataset configurations'	)
											test_parser.add_argument(
											    '--save_info'				, action='store_true'				, help='Save the dataset infos in the dataset card (README.md)'	)
											test_parser.add_argument(
											    '--ignore_verifications'				, action='store_true'				, help='Run the test without checksums and splits checks.'				, )
											test_parser.add_argument('--force_redownload'				, action='store_true'				, help='Force dataset redownload'	)
											test_parser.add_argument(
											    '--clear_cache'				, action='store_true'				, help='Remove downloaded files and cached datasets after each config test'				, )
											# aliases
											test_parser.add_argument('--save_infos'				, action='store_true'				, help='alias to save_info'	)
											test_parser.add_argument('dataset'				, type=A_				, help='Name of the dataset to download'	)
											test_parser.set_defaults(func=_test_command_factory	)





							def __init__(    self				, A_				, A_				, A_				, A_				, A_				, A_				, A_				, A_				, A_				, )->	Any:
											_SCREAMING_SNAKE_CASE      = dataset
											_SCREAMING_SNAKE_CASE      = name
											_SCREAMING_SNAKE_CASE      = cache_dir
											_SCREAMING_SNAKE_CASE      = data_dir
											_SCREAMING_SNAKE_CASE      = all_configs
											_SCREAMING_SNAKE_CASE      = save_infos
											_SCREAMING_SNAKE_CASE      = ignore_verifications
											_SCREAMING_SNAKE_CASE      = force_redownload
											_SCREAMING_SNAKE_CASE      = clear_cache
											if clear_cache and not cache_dir:
															print(
															    'When --clear_cache is used, specifying a cache directory is mandatory.\n'
															    'The \'download\' folder of the cache directory and the dataset builder cache will be deleted after each configuration test.\n'
															    'Please provide a --cache_dir that will be used to test the dataset script.'	)
															exit(1	)
											if save_infos:
															_SCREAMING_SNAKE_CASE      = True





							def 		__magic_name__			(    self	)->	Any:
											fl_logger().setLevel(A_	)
											if self._name is not None and self._all_configs:
															print('Both parameters `config` and `all_configs` can\'t be used at once.'	)
															exit(1	)
											_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      = self._dataset, self._name
											_SCREAMING_SNAKE_CASE      = dataset_module_factory(A_	)
											_SCREAMING_SNAKE_CASE      = import_main_class(module.module_path	)
											_SCREAMING_SNAKE_CASE      = len(builder_cls.BUILDER_CONFIGS	) if self._all_configs and builder_cls.BUILDER_CONFIGS else 1

											def get_builders() -> Generator[DatasetBuilder, None, None]:
															if self._all_configs and builder_cls.BUILDER_CONFIGS:
																			for i, config in enumerate(builder_cls.BUILDER_CONFIGS	):
																							if "config_name" in module.builder_kwargs:
																											yield builder_cls(
																											    cache_dir=self._cache_dir				, data_dir=self._data_dir				, **module.builder_kwargs				, )
																							else:
																											yield builder_cls(
																											    config_name=config.name				, cache_dir=self._cache_dir				, data_dir=self._data_dir				, **module.builder_kwargs				, )
															else:
																			if "config_name" in module.builder_kwargs:
																							yield builder_cls(cache_dir=self._cache_dir				, data_dir=self._data_dir				, **module.builder_kwargs	)
																			else:
																							yield builder_cls(
																							    config_name=A_				, cache_dir=self._cache_dir				, data_dir=self._data_dir				, **module.builder_kwargs				, )

											for j, builder in enumerate(get_builders()	):
															print(F'''Testing builder \'{builder.config.name}\' ({j + 1}/{n_builders})'''	)
															_SCREAMING_SNAKE_CASE      = os.path.exists(
															    os.path.join(builder.get_imported_module_dir()				, datasets.config.DATASETDICT_INFOS_FILENAME	)	)  # record checksums only if we need to update a (deprecated) dataset_infos.json
															builder.download_and_prepare(
															    download_mode=DownloadMode.REUSE_CACHE_IF_EXISTS
															    if not self._force_redownload
															    else DownloadMode.FORCE_REDOWNLOAD				, verification_mode=VerificationMode.NO_CHECKS
															    if self._ignore_verifications
															    else VerificationMode.ALL_CHECKS				, try_from_hf_gcs=A_				, )
															builder.as_dataset()
															if self._save_infos:
																			builder._save_infos()

															# If save_infos=True, the dataset card (README.md) is created next to the loaded module file.
															# The dataset_infos are saved in the YAML part of the README.md

															# Let's move it to the original directory of the dataset script, to allow the user to
															# upload them on S3 at the same time afterwards.
															if self._save_infos:
																			_SCREAMING_SNAKE_CASE      = os.path.join(builder_cls.get_imported_module_dir()				, 'README.md'	)
																			_SCREAMING_SNAKE_CASE      = Path(A_	).name + '.py'
																			_SCREAMING_SNAKE_CASE      = os.path.join(A_				, A_	)
																			if os.path.isfile(A_	):
																							_SCREAMING_SNAKE_CASE      = os.path.dirname(A_	)
																			elif os.path.isfile(A_	):
																							_SCREAMING_SNAKE_CASE      = path
																			elif os.path.isdir(A_	):  # for local directories containing only data files
																							_SCREAMING_SNAKE_CASE      = path
																			else:  # in case of a remote dataset
																							_SCREAMING_SNAKE_CASE      = None
																							print(F'''Dataset card saved at {dataset_readme_path}'''	)

																			# Move dataset_info back to the user
																			if dataset_dir is not None:
																							_SCREAMING_SNAKE_CASE      = os.path.join(A_				, 'README.md'	)
																							copyfile(A_				, A_	)
																							print(F'''Dataset card saved at {user_dataset_readme_path}'''	)

            # If clear_cache=True, the download folder and the dataset builder cache directory are deleted
															if self._clear_cache:
																			if os.path.isdir(builder._cache_dir	):
																							logger.warning(F'''Clearing cache at {builder._cache_dir}'''	)
																							rmtree(builder._cache_dir	)
																			_SCREAMING_SNAKE_CASE      = os.path.join(self._cache_dir				, datasets.config.DOWNLOADED_DATASETS_DIR	)
																			if os.path.isdir(A_	):
																							logger.warning(F'''Clearing cache at {download_dir}'''	)
																							rmtree(A_	)

											print('Test successful.'	)




import json
import os
from typing import TYPE_CHECKING, List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
							from transformers.pipelines.conversational import Conversation

snake_case			:      Any 							=   logging.get_logger(__name__)

snake_case			:      str 							=   {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt'}

snake_case			:      Optional[int] 							=   {
    'vocab_file': {
        'microsoft/deberta-base': 'https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json',
        'microsoft/deberta-large': 'https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json',
        'microsoft/deberta-xlarge': 'https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json',
        'microsoft/deberta-base-mnli': 'https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json',
        'microsoft/deberta-large-mnli': 'https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json',
        'microsoft/deberta-xlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'microsoft/deberta-base': 'https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt',
        'microsoft/deberta-large': 'https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt',
        'microsoft/deberta-xlarge': 'https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt',
        'microsoft/deberta-base-mnli': 'https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt',
        'microsoft/deberta-large-mnli': 'https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt',
        'microsoft/deberta-xlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt'
        ),
    },
}

snake_case			:      Tuple 							=   {
    'microsoft/deberta-base': 5_12,
    'microsoft/deberta-large': 5_12,
    'microsoft/deberta-xlarge': 5_12,
    'microsoft/deberta-base-mnli': 5_12,
    'microsoft/deberta-large-mnli': 5_12,
    'microsoft/deberta-xlarge-mnli': 5_12,
}

snake_case			:      Any 							=   {
    'microsoft/deberta-base': {'do_lower_case': False},
    'microsoft/deberta-large': {'do_lower_case': False},
}


def 				SCREAMING_SNAKE_CASE (     ):

				"""simple docstring"""






				_SCREAMING_SNAKE_CASE      = (
				    list(range(ord('!'							)   ,ord('~'							) + 1							)							) + list(range(ord('¡'							)   ,ord('¬'							) + 1							)							) + list(range(ord('®'							)   ,ord('ÿ'							) + 1							)							)
				)
				_SCREAMING_SNAKE_CASE      = bs[:]
				_SCREAMING_SNAKE_CASE      = 0
				for b in range(2**8							):
								if b not in bs:
												bs.append(UpperCAmelCase__							)
												cs.append(2**8 + n							)
												n += 1
				_SCREAMING_SNAKE_CASE      = [chr(UpperCAmelCase__							) for n in cs]
				return dict(zip(UpperCAmelCase__   ,UpperCAmelCase__							)							)


def 				SCREAMING_SNAKE_CASE (     UpperCAmelCase__							):

				"""simple docstring"""






				_SCREAMING_SNAKE_CASE      = set()
				_SCREAMING_SNAKE_CASE      = word[0]
				for char in word[1:]:
								pairs.add((prev_char, char)							)
								_SCREAMING_SNAKE_CASE      = char
				return pairs



class    __lowercase  (       UpperCamelCase							):

							"""simple docstring"""


							SCREAMING_SNAKE_CASE   :					int        =						VOCAB_FILES_NAMES
							SCREAMING_SNAKE_CASE   :					List[Any]        =						PRETRAINED_VOCAB_FILES_MAP
							SCREAMING_SNAKE_CASE   :					Any        =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							SCREAMING_SNAKE_CASE   :					Optional[Any]        =						["input_ids", "attention_mask", "token_type_ids"]





							def __init__(    self				, A_				, A_				, A_="replace"				, A_="[CLS]"				, A_="[SEP]"				, A_="[SEP]"				, A_="[CLS]"				, A_="[UNK]"				, A_="[PAD]"				, A_="[MASK]"				, A_=False				, A_=False				, **A_				, )->	List[str]:
											_SCREAMING_SNAKE_CASE      = AddedToken(A_				, lstrip=A_				, rstrip=A_	) if isinstance(A_				, A_	) else bos_token
											_SCREAMING_SNAKE_CASE      = AddedToken(A_				, lstrip=A_				, rstrip=A_	) if isinstance(A_				, A_	) else eos_token
											_SCREAMING_SNAKE_CASE      = AddedToken(A_				, lstrip=A_				, rstrip=A_	) if isinstance(A_				, A_	) else sep_token
											_SCREAMING_SNAKE_CASE      = AddedToken(A_				, lstrip=A_				, rstrip=A_	) if isinstance(A_				, A_	) else cls_token
											_SCREAMING_SNAKE_CASE      = AddedToken(A_				, lstrip=A_				, rstrip=A_	) if isinstance(A_				, A_	) else unk_token
											_SCREAMING_SNAKE_CASE      = AddedToken(A_				, lstrip=A_				, rstrip=A_	) if isinstance(A_				, A_	) else pad_token

											# Mask token behave like a normal word, i.e. include the space before it
											_SCREAMING_SNAKE_CASE      = AddedToken(A_				, lstrip=A_				, rstrip=A_	) if isinstance(A_				, A_	) else mask_token

											super().__init__(
											    errors=A_				, bos_token=A_				, eos_token=A_				, unk_token=A_				, sep_token=A_				, cls_token=A_				, pad_token=A_				, mask_token=A_				, add_prefix_space=A_				, add_bos_token=A_				, **A_				, )
											_SCREAMING_SNAKE_CASE      = add_bos_token

											with open(A_				, encoding='utf-8'	) as vocab_handle:
															_SCREAMING_SNAKE_CASE      = json.load(A_	)
											_SCREAMING_SNAKE_CASE      = {v: k for k, v in self.encoder.items()}
											_SCREAMING_SNAKE_CASE      = errors  # how to handle errors in decoding
											_SCREAMING_SNAKE_CASE      = bytes_to_unicode()
											_SCREAMING_SNAKE_CASE      = {v: k for k, v in self.byte_encoder.items()}
											with open(A_				, encoding='utf-8'	) as merges_handle:
															_SCREAMING_SNAKE_CASE      = merges_handle.read().split('\n'	)[1:-1]
											_SCREAMING_SNAKE_CASE      = [tuple(merge.split()	) for merge in bpe_merges]
											_SCREAMING_SNAKE_CASE      = dict(zip(A_				, range(len(A_	)	)	)	)
											_SCREAMING_SNAKE_CASE      = {}
											_SCREAMING_SNAKE_CASE      = add_prefix_space

											# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
											_SCREAMING_SNAKE_CASE      = re.compile(r'\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'	)





							@property
							# Copied from transformers.models.gpt2.tokenization_gpt2.GPT2Tokenizer.vocab_size
							def 		__magic_name__			(    self	)->	int:
											return len(self.encoder	)





							def 		__magic_name__			(    self	)->	Tuple:
											return dict(self.encoder				, **self.added_tokens_encoder	)





							def 		__magic_name__			(    self				, A_	)->	Optional[int]:
											if token in self.cache:
															return self.cache[token]
											_SCREAMING_SNAKE_CASE      = tuple(A_	)
											_SCREAMING_SNAKE_CASE      = get_pairs(A_	)

											if not pairs:
															return token

											while True:
															_SCREAMING_SNAKE_CASE      = min(A_				, key=lambda A_	: self.bpe_ranks.get(A_				, float('inf'	)	)	)
															if bigram not in self.bpe_ranks:
																			break
															_SCREAMING_SNAKE_CASE      ,      _SCREAMING_SNAKE_CASE      = bigram
															_SCREAMING_SNAKE_CASE      = []
															_SCREAMING_SNAKE_CASE      = 0
															while i < len(A_	):
																			try:
																							_SCREAMING_SNAKE_CASE      = word.index(A_				, A_	)
																			except ValueError:
																							new_word.extend(word[i:]	)
																							break
																			else:
																							new_word.extend(word[i:j]	)
																							_SCREAMING_SNAKE_CASE      = j

																			if word[i] == first and i < len(A_	) - 1 and word[i + 1] == second:
																							new_word.append(first + second	)
																							i += 2
																			else:
																							new_word.append(word[i]	)
																							i += 1
															_SCREAMING_SNAKE_CASE      = tuple(A_	)
															_SCREAMING_SNAKE_CASE      = new_word
															if len(A_	) == 1:
																			break
															else:
																			_SCREAMING_SNAKE_CASE      = get_pairs(A_	)
											_SCREAMING_SNAKE_CASE      = ' '.join(A_	)
											_SCREAMING_SNAKE_CASE      = word
											return word





							def 		__magic_name__			(    self				, A_				, A_ = None	)->	List[int]:
											if token_ids_a is None:
															return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
											_SCREAMING_SNAKE_CASE      = [self.cls_token_id]
											_SCREAMING_SNAKE_CASE      = [self.sep_token_id]
											return cls + token_ids_a + sep + token_ids_a + sep





							def 		__magic_name__			(    self				, A_				, A_ = None				, A_ = False	)->	List[int]:
											if already_has_special_tokens:
															return super().get_special_tokens_mask(
															    token_ids_a=A_				, token_ids_a=A_				, already_has_special_tokens=A_	)

											if token_ids_a is None:
															return [1] + ([0] * len(A_	)) + [1]
											return [1] + ([0] * len(A_	)) + [1] + ([0] * len(A_	)) + [1]





							def 		__magic_name__			(    self				, A_				, A_ = None	)->	List[int]:
											_SCREAMING_SNAKE_CASE      = [self.sep_token_id]
											_SCREAMING_SNAKE_CASE      = [self.cls_token_id]

											if token_ids_a is None:
															return len(cls + token_ids_a + sep	) * [0]
											return len(cls + token_ids_a + sep	) * [0] + len(token_ids_a + sep	) * [1]





							def 		__magic_name__			(    self				, A_	)->	Dict:
											_SCREAMING_SNAKE_CASE      = []
											for token in re.findall(self.pat				, A_	):
															_SCREAMING_SNAKE_CASE      = ''.join(
															    self.byte_encoder[b] for b in token.encode('utf-8'	)	)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
															bpe_tokens.extend(bpe_token for bpe_token in self.bpe(A_	).split(' '	)	)
											return bpe_tokens





							def 		__magic_name__			(    self				, A_	)->	Optional[Any]:
											return self.encoder.get(A_				, self.encoder.get(self.unk_token	)	)





							def 		__magic_name__			(    self				, A_	)->	Dict:
											return self.decoder.get(A_	)





							def 		__magic_name__			(    self				, A_	)->	Dict:
											_SCREAMING_SNAKE_CASE      = ''.join(A_	)
											_SCREAMING_SNAKE_CASE      = bytearray([self.byte_decoder[c] for c in text]	).decode('utf-8'				, errors=self.errors	)
											return text





							def 		__magic_name__			(    self				, A_				, A_ = None	)->	Tuple[str]:
											if not os.path.isdir(A_	):
															logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''	)
															return
											_SCREAMING_SNAKE_CASE      = os.path.join(
											    A_				, (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']	)
											_SCREAMING_SNAKE_CASE      = os.path.join(
											    A_				, (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['merges_file']	)

											with open(A_				, 'w'				, encoding='utf-8'	) as f:
															f.write(json.dumps(self.encoder				, indent=2				, sort_keys=A_				, ensure_ascii=A_	) + '\n'	)

											_SCREAMING_SNAKE_CASE      = 0
											with open(A_				, 'w'				, encoding='utf-8'	) as writer:
															writer.write('#version: 0.2\n'	)
															for bpe_tokens, token_index in sorted(self.bpe_ranks.items()				, key=lambda A_	: kv[1]	):
																			if index != token_index:
																							logger.warning(
																							    F'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
																							    ' Please check that the tokenizer is not corrupted!'	)
																							_SCREAMING_SNAKE_CASE      = token_index
																			writer.write(' '.join(A_	) + '\n'	)
																			index += 1

											return vocab_file, merge_file





							def 		__magic_name__			(    self				, A_				, A_=False				, **A_	)->	str:
											_SCREAMING_SNAKE_CASE      = kwargs.pop('add_prefix_space'				, self.add_prefix_space	)
											if (is_split_into_words or add_prefix_space) and (len(A_	) > 0 and not text[0].isspace()):
															_SCREAMING_SNAKE_CASE      = ' ' + text
											return (text, kwargs)





							def 		__magic_name__			(    self				, A_	)->	List[int]:
											_SCREAMING_SNAKE_CASE      = []
											for is_user, text in conversation.iter_texts():
															input_ids.extend(self.encode(A_				, add_special_tokens=A_	) + [self.eos_token_id]	)
											if len(A_	) > self.model_max_length:
															_SCREAMING_SNAKE_CASE      = input_ids[-self.model_max_length :]
											return input_ids
