






"""simple docstring"""



import json
import os
import unittest

from transformers import HerbertTokenizer, HerbertTokenizerFast
from transformers.models.herbert.tokenization_herbert import VOCAB_FILES_NAMES
from transformers.testing_utils import get_tests_dir, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class  _UpperCAmelCase	(			_A       ,						unittest.TestCase							):
					SCREAMING_SNAKE_CASE_		:    List[Any]							   =       HerbertTokenizer
					SCREAMING_SNAKE_CASE_		:    Optional[Any]							   =       HerbertTokenizerFast
					SCREAMING_SNAKE_CASE_		:    List[str]							   =       True




					def   A  (    self					:						Tuple       )  ->  Optional[Any]:
							super().setUp()

							# Use a simpler test file without japanese/chinese characters
							with open(F'''{get_tests_dir()}/fixtures/sample_text_no_unicode.txt'''   ,						encoding='''utf-8'''       ) as f_data:
									lowercase_			:			int								=       f_data.read().replace('''\n\n'''   ,						'''\n'''       ).strip()

							lowercase_			:			Optional[Any]								=       [
							    '''<s>''',
							    '''</s>''',
							    '''l''',
							    '''o''',
							    '''w''',
							    '''e''',
							    '''r''',
							    '''s''',
							    '''t''',
							    '''i''',
							    '''d''',
							    '''n''',
							    '''w</w>''',
							    '''r</w>''',
							    '''t</w>''',
							    '''lo''',
							    '''low''',
							    '''er</w>''',
							    '''low</w>''',
							    '''lowest</w>''',
							    '''newer</w>''',
							    '''wider</w>''',
							    ''',</w>''',
							    '''<unk>''',
							]
							lowercase_			:			Dict								=       dict(zip(A   ,						range(len(A       )       )       )       )
							lowercase_			:			Optional[int]								=       ['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

							lowercase_			:			Dict								=       os.path.join(self.tmpdirname   ,						VOCAB_FILES_NAMES['''vocab_file''']       )
							lowercase_			:			List[str]								=       os.path.join(self.tmpdirname   ,						VOCAB_FILES_NAMES['''merges_file''']       )
							with open(self.vocab_file   ,						'''w'''       ) as fp:
									fp.write(json.dumps(A       )       )
							with open(self.merges_file   ,						'''w'''       ) as fp:
									fp.write('''\n'''.join(A       )       )




					def   A  (    self					:						Any   ,						A					:						Union[str, Any]       )  ->  Union[str, Any]:
							lowercase_			:			Optional[Any]								=       '''lower newer'''
							lowercase_			:			int								=       '''lower newer'''
							return input_text, output_text




					def   A  (    self					:						Tuple       )  ->  List[Any]:
							lowercase_			:			Any								=       self.tokenizer_class(vocab_file=self.vocab_file   ,						merges_file=self.merges_file       )

							lowercase_			:			Any								=       '''lower'''
							lowercase_			:			Optional[Any]								=       ['''low''', '''er</w>''']
							lowercase_			:			List[Any]								=       tokenizer.tokenize(A       )
							self.assertListEqual(A   ,						A       )

							lowercase_			:			List[Any]								=       tokens + ['''<unk>''']
							lowercase_			:			str								=       [16, 17, 23]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(A       )   ,						A       )




					def   A  (    self					:						List[Any]       )  ->  Optional[Any]:
							if not self.test_rust_tokenizer:
									return

							lowercase_			:			Dict								=       self.get_tokenizer()
							lowercase_			:			Tuple								=       self.get_rust_tokenizer()

							lowercase_			:			str								=       '''lower,newer'''

							lowercase_			:			int								=       tokenizer.tokenize(A       )
							lowercase_			:			Any								=       rust_tokenizer.tokenize(A       )
							self.assertListEqual(A   ,						A       )

							lowercase_			:			List[str]								=       tokenizer.encode(A   ,						add_special_tokens=A       )
							lowercase_			:			Optional[int]								=       rust_tokenizer.encode(A   ,						add_special_tokens=A       )
							self.assertListEqual(A   ,						A       )

							lowercase_			:			Dict								=       self.get_rust_tokenizer()
							lowercase_			:			List[Any]								=       tokenizer.encode(A       )
							lowercase_			:			Any								=       rust_tokenizer.encode(A       )
							self.assertListEqual(A   ,						A       )






					@slow
					def   A  (    self					:						Dict       )  ->  Optional[int]:
							lowercase_			:			Union[str, Any]								=       self.tokenizer_class.from_pretrained('''allegro/herbert-base-cased'''       )

							lowercase_			:			List[Any]								=       tokenizer.encode('''konstruowanie sekwencji'''   ,						add_special_tokens=A       )
							lowercase_			:			Optional[int]								=       tokenizer.encode('''konstruowanie wielu sekwencji'''   ,						add_special_tokens=A       )

							lowercase_			:			Union[str, Any]								=       tokenizer.build_inputs_with_special_tokens(A       )
							lowercase_			:			Union[str, Any]								=       tokenizer.build_inputs_with_special_tokens(A   ,						A       )

							assert encoded_sentence == [0] + text + [2]
							assert encoded_pair == [0] + text + [2] + text_a + [2]











"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__A		:	Any      =		{
    '''configuration_realm''': ['''REALM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RealmConfig'''],
    '''tokenization_realm''': ['''RealmTokenizer'''],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__A		:	Tuple      =		['''RealmTokenizerFast''']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__A		:	Optional[int]      =		[
						    '''REALM_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''RealmEmbedder''',
						    '''RealmForOpenQA''',
						    '''RealmKnowledgeAugEncoder''',
						    '''RealmPreTrainedModel''',
						    '''RealmReader''',
						    '''RealmScorer''',
						    '''load_tf_weights_in_realm''',
						]
						__A		:	Union[str, Any]      =		['''RealmRetriever''']


if TYPE_CHECKING:
						from .configuration_realm import REALM_PRETRAINED_CONFIG_ARCHIVE_MAP, RealmConfig
						from .tokenization_realm import RealmTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_realm import RealmTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_realm import (
												    REALM_PRETRAINED_MODEL_ARCHIVE_LIST,
												    RealmEmbedder,
												    RealmForOpenQA,
												    RealmKnowledgeAugEncoder,
												    RealmPreTrainedModel,
												    RealmReader,
												    RealmScorer,
												    load_tf_weights_in_realm,
												)
												from .retrieval_realm import RealmRetriever


else:
						import sys

						__A		:	Optional[int]      =		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



