


'''simple docstring'''



import json
import os
import unittest

from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin






class  UpperCamelCase__(     lowerCAmelCase		,       unittest.TestCase ):
				__magic_name__       :   Tuple							= DebertaTokenizer
				__magic_name__       :   str							= True
				__magic_name__       :   Any							= DebertaTokenizerFast




				def   a__(	self    :				int   )->  Union[str, Any]:

										"""simple docstring"""




										super().setUp()

										# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
										UpperCAmelCase         =			[
										    '''l''',
										    '''o''',
										    '''w''',
										    '''e''',
										    '''r''',
										    '''s''',
										    '''t''',
										    '''i''',
										    '''d''',
										    '''n''',
										    '''\u0120''',
										    '''\u0120l''',
										    '''\u0120n''',
										    '''\u0120lo''',
										    '''\u0120low''',
										    '''er''',
										    '''\u0120lowest''',
										    '''\u0120newer''',
										    '''\u0120wider''',
										    '''[UNK]''',
										]
										UpperCAmelCase         =			dict(zip(lowerCAmelCase   ,					range(len(lowerCAmelCase   )   )   )   )
										UpperCAmelCase         =			['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
										UpperCAmelCase         =			{'''unk_token''': '''[UNK]'''}

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




				def   a__(	self    :				List[str]   ,					**lowerCAmelCase    :				Dict   )->  Tuple:

										"""simple docstring"""




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




				def   a__(	self    :				Any   ,					lowerCAmelCase    :				Any   )->  Dict:

										"""simple docstring"""




										UpperCAmelCase         =			'''lower newer'''
										UpperCAmelCase         =			'''lower newer'''
										return input_text, output_text




				def   a__(	self    :				Any   )->  List[str]:

										"""simple docstring"""




										UpperCAmelCase         =			self.get_tokenizer()
										UpperCAmelCase         =			'''lower newer'''
										UpperCAmelCase         =			['''l''', '''o''', '''w''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
										UpperCAmelCase         =			tokenizer.tokenize(lowerCAmelCase   )
										self.assertListEqual(lowerCAmelCase   ,					lowerCAmelCase   )

										UpperCAmelCase         =			tokens + [tokenizer.unk_token]
										UpperCAmelCase         =			[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
										self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase   )   ,					lowerCAmelCase   )




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

										"""simple docstring"""




										UpperCAmelCase         =			self.get_tokenizer()
										UpperCAmelCase         =			tokenizer('''Hello'''   ,					'''World'''   )
										UpperCAmelCase         =			[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
										self.assertListEqual(tokd['''token_type_ids''']   ,					lowerCAmelCase   )




				@slow
				def   a__(	self    :				List[str]   )->  Tuple:

										"""simple docstring"""




										UpperCAmelCase         =			self.tokenizer_class.from_pretrained('''microsoft/deberta-base'''   )

										UpperCAmelCase         =			tokenizer.encode('''sequence builders'''   ,					add_special_tokens=lowerCAmelCase   )
										UpperCAmelCase         =			tokenizer.encode('''multi-sequence build'''   ,					add_special_tokens=lowerCAmelCase   )

										UpperCAmelCase         =			tokenizer.encode(
										    '''sequence builders'''   ,					add_special_tokens=lowerCAmelCase   ,					add_prefix_space=lowerCAmelCase   )
										UpperCAmelCase         =			tokenizer.encode(
										    '''sequence builders'''   ,					'''multi-sequence build'''   ,					add_special_tokens=lowerCAmelCase   ,					add_prefix_space=lowerCAmelCase   )

										UpperCAmelCase         =			tokenizer.build_inputs_with_special_tokens(lowerCAmelCase   )
										UpperCAmelCase         =			tokenizer.build_inputs_with_special_tokens(lowerCAmelCase   ,					lowerCAmelCase   )

										assert encoded_sentence == encoded_text_from_decode
										assert encoded_pair == encoded_pair_from_decode





				@slow
				def   a__(	self    :				Tuple   )->  List[Any]:

										"""simple docstring"""




										UpperCAmelCase         =			[self.tokenizer_class]
										if self.test_rust_tokenizer:
																tokenizer_classes.append(self.rust_tokenizer_class   )

										for tokenizer_class in tokenizer_classes:
																UpperCAmelCase         =			tokenizer_class.from_pretrained('''microsoft/deberta-base'''   )

																UpperCAmelCase         =			[
																    '''ALBERT: A Lite BERT for Self-supervised Learning of Language Representations''',
																    '''ALBERT incorporates two parameter reduction techniques''',
																    '''The first one is a factorized embedding parameterization. By decomposing the large vocabulary'''
																    ''' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'''
																    ''' vocabulary embedding.''',
																]

																UpperCAmelCase         =			tokenizer(lowerCAmelCase   ,					padding=lowerCAmelCase   )
																UpperCAmelCase         =			[tokenizer.decode(lowerCAmelCase   ,					skip_special_tokens=lowerCAmelCase   ) for seq in encoding['''input_ids''']]

																# fmt: off
																UpperCAmelCase         =			{
																    '''input_ids''': [
																        [1, 2118, 11126, 565, 35, 83, 25191, 163, 18854, 13, 12156, 12, 16101, 25376, 13807, 9, 22205, 27893, 1635, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
																        [1, 2118, 11126, 565, 24536, 80, 43797, 4878, 7373, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
																        [1, 133, 78, 65, 16, 10, 3724, 1538, 33183, 11303, 43797, 1938, 4, 870, 24165, 29105, 5, 739, 32644, 33183, 11303, 36173, 88, 80, 650, 7821, 45940, 6, 52, 2559, 5, 1836, 9, 5, 7397, 13171, 31, 5, 1836, 9, 32644, 33183, 11303, 4, 2]
																    ],
																    '''token_type_ids''': [
																        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
																        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
																        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
																    ],
																    '''attention_mask''': [
																        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
																        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
																        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
																    ]
																}
																# fmt: on

																UpperCAmelCase         =			[
																    '''ALBERT: A Lite BERT for Self-supervised Learning of Language Representations''',
																    '''ALBERT incorporates two parameter reduction techniques''',
																    '''The first one is a factorized embedding parameterization. By decomposing the large vocabulary'''
																    ''' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'''
																    ''' vocabulary embedding.''',
																]

																self.assertDictEqual(encoding.data   ,					lowerCAmelCase   )

																for expected, decoded in zip(lowerCAmelCase   ,					lowerCAmelCase   ):
																						self.assertEqual(lowerCAmelCase   ,					lowerCAmelCase   )









'''simple docstring'''



from argparse import ArgumentParser

from . import BaseTransformersCLICommand


def    lowerCamelCase__   ( A     :    List[Any]  ):




						'''simple docstring'''






						return DownloadCommand(args.model   ,     args.cache_dir   ,     args.force   ,     args.trust_remote_code  )






class  UpperCamelCase__(     lowerCAmelCase ):




				@staticmethod
				def   a__(	lowerCAmelCase    :				ArgumentParser   )->  Optional[Any]:

										"""simple docstring"""




										UpperCAmelCase         =			parser.add_parser('''download'''   )
										download_parser.add_argument(
										    '''--cache-dir'''   ,					type=lowerCAmelCase   ,					default=lowerCAmelCase   ,					help='''Path to location to store the models'''   )
										download_parser.add_argument(
										    '''--force'''   ,					action='''store_true'''   ,					help='''Force the model to be download even if already in cache-dir'''   )
										download_parser.add_argument(
										    '''--trust-remote-code'''   ,					action='''store_true'''   ,					help='''Whether or not to allow for custom models defined on the Hub in their own modeling files. Use only if you\'ve reviewed the code as it will execute on your local machine'''   ,					)
										download_parser.add_argument('''model'''   ,					type=lowerCAmelCase   ,					help='''Name of the model to download'''   )
										download_parser.set_defaults(func=lowerCAmelCase   )




				def __init__(	self    :				Dict   ,					lowerCAmelCase    :				str   ,					lowerCAmelCase    :				str   ,					lowerCAmelCase    :				bool   ,					lowerCAmelCase    :				bool   )->  Any:

										"""simple docstring"""




										UpperCAmelCase         =			model
										UpperCAmelCase         =			cache
										UpperCAmelCase         =			force
										UpperCAmelCase         =			trust_remote_code





				def   a__(	self    :				int   )->  Optional[Any]:

										"""simple docstring"""




										from ..models.auto import AutoModel, AutoTokenizer

										AutoModel.from_pretrained(
										    self._model   ,					cache_dir=self._cache   ,					force_download=self._force   ,					trust_remote_code=self._trust_remote_code   )
										AutoTokenizer.from_pretrained(
										    self._model   ,					cache_dir=self._cache   ,					force_download=self._force   ,					trust_remote_code=self._trust_remote_code   )





