

from argparse import ArgumentParser

from . import BaseTransformersCLICommand



def   __UpperCAmelCase					(      __a :     Union[str, Any]      )     ->	List[Any]:

					"""simple docstring"""

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







class 							UpperCAmelCase_						(     __snake_case  ):
							"""simple docstring"""







							@staticmethod
							def 							__lowercase							(     _a			)       ->       Optional[int]:
												_a      :       str   =						parser.add_parser('''download'''			)
												download_parser.add_argument(
												    '''--cache-dir'''			,				type=_a			,				default=_a			,				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=_a			,				help='''Name of the model to download'''			)
												download_parser.set_defaults(func=_a			)


							def __init__(     self			,				_a			,				_a			,				_a			,				_a			)       ->       List[Any]:
												_a      :       List[str]   =						model
												_a      :       Union[str, Any]   =						cache
												_a      :       str   =						force
												_a      :       List[Any]   =						trust_remote_code




							def 							__lowercase							(     self			)       ->       Tuple:
												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			)





'''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 					A							(				__snake_case       ,     unittest.TestCase		):
     __magic_name__          =						DebertaTokenizer
     __magic_name__          =						True
     __magic_name__          =						DebertaTokenizerFast





     def 							__lowerCAmelCase				(				self      )					->							Tuple:






         """simple docstring"""






         super().setUp()

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

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





     def 							__lowerCAmelCase				(				self		,     **SCREAMING_SNAKE_CASE      )					->							Optional[Any]:






         """simple docstring"""






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





     def 							__lowerCAmelCase				(				self		,     SCREAMING_SNAKE_CASE      )					->							str:






         """simple docstring"""






         A			:  Union[str, Any]   =		'''lower newer'''
         A			:  List[Any]   =		'''lower newer'''
         return input_text, output_text





     def 							__lowerCAmelCase				(				self      )					->							str:






         """simple docstring"""






         A			:  str   =		self.get_tokenizer()
         A			:  List[str]   =		'''lower newer'''
         A			:  int   =		['''l''', '''o''', '''w''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
         A			:  Any   =		tokenizer.tokenize(SCREAMING_SNAKE_CASE      )
         self.assertListEqual(SCREAMING_SNAKE_CASE		,     SCREAMING_SNAKE_CASE      )

         A			:  str   =		tokens + [tokenizer.unk_token]
         A			:  str   =		[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
         self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE      )		,     SCREAMING_SNAKE_CASE      )





     def 							__lowerCAmelCase				(				self      )					->							str:






         """simple docstring"""






         A			:  List[Any]   =		self.get_tokenizer()
         A			:  Tuple   =		tokenizer('''Hello'''		,     '''World'''      )
         A			:  str   =		[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
         self.assertListEqual(tokd['''token_type_ids''']		,     SCREAMING_SNAKE_CASE      )





     @slow
     def 							__lowerCAmelCase				(				self      )					->							int:






         """simple docstring"""






         A			:  Union[str, Any]   =		self.tokenizer_class.from_pretrained('''microsoft/deberta-base'''      )

         A			:  Union[str, Any]   =		tokenizer.encode('''sequence builders'''		,     add_special_tokens=SCREAMING_SNAKE_CASE      )
         A			:  Any   =		tokenizer.encode('''multi-sequence build'''		,     add_special_tokens=SCREAMING_SNAKE_CASE      )

         A			:  Tuple   =		tokenizer.encode(
             '''sequence builders'''		,     add_special_tokens=SCREAMING_SNAKE_CASE		,     add_prefix_space=SCREAMING_SNAKE_CASE      )
         A			:  Union[str, Any]   =		tokenizer.encode(
             '''sequence builders'''		,     '''multi-sequence build'''		,     add_special_tokens=SCREAMING_SNAKE_CASE		,     add_prefix_space=SCREAMING_SNAKE_CASE      )

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

         assert encoded_sentence == encoded_text_from_decode
         assert encoded_pair == encoded_pair_from_decode





     @slow
     def 							__lowerCAmelCase				(				self      )					->							int:






         """simple docstring"""






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

         for tokenizer_class in tokenizer_classes:
             A			:  List[str]   =		tokenizer_class.from_pretrained('''microsoft/deberta-base'''      )

             A			:  Dict   =		[
                 '''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.''',
             ]

             A			:  List[Any]   =		tokenizer(SCREAMING_SNAKE_CASE		,     padding=SCREAMING_SNAKE_CASE      )
             A			:  List[str]   =		[tokenizer.decode(SCREAMING_SNAKE_CASE		,     skip_special_tokens=SCREAMING_SNAKE_CASE      ) for seq in encoding['''input_ids''']]

             # fmt: off
             A			:  List[str]   =		{
                 '''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

             A			:  Any   =		[
                 '''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		,     SCREAMING_SNAKE_CASE      )

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




