
"""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  __lowerCamelCase  ( lowerCAmelCase						,	unittest.TestCase			):
				a__:      Dict          =    DebertaTokenizer
				a__:      int          =    True
				a__:      List[str]          =    DebertaTokenizerFast




				def        UpperCAmelCase__						(				self   ):
							super().setUp()

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

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




				def        UpperCAmelCase__						(				self							,						**UpperCAmelCase   ):
							kwargs.update(self.special_tokens_map   )
							return self.tokenizer_class.from_pretrained(self.tmpdirname							,						**UpperCAmelCase   )




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase   ):
							lowerCamelCase_					=  '''lower newer'''
							lowerCamelCase_					=  '''lower newer'''
							return input_text, output_text




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

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




				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.get_tokenizer()
							lowerCamelCase_					=  tokenizer('''Hello'''							,						'''World'''   )
							lowerCamelCase_					=  [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
							self.assertListEqual(tokd['''token_type_ids''']							,						UpperCAmelCase   )




				@slow
				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  self.tokenizer_class.from_pretrained('''microsoft/deberta-base'''   )

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

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

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

							assert encoded_sentence == encoded_text_from_decode
							assert encoded_pair == encoded_pair_from_decode





				@slow
				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  [self.tokenizer_class]
							if self.test_rust_tokenizer:
										tokenizer_classes.append(self.rust_tokenizer_class   )

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

										lowerCamelCase_					=  [
										    '''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.''',
										]

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

										# fmt: off
										lowerCamelCase_					=  {
										    '''input_ids''': [
										        [1, 2118, 1_1126, 565, 35, 83, 2_5191, 163, 1_8854, 13, 1_2156, 12, 1_6101, 2_5376, 1_3807, 9, 2_2205, 2_7893, 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, 1_1126, 565, 2_4536, 80, 4_3797, 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, 3_3183, 1_1303, 4_3797, 1938, 4, 870, 2_4165, 2_9105, 5, 739, 3_2644, 3_3183, 1_1303, 3_6173, 88, 80, 650, 7821, 4_5940, 6, 52, 2559, 5, 1836, 9, 5, 7397, 1_3171, 31, 5, 1836, 9, 3_2644, 3_3183, 1_1303, 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

										lowerCamelCase_					=  [
										    '''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							,						UpperCAmelCase   )

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





"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


A_    							=					{
    """configuration_nllb_moe""": [
        """NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """NllbMoeConfig""",
    ]
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_    							=					[
						    """NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """NllbMoeForConditionalGeneration""",
						    """NllbMoeModel""",
						    """NllbMoePreTrainedModel""",
						    """NllbMoeTop2Router""",
						    """NllbMoeSparseMLP""",
						]


if TYPE_CHECKING:
						from .configuration_nllb_moe import (
						    NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    NllbMoeConfig,
						)

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_nllb_moe import (
												    NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST,
												    NllbMoeForConditionalGeneration,
												    NllbMoeModel,
												    NllbMoePreTrainedModel,
												    NllbMoeSparseMLP,
												    NllbMoeTopaRouter,
												)


else:
						import sys

						A_    							=					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



