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      (						_UpperCAmelCase						,unittest.TestCase ):


			"""simple docstring"""






			__a  : List[str]			 =						DebertaTokenizer
			__a  : List[Any]			 =						True
			__a  : Optional[Any]			 =						DebertaTokenizerFast





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Any:

							'''simple docstring'''


							super().setUp()

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

							__lowercase 			=							os.path.join(self.tmpdirname      ,   VOCAB_FILES_NAMES['''vocab_file''']						)
							__lowercase 			=							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        _SCREAMING_SNAKE_CASE			(   self      ,   **lowerCAmelCase__						)     ->					Optional[int]:

							'''simple docstring'''


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





			def        _SCREAMING_SNAKE_CASE			(   self      ,   lowerCAmelCase__						)     ->					Union[str, Any]:

							'''simple docstring'''


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





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Any:

							'''simple docstring'''


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

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





			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					List[str]:

							'''simple docstring'''


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





			@slow
			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Any:

							'''simple docstring'''


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

							__lowercase 			=							tokenizer.encode('''sequence builders'''      ,   add_special_tokens=lowerCAmelCase__						)
							__lowercase 			=							tokenizer.encode('''multi-sequence build'''      ,   add_special_tokens=lowerCAmelCase__						)

							__lowercase 			=							tokenizer.encode(
							    '''sequence builders'''      ,   add_special_tokens=lowerCAmelCase__      ,   add_prefix_space=lowerCAmelCase__						)
							__lowercase 			=							tokenizer.encode(
							    '''sequence builders'''      ,   '''multi-sequence build'''      ,   add_special_tokens=lowerCAmelCase__      ,   add_prefix_space=lowerCAmelCase__						)

							__lowercase 			=							tokenizer.build_inputs_with_special_tokens(lowerCAmelCase__						)
							__lowercase 			=							tokenizer.build_inputs_with_special_tokens(lowerCAmelCase__      ,   lowerCAmelCase__						)

							assert encoded_sentence == encoded_text_from_decode
							assert encoded_pair == encoded_pair_from_decode





			@slow
			def        _SCREAMING_SNAKE_CASE			(   self						)     ->					Optional[Any]:

							'''simple docstring'''


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

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

											__lowercase 			=							[
											    '''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.''',
											]

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

											# fmt: off
											__lowercase 			=							{
											    '''input_ids''': [
											        [1, 21_18, 1_11_26, 5_65, 35, 83, 2_51_91, 1_63, 1_88_54, 13, 1_21_56, 12, 1_61_01, 2_53_76, 1_38_07, 9, 2_22_05, 2_78_93, 16_35, 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, 21_18, 1_11_26, 5_65, 2_45_36, 80, 4_37_97, 48_78, 73_73, 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, 1_33, 78, 65, 16, 10, 37_24, 15_38, 3_31_83, 1_13_03, 4_37_97, 19_38, 4, 8_70, 2_41_65, 2_91_05, 5, 7_39, 3_26_44, 3_31_83, 1_13_03, 3_61_73, 88, 80, 6_50, 78_21, 4_59_40, 6, 52, 25_59, 5, 18_36, 9, 5, 73_97, 1_31_71, 31, 5, 18_36, 9, 3_26_44, 3_31_83, 1_13_03, 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

											__lowercase 			=							[
											    '''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__						)
from typing import TYPE_CHECKING

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


__a  :     Any       =  {
    """configuration_bloom""": ["""BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BloomConfig""", """BloomOnnxConfig"""],
}
try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__a  :     Optional[int]       =  ["""BloomTokenizerFast"""]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__a  :     str       =  [
		    """BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """BloomForCausalLM""",
		    """BloomModel""",
		    """BloomPreTrainedModel""",
		    """BloomForSequenceClassification""",
		    """BloomForTokenClassification""",
		    """BloomForQuestionAnswering""",
		]

if TYPE_CHECKING:
		from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_bloom_fast import BloomTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_bloom import (
				    BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
				    BloomForCausalLM,
				    BloomForQuestionAnswering,
				    BloomForSequenceClassification,
				    BloomForTokenClassification,
				    BloomModel,
				    BloomPreTrainedModel,
				)

else:
		import sys

		__a  :     Dict       =  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)