

import unittest

from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


lowerCAmelCase_       	=	get_tests_dir('''fixtures/test_sentencepiece.model''')





@require_sentencepiece
@require_tokenizers
class 	snake_case_   (		__A	,       unittest.TestCase					):







			'''simple docstring'''

			SCREAMING_SNAKE_CASE     :   Optional[int]									=				ReformerTokenizer
			SCREAMING_SNAKE_CASE     :   int									=				ReformerTokenizerFast
			SCREAMING_SNAKE_CASE     :   int									=				True
			SCREAMING_SNAKE_CASE     :   Optional[Any]									=				False
			SCREAMING_SNAKE_CASE     :   str									=				True







			def 				snake_case__(     self :				Dict							)      ->List[str]:
									super().setUp()

									snake_case_   =	ReformerTokenizer(_UpperCamelCase							,   keep_accents=_UpperCamelCase							)
									tokenizer.save_pretrained(self.tmpdirname							)







			def 				snake_case__(     self :				Optional[Any]							)      ->int:
									snake_case_   =	'''<s>'''
									snake_case_   =	1

									self.assertEqual(self.get_tokenizer()._convert_token_to_id(_UpperCamelCase							)							,   _UpperCamelCase							)
									self.assertEqual(self.get_tokenizer()._convert_id_to_token(_UpperCamelCase							)							,   _UpperCamelCase							)







			def 				snake_case__(     self :				Optional[Any]							)      ->Dict:
									snake_case_   =	list(self.get_tokenizer().get_vocab().keys()							)

									self.assertEqual(vocab_keys[0]							,   '''<unk>'''							)
									self.assertEqual(vocab_keys[1]							,   '''<s>'''							)
									self.assertEqual(vocab_keys[-1]							,   '''j'''							)
									self.assertEqual(len(_UpperCamelCase							)							,   1_0_0_0							)







			def 				snake_case__(     self :				Union[str, Any]							)      ->Dict:
									self.assertEqual(self.get_tokenizer().vocab_size							,   1_0_0_0							)







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

									snake_case_   =	self.get_tokenizer()
									snake_case_   =	self.get_rust_tokenizer()

									snake_case_   =	'''I was born in 92000, and this is falsé.'''

									snake_case_   =	tokenizer.tokenize(_UpperCamelCase							)
									snake_case_   =	rust_tokenizer.tokenize(_UpperCamelCase							)
									self.assertListEqual(_UpperCamelCase							,   _UpperCamelCase							)

									snake_case_   =	tokenizer.encode(_UpperCamelCase							,   add_special_tokens=_UpperCamelCase							)
									snake_case_   =	rust_tokenizer.encode(_UpperCamelCase							,   add_special_tokens=_UpperCamelCase							)
									self.assertListEqual(_UpperCamelCase							,   _UpperCamelCase							)

									snake_case_   =	self.get_rust_tokenizer()
									snake_case_   =	tokenizer.encode(_UpperCamelCase							)
									snake_case_   =	rust_tokenizer.encode(_UpperCamelCase							)
									self.assertListEqual(_UpperCamelCase							,   _UpperCamelCase							)







			def 				snake_case__(     self :				Optional[int]							,   _UpperCamelCase :				Tuple=1_5							)      ->Tuple:
									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
															with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''							):
																					snake_case_   =	self.rust_tokenizer_class.from_pretrained(_UpperCamelCase							,   **_UpperCamelCase							)

																					# Simple input
																					snake_case_   =	'''This is a simple input'''
																					snake_case_   =	['''This is a simple input 1''', '''This is a simple input 2''']
																					snake_case_   =	('''This is a simple input''', '''This is a pair''')
																					snake_case_   =	[
																					    ('''This is a simple input 1''', '''This is a simple input 2'''),
																					    ('''This is a simple pair 1''', '''This is a simple pair 2'''),
																					]

																					# Simple input tests
																					self.assertRaises(_UpperCamelCase							,   tokenizer_r.encode							,   _UpperCamelCase							,   max_length=_UpperCamelCase							,   padding='''max_length'''							)

																					# Simple input
																					self.assertRaises(_UpperCamelCase							,   tokenizer_r.encode_plus							,   _UpperCamelCase							,   max_length=_UpperCamelCase							,   padding='''max_length'''							)

																					# Simple input
																					self.assertRaises(
																					    _UpperCamelCase							,   tokenizer_r.batch_encode_plus							,   _UpperCamelCase							,   max_length=_UpperCamelCase							,   padding='''max_length'''							,   )

																					# Pair input
																					self.assertRaises(_UpperCamelCase							,   tokenizer_r.encode							,   _UpperCamelCase							,   max_length=_UpperCamelCase							,   padding='''max_length'''							)

																					# Pair input
																					self.assertRaises(_UpperCamelCase							,   tokenizer_r.encode_plus							,   _UpperCamelCase							,   max_length=_UpperCamelCase							,   padding='''max_length'''							)

																					# Pair input
																					self.assertRaises(
																					    _UpperCamelCase							,   tokenizer_r.batch_encode_plus							,   _UpperCamelCase							,   max_length=_UpperCamelCase							,   padding='''max_length'''							,   )







			def 				snake_case__(     self :				Dict							)      ->int:
									pass







			def 				snake_case__(     self :				Dict							)      ->str:
									snake_case_   =	ReformerTokenizer(_UpperCamelCase							,   keep_accents=_UpperCamelCase							)

									snake_case_   =	tokenizer.tokenize('''This is a test'''							)
									self.assertListEqual(_UpperCamelCase							,   ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']							)

									self.assertListEqual(
									    tokenizer.convert_tokens_to_ids(_UpperCamelCase							)							,   [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]							,   )

									snake_case_   =	tokenizer.tokenize('''I was born in 92000, and this is falsé.'''							)
									self.assertListEqual(
									    _UpperCamelCase							,   [
									        SPIECE_UNDERLINE + '''I''',
									        SPIECE_UNDERLINE + '''was''',
									        SPIECE_UNDERLINE + '''b''',
									        '''or''',
									        '''n''',
									        SPIECE_UNDERLINE + '''in''',
									        SPIECE_UNDERLINE + '''''',
									        '''9''',
									        '''2''',
									        '''0''',
									        '''0''',
									        '''0''',
									        ''',''',
									        SPIECE_UNDERLINE + '''and''',
									        SPIECE_UNDERLINE + '''this''',
									        SPIECE_UNDERLINE + '''is''',
									        SPIECE_UNDERLINE + '''f''',
									        '''al''',
									        '''s''',
									        '''é''',
									        '''.''',
									    ]							,   )
									snake_case_   =	tokenizer.convert_tokens_to_ids(_UpperCamelCase							)
									self.assertListEqual(
									    _UpperCamelCase							,   [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 0, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 0, 4]							,   )

									snake_case_   =	tokenizer.convert_ids_to_tokens(_UpperCamelCase							)
									self.assertListEqual(
									    _UpperCamelCase							,   [
									        SPIECE_UNDERLINE + '''I''',
									        SPIECE_UNDERLINE + '''was''',
									        SPIECE_UNDERLINE + '''b''',
									        '''or''',
									        '''n''',
									        SPIECE_UNDERLINE + '''in''',
									        SPIECE_UNDERLINE + '''''',
									        '''<unk>''',
									        '''2''',
									        '''0''',
									        '''0''',
									        '''0''',
									        ''',''',
									        SPIECE_UNDERLINE + '''and''',
									        SPIECE_UNDERLINE + '''this''',
									        SPIECE_UNDERLINE + '''is''',
									        SPIECE_UNDERLINE + '''f''',
									        '''al''',
									        '''s''',
									        '''<unk>''',
									        '''.''',
									    ]							,   )







			@cached_property
			def 				snake_case__(     self :				Optional[int]							)      ->Tuple:
									return ReformerTokenizer.from_pretrained('''google/reformer-crime-and-punishment'''							)







			@slow
			def 				snake_case__(     self :				Optional[Any]							)      ->int:
									snake_case_   =	'''Hello World!'''
									snake_case_   =	[1_2_6, 3_2, 2_6_2, 1_5_2, 3_8, 7_2, 2_8_7]

									self.assertListEqual(_UpperCamelCase							,   self.big_tokenizer.encode(_UpperCamelCase							)							)







			@slow
			def 				snake_case__(     self :				str							)      ->str:
									snake_case_   =	(
									    '''This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'''
									    ''' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'''
									)
									snake_case_   =	[
									    1_0_8,
									    2_6_5,
									    2_4,
									    1_1_1,
									    4,
									    2_5_8,
									    1_5_6,
									    3_5,
									    2_8,
									    2_7_5,
									    3,
									    2_5_9,
									    2_9_7,
									    2_6_0,
									    8_4,
									    4,
									    3_5,
									    1_1_0,
									    4_4,
									    8,
									    2_5_9,
									    9_1,
									    2_6_8,
									    2_1,
									    1_1,
									    2_0_9,
									    2_7_4,
									    1_0_9,
									    2_6_6,
									    2_7_7,
									    1_1_7,
									    8_6,
									    9_3,
									    3_1_5,
									    2_5_8,
									    2_7_8,
									    2_5_8,
									    2_7_7,
									    2_5_8,
									    0,
									    2_5_8,
									    2_8_8,
									    2_5_8,
									    3_1_9,
									    2_5_8,
									    0,
									    2_5_8,
									    0,
									    2_5_8,
									    0,
									    2_5_8,
									    0,
									    2_5_8,
									    2_8_7,
									    2_5_8,
									    3_1_5,
									    2_5_8,
									    2_8_9,
									    2_5_8,
									    2_7_8,
									    9_9,
									    2_6_9,
									    2_6_6,
									    2_6_2,
									    8,
									    2_5_9,
									    2_4_1,
									    4,
									    2_1_7,
									    2_3_0,
									    2_6_8,
									    2_6_6,
									    5_5,
									    1_6_8,
									    1_0_6,
									    7_5,
									    1_9_3,
									    2_6_6,
									    2_2_3,
									    2_7,
									    4_9,
									    2_6,
									    2_8_2,
									    2_5,
									    2_6_4,
									    2_9_9,
									    1_9,
									    2_6,
									    0,
									    2_5_8,
									    2_7_7,
									    1_1_7,
									    8_6,
									    9_3,
									    1_7_6,
									    1_8_3,
									    2_7_0,
									    1_1,
									    2_6_2,
									    4_2,
									    6_1,
									    2_6_5,
									]

									self.assertListEqual(_UpperCamelCase							,   self.big_tokenizer.encode(_UpperCamelCase							)							)







			@require_torch
			@slow
			def 				snake_case__(     self :				List[str]							)      ->List[str]:
									import torch

									from transformers import ReformerConfig, ReformerModel

									# Build sequence
									snake_case_   =	list(self.big_tokenizer.get_vocab().keys()							)[:1_0]
									snake_case_   =	''' '''.join(_UpperCamelCase							)
									snake_case_   =	self.big_tokenizer.encode_plus(_UpperCamelCase							,   return_tensors='''pt'''							)
									snake_case_   =	self.big_tokenizer.batch_encode_plus([sequence, sequence]							,   return_tensors='''pt'''							)

									snake_case_   =	ReformerConfig()
									# The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
									snake_case_   =	encoded_sequence['''input_ids'''].shape
									snake_case_   =	ReformerModel(_UpperCamelCase							)

									# Reformer has config.vocab_size == tokenizer.vocab_size == len(tokenizer) - 1 = 320; len(tokenizer) is 321 (including a pad token with id 320)
									assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

									with torch.no_grad():
															model(**_UpperCamelCase							)
															model(**_UpperCamelCase							)







			@slow
			def 				snake_case__(     self :				List[Any]							)      ->Dict:
									# fmt: off
									snake_case_   =	{'''input_ids''': [[1_0_8, 2_6_5, 2_4, 1_1_1, 4, 2_5_8, 1_5_6, 7, 5_1, 2_7_9, 5_8, 7, 7_6, 2_5, 6_9, 2_7_8], [1_4_0, 2_4_3, 2_6_4, 1_3_4, 1_7, 2_6_7, 7_7, 2_6_3, 2_2, 2_6_2, 2_9_7, 2_5_8, 3_0_4, 1_7_7, 2_7_9, 2_6_6, 1_4, 8_9, 1_3, 3_5, 2_6_1, 2_9_9, 2_7_2, 1_3_7, 2_7_5, 2_7_8]], '''attention_mask''': [[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]]}  # noqa: E501
									# fmt: on

									# This tokenizer does not know some characters like ")".
									# That is the reason why we use very simple texts here.
									# Also see https://github.com/huggingface/transformers/pull/11737#issuecomment-850769064
									snake_case_   =	[
									    '''This is a very simple sentence.''',
									    '''The quick brown fox jumps over the lazy dog.''',
									]

									self.tokenizer_integration_test_util(
									    expected_encoding=_UpperCamelCase							,   model_name='''google/reformer-crime-and-punishment'''							,   revision='''0e6c3decb8211d49bf881013425dc8b0448b3f5a'''							,   padding=_UpperCamelCase							,   sequences=_UpperCamelCase							,   )


from __future__ import annotations


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__       ):
						return [ord(SCREAMING_SNAKE_CASE__       ) - 96 for elem in plain]


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__       ):
						return "".join(chr(elem + 96       ) for elem in encoded       )


def   __SCREAMING_SNAKE_CASE					():
						snake_case_   =	encode(input('''-> '''       ).strip().lower()       )
						print('''Encoded: '''  ,		SCREAMING_SNAKE_CASE__       )
						print('''Decoded:'''  ,		decode(SCREAMING_SNAKE_CASE__       )       )


if __name__ == "__main__":
					main()