

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


_UpperCAmelCase		:       Any						    =	get_tests_dir("""fixtures/test_sentencepiece.model""")







@require_sentencepiece
@require_tokenizers
class 			lowercase    (	lowercase_     ,  unittest.TestCase      ):
				__SCREAMING_SNAKE_CASE :			Dict	      =      ReformerTokenizer
				__SCREAMING_SNAKE_CASE :			Tuple	      =      ReformerTokenizerFast
				__SCREAMING_SNAKE_CASE :			List[Any]	      =      True
				__SCREAMING_SNAKE_CASE :			str	      =      False
				__SCREAMING_SNAKE_CASE :			Union[str, Any]	      =      True




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

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




				def    a							( self	):
											snake_case_          =   '<s>'
											snake_case_          =   1

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




				def    a							( self	):
											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(snake_case	)				,       1000	)




				def    a							( self	):
											self.assertEqual(self.get_tokenizer().vocab_size				,       1000	)




				def    a							( self	):
											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(snake_case	)
											snake_case_          =   rust_tokenizer.tokenize(snake_case	)
											self.assertListEqual(snake_case				,       snake_case	)

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

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




				def    a							( self				,       snake_case=15	):
											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(snake_case				,       **snake_case	)

																									# 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(snake_case				,       tokenizer_r.encode				,       snake_case				,       max_length=snake_case				,       padding='max_length'	)

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

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

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

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

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




				def    a							( self	):
											pass




				def    a							( self	):
											snake_case_          =   ReformerTokenizer(snake_case				,       keep_accents=snake_case	)

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

											self.assertListEqual(
											    tokenizer.convert_tokens_to_ids(snake_case	)				,       [285, 46, 10, 170, 382]				,       )

											snake_case_          =   tokenizer.tokenize('I was born in 92000, and this is falsé.'	)
											self.assertListEqual(
											    snake_case				,       [
											        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(snake_case	)
											self.assertListEqual(
											    snake_case				,       [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]				,       )

											snake_case_          =   tokenizer.convert_ids_to_tokens(snake_case	)
											self.assertListEqual(
											    snake_case				,       [
											        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    a							( self	):
											return ReformerTokenizer.from_pretrained('google/reformer-crime-and-punishment'	)




				@slow
				def    a							( self	):
											snake_case_          =   'Hello World!'
											snake_case_          =   [126, 32, 262, 152, 38, 72, 287]

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




				@slow
				def    a							( self	):
											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_          =   [
											    108,
											    265,
											    24,
											    111,
											    4,
											    258,
											    156,
											    35,
											    28,
											    275,
											    3,
											    259,
											    297,
											    260,
											    84,
											    4,
											    35,
											    110,
											    44,
											    8,
											    259,
											    91,
											    268,
											    21,
											    11,
											    209,
											    274,
											    109,
											    266,
											    277,
											    117,
											    86,
											    93,
											    315,
											    258,
											    278,
											    258,
											    277,
											    258,
											    0,
											    258,
											    288,
											    258,
											    319,
											    258,
											    0,
											    258,
											    0,
											    258,
											    0,
											    258,
											    0,
											    258,
											    287,
											    258,
											    315,
											    258,
											    289,
											    258,
											    278,
											    99,
											    269,
											    266,
											    262,
											    8,
											    259,
											    241,
											    4,
											    217,
											    230,
											    268,
											    266,
											    55,
											    168,
											    106,
											    75,
											    193,
											    266,
											    223,
											    27,
											    49,
											    26,
											    282,
											    25,
											    264,
											    299,
											    19,
											    26,
											    0,
											    258,
											    277,
											    117,
											    86,
											    93,
											    176,
											    183,
											    270,
											    11,
											    262,
											    42,
											    61,
											    265,
											]

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




				@require_torch
				@slow
				def    a							( self	):
											import torch

											from transformers import ReformerConfig, ReformerModel

											# Build sequence
											snake_case_          =   list(self.big_tokenizer.get_vocab().keys()	)[:10]
											snake_case_          =   ' '.join(snake_case	)
											snake_case_          =   self.big_tokenizer.encode_plus(snake_case				,       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(snake_case	)

											# 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(**snake_case	)
																		model(**snake_case	)




				@slow
				def    a							( self	):
											# fmt: off
											snake_case_          =   {'input_ids': [[108, 265, 24, 111, 4, 258, 156, 7, 51, 279, 58, 7, 76, 25, 69, 278], [140, 243, 264, 134, 17, 267, 77, 263, 22, 262, 297, 258, 304, 177, 279, 266, 14, 89, 13, 35, 261, 299, 272, 137, 275, 278]], '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=snake_case				,       model_name='google/reformer-crime-and-punishment'				,       revision='0e6c3decb8211d49bf881013425dc8b0448b3f5a'				,       padding=snake_case				,       sequences=snake_case				,       )







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_UpperCAmelCase		:       Tuple						    =	{
    """configuration_xlm_roberta_xl""": [
        """XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """XLMRobertaXLConfig""",
        """XLMRobertaXLOnnxConfig""",
    ],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCAmelCase		:       int						    =	[
					    """XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """XLMRobertaXLForCausalLM""",
					    """XLMRobertaXLForMaskedLM""",
					    """XLMRobertaXLForMultipleChoice""",
					    """XLMRobertaXLForQuestionAnswering""",
					    """XLMRobertaXLForSequenceClassification""",
					    """XLMRobertaXLForTokenClassification""",
					    """XLMRobertaXLModel""",
					    """XLMRobertaXLPreTrainedModel""",
					]

if TYPE_CHECKING:
					from .configuration_xlm_roberta_xl import (
					    XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    XLMRobertaXLConfig,
					    XLMRobertaXLOnnxConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_xlm_roberta_xl import (
										    XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
										    XLMRobertaXLForCausalLM,
										    XLMRobertaXLForMaskedLM,
										    XLMRobertaXLForMultipleChoice,
										    XLMRobertaXLForQuestionAnswering,
										    XLMRobertaXLForSequenceClassification,
										    XLMRobertaXLForTokenClassification,
										    XLMRobertaXLModel,
										    XLMRobertaXLPreTrainedModel,
										)

else:
					import sys

					_UpperCAmelCase		:       Optional[Any]						    =	_LazyModule(__name__, globals()["""__file__"""], _import_structure)




