
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


SCREAMING_SNAKE_CASE__   :			str												=    get_tests_dir("fixtures/test_sentencepiece.model")

@require_sentencepiece
@require_tokenizers
class    lowerCAmelCase__     (	__lowercase  ,	unittest.TestCase			):
		a__       :						Dict        =		ReformerTokenizer
		a__       :						List[str]        =		ReformerTokenizerFast
		a__       :						List[str]        =		True
		a__       :						List[str]        =		False
		a__       :						Tuple        =		True






		def  __A       (		self		:							Dict  )     ->							Optional[Any]:
						super().setUp()

						__lowerCamelCase    					=	ReformerTokenizer(SCREAMING_SNAKE_CASE__							,			keep_accents=SCREAMING_SNAKE_CASE__  )
						tokenizer.save_pretrained(self.tmpdirname  )






		def  __A       (		self		:							Any  )     ->							Union[str, Any]:
						__lowerCamelCase    					=	'''<s>'''
						__lowerCamelCase    					=	1

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






		def  __A       (		self		:							List[str]  )     ->							Dict:
						__lowerCamelCase    					=	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(SCREAMING_SNAKE_CASE__  )							,			10_00  )






		def  __A       (		self		:							Tuple  )     ->							Any:
						self.assertEqual(self.get_tokenizer().vocab_size							,			10_00  )






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

						__lowerCamelCase    					=	self.get_tokenizer()
						__lowerCamelCase    					=	self.get_rust_tokenizer()

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

						__lowerCamelCase    					=	tokenizer.tokenize(SCREAMING_SNAKE_CASE__  )
						__lowerCamelCase    					=	rust_tokenizer.tokenize(SCREAMING_SNAKE_CASE__  )
						self.assertListEqual(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )

						__lowerCamelCase    					=	tokenizer.encode(SCREAMING_SNAKE_CASE__							,			add_special_tokens=SCREAMING_SNAKE_CASE__  )
						__lowerCamelCase    					=	rust_tokenizer.encode(SCREAMING_SNAKE_CASE__							,			add_special_tokens=SCREAMING_SNAKE_CASE__  )
						self.assertListEqual(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )

						__lowerCamelCase    					=	self.get_rust_tokenizer()
						__lowerCamelCase    					=	tokenizer.encode(SCREAMING_SNAKE_CASE__  )
						__lowerCamelCase    					=	rust_tokenizer.encode(SCREAMING_SNAKE_CASE__  )
						self.assertListEqual(SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__  )






		def  __A       (		self		:							Union[str, Any]							,			SCREAMING_SNAKE_CASE__		:							Optional[Any]=15  )     ->							int:
						for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
										with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''  ):
														__lowerCamelCase    					=	self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__							,			**SCREAMING_SNAKE_CASE__  )

														# Simple input
														__lowerCamelCase    					=	'''This is a simple input'''
														__lowerCamelCase    					=	['''This is a simple input 1''', '''This is a simple input 2''']
														__lowerCamelCase    					=	('''This is a simple input''', '''This is a pair''')
														__lowerCamelCase    					=	[
														    ('''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(SCREAMING_SNAKE_CASE__							,			tokenizer_r.encode							,			SCREAMING_SNAKE_CASE__							,			max_length=SCREAMING_SNAKE_CASE__							,			padding='''max_length'''  )

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

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

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

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

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






		def  __A       (		self		:							Optional[Any]  )     ->							Union[str, Any]:
						pass






		def  __A       (		self		:							int  )     ->							Optional[int]:
						__lowerCamelCase    					=	ReformerTokenizer(SCREAMING_SNAKE_CASE__							,			keep_accents=SCREAMING_SNAKE_CASE__  )

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

						self.assertListEqual(
						    tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__  )							,			[2_85, 46, 10, 1_70, 3_82]							,			)

						__lowerCamelCase    					=	tokenizer.tokenize('''I was born in 92000, and this is falsé.'''  )
						self.assertListEqual(
						    SCREAMING_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''',
						        '''.''',
						    ]							,			)
						__lowerCamelCase    					=	tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__  )
						self.assertListEqual(
						    SCREAMING_SNAKE_CASE__							,			[8, 21, 84, 55, 24, 19, 7, 0, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 0, 4]							,			)

						__lowerCamelCase    					=	tokenizer.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__  )
						self.assertListEqual(
						    SCREAMING_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		:							List[Any]  )     ->							int:
						return ReformerTokenizer.from_pretrained('''google/reformer-crime-and-punishment'''  )






		@slow
		def  __A       (		self		:							str  )     ->							Tuple:
						__lowerCamelCase    					=	'''Hello World!'''
						__lowerCamelCase    					=	[1_26, 32, 2_62, 1_52, 38, 72, 2_87]

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






		@slow
		def  __A       (		self		:							Dict  )     ->							Union[str, Any]:
						__lowerCamelCase    					=	(
						    '''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'''
						)
						__lowerCamelCase    					=	[
						    1_08,
						    2_65,
						    24,
						    1_11,
						    4,
						    2_58,
						    1_56,
						    35,
						    28,
						    2_75,
						    3,
						    2_59,
						    2_97,
						    2_60,
						    84,
						    4,
						    35,
						    1_10,
						    44,
						    8,
						    2_59,
						    91,
						    2_68,
						    21,
						    11,
						    2_09,
						    2_74,
						    1_09,
						    2_66,
						    2_77,
						    1_17,
						    86,
						    93,
						    3_15,
						    2_58,
						    2_78,
						    2_58,
						    2_77,
						    2_58,
						    0,
						    2_58,
						    2_88,
						    2_58,
						    3_19,
						    2_58,
						    0,
						    2_58,
						    0,
						    2_58,
						    0,
						    2_58,
						    0,
						    2_58,
						    2_87,
						    2_58,
						    3_15,
						    2_58,
						    2_89,
						    2_58,
						    2_78,
						    99,
						    2_69,
						    2_66,
						    2_62,
						    8,
						    2_59,
						    2_41,
						    4,
						    2_17,
						    2_30,
						    2_68,
						    2_66,
						    55,
						    1_68,
						    1_06,
						    75,
						    1_93,
						    2_66,
						    2_23,
						    27,
						    49,
						    26,
						    2_82,
						    25,
						    2_64,
						    2_99,
						    19,
						    26,
						    0,
						    2_58,
						    2_77,
						    1_17,
						    86,
						    93,
						    1_76,
						    1_83,
						    2_70,
						    11,
						    2_62,
						    42,
						    61,
						    2_65,
						]

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






		@require_torch
		@slow
		def  __A       (		self		:							Dict  )     ->							Tuple:
						import torch

						from transformers import ReformerConfig, ReformerModel

						# Build sequence
						__lowerCamelCase    					=	list(self.big_tokenizer.get_vocab().keys()  )[:10]
						__lowerCamelCase    					=	''' '''.join(SCREAMING_SNAKE_CASE__  )
						__lowerCamelCase    					=	self.big_tokenizer.encode_plus(SCREAMING_SNAKE_CASE__							,			return_tensors='''pt'''  )
						__lowerCamelCase    					=	self.big_tokenizer.batch_encode_plus([sequence, sequence]							,			return_tensors='''pt'''  )

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







		@slow
		def  __A       (		self		:							int  )     ->							Dict:
						# fmt: off
						__lowerCamelCase    					=	{'''input_ids''': [[1_08, 2_65, 24, 1_11, 4, 2_58, 1_56, 7, 51, 2_79, 58, 7, 76, 25, 69, 2_78], [1_40, 2_43, 2_64, 1_34, 17, 2_67, 77, 2_63, 22, 2_62, 2_97, 2_58, 3_04, 1_77, 2_79, 2_66, 14, 89, 13, 35, 2_61, 2_99, 2_72, 1_37, 2_75, 2_78]], '''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
						__lowerCamelCase    					=	[
						    '''This is a very simple sentence.''',
						    '''The quick brown fox jumps over the lazy dog.''',
						]

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





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE__   :			List[Any]												=    {
    "configuration_resnet": ["RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "ResNetConfig", "ResNetOnnxConfig"]
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   :			int												=    [
					    "RESNET_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "ResNetForImageClassification",
					    "ResNetModel",
					    "ResNetPreTrainedModel",
					    "ResNetBackbone",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   :			List[Any]												=    [
					    "TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "TFResNetForImageClassification",
					    "TFResNetModel",
					    "TFResNetPreTrainedModel",
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE__   :			Union[str, Any]												=    [
					    "FlaxResNetForImageClassification",
					    "FlaxResNetModel",
					    "FlaxResNetPreTrainedModel",
					]

if TYPE_CHECKING:
					from .configuration_resnet import RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ResNetConfig, ResNetOnnxConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_resnet import (
										    RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
										    ResNetBackbone,
										    ResNetForImageClassification,
										    ResNetModel,
										    ResNetPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_resnet import (
										    TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFResNetForImageClassification,
										    TFResNetModel,
										    TFResNetPreTrainedModel,
										)

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel, FlaxResNetPreTrainedModel


else:
					import sys

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



