


"""simple docstring"""




import unittest

from transformers import BigBirdTokenizer, BigBirdTokenizerFast
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						=      '''▁'''

__UpperCamelCase						=      get_tests_dir('''fixtures/test_sentencepiece.model''')
@require_sentencepiece
@require_tokenizers
class 					lowerCAmelCase	(					_UpperCAmelCase	,    unittest.TestCase    ):




		'''simple docstring'''







		SCREAMING_SNAKE_CASE_						:     str								=     BigBirdTokenizer
		SCREAMING_SNAKE_CASE_						:     Any								=     BigBirdTokenizerFast
		SCREAMING_SNAKE_CASE_						:     List[Any]								=     True
		SCREAMING_SNAKE_CASE_						:     int								=     True







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

								SCREAMING_SNAKE_CASE													=					self.tokenizer_class(lowerCAmelCase__						,       keep_accents=lowerCAmelCase__				)
								tokenizer.save_pretrained(self.tmpdirname				)







		def 							__A						(	self				)     ->							List[Any]:
								SCREAMING_SNAKE_CASE													=					'<s>'
								SCREAMING_SNAKE_CASE													=					1

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







		def 							__A						(	self				)     ->							List[str]:
								SCREAMING_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]						,       '[MASK]'				)
								self.assertEqual(len(lowerCAmelCase__				)						,       1_004				)







		def 							__A						(	self				)     ->							Union[str, Any]:
								self.assertEqual(self.get_tokenizer().vocab_size						,       1_000				)







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

								SCREAMING_SNAKE_CASE													=					self.get_tokenizer()
								SCREAMING_SNAKE_CASE													=					self.get_rust_tokenizer()

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

								SCREAMING_SNAKE_CASE													=					tokenizer.tokenize(lowerCAmelCase__				)
								SCREAMING_SNAKE_CASE													=					rust_tokenizer.tokenize(lowerCAmelCase__				)
								self.assertListEqual(lowerCAmelCase__						,       lowerCAmelCase__				)

								SCREAMING_SNAKE_CASE													=					tokenizer.encode(lowerCAmelCase__						,       add_special_tokens=lowerCAmelCase__				)
								SCREAMING_SNAKE_CASE													=					rust_tokenizer.encode(lowerCAmelCase__						,       add_special_tokens=lowerCAmelCase__				)
								self.assertListEqual(lowerCAmelCase__						,       lowerCAmelCase__				)

								SCREAMING_SNAKE_CASE													=					self.get_rust_tokenizer()
								SCREAMING_SNAKE_CASE													=					tokenizer.encode(lowerCAmelCase__				)
								SCREAMING_SNAKE_CASE													=					rust_tokenizer.encode(lowerCAmelCase__				)
								self.assertListEqual(lowerCAmelCase__						,       lowerCAmelCase__				)







		def 							__A						(	self				)     ->							int:
								SCREAMING_SNAKE_CASE													=					BigBirdTokenizer(lowerCAmelCase__						,       keep_accents=lowerCAmelCase__				)

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

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

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

								SCREAMING_SNAKE_CASE													=					tokenizer.convert_ids_to_tokens(lowerCAmelCase__				)
								self.assertListEqual(
								    lowerCAmelCase__						,       [
								        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				)     ->							Tuple:
								return BigBirdTokenizer.from_pretrained('google/bigbird-roberta-base'				)







		@slow
		def 							__A						(	self				)     ->							Tuple:
								SCREAMING_SNAKE_CASE													=					'Hello World!'
								SCREAMING_SNAKE_CASE													=					[65, 18_536, 2_260, 101, 66]

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







		@slow
		def 							__A						(	self				)     ->							int:
								SCREAMING_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'
								)
								# fmt: off
								SCREAMING_SNAKE_CASE													=					[65, 871, 419, 358, 946, 991, 2_521, 452, 358, 1_357, 387, 7_751, 3_536, 112, 985, 456, 126, 865, 938, 5_400, 5_734, 458, 1_368, 467, 786, 2_462, 5_246, 1_159, 633, 865, 4_519, 457, 582, 852, 2_557, 427, 916, 508, 405, 34_324, 497, 391, 408, 11_342, 1_244, 385, 100, 938, 985, 456, 574, 362, 12_597, 3_200, 3_129, 1_172, 66]  # noqa: E231
								# fmt: on
								self.assertListEqual(lowerCAmelCase__						,       self.big_tokenizer.encode(lowerCAmelCase__				)				)







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

								from transformers import BigBirdConfig, BigBirdModel

								# Build sequence
								SCREAMING_SNAKE_CASE													=					list(self.big_tokenizer.get_vocab().keys()				)[:10]
								SCREAMING_SNAKE_CASE													=					' '.join(lowerCAmelCase__				)
								SCREAMING_SNAKE_CASE													=					self.big_tokenizer.encode_plus(lowerCAmelCase__						,       return_tensors='pt'						,       return_token_type_ids=lowerCAmelCase__				)
								SCREAMING_SNAKE_CASE													=					self.big_tokenizer.batch_encode_plus(
								    [sequence + ' ' + sequence]						,       return_tensors='pt'						,       return_token_type_ids=lowerCAmelCase__				)

								SCREAMING_SNAKE_CASE													=					BigBirdConfig(attention_type='original_full'				)
								SCREAMING_SNAKE_CASE													=					BigBirdModel(lowerCAmelCase__				)

								assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

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







		@slow
		def 							__A						(	self				)     ->							List[str]:
								SCREAMING_SNAKE_CASE													=					BigBirdTokenizer.from_pretrained('google/bigbird-roberta-base'				)
								SCREAMING_SNAKE_CASE													=					tokenizer.decode(tokenizer('Paris is the [MASK].'				).input_ids				)

								self.assertTrue(decoded_text == '[CLS] Paris is the[MASK].[SEP]'				)







		@slow
		def 							__A						(	self				)     ->							Optional[int]:
								# fmt: off
								SCREAMING_SNAKE_CASE													=					{'input_ids': [[65, 39_286, 458, 36_335, 2_001, 456, 13_073, 13_266, 455, 113, 7_746, 1_741, 11_157, 391, 13_073, 13_266, 455, 113, 3_967, 35_412, 113, 4_936, 109, 3_870, 2_377, 113, 30_084, 45_720, 458, 134, 17_496, 112, 503, 11_672, 113, 118, 112, 5_665, 13_347, 38_687, 112, 1_496, 31_389, 112, 3_268, 47_264, 134, 962, 112, 16_377, 8_035, 23_130, 430, 12_169, 15_518, 28_592, 458, 146, 41_697, 109, 391, 12_169, 15_518, 16_689, 458, 146, 41_358, 109, 452, 726, 4_034, 111, 763, 35_412, 5_082, 388, 1_903, 111, 9_051, 391, 2_870, 48_918, 1_900, 1_123, 550, 998, 112, 9_586, 15_985, 455, 391, 410, 22_955, 37_636, 114, 66], [65, 448, 17_496, 419, 3_663, 385, 763, 113, 27_533, 2_870, 3_283, 13_043, 1_639, 24_713, 523, 656, 24_013, 18_550, 2_521, 517, 27_014, 21_244, 420, 1_212, 1_465, 391, 927, 4_833, 388, 578, 11_786, 114, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [65, 484, 2_169, 7_687, 21_932, 18_146, 726, 363, 17_032, 3_391, 114, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 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, 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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
								# fmt: on

								self.tokenizer_integration_test_util(
								    expected_encoding=lowerCAmelCase__						,       model_name='google/bigbird-roberta-base'						,       revision='215c99f1600e06f83acce68422f2035b2b5c3510'						,       )









"""simple docstring"""





from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE__					=   {"configuration_focalnet": ["FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "FocalNetConfig"]}


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       SCREAMING_SNAKE_CASE__					=   [
           "FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST",
           "FocalNetForImageClassification",
           "FocalNetForMaskedImageModeling",
           "FocalNetBackbone",
           "FocalNetModel",
           "FocalNetPreTrainedModel",
       ]

if TYPE_CHECKING:
       from .configuration_focalnet import FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FocalNetConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_focalnet import (
                  FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                  FocalNetBackbone,
                  FocalNetForImageClassification,
                  FocalNetForMaskedImageModeling,
                  FocalNetModel,
                  FocalNetPreTrainedModel,
              )

else:
       import sys

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






