


'''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


_lowercase      :      List[str]            =					"""▁"""

_lowercase      :      Union[str, Any]            =					get_tests_dir("""fixtures/test_sentencepiece.model""")






@require_sentencepiece
@require_tokenizers
class 	UpperCamelCase__(       lowerCAmelCase						,    unittest.TestCase	):
				__magic_name__    :				Union[str, Any]  		=	BigBirdTokenizer
				__magic_name__    :				List[Any]  		=	BigBirdTokenizerFast
				__magic_name__    :				int  		=	True
				__magic_name__    :				str  		=	True




				def 					a__(		self      :			Dict				)->					Union[str, Any]:

										"""simple docstring"""




										super().setUp()

										UpperCAmelCase					    =     self.tokenizer_class(lowerCAmelCase				,   keep_accents=lowerCAmelCase				)
										tokenizer.save_pretrained(self.tmpdirname				)




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

										"""simple docstring"""




										UpperCAmelCase					    =     '''<s>'''
										UpperCAmelCase					    =     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      :			Tuple				)->					List[Any]:

										"""simple docstring"""




										UpperCAmelCase					    =     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				)				,   1004				)




				def 					a__(		self      :			Tuple				)->					str:

										"""simple docstring"""




										self.assertEqual(self.get_tokenizer().vocab_size				,   1000				)




				def 					a__(		self      :			Any				)->					int:

										"""simple docstring"""




										if not self.test_rust_tokenizer:
																return

										UpperCAmelCase					    =     self.get_tokenizer()
										UpperCAmelCase					    =     self.get_rust_tokenizer()

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

										UpperCAmelCase					    =     tokenizer.tokenize(lowerCAmelCase				)
										UpperCAmelCase					    =     rust_tokenizer.tokenize(lowerCAmelCase				)
										self.assertListEqual(lowerCAmelCase				,   lowerCAmelCase				)

										UpperCAmelCase					    =     tokenizer.encode(lowerCAmelCase				,   add_special_tokens=lowerCAmelCase				)
										UpperCAmelCase					    =     rust_tokenizer.encode(lowerCAmelCase				,   add_special_tokens=lowerCAmelCase				)
										self.assertListEqual(lowerCAmelCase				,   lowerCAmelCase				)

										UpperCAmelCase					    =     self.get_rust_tokenizer()
										UpperCAmelCase					    =     tokenizer.encode(lowerCAmelCase				)
										UpperCAmelCase					    =     rust_tokenizer.encode(lowerCAmelCase				)
										self.assertListEqual(lowerCAmelCase				,   lowerCAmelCase				)




				def 					a__(		self      :			Dict				)->					Any:

										"""simple docstring"""




										UpperCAmelCase					    =     BigBirdTokenizer(lowerCAmelCase				,   keep_accents=lowerCAmelCase				)

										UpperCAmelCase					    =     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]				,   )

										UpperCAmelCase					    =     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''',
										        '''.''',
										    ]				,   )
										UpperCAmelCase					    =     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]				,   )

										UpperCAmelCase					    =     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      :			Any				)->					str:

										"""simple docstring"""




										return BigBirdTokenizer.from_pretrained('''google/bigbird-roberta-base'''				)




				@slow
				def 					a__(		self      :			List[str]				)->					str:

										"""simple docstring"""




										UpperCAmelCase					    =     '''Hello World!'''
										UpperCAmelCase					    =     [65, 18536, 2260, 101, 66]

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




				@slow
				def 					a__(		self      :			Union[str, Any]				)->					Union[str, Any]:

										"""simple docstring"""




										UpperCAmelCase					    =     (
										    '''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
										UpperCAmelCase					    =     [65, 871, 419, 358, 946, 991, 2521, 452, 358, 1357, 387, 7751, 3536, 112, 985, 456, 126, 865, 938, 5400, 5734, 458, 1368, 467, 786, 2462, 5246, 1159, 633, 865, 4519, 457, 582, 852, 2557, 427, 916, 508, 405, 34324, 497, 391, 408, 11342, 1244, 385, 100, 938, 985, 456, 574, 362, 12597, 3200, 3129, 1172, 66]  # noqa: E231
										# fmt: on
										self.assertListEqual(lowerCAmelCase				,   self.big_tokenizer.encode(lowerCAmelCase				)				)




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

										"""simple docstring"""




										import torch

										from transformers import BigBirdConfig, BigBirdModel

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

										UpperCAmelCase					    =     BigBirdConfig(attention_type='''original_full'''				)
										UpperCAmelCase					    =     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]				)->					Union[str, Any]:

										"""simple docstring"""




										UpperCAmelCase					    =     BigBirdTokenizer.from_pretrained('''google/bigbird-roberta-base'''				)
										UpperCAmelCase					    =     tokenizer.decode(tokenizer('''Paris is the [MASK].'''				).input_ids				)

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





				@slow
				def 					a__(		self      :			str				)->					Optional[int]:

										"""simple docstring"""




										UpperCAmelCase					    =     {'''input_ids''': [[65, 39286, 458, 36335, 2001, 456, 13073, 13266, 455, 113, 7746, 1741, 11157, 391, 13073, 13266, 455, 113, 3967, 35412, 113, 4936, 109, 3870, 2377, 113, 30084, 45720, 458, 134, 17496, 112, 503, 11672, 113, 118, 112, 5665, 13347, 38687, 112, 1496, 31389, 112, 3268, 47264, 134, 962, 112, 16377, 8035, 23130, 430, 12169, 15518, 28592, 458, 146, 41697, 109, 391, 12169, 15518, 16689, 458, 146, 41358, 109, 452, 726, 4034, 111, 763, 35412, 5082, 388, 1903, 111, 9051, 391, 2870, 48918, 1900, 1123, 550, 998, 112, 9586, 15985, 455, 391, 410, 22955, 37636, 114, 66], [65, 448, 17496, 419, 3663, 385, 763, 113, 27533, 2870, 3283, 13043, 1639, 24713, 523, 656, 24013, 18550, 2521, 517, 27014, 21244, 420, 1212, 1465, 391, 927, 4833, 388, 578, 11786, 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, 2169, 7687, 21932, 18146, 726, 363, 17032, 3391, 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'''



# limitations under the License.

# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works

from .pipelines import DiffusionPipeline, ImagePipelineOutput  # noqa: F401
from .utils import deprecate


deprecate(
    """pipelines_utils""",
    """0.22.0""",
    """Importing `DiffusionPipeline` or `ImagePipelineOutput` from diffusers.pipeline_utils is deprecated. Please import from diffusers.pipelines.pipeline_utils instead.""",
    standard_warn=False,
    stacklevel=3,
)





