"""simple docstring"""




import unittest

from transformers import AlbertTokenizer, AlbertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


_a				:		int	   =       get_tests_dir('fixtures/spiece.model')
@require_sentencepiece
@require_tokenizers
class 						__A							(			SCREAMING_SNAKE_CASE_   ,     unittest.TestCase		):
     _UpperCamelCase							:      Any		 =		AlbertTokenizer
     _UpperCamelCase							:      Optional[Any]		 =		AlbertTokenizerFast
     _UpperCamelCase							:      Optional[int]		 =		True
     _UpperCamelCase							:      Optional[Any]		 =		True
     _UpperCamelCase							:      Dict		 =		True






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

       # We have a SentencePiece fixture for testing
       _lowerCAmelCase						:	List[str]        =					AlbertTokenizer(a__  )
       tokenizer.save_pretrained(self.tmpdirname  )






     def    __A  (		self    ,					a__  ):
       _lowerCAmelCase						:	Tuple        =					"""this is a test"""
       _lowerCAmelCase						:	Tuple        =					"""this is a test"""
       return input_text, output_text






     def    __A  (		self  ):
       _lowerCAmelCase						:	List[Any]        =					"""<pad>"""
       _lowerCAmelCase						:	Tuple        =					0

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






     def    __A  (		self  ):
       _lowerCAmelCase						:	str        =					list(self.get_tokenizer().get_vocab().keys()  )

       self.assertEqual(vocab_keys[0]    ,					"""<pad>"""  )
       self.assertEqual(vocab_keys[1]    ,					"""<unk>"""  )
       self.assertEqual(vocab_keys[-1]    ,					"""▁eloquent"""  )
       self.assertEqual(len(a__  )    ,					30000  )






     def    __A  (		self  ):
       self.assertEqual(self.get_tokenizer().vocab_size    ,					30000  )






     def    __A  (		self  ):
       if not self.test_rust_tokenizer:
         return

       _lowerCAmelCase						:	Union[str, Any]        =					self.get_tokenizer()
       _lowerCAmelCase						:	int        =					self.get_rust_tokenizer()

       _lowerCAmelCase						:	Union[str, Any]        =					"""I was born in 92000, and this is falsé."""

       _lowerCAmelCase						:	int        =					tokenizer.tokenize(a__  )
       _lowerCAmelCase						:	List[Any]        =					rust_tokenizer.tokenize(a__  )
       self.assertListEqual(a__    ,					a__  )

       _lowerCAmelCase						:	Tuple        =					tokenizer.encode(a__    ,					add_special_tokens=a__  )
       _lowerCAmelCase						:	Tuple        =					rust_tokenizer.encode(a__    ,					add_special_tokens=a__  )
       self.assertListEqual(a__    ,					a__  )

       _lowerCAmelCase						:	Union[str, Any]        =					self.get_rust_tokenizer()
       _lowerCAmelCase						:	Any        =					tokenizer.encode(a__  )
       _lowerCAmelCase						:	Dict        =					rust_tokenizer.encode(a__  )
       self.assertListEqual(a__    ,					a__  )






     def    __A  (		self  ):
       _lowerCAmelCase						:	Optional[Any]        =					AlbertTokenizer(a__    ,					keep_accents=a__  )

       _lowerCAmelCase						:	Optional[Any]        =					tokenizer.tokenize("""This is a test"""  )
       self.assertListEqual(a__    ,					["""▁this""", """▁is""", """▁a""", """▁test"""]  )

       self.assertListEqual(tokenizer.convert_tokens_to_ids(a__  )    ,					[48, 25, 21, 1289]  )

       _lowerCAmelCase						:	Union[str, Any]        =					tokenizer.tokenize("""I was born in 92000, and this is falsé."""  )
       self.assertListEqual(
           a__    ,					["""▁i""", """▁was""", """▁born""", """▁in""", """▁9""", """2000""", """,""", """▁and""", """▁this""", """▁is""", """▁fal""", """s""", """é""", """."""]  )
       _lowerCAmelCase						:	List[Any]        =					tokenizer.convert_tokens_to_ids(a__  )
       self.assertListEqual(a__    ,					[31, 23, 386, 19, 561, 3050, 15, 17, 48, 25, 8256, 18, 1, 9]  )

       _lowerCAmelCase						:	Dict        =					tokenizer.convert_ids_to_tokens(a__  )
       self.assertListEqual(
           a__    ,					["""▁i""", """▁was""", """▁born""", """▁in""", """▁9""", """2000""", """,""", """▁and""", """▁this""", """▁is""", """▁fal""", """s""", """<unk>""", """."""]    ,					)






     def    __A  (		self  ):
       _lowerCAmelCase						:	Any        =					AlbertTokenizer(a__  )

       _lowerCAmelCase						:	List[Any]        =					tokenizer.encode("""sequence builders"""  )
       _lowerCAmelCase						:	Optional[int]        =					tokenizer.encode("""multi-sequence build"""  )

       _lowerCAmelCase						:	str        =					tokenizer.build_inputs_with_special_tokens(a__  )
       _lowerCAmelCase						:	str        =					tokenizer.build_inputs_with_special_tokens(a__    ,					a__  )

       assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
       assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
           tokenizer.sep_token_id
       ]






     @slow
     def    __A  (		self  ):
       # fmt: off
       _lowerCAmelCase						:	Optional[int]        =					{"""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, 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], [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, 0, 0, 0, 0, 0]], """input_ids""": [[2, 21970, 13, 5, 6092, 167, 28, 7103, 2153, 673, 8, 7028, 12051, 18, 17, 7103, 2153, 673, 8, 3515, 18684, 8, 4461, 6, 1927, 297, 8, 12060, 2607, 18, 13, 5, 4461, 15, 10538, 38, 8, 135, 15, 822, 58, 15, 993, 10363, 15, 1460, 8005, 4461, 15, 993, 255, 2328, 9, 9, 9, 6, 26, 1112, 816, 3260, 13, 5, 103, 2377, 6, 17, 1112, 816, 2782, 13, 5, 103, 10641, 6, 29, 84, 2512, 2430, 782, 18684, 2761, 19, 808, 2430, 2556, 17, 855, 1480, 9477, 4091, 128, 11712, 15, 7103, 2153, 673, 17, 24883, 9990, 9, 3], [2, 11502, 25, 1006, 20, 782, 8, 11809, 855, 1732, 19393, 18667, 37, 367, 21018, 69, 1854, 34, 11860, 19124, 27, 156, 225, 17, 193, 4141, 19, 65, 9124, 9, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 14, 2231, 886, 2385, 17659, 84, 14, 16792, 1952, 9, 3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], """token_type_ids""": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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=a__    ,					model_name="""albert-base-v2"""    ,					revision="""6b6560eaf5ff2e250b00c50f380c5389a9c2d82e"""    ,					)





"""simple docstring"""




from ...configuration_utils import PretrainedConfig
from ...utils import logging


_a				:		Any	   =       logging.get_logger(__name__)

_a				:		Any	   =       {
    'uclanlp/visualbert-vqa': 'https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json',
    'uclanlp/visualbert-vqa-pre': 'https://huggingface.co/uclanlp/visualbert-vqa-pre/resolve/main/config.json',
    'uclanlp/visualbert-vqa-coco-pre': (
        'https://huggingface.co/uclanlp/visualbert-vqa-coco-pre/resolve/main/config.json'
    ),
    'uclanlp/visualbert-vcr': 'https://huggingface.co/uclanlp/visualbert-vcr/resolve/main/config.json',
    'uclanlp/visualbert-vcr-pre': 'https://huggingface.co/uclanlp/visualbert-vcr-pre/resolve/main/config.json',
    'uclanlp/visualbert-vcr-coco-pre': (
        'https://huggingface.co/uclanlp/visualbert-vcr-coco-pre/resolve/main/config.json'
    ),
    'uclanlp/visualbert-nlvr2': 'https://huggingface.co/uclanlp/visualbert-nlvr2/resolve/main/config.json',
    'uclanlp/visualbert-nlvr2-pre': 'https://huggingface.co/uclanlp/visualbert-nlvr2-pre/resolve/main/config.json',
    'uclanlp/visualbert-nlvr2-coco-pre': (
        'https://huggingface.co/uclanlp/visualbert-nlvr2-coco-pre/resolve/main/config.json'
    )
    # See all VisualBERT models at https://huggingface.co/models?filter=visual_bert
}
class 						__A							(			SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:      List[Any]		 =		"visual_bert"






     def __init__(		self    ,					a__=30522    ,					a__=768    ,					a__=512    ,					a__=12    ,					a__=12    ,					a__=3072    ,					a__="gelu"    ,					a__=0.1    ,					a__=0.1    ,					a__=512    ,					a__=2    ,					a__=0.0_2    ,					a__=1e-12    ,					a__=False    ,					a__=True    ,					a__=1    ,					a__=0    ,					a__=2    ,					**a__    ,					):
       super().__init__(pad_token_id=a__    ,					bos_token_id=a__    ,					eos_token_id=a__    ,					**a__  )

       _lowerCAmelCase						:	Optional[Any]        =					vocab_size
       _lowerCAmelCase						:	int        =					max_position_embeddings
       _lowerCAmelCase						:	Optional[Any]        =					hidden_size
       _lowerCAmelCase						:	Optional[int]        =					visual_embedding_dim
       _lowerCAmelCase						:	Optional[int]        =					num_hidden_layers
       _lowerCAmelCase						:	List[Any]        =					num_attention_heads
       _lowerCAmelCase						:	Any        =					intermediate_size
       _lowerCAmelCase						:	List[Any]        =					hidden_act
       _lowerCAmelCase						:	int        =					hidden_dropout_prob
       _lowerCAmelCase						:	Optional[Any]        =					attention_probs_dropout_prob
       _lowerCAmelCase						:	Any        =					initializer_range
       _lowerCAmelCase						:	List[Any]        =					type_vocab_size
       _lowerCAmelCase						:	Union[str, Any]        =					layer_norm_eps
       _lowerCAmelCase						:	Optional[Any]        =					bypass_transformer
       _lowerCAmelCase						:	List[Any]        =					special_visual_initialize




