





import json
import os
import unittest

from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
    VOCAB_FILES_NAMES,
    BlenderbotSmallTokenizer,
)

from ...test_tokenization_common import TokenizerTesterMixin



class    __lowerCAmelCase			(     _a,	unittest.TestCase	):
			lowerCamelCase_	:	Optional[int]			     = BlenderbotSmallTokenizer
			lowerCamelCase_	:	Dict			     = False






			def      lowerCamelCase    (self       )	->  List[str]:


							'''simple docstring'''



							super().setUp()

							snake_case_		:       Optional[int]           =    ['''__start__''', '''adapt''', '''act''', '''ap@@''', '''te''', '''__end__''', '''__unk__''']
							snake_case_		:       str           =    dict(zip(__magic_name__   ,				range(len(__magic_name__       )       )       )       )

							snake_case_		:       Tuple           =    ['''#version: 0.2''', '''a p''', '''t e</w>''', '''ap t</w>''', '''a d''', '''ad apt</w>''', '''a c''', '''ac t</w>''', '''''']
							snake_case_		:       List[str]           =    {'''unk_token''': '''__unk__''', '''bos_token''': '''__start__''', '''eos_token''': '''__end__'''}

							snake_case_		:       Optional[int]           =    os.path.join(self.tmpdirname   ,				VOCAB_FILES_NAMES['''vocab_file''']       )
							snake_case_		:       Optional[Any]           =    os.path.join(self.tmpdirname   ,				VOCAB_FILES_NAMES['''merges_file''']       )
							with open(self.vocab_file   ,				'''w'''   ,				encoding='''utf-8'''       ) as fp:
											fp.write(json.dumps(__magic_name__       ) + '''\n'''       )
							with open(self.merges_file   ,				'''w'''   ,				encoding='''utf-8'''       ) as fp:
											fp.write('''\n'''.join(__magic_name__       )       )






			def      lowerCamelCase    (self   ,				**__magic_name__       )	->  int:


							'''simple docstring'''



							kwargs.update(self.special_tokens_map       )
							return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname   ,				**__magic_name__       )






			def      lowerCamelCase    (self   ,				__magic_name__       )	->  Optional[Any]:


							'''simple docstring'''



							snake_case_		:       Optional[int]           =    '''adapt act apte'''
							snake_case_		:       int           =    '''adapt act apte'''
							return input_text, output_text






			def      lowerCamelCase    (self       )	->  Tuple:


							'''simple docstring'''



							snake_case_		:       Any           =    BlenderbotSmallTokenizer(self.vocab_file   ,				self.merges_file   ,				**self.special_tokens_map       )
							snake_case_		:       List[Any]           =    '''adapt act apte'''
							snake_case_		:       Dict           =    ['''adapt''', '''act''', '''ap@@''', '''te''']
							snake_case_		:       Any           =    tokenizer.tokenize(__magic_name__       )
							self.assertListEqual(__magic_name__   ,				__magic_name__       )

							snake_case_		:       Tuple           =    [tokenizer.bos_token] + tokens + [tokenizer.eos_token]

							snake_case_		:       str           =    [0, 1, 2, 3, 4, 5]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(__magic_name__       )   ,				__magic_name__       )






			def      lowerCamelCase    (self       )	->  int:


							'''simple docstring'''



							snake_case_		:       int           =    BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''       )
							assert tok('''sam'''       ).input_ids == [1384]
							snake_case_		:       int           =    '''I am a small frog.'''
							snake_case_		:       Dict           =    tok([src_text]   ,				padding=__magic_name__   ,				truncation=__magic_name__       )['''input_ids''']
							snake_case_		:       str           =    tok.batch_decode(__magic_name__   ,				skip_special_tokens=__magic_name__   ,				clean_up_tokenization_spaces=__magic_name__       )[0]
							assert src_text != decoded  # I wish it did!
							assert decoded == "i am a small frog ."






			def      lowerCamelCase    (self       )	->  int:


							'''simple docstring'''



							snake_case_		:       str           =    BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''       )
							snake_case_		:       Tuple           =    '''I am a small frog .'''
							snake_case_		:       Optional[Any]           =    '''.'''
							snake_case_		:       int           =    tok(__magic_name__       )['''input_ids''']
							snake_case_		:       Any           =    tok(__magic_name__       )['''input_ids''']

							assert encoded[-1] == encoded_dot[0]




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


snake_case							:       List[Any]										=	logging.get_logger(__name__)

snake_case							:       Dict										=	{
    '''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 						_snake_case    ( _snake_case				):
 SCREAMING_SNAKE_CASE__ =			'visual_bert'




 def __init__(						self   ,    _lowerCamelCase=3_0522   ,    _lowerCamelCase=768   ,    _lowerCamelCase=512   ,    _lowerCamelCase=12   ,    _lowerCamelCase=12   ,    _lowerCamelCase=3072   ,    _lowerCamelCase="gelu"   ,    _lowerCamelCase=0.1   ,    _lowerCamelCase=0.1   ,    _lowerCamelCase=512   ,    _lowerCamelCase=2   ,    _lowerCamelCase=0.02   ,    _lowerCamelCase=1e-12   ,    _lowerCamelCase=False   ,    _lowerCamelCase=True   ,    _lowerCamelCase=1   ,    _lowerCamelCase=0   ,    _lowerCamelCase=2   ,    **_lowerCamelCase   ,    ):
       super().__init__(pad_token_id=_lowerCamelCase   ,    bos_token_id=_lowerCamelCase   ,    eos_token_id=_lowerCamelCase   ,    **_lowerCamelCase       )

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





