
'''simple docstring'''




from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase			:				Dict    		=			logging.get_logger(__name__)

lowercase			:				Dict    		=			{
    'facebook/data2vec-vision-base-ft': (
        'https://huggingface.co/facebook/data2vec-vision-base-ft/resolve/main/config.json'
    ),
}





class  A				(							__snake_case						):
     __magic_name__						=		'''data2vec-vision'''





     def __init__(							self						,			SCREAMING_SNAKE_CASE=768						,			SCREAMING_SNAKE_CASE=12						,			SCREAMING_SNAKE_CASE=12						,			SCREAMING_SNAKE_CASE=3072						,			SCREAMING_SNAKE_CASE="gelu"						,			SCREAMING_SNAKE_CASE=0.0						,			SCREAMING_SNAKE_CASE=0.0						,			SCREAMING_SNAKE_CASE=0.02						,			SCREAMING_SNAKE_CASE=1e-12						,			SCREAMING_SNAKE_CASE=224						,			SCREAMING_SNAKE_CASE=16						,			SCREAMING_SNAKE_CASE=3						,			SCREAMING_SNAKE_CASE=False						,			SCREAMING_SNAKE_CASE=False						,			SCREAMING_SNAKE_CASE=False						,			SCREAMING_SNAKE_CASE=False						,			SCREAMING_SNAKE_CASE=0.1						,			SCREAMING_SNAKE_CASE=0.1						,			SCREAMING_SNAKE_CASE=True						,			SCREAMING_SNAKE_CASE=[3, 5, 7, 11]						,			SCREAMING_SNAKE_CASE=[1, 2, 3, 6]						,			SCREAMING_SNAKE_CASE=True						,			SCREAMING_SNAKE_CASE=0.4						,			SCREAMING_SNAKE_CASE=256						,			SCREAMING_SNAKE_CASE=1						,			SCREAMING_SNAKE_CASE=False						,			SCREAMING_SNAKE_CASE=255						,			**SCREAMING_SNAKE_CASE						,			) ->				Optional[int]:






         """simple docstring"""






         super().__init__(**SCREAMING_SNAKE_CASE	)

         A       :   Tuple					=						hidden_size
         A       :   Optional[int]					=						num_hidden_layers
         A       :   Union[str, Any]					=						num_attention_heads
         A       :   Dict					=						intermediate_size
         A       :   Any					=						hidden_act
         A       :   Optional[int]					=						hidden_dropout_prob
         A       :   Optional[Any]					=						attention_probs_dropout_prob
         A       :   List[Any]					=						initializer_range
         A       :   Any					=						layer_norm_eps

         A       :   Any					=						image_size
         A       :   Union[str, Any]					=						patch_size
         A       :   Optional[Any]					=						num_channels
         A       :   Union[str, Any]					=						use_mask_token
         A       :   int					=						use_absolute_position_embeddings
         A       :   List[str]					=						use_relative_position_bias
         A       :   List[Any]					=						use_shared_relative_position_bias
         A       :   str					=						layer_scale_init_value
         A       :   int					=						drop_path_rate
         A       :   Tuple					=						use_mean_pooling
         # decode head attributes (semantic segmentation)
         A       :   Tuple					=						out_indices
         A       :   Optional[int]					=						pool_scales
         # auxiliary head attributes (semantic segmentation)
         A       :   int					=						use_auxiliary_head
         A       :   List[Any]					=						auxiliary_loss_weight
         A       :   str					=						auxiliary_channels
         A       :   Union[str, Any]					=						auxiliary_num_convs
         A       :   List[Any]					=						auxiliary_concat_input
         A       :   Dict					=						semantic_loss_ignore_index





class  A				(							__snake_case						):
     __magic_name__						=		version.parse('''1.11'''						)





     @property
     def 				__lowerCAmelCase							(							self	) ->				Mapping[str, Mapping[int, str]]:






         """simple docstring"""






         return OrderedDict(
             [
                 ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
             ]	)





     @property
     def 				__lowerCAmelCase							(							self	) ->				float:






         """simple docstring"""






         return 1e-4






'''simple docstring'''




from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow

from ..bert.test_tokenization_bert import BertTokenizationTest





@require_tokenizers
class  A				(							__snake_case						):
     __magic_name__						=		DistilBertTokenizer
     __magic_name__						=		DistilBertTokenizerFast
     __magic_name__						=		True





     @slow
     def 				__lowerCAmelCase							(							self	) ->				Any:






         """simple docstring"""






         A       :   List[Any]					=						DistilBertTokenizer.from_pretrained('''distilbert-base-uncased'''	)

         A       :   Dict					=						tokenizer.encode('''sequence builders'''						,			add_special_tokens=SCREAMING_SNAKE_CASE	)
         A       :   List[str]					=						tokenizer.encode('''multi-sequence build'''						,			add_special_tokens=SCREAMING_SNAKE_CASE	)

         A       :   Optional[Any]					=						tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE	)
         A       :   Tuple					=						tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE	)

         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
         ]




