



"""simple docstring"""


from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_clip import CLIPTokenizer


snake_case_								=			logging.get_logger(__name__)

snake_case_								=			{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

snake_case_								=			{
    """vocab_file""": {
        """openai/clip-vit-base-patch32""": """https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """openai/clip-vit-base-patch32""": """https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """openai/clip-vit-base-patch32""": (
            """https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/tokenizer.json"""
        ),
    },
}

snake_case_								=			{
    """openai/clip-vit-base-patch32""": 77,
}







class  A_     (	SCREAMING_SNAKE_CASE_							):



						"""simple docstring"""
						__UpperCamelCase													=				VOCAB_FILES_NAMES
						__UpperCamelCase													=				PRETRAINED_VOCAB_FILES_MAP
						__UpperCamelCase													=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						__UpperCamelCase													=				["""input_ids""", """attention_mask"""]
						__UpperCamelCase													=				CLIPTokenizer






						def __init__(   self					:int  ,    lowercase_					:Optional[int]=None  ,    lowercase_					:int=None  ,    lowercase_					:Optional[Any]=None  ,    lowercase_					:Dict="<|endoftext|>"  ,    lowercase_					:List[Any]="<|startoftext|>"  ,    lowercase_					:Any="<|endoftext|>"  ,    lowercase_					:List[str]="<|endoftext|>"  ,    **lowercase_					:Tuple  ,    )							->     List[Any]:
												super().__init__(
												    lowercase_  ,    lowercase_  ,    tokenizer_file=lowercase_  ,    unk_token=lowercase_  ,    bos_token=lowercase_  ,    eos_token=lowercase_  ,    pad_token=lowercase_  ,    **lowercase_  ,    )

												if not isinstance(self.backend_tokenizer.pre_tokenizer  ,    pre_tokenizers.Sequence   ):
																		raise ValueError(
																		    'The `backend_tokenizer` provided does not match the expected format. The CLIP tokenizer has been'
																		    ' heavily modified from transformers version 4.17.0. You need to convert the tokenizer you are using'
																		    ' to be compatible with this version.The easiest way to do so is'
																		    ' `CLIPTokenizerFast.from_pretrained("path_to_local_folder_or_hub_repo, from_slow=True)`. If you want'
																		    ' to use your existing tokenizer, you will have to revert to a version prior to 4.17.0 of'
																		    ' transformers.'   )

												self._wrap_decode_method_backend_tokenizer()






						def       UpperCAmelCase__	(   self					:Dict   )							->     List[Any]:
												UpperCAmelCase			 =					self.backend_tokenizer.decode

												def new_decode_method(*lowercase_					:List[Any]  ,    **lowercase_					:Optional[Any]   ):
																		UpperCAmelCase			 =					orig_decode_method(*lowercase_  ,    **lowercase_   )
																		UpperCAmelCase			 =					text.replace(self.backend_tokenizer.model.end_of_word_suffix  ,    ' '   ).strip()
																		return text

												UpperCAmelCase			 =					new_decode_method






						def       UpperCAmelCase__	(   self					:Optional[int]  ,    lowercase_					:List[int]  ,    lowercase_					:Optional[List[int]] = None   )							->     List[int]:
												UpperCAmelCase			 =					[self.bos_token_id]
												UpperCAmelCase			 =					[self.eos_token_id]

												if token_ids_a is None:
																		return bos_token + token_ids_a + eos_token
												return bos_token + token_ids_a + eos_token + eos_token + token_ids_a + eos_token






						def       UpperCAmelCase__	(   self					:Dict  ,    lowercase_					:List[int]  ,    lowercase_					:Optional[List[int]] = None   )							->     List[int]:
												UpperCAmelCase			 =					[self.bos_token_id]
												UpperCAmelCase			 =					[self.eos_token_id]

												if token_ids_a is None:
																		return len(bos_token + token_ids_a + eos_token   ) * [0]
												return len(bos_token + token_ids_a + eos_token + eos_token + token_ids_a + eos_token   ) * [0]






						def       UpperCAmelCase__	(   self					:Optional[Any]  ,    lowercase_					:str  ,    lowercase_					:Optional[str] = None   )							->     Tuple[str]:
												UpperCAmelCase			 =					self._tokenizer.model.save(lowercase_  ,    name=lowercase_   )
												return tuple(lowercase_   )












import copy
from typing import Dict

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
from ..bit import BitConfig


a_    :				Union[str, Any]					=			logging.get_logger(__name__)

a_    :				Optional[Any]					=			{
    'google/vit-hybrid-base-bit-384': 'https://huggingface.co/vit-hybrid-base-bit-384/resolve/main/config.json',
    # See all ViT hybrid models at https://huggingface.co/models?filter=vit
}



class  SCREAMING_SNAKE_CASE_  ( SCREAMING_SNAKE_CASE__			):


  """simple docstring"""





  _a								=							"""vit-hybrid"""





  def __init__(  self	,  A=None	,  A=7_68	,  A=12	,  A=12	,  A=30_72	,  A="gelu"	,  A=0.0	,  A=0.0	,  A=0.02	,  A=1E-12	,  A=2_24	,  A=1	,  A=3	,  A=[1, 10_24, 24, 24]	,  A=True	,  **A	,  )		->   Union[str, Any]:





       '''simple docstring'''







       super().__init__(**A						)

       if backbone_config is None:
            logger.info('''`backbone_config` is `None`. Initializing the config with a `BiT` backbone.'''						)
            __magic_name__				=       {
                '''global_padding''': '''same''',
                '''layer_type''': '''bottleneck''',
                '''depths''': [3, 4, 9],
                '''out_features''': ['''stage3'''],
                '''embedding_dynamic_padding''': True,
            }

       if isinstance(A	,  A						):
            if "model_type" in backbone_config:
                 __magic_name__				=       CONFIG_MAPPING[backbone_config['''model_type''']]
            else:
                 logger.info(
                     '''`model_type` is not found in `backbone_config`. Use `Bit` as the backbone configuration class.'''						)
                 __magic_name__				=       BitConfig
            __magic_name__				=       backbone_config_class(**A						)

       __magic_name__				=       backbone_featmap_shape
       __magic_name__				=       backbone_config
       __magic_name__				=       hidden_size
       __magic_name__				=       num_hidden_layers
       __magic_name__				=       num_attention_heads
       __magic_name__				=       intermediate_size
       __magic_name__				=       hidden_act
       __magic_name__				=       hidden_dropout_prob
       __magic_name__				=       attention_probs_dropout_prob
       __magic_name__				=       initializer_range
       __magic_name__				=       layer_norm_eps
       __magic_name__				=       image_size
       __magic_name__				=       patch_size
       __magic_name__				=       num_channels
       __magic_name__				=       qkv_bias







  def 			__A      (  self						)		->   Dict[str, any]:





       '''simple docstring'''







       __magic_name__				=       copy.deepcopy(self.__dict__						)
       __magic_name__				=       self.backbone_config.to_dict()
       __magic_name__				=       self.__class__.model_type
       return output