



'''simple docstring'''






import torch

from transformers import AutoTokenizer



class 							lowercase (						_SCREAMING_SNAKE_CASE			):


 """simple docstring"""


 def __init__(							self						,a_							)	->     str:
        _UpperCAmelCase						:		Union[str, Any]      =				AutoTokenizer.from_pretrained(UpperCamelCase__							)







 def _snake_case				(							self						,a_							)	->     Tuple:

        if isinstance(UpperCamelCase__						,UpperCamelCase__							) and all(isinstance(_x						,UpperCamelCase__							) for _x in x							):
               _UpperCAmelCase						:		int      =				None
               for l in x:
                      _UpperCAmelCase						:		Any      =				self.tokenizer(
                          UpperCamelCase__						,padding="""max_length"""						,max_length=384						,truncation=UpperCamelCase__						,return_tensors="""pt"""						,)
                      _UpperCAmelCase						:		Optional[int]      =				torch.tensor([len(UpperCamelCase__							)]							)
                      if d is not None:
                             for k in d.keys():
                                    _UpperCAmelCase						:		int      =				torch.cat((d[k], t[k])						,0							)
                      else:
                             _UpperCAmelCase						:		Optional[Any]      =				t

               _UpperCAmelCase						:		List[Any]      =				torch.tensor(self.tokenizer.convert_tokens_to_ids("""[E]"""							)							)
               _UpperCAmelCase						:		Optional[Any]      =				torch.tensor(self.tokenizer.convert_tokens_to_ids("""[/E]"""							)							)

        elif isinstance(UpperCamelCase__						,UpperCamelCase__							) and all(isinstance(_x						,UpperCamelCase__							) for _x in x							):
               _UpperCAmelCase						:		Any      =				self.tokenizer(
                   UpperCamelCase__						,padding="""max_length"""						,max_length=384						,truncation=UpperCamelCase__						,return_tensors="""pt"""						,)

        else:
               raise Exception(
                   """Type of parameter x was not recognized! Only `list of strings` for query or `list of lists of"""
                   """ strings` for supports are supported."""							)

        return d







 def _snake_case				(							self						,a_						,a_						,a_						,a_						,a_=0.70							)	->     Union[str, Any]:
        _UpperCAmelCase						:		List[str]      =				[]
        for idx in range(len(W_query["""input_ids"""]							)							):
               _UpperCAmelCase						:		Any      =				range(p_start.shape[1]							)

               _UpperCAmelCase						:		List[Any]      =				[]
               for start_id in start_indexes:
                      for end_id in end_indexes:
                             if start_id < end_id:
                                    output.append(
                                        (
                                            start_id,
                                            end_id,
                                            p_start[idx][start_id].item(),
                                            p_end[idx][end_id].item(),
                                        )							)

               output.sort(key=lambda a_							: (tup[2] * tup[3])						,reverse=UpperCamelCase__							)
               _UpperCAmelCase						:		Any      =				[]
               for k in range(len(UpperCamelCase__							)							):
                      if output[k][2] * output[k][3] >= thresh:
                             _UpperCAmelCase  ,_UpperCAmelCase						:		Optional[Any]      =				output[k][0], output[k][1]
                             _UpperCAmelCase						:		Tuple      =				self.tokenizer.decode(W_query["""input_ids"""][idx][c_start_pos:c_end_pos]							)
                             temp.append((decoded, output[k][2] * output[k][3])							)

               final_outputs.append(UpperCamelCase__							)

        return final_outputs








'''simple docstring'''






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_							:				List[str]						=			logging.get_logger(__name__)

A_							:				Optional[int]						=			{
    """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 							lowercase (						_lowerCamelCase       ):


 """simple docstring"""


 UpperCAmelCase					=						"""vit-hybrid"""







 def __init__(							self						,a_=None						,a_=768						,a_=12						,a_=12						,a_=3_072						,a_="gelu"						,a_=0.0						,a_=0.0						,a_=0.02						,a_=1E-1_2						,a_=224						,a_=1						,a_=3						,a_=[1, 1_024, 24, 24]						,a_=True						,**a_						,)	->     Union[str, Any]:
        super().__init__(**a_							)

        if backbone_config is None:
               logger.info("""`backbone_config` is `None`. Initializing the config with a `BiT` backbone."""							)
               _UpperCAmelCase						:		Dict      =				{
                   """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:
                      _UpperCAmelCase						:		Dict      =				CONFIG_MAPPING[backbone_config["""model_type"""]]
               else:
                      logger.info(
                          """`model_type` is not found in `backbone_config`. Use `Bit` as the backbone configuration class."""							)
                      _UpperCAmelCase						:		str      =				BitConfig
               _UpperCAmelCase						:		List[str]      =				backbone_config_class(**a_							)

        _UpperCAmelCase						:		Dict      =				backbone_featmap_shape
        _UpperCAmelCase						:		str      =				backbone_config
        _UpperCAmelCase						:		List[str]      =				hidden_size
        _UpperCAmelCase						:		int      =				num_hidden_layers
        _UpperCAmelCase						:		List[Any]      =				num_attention_heads
        _UpperCAmelCase						:		int      =				intermediate_size
        _UpperCAmelCase						:		str      =				hidden_act
        _UpperCAmelCase						:		Tuple      =				hidden_dropout_prob
        _UpperCAmelCase						:		int      =				attention_probs_dropout_prob
        _UpperCAmelCase						:		int      =				initializer_range
        _UpperCAmelCase						:		int      =				layer_norm_eps
        _UpperCAmelCase						:		Dict      =				image_size
        _UpperCAmelCase						:		Optional[int]      =				patch_size
        _UpperCAmelCase						:		List[Any]      =				num_channels
        _UpperCAmelCase						:		List[Any]      =				qkv_bias







 def _snake_case				(							self							)	->     Dict[str, any]:
        _UpperCAmelCase						:		Optional[Any]      =				copy.deepcopy(self.__dict__							)
        _UpperCAmelCase						:		Optional[Any]      =				self.backbone_config.to_dict()
        _UpperCAmelCase						:		Tuple      =				self.__class__.model_type
        return output



