




"""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_            =   logging.get_logger(__name__)

a_            =   {
    '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        UpperCAmelCase_  (	snake_case						):
		UpperCamelCase									="vit-hybrid"
		def __init__(		self							,		UpperCamelCase_=None							,		UpperCamelCase_=7_68							,		UpperCamelCase_=12							,		UpperCamelCase_=12							,		UpperCamelCase_=30_72							,		UpperCamelCase_="gelu"							,		UpperCamelCase_=0.0							,		UpperCamelCase_=0.0							,		UpperCamelCase_=0.0_2							,		UpperCamelCase_=1E-12							,		UpperCamelCase_=2_24							,		UpperCamelCase_=1							,		UpperCamelCase_=3							,		UpperCamelCase_=[1, 10_24, 24, 24]							,		UpperCamelCase_=True							,		**UpperCamelCase_							,		)		->   str:
					super().__init__(**UpperCamelCase_   )

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

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

					__lowercase	:       Union[str, Any]				=   backbone_featmap_shape
					__lowercase	:       Tuple				=   backbone_config
					__lowercase	:       Optional[Any]				=   hidden_size
					__lowercase	:       Any				=   num_hidden_layers
					__lowercase	:       Union[str, Any]				=   num_attention_heads
					__lowercase	:       List[Any]				=   intermediate_size
					__lowercase	:       Optional[int]				=   hidden_act
					__lowercase	:       List[str]				=   hidden_dropout_prob
					__lowercase	:       List[str]				=   attention_probs_dropout_prob
					__lowercase	:       List[str]				=   initializer_range
					__lowercase	:       Union[str, Any]				=   layer_norm_eps
					__lowercase	:       Union[str, Any]				=   image_size
					__lowercase	:       int				=   patch_size
					__lowercase	:       Optional[Any]				=   num_channels
					__lowercase	:       Optional[int]				=   qkv_bias
		def 					_lowerCamelCase    (		self   )		->   Dict[str, any]:
					__lowercase	:       Union[str, Any]				=   copy.deepcopy(self.__dict__   )
					__lowercase	:       Dict				=   self.backbone_config.to_dict()
					__lowercase	:       List[Any]				=   self.__class__.model_type
					return output









"""simple docstring"""




from collections import defaultdict







def       __UpperCAmelCase      (				__UpperCamelCase					):
			__lowercase	:       defaultdict[str, int]				=   defaultdict(__UpperCamelCase					)
			# Creating a dictionary containing count of each word
			for word in sentence.split():
						occurrence[word] += 1
			return occurrence


if __name__ == "__main__":
			for word, count in word_occurrence('INPUT STRING').items():
						print(F"{word}: {count}")



