



"""simple docstring"""






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


__snake_case							:     Any   =					logging.get_logger(__name__)

__snake_case							:     Union[str, Any]   =					{
    'caidas/swin2sr-classicalsr-x2-64': (
        'https://huggingface.co/caidas/swin2sr-classicalsr-x2-64/resolve/main/config.json'
    ),
}



class     A__     (						_UpperCamelCase							):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE					   =			'swin2sr'

	SCREAMING_SNAKE_CASE					   =			{
	    'hidden_size': 'embed_dim',
	    'num_attention_heads': 'num_heads',
	    'num_hidden_layers': 'num_layers',
	}

	def __init__(  self:	Optional[int] ,    _SCREAMING_SNAKE_CASE:	List[str]=64 ,    _SCREAMING_SNAKE_CASE:	Any=1 ,    _SCREAMING_SNAKE_CASE:	Optional[Any]=3 ,    _SCREAMING_SNAKE_CASE:	int=180 ,    _SCREAMING_SNAKE_CASE:	str=[6, 6, 6, 6, 6, 6] ,    _SCREAMING_SNAKE_CASE:	int=[6, 6, 6, 6, 6, 6] ,    _SCREAMING_SNAKE_CASE:	Tuple=8 ,    _SCREAMING_SNAKE_CASE:	List[str]=2.0 ,    _SCREAMING_SNAKE_CASE:	Union[str, Any]=True ,    _SCREAMING_SNAKE_CASE:	str=0.0 ,    _SCREAMING_SNAKE_CASE:	List[str]=0.0 ,    _SCREAMING_SNAKE_CASE:	int=0.1 ,    _SCREAMING_SNAKE_CASE:	List[str]="gelu" ,    _SCREAMING_SNAKE_CASE:	Dict=False ,    _SCREAMING_SNAKE_CASE:	List[str]=0.02 ,    _SCREAMING_SNAKE_CASE:	str=1e-5 ,    _SCREAMING_SNAKE_CASE:	List[str]=2 ,    _SCREAMING_SNAKE_CASE:	Any=1.0 ,    _SCREAMING_SNAKE_CASE:	Any="1conv" ,    _SCREAMING_SNAKE_CASE:	List[Any]="pixelshuffle" ,    **_SCREAMING_SNAKE_CASE:	Dict ,    )       -> Optional[int]:

				"""simple docstring"""

				super().__init__(**_UpperCAmelCase)

				__lowerCAmelCase				= image_size
				__lowerCAmelCase				= patch_size
				__lowerCAmelCase				= num_channels
				__lowerCAmelCase				= embed_dim
				__lowerCAmelCase				= depths
				__lowerCAmelCase				= len(_UpperCAmelCase)
				__lowerCAmelCase				= num_heads
				__lowerCAmelCase				= window_size
				__lowerCAmelCase				= mlp_ratio
				__lowerCAmelCase				= qkv_bias
				__lowerCAmelCase				= hidden_dropout_prob
				__lowerCAmelCase				= attention_probs_dropout_prob
				__lowerCAmelCase				= drop_path_rate
				__lowerCAmelCase				= hidden_act
				__lowerCAmelCase				= use_absolute_embeddings
				__lowerCAmelCase				= layer_norm_eps
				__lowerCAmelCase				= initializer_range
				__lowerCAmelCase				= upscale
				__lowerCAmelCase				= img_range
				__lowerCAmelCase				= resi_connection
				__lowerCAmelCase				= upsampler




"""simple docstring"""






import string
from math import logaa


def     _lowercase       (						__snake_case      ,__snake_case			)		->     int:
			__lowerCAmelCase   :						int				= document.translate(
			    str.maketrans(""      ,""      ,string.punctuation			)			).replace("\n"      ,""			)
			__lowerCAmelCase   :						Dict				= document_without_punctuation.split(" "			)  # word tokenization
			return len([word for word in tokenize_document if word.lower() == term.lower()]			)


def     _lowercase       (						__snake_case      ,__snake_case			)		->     tuple[int, int]:
			__lowerCAmelCase   :						Optional[Any]				= corpus.lower().translate(
			    str.maketrans(""      ,""      ,string.punctuation			)			)  # strip all punctuation and replace it with ''
			__lowerCAmelCase   :						List[str]				= corpus_without_punctuation.split("\n"			)
			__lowerCAmelCase   :						str				= term.lower()
			return (len([doc for doc in docs if term in doc]			), len(__snake_case			))


def     _lowercase       (						__snake_case      ,__snake_case      ,__snake_case=False			)		->     float:
			if smoothing:
						if n == 0:
									raise ValueError("log10(0) is undefined."			)
						return round(1 + logaa(n / (1 + df)			)      ,3			)

			if df == 0:
						raise ZeroDivisionError("df must be > 0"			)
			elif n == 0:
						raise ValueError("log10(0) is undefined."			)
			return round(logaa(n / df			)      ,3			)





def     _lowercase       (						__snake_case      ,__snake_case			)		->     float:
			return round(tf * idf      ,3			)