




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


lowerCamelCase				:		Optional[Any]     =   logging.get_logger(__name__)

lowerCamelCase				:		str     =   {
    "vinvino02/glpn-kitti": "https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json",
    # See all GLPN models at https://huggingface.co/models?filter=glpn
}







class   A(						_a   ):



 '''simple docstring'''


 UpperCamelCase						   =						'''glpn'''

 def __init__(				self					:  Optional[Any]						,							A_					:  List[str]=3						,							A_					:  Dict=4						,							A_					:  List[Any]=[2, 2, 2, 2]						,							A_					:  int=[8, 4, 2, 1]						,							A_					:  List[str]=[32, 64, 160, 256]						,							A_					:  Tuple=[7, 3, 3, 3]						,							A_					:  List[Any]=[4, 2, 2, 2]						,							A_					:  Tuple=[1, 2, 5, 8]						,							A_					:  List[str]=[4, 4, 4, 4]						,							A_					:  Optional[int]="gelu"						,							A_					:  Dict=0.0						,							A_					:  Union[str, Any]=0.0						,							A_					:  List[Any]=0.02						,							A_					:  Tuple=0.1						,							A_					:  Any=1E-6						,							A_					:  Dict=64						,							A_					:  Tuple=10						,							A_					:  List[Any]=-1						,							**A_					:  Optional[Any]						,							)   ->	Dict:

       """simple docstring"""






       super().__init__(**snake_case_  )

       lowerCamelCase_		     =    num_channels
       lowerCamelCase_		     =    num_encoder_blocks
       lowerCamelCase_		     =    depths
       lowerCamelCase_		     =    sr_ratios
       lowerCamelCase_		     =    hidden_sizes
       lowerCamelCase_		     =    patch_sizes
       lowerCamelCase_		     =    strides
       lowerCamelCase_		     =    mlp_ratios
       lowerCamelCase_		     =    num_attention_heads
       lowerCamelCase_		     =    hidden_act
       lowerCamelCase_		     =    hidden_dropout_prob
       lowerCamelCase_		     =    attention_probs_dropout_prob
       lowerCamelCase_		     =    initializer_range
       lowerCamelCase_		     =    drop_path_rate
       lowerCamelCase_		     =    layer_norm_eps
       lowerCamelCase_		     =    decoder_hidden_size
       lowerCamelCase_		     =    max_depth
       lowerCamelCase_		     =    head_in_index








'''simple docstring'''





import string
from math import logaa





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





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





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