





'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


__lowerCAmelCase				:      str						      =logging.get_logger(__name__)

__lowerCAmelCase				:      List[Any]						      ={
    "microsoft/table-transformer-detection": (
        "https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json"
    ),
}






class   UpperCAmelCase      (			UpperCamelCase__							):
 __lowercase  						=							"""table-transformer"""
 __lowercase  						=							["""past_key_values"""]
 __lowercase  						=							{
     """hidden_size""": """d_model""",
     """num_attention_heads""": """encoder_attention_heads""",
 }
 def __init__(							self :Union[str, Any]		,			lowercase_ :List[str]=True		,			lowercase_ :Union[str, Any]=None		,			lowercase_ :Dict=3		,			lowercase_ :str=1_00		,			lowercase_ :Any=6		,			lowercase_ :Union[str, Any]=20_48		,			lowercase_ :List[Any]=8		,			lowercase_ :Optional[int]=6		,			lowercase_ :List[str]=20_48		,			lowercase_ :Optional[Any]=8		,			lowercase_ :Optional[Any]=0.0		,			lowercase_ :Optional[Any]=0.0		,			lowercase_ :str=True		,			lowercase_ :Dict="relu"		,			lowercase_ :str=2_56		,			lowercase_ :Any=0.1		,			lowercase_ :Dict=0.0		,			lowercase_ :str=0.0		,			lowercase_ :str=0.0_2		,			lowercase_ :str=1.0		,			lowercase_ :Optional[Any]=False		,			lowercase_ :Union[str, Any]="sine"		,			lowercase_ :Dict="resnet50"		,			lowercase_ :str=True		,			lowercase_ :Union[str, Any]=False		,			lowercase_ :Union[str, Any]=1		,			lowercase_ :str=5		,			lowercase_ :Tuple=2		,			lowercase_ :List[Any]=1		,			lowercase_ :int=1		,			lowercase_ :Any=5		,			lowercase_ :str=2		,			lowercase_ :Optional[int]=0.1		,			**lowercase_ :Dict		,			)->				List[Any]:
     if backbone_config is not None and use_timm_backbone:
         raise ValueError("You can't specify both `backbone_config` and `use_timm_backbone`."				)

     if not use_timm_backbone:
         if backbone_config is None:
             logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."				)
             A__							=     CONFIG_MAPPING["resnet"](out_features=["stage4"]				)
         elif isinstance(lowercase_		,			lowercase_				):
             A__							=     backbone_config.get("model_type"				)
             A__							=     CONFIG_MAPPING[backbone_model_type]
             A__							=     config_class.from_dict(lowercase_				)
         # set timm attributes to None
         A__,      A__,      A__							=     None, None, None

     A__							=     use_timm_backbone
     A__							=     backbone_config
     A__							=     num_channels
     A__							=     num_queries
     A__							=     d_model
     A__							=     encoder_ffn_dim
     A__							=     encoder_layers
     A__							=     encoder_attention_heads
     A__							=     decoder_ffn_dim
     A__							=     decoder_layers
     A__							=     decoder_attention_heads
     A__							=     dropout
     A__							=     attention_dropout
     A__							=     activation_dropout
     A__							=     activation_function
     A__							=     init_std
     A__							=     init_xavier_std
     A__							=     encoder_layerdrop
     A__							=     decoder_layerdrop
     A__							=     encoder_layers
     A__							=     auxiliary_loss
     A__							=     position_embedding_type
     A__							=     backbone
     A__							=     use_pretrained_backbone
     A__							=     dilation
     # Hungarian matcher
     A__							=     class_cost
     A__							=     bbox_cost
     A__							=     giou_cost
     # Loss coefficients
     A__							=     mask_loss_coefficient
     A__							=     dice_loss_coefficient
     A__							=     bbox_loss_coefficient
     A__							=     giou_loss_coefficient
     A__							=     eos_coefficient
     super().__init__(is_encoder_decoder=lowercase_		,			**lowercase_				)
 @property
 def 	UpperCAmelCase_				(							self :Optional[int]				)->				int:
     return self.encoder_attention_heads



 @property
 def 	UpperCAmelCase_				(							self :List[str]				)->				int:
     return self.d_model






class   UpperCAmelCase      (			UpperCamelCase__							):
 __lowercase  						=							version.parse("""1.11"""							)
 @property
 def 	UpperCAmelCase_				(							self :int				)->				Mapping[str, Mapping[int, str]]:
     return OrderedDict(
         [
             ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
             ("pixel_mask", {0: "batch"}),
         ]				)
 @property
 def 	UpperCAmelCase_				(							self :Union[str, Any]				)->				float:
     return 1E-5



 @property
 def 	UpperCAmelCase_				(							self :int				)->				int:
     return 12










'''simple docstring'''

import string
from math import logaa





def 		UpperCamelCase							(				_lowerCamelCase   :   str		,			_lowerCamelCase   :   str			):
    A__							=     document.translate(
        str.maketrans(""		,			""		,			string.punctuation			)			).replace("\n"		,			""			)
    A__							=     document_without_punctuation.split(" "			)  # word tokenization
    return len([word for word in tokenize_document if word.lower() == term.lower()]			)





def 		UpperCamelCase							(				_lowerCamelCase   :   str		,			_lowerCamelCase   :   str			):
    A__							=     corpus.lower().translate(
        str.maketrans(""		,			""		,			string.punctuation			)			)  # strip all punctuation and replace it with ''
    A__							=     corpus_without_punctuation.split("\n"			)
    A__							=     term.lower()
    return (len([doc for doc in docs if term in doc]			), len(_lowerCamelCase			))





def 		UpperCamelCase							(				_lowerCamelCase   :   int		,			_lowerCamelCase   :   int		,			_lowerCamelCase   :   Tuple=False			):
    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 		UpperCamelCase							(				_lowerCamelCase   :   int		,			_lowerCamelCase   :   int			):
    return round(tf * idf		,			3			)



