
'''simple docstring'''





from collections import Counter





def 							snake_case				(		UpperCAmelCase						)->      int:







      """simple docstring"""
      return sum(socks_by_color // 2 for socks_by_color in Counter(UpperCAmelCase						).values()						)


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 a__		: List[Any]  							=							[int(x) for x in input("Enter socks by color :").rstrip().split()]
 print(f'''sock_merchant({colors}) = {sock_merchant(colors)}''')








from collections import OrderedDict
from typing import Mapping

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


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

UpperCAmelCase__						:	str	     = {
    'flaubert/flaubert_small_cased': 'https://huggingface.co/flaubert/flaubert_small_cased/resolve/main/config.json',
    'flaubert/flaubert_base_uncased': 'https://huggingface.co/flaubert/flaubert_base_uncased/resolve/main/config.json',
    'flaubert/flaubert_base_cased': 'https://huggingface.co/flaubert/flaubert_base_cased/resolve/main/config.json',
    'flaubert/flaubert_large_cased': 'https://huggingface.co/flaubert/flaubert_large_cased/resolve/main/config.json',
}






class UpperCAmelCase  (		SCREAMING_SNAKE_CASE__  ):





   '''simple docstring'''
   __UpperCamelCase						:       Tuple												=       '''flaubert'''
   __UpperCamelCase						:       str												=       {
       '''hidden_size''': '''emb_dim''',
       '''num_attention_heads''': '''n_heads''',
       '''num_hidden_layers''': '''n_layers''',
       '''n_words''': '''vocab_size''',  # For backward compatibility
   }


   def __init__(   self	:				Union[str, Any]     ,     lowerCAmelCase_	:				Union[str, Any]=False     ,     lowerCAmelCase_	:				List[str]=0.0     ,     lowerCAmelCase_	:				Dict=3_0_1_4_5     ,     lowerCAmelCase_	:				Tuple=2_0_4_8     ,     lowerCAmelCase_	:				List[Any]=1_2     ,     lowerCAmelCase_	:				str=1_6     ,     lowerCAmelCase_	:				Union[str, Any]=0.1     ,     lowerCAmelCase_	:				List[str]=0.1     ,     lowerCAmelCase_	:				str=True     ,     lowerCAmelCase_	:				Optional[int]=False     ,     lowerCAmelCase_	:				Any=False     ,     lowerCAmelCase_	:				int=False     ,     lowerCAmelCase_	:				int=1     ,     lowerCAmelCase_	:				List[str]=True     ,     lowerCAmelCase_	:				Dict=5_1_2     ,     lowerCAmelCase_	:				Any=2_0_4_8**-0.5     ,     lowerCAmelCase_	:				List[str]=1e-12     ,     lowerCAmelCase_	:				Any=0.02     ,     lowerCAmelCase_	:				Optional[Any]=0     ,     lowerCAmelCase_	:				Union[str, Any]=1     ,     lowerCAmelCase_	:				List[Any]=2     ,     lowerCAmelCase_	:				Dict=3     ,     lowerCAmelCase_	:				Optional[Any]=5     ,     lowerCAmelCase_	:				Any=True     ,     lowerCAmelCase_	:				Union[str, Any]="first"     ,     lowerCAmelCase_	:				List[Any]=True     ,     lowerCAmelCase_	:				Tuple=None     ,     lowerCAmelCase_	:				Optional[int]=True     ,     lowerCAmelCase_	:				int=0.1     ,     lowerCAmelCase_	:				Optional[int]=5     ,     lowerCAmelCase_	:				Union[str, Any]=5     ,     lowerCAmelCase_	:				List[Any]=0     ,     lowerCAmelCase_	:				Dict=0     ,     lowerCAmelCase_	:				Union[str, Any]=2     ,     lowerCAmelCase_	:				Optional[int]=0     ,     **lowerCAmelCase_	:				Any     ,     ):

      """simple docstring"""
      _A:    List[str]						     =			pre_norm
      _A:    List[Any]						     =			layerdrop
      _A:    Dict						     =			vocab_size
      _A:    List[Any]						     =			emb_dim
      _A:    Optional[Any]						     =			n_layers
      _A:    Union[str, Any]						     =			n_heads
      _A:    Optional[Any]						     =			dropout
      _A:    Optional[Any]						     =			attention_dropout
      _A:    Dict						     =			gelu_activation
      _A:    Tuple						     =			sinusoidal_embeddings
      _A:    Tuple						     =			causal
      _A:    List[Any]						     =			asm
      _A:    Dict						     =			n_langs
      _A:    Optional[int]						     =			use_lang_emb
      _A:    Any						     =			layer_norm_eps
      _A:    str						     =			bos_index
      _A:    Optional[int]						     =			eos_index
      _A:    int						     =			pad_index
      _A:    Tuple						     =			unk_index
      _A:    int						     =			mask_index
      _A:    int						     =			is_encoder
      _A:    Union[str, Any]						     =			max_position_embeddings
      _A:    List[str]						     =			embed_init_std
      _A:    List[str]						     =			init_std
      _A:    Tuple						     =			summary_type
      _A:    List[str]						     =			summary_use_proj
      _A:    Union[str, Any]						     =			summary_activation
      _A:    Union[str, Any]						     =			summary_proj_to_labels
      _A:    int						     =			summary_first_dropout
      _A:    Optional[Any]						     =			start_n_top
      _A:    Optional[Any]						     =			end_n_top
      _A:    Dict						     =			mask_token_id
      _A:    str						     =			lang_id

      if "n_words" in kwargs:
         _A:    int						     =			kwargs['''n_words''']

      super().__init__(pad_token_id=lowerCAmelCase_     ,     bos_token_id=lowerCAmelCase_     ,     **lowerCAmelCase_	)






class UpperCAmelCase  (		SCREAMING_SNAKE_CASE__  ):





   '''simple docstring'''


   @property
   def 					__magic_name__							(   self	:				Tuple	):

      """simple docstring"""
      if self.task == "multiple-choice":
         _A:    int						     =			{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
      else:
         _A:    Any						     =			{0: '''batch''', 1: '''sequence'''}
      return OrderedDict(
          [
              ('''input_ids''', dynamic_axis),
              ('''attention_mask''', dynamic_axis),
          ]	)



