


'''simple docstring'''




def    UpperCAmelCase_  (       A      ):
   '''simple docstring'''





   if collection == []:
      return []

   # get some information about the collection
   _a					:  Dict						=  len(A      )
   _a					:  Optional[Any]						=  max(A      )
   _a					:  Union[str, Any]						=  min(A      )

   # create the counting array
   _a					:  Dict						=  coll_max + 1 - coll_min
   _a					:  str						=  [0] * counting_arr_length

   # count how much a number appears in the collection
   for number in collection:
      counting_arr[number - coll_min] += 1

   # sum each position with it's predecessors. now, counting_arr[i] tells
   # us how many elements <= i has in the collection
   for i in range(1		,	A      ):
      _a					:  List[Any]						=  counting_arr[i] + counting_arr[i - 1]

   # create the output collection
   _a					:  Union[str, Any]						=  [0] * coll_len

   # place the elements in the output, respecting the original order (stable
   # sort) from end to begin, updating counting_arr
   for i in reversed(range(0		,	A      )      ):
      _a					:  Union[str, Any]						=  collection[i]
      counting_arr[collection[i] - coll_min] -= 1

   return ordered


def    UpperCAmelCase_  (       A      ):
   '''simple docstring'''





   return "".join([chr(A      ) for i in counting_sort([ord(A      ) for c in string]      )]      )


if __name__ == "__main__":
 # Test string sort
 assert counting_sort_string("thisisthestring") == "eghhiiinrsssttt"

 UpperCAmelCase_      :		Any          =		input("Enter numbers separated by a comma:\n").strip()
 UpperCAmelCase_      :		Union[str, Any]          =		[int(item) for item in user_input.split(",")]
 print(counting_sort(unsorted))






'''simple docstring'''




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


UpperCAmelCase_      :		Dict          =		logging.get_logger(__name__)

UpperCAmelCase_      :		List[str]          =		{
    "uclanlp/visualbert-vqa": "https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json",
    "uclanlp/visualbert-vqa-pre": "https://huggingface.co/uclanlp/visualbert-vqa-pre/resolve/main/config.json",
    "uclanlp/visualbert-vqa-coco-pre": (
        "https://huggingface.co/uclanlp/visualbert-vqa-coco-pre/resolve/main/config.json"
    ),
    "uclanlp/visualbert-vcr": "https://huggingface.co/uclanlp/visualbert-vcr/resolve/main/config.json",
    "uclanlp/visualbert-vcr-pre": "https://huggingface.co/uclanlp/visualbert-vcr-pre/resolve/main/config.json",
    "uclanlp/visualbert-vcr-coco-pre": (
        "https://huggingface.co/uclanlp/visualbert-vcr-coco-pre/resolve/main/config.json"
    ),
    "uclanlp/visualbert-nlvr2": "https://huggingface.co/uclanlp/visualbert-nlvr2/resolve/main/config.json",
    "uclanlp/visualbert-nlvr2-pre": "https://huggingface.co/uclanlp/visualbert-nlvr2-pre/resolve/main/config.json",
    "uclanlp/visualbert-nlvr2-coco-pre": (
        "https://huggingface.co/uclanlp/visualbert-nlvr2-coco-pre/resolve/main/config.json"
    )
    # See all VisualBERT models at https://huggingface.co/models?filter=visual_bert
}







class 					a			(				snake_case__  ):
 '''simple docstring'''




 __lowerCAmelCase						: int							  =		"""visual_bert"""




 def __init__(    self		,      lowerCamelCase_=3_0_5_2_2		,      lowerCamelCase_=7_6_8		,      lowerCamelCase_=5_1_2		,      lowerCamelCase_=1_2		,      lowerCamelCase_=1_2		,      lowerCamelCase_=3_0_7_2		,      lowerCamelCase_="gelu"		,      lowerCamelCase_=0.1		,      lowerCamelCase_=0.1		,      lowerCamelCase_=5_1_2		,      lowerCamelCase_=2		,      lowerCamelCase_=0.02		,      lowerCamelCase_=1e-12		,      lowerCamelCase_=False		,      lowerCamelCase_=True		,      lowerCamelCase_=1		,      lowerCamelCase_=0		,      lowerCamelCase_=2		,      **lowerCamelCase_		,      )      ->    Optional[Any]:
    super().__init__(pad_token_id=lowerCamelCase_		,      bos_token_id=lowerCamelCase_		,      eos_token_id=lowerCamelCase_		,      **lowerCamelCase_      )

    _a					:  List[str]						=  vocab_size
    _a					:  Union[str, Any]						=  max_position_embeddings
    _a					:  Tuple						=  hidden_size
    _a					:  Tuple						=  visual_embedding_dim
    _a					:  Optional[Any]						=  num_hidden_layers
    _a					:  str						=  num_attention_heads
    _a					:  Union[str, Any]						=  intermediate_size
    _a					:  List[str]						=  hidden_act
    _a					:  str						=  hidden_dropout_prob
    _a					:  List[Any]						=  attention_probs_dropout_prob
    _a					:  Optional[Any]						=  initializer_range
    _a					:  List[Any]						=  type_vocab_size
    _a					:  Any						=  layer_norm_eps
    _a					:  Tuple						=  bypass_transformer
    _a					:  str						=  special_visual_initialize


