



import math






def     a(      A       :    list ,     A       :    int       )		->       int:
					"""simple docstring"""





					a					     =   len(A       )
					a					     =   int(math.floor(math.sqrt(A       )       )       )
					a					     =   0
					while arr[min(A ,     A       ) - 1] < x:
										a					     =   step
										step += int(math.floor(math.sqrt(A       )       )       )
										if prev >= n:
															return -1

					while arr[prev] < x:
										a					     =   prev + 1
										if prev == min(A ,     A       ):
															return -1
					if arr[prev] == x:
										return prev
					return -1


if __name__ == "__main__":
				_lowercase: Tuple      			=		input("Enter numbers separated by a comma:\n").strip()
				_lowercase: str      			=		[int(item) for item in user_input.split(",")]
				_lowercase: Dict      			=		int(input("Enter the number to be searched:\n"))
				_lowercase: List[Any]      			=		jump_search(arr, x)
				if res == -1:
								print("Number not found!")
				else:
								print(F"""Number {x} is at index {res}""")





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


_lowercase: str      			=		logging.get_logger(__name__)

_lowercase: List[str]      			=		{
    "microsoft/trocr-base-handwritten": (
        "https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json"
    ),
    # See all TrOCR models at https://huggingface.co/models?filter=trocr
}



class 	_lowercase							(   lowerCAmelCase	):





	"""simple docstring"""
	__A							=							"trocr"
	__A							=							["past_key_values"]
	__A							=							{
	    "num_attention_heads": "decoder_attention_heads",
	    "hidden_size": "d_model",
	    "num_hidden_layers": "decoder_layers",
	}




	def __init__(self		,       lowerCamelCase_=50265		,       lowerCamelCase_=1024		,       lowerCamelCase_=12		,       lowerCamelCase_=16		,       lowerCamelCase_=4096		,       lowerCamelCase_="gelu"		,       lowerCamelCase_=512		,       lowerCamelCase_=0.1		,       lowerCamelCase_=0.0		,       lowerCamelCase_=0.0		,       lowerCamelCase_=2		,       lowerCamelCase_=0.02		,       lowerCamelCase_=0.0		,       lowerCamelCase_=True		,       lowerCamelCase_=False		,       lowerCamelCase_=True		,       lowerCamelCase_=True		,       lowerCamelCase_=1		,       lowerCamelCase_=0		,       lowerCamelCase_=2		,       **lowerCamelCase_		,       ):
						"""simple docstring"""

						a					     =   vocab_size
						a					     =   d_model
						a					     =   decoder_layers
						a					     =   decoder_attention_heads
						a					     =   decoder_ffn_dim
						a					     =   activation_function
						a					     =   max_position_embeddings
						a					     =   dropout
						a					     =   attention_dropout
						a					     =   activation_dropout
						a					     =   init_std
						a					     =   decoder_layerdrop
						a					     =   use_cache
						a					     =   scale_embedding
						a					     =   use_learned_position_embeddings
						a					     =   layernorm_embedding

						super().__init__(
						    pad_token_id=lowerCamelCase_		,       bos_token_id=lowerCamelCase_		,       eos_token_id=lowerCamelCase_		,       decoder_start_token_id=lowerCamelCase_		,       **lowerCamelCase_		,       )
