
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping

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


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

lowercase		:						List[Any] =					{
    'uclanlp/plbart-base': 'https://huggingface.co/uclanlp/plbart-base/resolve/main/config.json',
    # See all PLBART models at https://huggingface.co/models?filter=plbart
}




class    _snake_case  (						_lowercase       ):
							SCREAMING_SNAKE_CASE		     =						'''plbart'''
							SCREAMING_SNAKE_CASE		     =						['''past_key_values''']
							SCREAMING_SNAKE_CASE		     =						{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}






							def __init__(					self						:			Any						, snake_case__						:			str=5_0_0_0_5						, snake_case__						:			Union[str, Any]=1_0_2_4						, snake_case__						:			Dict=6						, snake_case__						:			Optional[int]=3_0_7_2						, snake_case__						:			Union[str, Any]=1_2						, snake_case__						:			Optional[int]=6						, snake_case__						:			str=3_0_7_2						, snake_case__						:			Union[str, Any]=1_2						, snake_case__						:			List[str]=0.0						, snake_case__						:			List[str]=0.0						, snake_case__						:			Dict=True						, snake_case__						:			Tuple=True						, snake_case__						:			int="gelu"						, snake_case__						:			List[str]=7_6_8						, snake_case__						:			Optional[int]=0.1						, snake_case__						:			str=0.1						, snake_case__						:			Optional[int]=0.0						, snake_case__						:			Dict=0.02						, snake_case__						:			List[str]=0.0						, snake_case__						:			str=True						, snake_case__						:			Any=1						, snake_case__						:			Any=0						, snake_case__						:			int=2						, snake_case__						:			List[Any]=2						, **snake_case__						:			Tuple						, ):






												"""simple docstring"""



												__a:						List[str]		    =					vocab_size
												__a:						List[str]		    =					max_position_embeddings
												__a:						Optional[int]		    =					d_model
												__a:						Tuple		    =					encoder_ffn_dim
												__a:						List[str]		    =					encoder_layers
												__a:						int		    =					encoder_attention_heads
												__a:						Union[str, Any]		    =					decoder_ffn_dim
												__a:						str		    =					decoder_layers
												__a:						str		    =					decoder_attention_heads
												__a:						int		    =					dropout
												__a:						Tuple		    =					attention_dropout
												__a:						int		    =					activation_dropout
												__a:						Optional[Any]		    =					activation_function
												__a:						int		    =					init_std
												__a:						List[Any]		    =					encoder_layerdrop
												__a:						List[str]		    =					decoder_layerdrop
												__a:						Optional[Any]		    =					classifier_dropout
												__a:						str		    =					use_cache
												__a:						List[Any]		    =					encoder_layers
												__a:						int		    =					scale_embedding  # scale factor will be sqrt(d_model) if True
												super().__init__(
												    pad_token_id=snake_case__						, bos_token_id=snake_case__						, eos_token_id=snake_case__						, is_encoder_decoder=snake_case__						, forced_eos_token_id=snake_case__						, **snake_case__						, )






class    _snake_case  (						_lowercase       ):






							@property
							def 							__lowercase  (					self						:			List[str]    ):






												"""simple docstring"""



												return OrderedDict(
												    [
												        ("""input_ids""", {0: """batch""", 1: """sequence"""}),
												        ("""attention_mask""", {0: """batch""", 1: """sequence"""}),
												    ]    )






							@property
							def 							__lowercase  (					self						:			Tuple    ):






												"""simple docstring"""



												if self.use_past:
																	return OrderedDict(
																	    [
																	        ("""last_hidden_state""", {0: """batch""", 1: """sequence"""}),
																	        ("""past_keys""", {0: """batch""", 2: """sequence"""}),
																	        ("""encoder_last_hidden_state""", {0: """batch""", 1: """sequence"""}),
																	    ]    )
												else:
																	return OrderedDict(
																	    [
																	        ("""last_hidden_state""", {0: """batch""", 1: """sequence"""}),
																	        ("""encoder_last_hidden_state""", {0: """batch""", 1: """sequence"""}),
																	    ]    )


"""simple docstring"""
def     lowerCamelCase_    (	lowerCamelCase_	,				lowerCamelCase_ )     ->			int:


					'''simple docstring'''
					for index, item in enumerate(lowerCamelCase_ ):
										if item == target:
															return index
					return -1


def     lowerCamelCase_    (	lowerCamelCase_	,				lowerCamelCase_	,				lowerCamelCase_	,				lowerCamelCase_ )     ->			int:


					'''simple docstring'''
					if not (0 <= high < len(lowerCamelCase_ ) and 0 <= low < len(lowerCamelCase_ )):
										raise Exception("""Invalid upper or lower bound!""" )
					if high < low:
										return -1
					if sequence[low] == target:
										return low
					if sequence[high] == target:
										return high
					return rec_linear_search(lowerCamelCase_	,				low + 1	,				high - 1	,				lowerCamelCase_ )


if __name__ == "__main__":
			lowercase		:						str =					input('Enter numbers separated by comma:\n').strip()
			lowercase		:						int =					[int(item.strip()) for item in user_input.split(',')]

			lowercase		:						Optional[Any] =					int(input('Enter a single number to be found in the list:\n').strip())
			lowercase		:						str =					linear_search(sequence, target)
			if result != -1:
						print(F"linear_search({sequence}, {target}) = {result}")
			else:
						print(F"{target} was not found in {sequence}")
