
"""simple docstring"""







from __future__ import annotations

import bisect
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE = 0							,    SCREAMING_SNAKE_CASE = -1 )  ->     int:







							"""simple docstring"""


							if hi < 0:
														__snake_case     			=       len(_SCREAMING_SNAKE_CASE )

							while lo < hi:
														__snake_case     			=       lo + (hi - lo) // 2
														if sorted_collection[mid] < item:
																					__snake_case     			=       mid + 1
														else:
																					__snake_case     			=       mid

							return lo
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE = 0							,    SCREAMING_SNAKE_CASE = -1 )  ->     int:







							"""simple docstring"""


							if hi < 0:
														__snake_case     			=       len(_SCREAMING_SNAKE_CASE )

							while lo < hi:
														__snake_case     			=       lo + (hi - lo) // 2
														if sorted_collection[mid] <= item:
																					__snake_case     			=       mid + 1
														else:
																					__snake_case     			=       mid

							return lo
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE = 0							,    SCREAMING_SNAKE_CASE = -1 )  ->     None:







							"""simple docstring"""


							sorted_collection.insert(bisect_left(_SCREAMING_SNAKE_CASE							,    _SCREAMING_SNAKE_CASE							,    _SCREAMING_SNAKE_CASE							,    _SCREAMING_SNAKE_CASE )							,    _SCREAMING_SNAKE_CASE )
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE = 0							,    SCREAMING_SNAKE_CASE = -1 )  ->     None:







							"""simple docstring"""


							sorted_collection.insert(bisect_right(_SCREAMING_SNAKE_CASE							,    _SCREAMING_SNAKE_CASE							,    _SCREAMING_SNAKE_CASE							,    _SCREAMING_SNAKE_CASE )							,    _SCREAMING_SNAKE_CASE )
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )  ->     int | None:







							"""simple docstring"""


							__snake_case     			=       0
							__snake_case     			=       len(_SCREAMING_SNAKE_CASE ) - 1

							while left <= right:
														__snake_case     			=       left + (right - left) // 2
														__snake_case     			=       sorted_collection[midpoint]
														if current_item == item:
																					return midpoint
														elif item < current_item:
																					__snake_case     			=       midpoint - 1
														else:
																					__snake_case     			=       midpoint + 1
							return None
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )  ->     int | None:







							"""simple docstring"""


							__snake_case     			=       bisect.bisect_left(_SCREAMING_SNAKE_CASE							,    _SCREAMING_SNAKE_CASE )
							if index != len(_SCREAMING_SNAKE_CASE ) and sorted_collection[index] == item:
														return index
							return None
def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE )  ->     int | None:







							"""simple docstring"""


							if right < left:
														return None

							__snake_case     			=       left + (right - left) // 2

							if sorted_collection[midpoint] == item:
														return midpoint
							elif sorted_collection[midpoint] > item:
														return binary_search_by_recursion(_SCREAMING_SNAKE_CASE							,    _SCREAMING_SNAKE_CASE							,    _SCREAMING_SNAKE_CASE							,    midpoint - 1 )
							else:
														return binary_search_by_recursion(_SCREAMING_SNAKE_CASE							,    _SCREAMING_SNAKE_CASE							,    midpoint + 1							,    _SCREAMING_SNAKE_CASE )


if __name__ == "__main__":
		_SCREAMING_SNAKE_CASE	       =      input("""Enter numbers separated by comma:\n""").strip()
		_SCREAMING_SNAKE_CASE	       =      sorted(int(item) for item in user_input.split(""","""))
		_SCREAMING_SNAKE_CASE	       =      int(input("""Enter a single number to be found in the list:\n"""))
		_SCREAMING_SNAKE_CASE	       =      binary_search(collection, target)
		if result is None:
				print(F"""{target} was not found in {collection}.""")
		else:
				print(F"""{target} was found at position {result} in {collection}.""")


"""simple docstring"""







import math

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


_SCREAMING_SNAKE_CASE	       =      logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE	       =      {
    """facebook/data2vec-base-960h""": """https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main/config.json""",
    # See all Data2VecAudio models at https://huggingface.co/models?filter=data2vec-audio
}







class 							__magic_name__     (		lowercase__   ):
					_SCREAMING_SNAKE_CASE       :					Any     							=   'data2vec-audio'





					def __init__(				self   :							Any							,						snake_case_   :							Optional[Any]=32							,						snake_case_   :							Tuple=768							,						snake_case_   :							Optional[int]=12							,						snake_case_   :							str=12							,						snake_case_   :							int=3072							,						snake_case_   :							Tuple="gelu"							,						snake_case_   :							Optional[Any]=0.1							,						snake_case_   :							str=0.1							,						snake_case_   :							int=0.1							,						snake_case_   :							int=0.0							,						snake_case_   :							Optional[int]=0.1							,						snake_case_   :							Dict=0.1							,						snake_case_   :							Dict=0.02							,						snake_case_   :							Optional[int]=1e-5							,						snake_case_   :							Optional[Any]="gelu"							,						snake_case_   :							Tuple=(512, 512, 512, 512, 512, 512, 512)							,						snake_case_   :							int=(5, 2, 2, 2, 2, 2, 2)							,						snake_case_   :							Union[str, Any]=(10, 3, 3, 3, 3, 2, 2)							,						snake_case_   :							Optional[int]=False							,						snake_case_   :							int=16							,						snake_case_   :							List[Any]=19							,						snake_case_   :							str=5							,						snake_case_   :							List[Any]=0.05							,						snake_case_   :							Dict=10							,						snake_case_   :							Dict=2							,						snake_case_   :							Dict=0.0							,						snake_case_   :							Union[str, Any]=10							,						snake_case_   :							Optional[Any]=0							,						snake_case_   :							Optional[Any]="sum"							,						snake_case_   :							List[str]=False							,						snake_case_   :							List[Any]=False							,						snake_case_   :							Optional[int]=256							,						snake_case_   :							int=(512, 512, 512, 512, 1500)							,						snake_case_   :							List[Any]=(5, 3, 3, 1, 1)							,						snake_case_   :							Any=(1, 2, 3, 1, 1)							,						snake_case_   :							List[Any]=512							,						snake_case_   :							Optional[int]=0							,						snake_case_   :							Dict=1							,						snake_case_   :							List[str]=2							,						snake_case_   :							Optional[Any]=False							,						snake_case_   :							Optional[Any]=3							,						snake_case_   :							Tuple=2							,						snake_case_   :							Any=3							,						snake_case_   :							Union[str, Any]=None							,						**snake_case_   :							int							,						):
												super().__init__(**snake_case_							,						pad_token_id=snake_case_							,						bos_token_id=snake_case_							,						eos_token_id=snake_case_    )
												__snake_case     			=       hidden_size
												__snake_case     			=       feat_extract_activation
												__snake_case     			=       list(snake_case_    )
												__snake_case     			=       list(snake_case_    )
												__snake_case     			=       list(snake_case_    )
												__snake_case     			=       conv_bias
												__snake_case     			=       num_conv_pos_embeddings
												__snake_case     			=       num_conv_pos_embedding_groups
												__snake_case     			=       conv_pos_kernel_size
												__snake_case     			=       len(self.conv_dim    )
												__snake_case     			=       num_hidden_layers
												__snake_case     			=       intermediate_size
												__snake_case     			=       hidden_act
												__snake_case     			=       num_attention_heads
												__snake_case     			=       hidden_dropout
												__snake_case     			=       attention_dropout
												__snake_case     			=       activation_dropout
												__snake_case     			=       feat_proj_dropout
												__snake_case     			=       final_dropout
												__snake_case     			=       layerdrop
												__snake_case     			=       layer_norm_eps
												__snake_case     			=       initializer_range
												__snake_case     			=       vocab_size
												__snake_case     			=       use_weighted_layer_sum

												if (
												    (len(self.conv_stride    ) != self.num_feat_extract_layers)
												    or (len(self.conv_kernel    ) != self.num_feat_extract_layers)
												    or (len(self.conv_dim    ) != self.num_feat_extract_layers)
												):
																			raise ValueError(
																			    "Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="
																			    " `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="
																			    F''' {len(self.conv_dim    )}`, `len(config.conv_stride) = {len(self.conv_stride    )}`,'''
																			    F''' `len(config.conv_kernel) = {len(self.conv_kernel    )}`.'''    )

												# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
												__snake_case     			=       mask_time_prob
												__snake_case     			=       mask_time_length
												__snake_case     			=       mask_time_min_masks
												__snake_case     			=       mask_feature_prob
												__snake_case     			=       mask_feature_length
												__snake_case     			=       mask_feature_min_masks

												# ctc loss
												__snake_case     			=       ctc_loss_reduction
												__snake_case     			=       ctc_zero_infinity

												# adapter
												__snake_case     			=       add_adapter
												__snake_case     			=       adapter_kernel_size
												__snake_case     			=       adapter_stride
												__snake_case     			=       num_adapter_layers
												__snake_case     			=       output_hidden_size or hidden_size

												# SequenceClassification-specific parameter. Feel free to ignore for other classes.
												__snake_case     			=       classifier_proj_size

												# XVector-specific parameters. Feel free to ignore for other classes.
												__snake_case     			=       list(snake_case_    )
												__snake_case     			=       list(snake_case_    )
												__snake_case     			=       list(snake_case_    )
												__snake_case     			=       xvector_output_dim





					@property
					def 							lowerCAmelCase      (				self   :							List[str]    ):
												return math.prod(self.conv_stride    )
