



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


snake_case__					:     str    		=   {
    """configuration_roberta_prelayernorm""": [
        """ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """RobertaPreLayerNormConfig""",
        """RobertaPreLayerNormOnnxConfig""",
    ],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			snake_case__					:     Union[str, Any]    		=   [
			    """ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """RobertaPreLayerNormForCausalLM""",
			    """RobertaPreLayerNormForMaskedLM""",
			    """RobertaPreLayerNormForMultipleChoice""",
			    """RobertaPreLayerNormForQuestionAnswering""",
			    """RobertaPreLayerNormForSequenceClassification""",
			    """RobertaPreLayerNormForTokenClassification""",
			    """RobertaPreLayerNormModel""",
			    """RobertaPreLayerNormPreTrainedModel""",
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			snake_case__					:     Dict    		=   [
			    """TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """TFRobertaPreLayerNormForCausalLM""",
			    """TFRobertaPreLayerNormForMaskedLM""",
			    """TFRobertaPreLayerNormForMultipleChoice""",
			    """TFRobertaPreLayerNormForQuestionAnswering""",
			    """TFRobertaPreLayerNormForSequenceClassification""",
			    """TFRobertaPreLayerNormForTokenClassification""",
			    """TFRobertaPreLayerNormMainLayer""",
			    """TFRobertaPreLayerNormModel""",
			    """TFRobertaPreLayerNormPreTrainedModel""",
			]

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			snake_case__					:     Dict    		=   [
			    """FlaxRobertaPreLayerNormForCausalLM""",
			    """FlaxRobertaPreLayerNormForMaskedLM""",
			    """FlaxRobertaPreLayerNormForMultipleChoice""",
			    """FlaxRobertaPreLayerNormForQuestionAnswering""",
			    """FlaxRobertaPreLayerNormForSequenceClassification""",
			    """FlaxRobertaPreLayerNormForTokenClassification""",
			    """FlaxRobertaPreLayerNormModel""",
			    """FlaxRobertaPreLayerNormPreTrainedModel""",
			]


if TYPE_CHECKING:
			from .configuration_roberta_prelayernorm import (
			    ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    RobertaPreLayerNormConfig,
			    RobertaPreLayerNormOnnxConfig,
			)

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_roberta_prelayernorm import (
						    ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
						    RobertaPreLayerNormForCausalLM,
						    RobertaPreLayerNormForMaskedLM,
						    RobertaPreLayerNormForMultipleChoice,
						    RobertaPreLayerNormForQuestionAnswering,
						    RobertaPreLayerNormForSequenceClassification,
						    RobertaPreLayerNormForTokenClassification,
						    RobertaPreLayerNormModel,
						    RobertaPreLayerNormPreTrainedModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_roberta_prelayernorm import (
						    TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFRobertaPreLayerNormForCausalLM,
						    TFRobertaPreLayerNormForMaskedLM,
						    TFRobertaPreLayerNormForMultipleChoice,
						    TFRobertaPreLayerNormForQuestionAnswering,
						    TFRobertaPreLayerNormForSequenceClassification,
						    TFRobertaPreLayerNormForTokenClassification,
						    TFRobertaPreLayerNormMainLayer,
						    TFRobertaPreLayerNormModel,
						    TFRobertaPreLayerNormPreTrainedModel,
						)

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_roberta_prelayernorm import (
						    FlaxRobertaPreLayerNormForCausalLM,
						    FlaxRobertaPreLayerNormForMaskedLM,
						    FlaxRobertaPreLayerNormForMultipleChoice,
						    FlaxRobertaPreLayerNormForQuestionAnswering,
						    FlaxRobertaPreLayerNormForSequenceClassification,
						    FlaxRobertaPreLayerNormForTokenClassification,
						    FlaxRobertaPreLayerNormModel,
						    FlaxRobertaPreLayerNormPreTrainedModel,
						)

else:
			import sys

			snake_case__					:     int    		=   _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)








from __future__ import annotations

import bisect



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE = 0  ,	_SCREAMING_SNAKE_CASE = -1   ):
	if hi < 0:
		__lowercase		       =			len(_SCREAMING_SNAKE_CASE   )

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

	return lo



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE = 0  ,	_SCREAMING_SNAKE_CASE = -1   ):
	if hi < 0:
		__lowercase		       =			len(_SCREAMING_SNAKE_CASE   )

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

	return lo



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE = 0  ,	_SCREAMING_SNAKE_CASE = -1   ):
	sorted_collection.insert(bisect_left(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   )  ,	_SCREAMING_SNAKE_CASE   )



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE = 0  ,	_SCREAMING_SNAKE_CASE = -1   ):
	sorted_collection.insert(bisect_right(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   )  ,	_SCREAMING_SNAKE_CASE   )



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   ):
	__lowercase		       =			0
	__lowercase		       =			len(_SCREAMING_SNAKE_CASE   ) - 1

	while left <= right:
		__lowercase		       =			left + (right - left) // 2
		__lowercase		       =			sorted_collection[midpoint]
		if current_item == item:
			return midpoint
		elif item < current_item:
			__lowercase		       =			midpoint - 1
		else:
			__lowercase		       =			midpoint + 1
	return None



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   ):
	__lowercase		       =			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    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   ):
	if right < left:
		return None

	__lowercase		       =			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__":
	snake_case__					:     Optional[Any]    		=   input("""Enter numbers separated by comma:\n""").strip()
	snake_case__					:     Any    		=   sorted(int(item) for item in user_input.split(""","""))
	snake_case__					:     Any    		=   int(input("""Enter a single number to be found in the list:\n"""))
	snake_case__					:     List[Any]    		=   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}.''')



