






'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_A     :      str   					=     {
    'configuration_conditional_detr': [
        'CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'ConditionalDetrConfig',
        'ConditionalDetrOnnxConfig',
    ]
}

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A     :      Tuple   					=     ['ConditionalDetrFeatureExtractor']
			_A     :      Union[str, Any]   					=     ['ConditionalDetrImageProcessor']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A     :      List[str]   					=     [
			    'CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'ConditionalDetrForObjectDetection',
			    'ConditionalDetrForSegmentation',
			    'ConditionalDetrModel',
			    'ConditionalDetrPreTrainedModel',
			]


if TYPE_CHECKING:
			from .configuration_conditional_detr import (
			    CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    ConditionalDetrConfig,
			    ConditionalDetrOnnxConfig,
			)

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .feature_extraction_conditional_detr import ConditionalDetrFeatureExtractor
						from .image_processing_conditional_detr import ConditionalDetrImageProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_conditional_detr import (
						    CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
						    ConditionalDetrForObjectDetection,
						    ConditionalDetrForSegmentation,
						    ConditionalDetrModel,
						    ConditionalDetrPreTrainedModel,
						)

else:
			import sys

			_A     :      str   					=     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)









'''simple docstring'''







from __future__ import annotations





def 			SCREAMING_SNAKE_CASE_	(UpperCamelCase    ,      UpperCamelCase				)				->	list[str]:
							if nth_term == "":
														return [""]
							lowerCamelCase__ :     str	  =	int(UpperCamelCase				)
							lowerCamelCase__ :     Union[str, Any]	  =	int(UpperCamelCase				)
							lowerCamelCase__ :     list[str]	  =	[]
							for temp in range(int(UpperCamelCase				)				):
														series.append(f'''1 / {pow(temp + 1    ,      int(UpperCamelCase				)				)}''' if series else """1"""				)
							return series


if __name__ == "__main__":
			import doctest

			doctest.testmod()

			_A     :      Optional[Any]   					=int(input('''Enter the last number (nth term) of the P-Series'''))
			_A     :      List[str]   					=int(input('''Enter the power for  P-Series'''))
			print('''Formula of P-Series => 1+1/2^p+1/3^p ..... 1/n^p''')
			print(p_series(nth_term, power))

