






'''simple docstring'''







from __future__ import annotations





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase      )      ->       list[str]:
							if nth_term == "":
														return [""]
							lowerCamelCase__					:	Any         =       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	:			List[str]         =int(input('''Enter the last number (nth term) of the P-Series'''))
			_A	:			int         =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))









'''simple docstring'''







import inspect
import unittest

from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch

			from transformers import MobileNetVaForImageClassification, MobileNetVaModel
			from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
			from PIL import Image

			from transformers import MobileNetVaImageProcessor

class _lowercase	(				_lowercase		):




							def 		lowerCamelCase_				(							self:				Any     ):
														lowerCamelCase__					:	str         =       self.config_class(**self.inputs_dict     )
														self.parent.assertTrue(hasattr(UpperCamelCase__			,							"""tf_padding"""     )     )
														self.parent.assertTrue(hasattr(UpperCamelCase__			,							"""depth_multiplier"""     )     )



class _lowercase	:




							def __init__(							self:				str			,							UpperCamelCase__:				Dict			,							UpperCamelCase__:				Tuple=13			,							UpperCamelCase__:				Optional[int]=3			,							UpperCamelCase__:				List[Any]=32			,							UpperCamelCase__:				Optional[Any]=0.25			,							UpperCamelCase__:				int=8			,							UpperCamelCase__:				Any=True			,							UpperCamelCase__:				Dict=1_024			,							UpperCamelCase__:				Optional[int]=32			,							UpperCamelCase__:				Tuple="relu6"			,							UpperCamelCase__:				int=0.1			,							UpperCamelCase__:				List[Any]=0.02			,							UpperCamelCase__:				Optional[Any]=True			,							UpperCamelCase__:				Union[str, Any]=True			,							UpperCamelCase__:				Union[str, Any]=10			,							UpperCamelCase__:				str=None			,							):
														lowerCamelCase__					:	Optional[Any]         =       parent
														lowerCamelCase__					:	List[str]         =       batch_size
														lowerCamelCase__					:	Optional[int]         =       num_channels
														lowerCamelCase__					:	Optional[int]         =       image_size
														lowerCamelCase__					:	Optional[Any]         =       depth_multiplier
														lowerCamelCase__					:	Union[str, Any]         =       min_depth
														lowerCamelCase__					:	Optional[Any]         =       tf_padding
														lowerCamelCase__					:	str         =       int(last_hidden_size * depth_multiplier     )
														lowerCamelCase__					:	Any         =       output_stride
														lowerCamelCase__					:	int         =       hidden_act
														lowerCamelCase__					:	Tuple         =       classifier_dropout_prob
														lowerCamelCase__					:	Dict         =       use_labels
														lowerCamelCase__					:	Tuple         =       is_training
														lowerCamelCase__					:	Optional[Any]         =       num_labels
														lowerCamelCase__					:	Union[str, Any]         =       initializer_range
														lowerCamelCase__					:	Optional[Any]         =       scope




							def 		lowerCamelCase_				(							self:				List[str]     ):
														lowerCamelCase__					:	Optional[Any]         =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

														lowerCamelCase__					:	Optional[Any]         =       None
														lowerCamelCase__					:	Dict         =       None
														if self.use_labels:
																					lowerCamelCase__					:	Union[str, Any]         =       ids_tensor([self.batch_size]			,							self.num_labels     )
																					lowerCamelCase__					:	Dict         =       ids_tensor([self.batch_size, self.image_size, self.image_size]			,							self.num_labels     )

														lowerCamelCase__					:	Dict         =       self.get_config()

														return config, pixel_values, labels, pixel_labels




							def 		lowerCamelCase_				(							self:				str     ):
														return MobileNetVaConfig(
														    num_channels=self.num_channels			,							image_size=self.image_size			,							depth_multiplier=self.depth_multiplier			,							min_depth=self.min_depth			,							tf_padding=self.tf_padding			,							hidden_act=self.hidden_act			,							classifier_dropout_prob=self.classifier_dropout_prob			,							initializer_range=self.initializer_range			,							)




							def 		lowerCamelCase_				(							self:				Tuple			,							UpperCamelCase__:				int			,							UpperCamelCase__:				str			,							UpperCamelCase__:				Any			,							UpperCamelCase__:				Optional[int]     ):
														lowerCamelCase__					:	List[str]         =       MobileNetVaModel(config=UpperCamelCase__     )
														model.to(UpperCamelCase__     )
														model.eval()
														lowerCamelCase__					:	List[str]         =       model(UpperCamelCase__     )
														self.parent.assertEqual(
														    result.last_hidden_state.shape			,							(
														        self.batch_size,
														        self.last_hidden_size,
														        self.image_size // self.output_stride,
														        self.image_size // self.output_stride,
														    )			,							)




							def 		lowerCamelCase_				(							self:				str			,							UpperCamelCase__:				Tuple			,							UpperCamelCase__:				Optional[int]			,							UpperCamelCase__:				List[Any]			,							UpperCamelCase__:				Union[str, Any]     ):
														lowerCamelCase__					:	List[str]         =       self.num_labels
														lowerCamelCase__					:	Optional[Any]         =       MobileNetVaForImageClassification(UpperCamelCase__     )
														model.to(UpperCamelCase__     )
														model.eval()
														lowerCamelCase__					:	List[Any]         =       model(UpperCamelCase__			,							labels=UpperCamelCase__     )
														self.parent.assertEqual(result.logits.shape			,							(self.batch_size, self.num_labels)     )




							def 		lowerCamelCase_				(							self:				Optional[Any]     ):
														lowerCamelCase__					:	str         =       self.prepare_config_and_inputs()
														lowerCamelCase__  ,		lowerCamelCase__  ,		lowerCamelCase__  ,		lowerCamelCase__					:	int         =       config_and_inputs
														lowerCamelCase__					:	Optional[int]         =       {"""pixel_values""": pixel_values}
														return config, inputs_dict



@require_torch
class _lowercase	(				_lowercase				,      _lowercase				,      unittest.TestCase		):
							a				      =       (MobileNetVaModel, MobileNetVaForImageClassification) if is_torch_available() else ()
							a				      =       (
							    {"""feature-extraction""": MobileNetVaModel, """image-classification""": MobileNetVaForImageClassification}
							    if is_torch_available()
							    else {}
							)

							a				      =       False
							a				      =       False
							a				      =       False
							a				      =       False




							def 		lowerCamelCase_				(							self:				List[str]     ):
														lowerCamelCase__					:	Optional[int]         =       MobileNetVaModelTester(self     )
														lowerCamelCase__					:	List[str]         =       MobileNetVaConfigTester(self			,							config_class=UpperCamelCase__			,							has_text_modality=UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				Union[str, Any]     ):
														self.config_tester.run_common_tests()




							@unittest.skip(reason="""MobileNetV1 does not use inputs_embeds"""     )
							def 		lowerCamelCase_				(							self:				Optional[int]     ):
														pass




							@unittest.skip(reason="""MobileNetV1 does not support input and output embeddings"""     )
							def 		lowerCamelCase_				(							self:				Optional[Any]     ):
														pass




							@unittest.skip(reason="""MobileNetV1 does not output attentions"""     )
							def 		lowerCamelCase_				(							self:				Any     ):
														pass




							def 		lowerCamelCase_				(							self:				Any     ):
														lowerCamelCase__  ,		lowerCamelCase__					:	Optional[Any]         =       self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					lowerCamelCase__					:	Dict         =       model_class(UpperCamelCase__     )
																					lowerCamelCase__					:	Dict         =       inspect.signature(model.forward     )
																					# signature.parameters is an OrderedDict => so arg_names order is deterministic
																					lowerCamelCase__					:	List[Any]         =       [*signature.parameters.keys()]

																					lowerCamelCase__					:	Dict         =       ["""pixel_values"""]
																					self.assertListEqual(arg_names[:1]			,							UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				str     ):
														lowerCamelCase__					:	Tuple         =       self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				str     ):
														def check_hidden_states_output(UpperCamelCase__:				List[Any]			,							UpperCamelCase__:				Dict			,							UpperCamelCase__:				List[Any]     ):
																					lowerCamelCase__					:	str         =       model_class(UpperCamelCase__     )
																					model.to(UpperCamelCase__     )
																					model.eval()

																					with torch.no_grad():
																												lowerCamelCase__					:	Union[str, Any]         =       model(**self._prepare_for_class(UpperCamelCase__			,							UpperCamelCase__     )     )

																					lowerCamelCase__					:	List[Any]         =       outputs.hidden_states

																					lowerCamelCase__					:	Tuple         =       26
																					self.assertEqual(len(UpperCamelCase__     )			,							UpperCamelCase__     )

														lowerCamelCase__  ,		lowerCamelCase__					:	List[str]         =       self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					lowerCamelCase__					:	List[Any]         =       True
																					check_hidden_states_output(UpperCamelCase__			,							UpperCamelCase__			,							UpperCamelCase__     )

																					# check that output_hidden_states also work using config
																					del inputs_dict["output_hidden_states"]
																					lowerCamelCase__					:	Optional[Any]         =       True

																					check_hidden_states_output(UpperCamelCase__			,							UpperCamelCase__			,							UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				Dict     ):
														lowerCamelCase__					:	int         =       self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_image_classification(*UpperCamelCase__     )




							@slow
							def 		lowerCamelCase_				(							self:				List[str]     ):
														for model_name in MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																					lowerCamelCase__					:	Dict         =       MobileNetVaModel.from_pretrained(UpperCamelCase__     )
																					self.assertIsNotNone(UpperCamelCase__     )





def  SCREAMING_SNAKE_CASE_       ()      ->       Union[str, Any]:
							lowerCamelCase__					:	Optional[int]         =       Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""      )
							return image



@require_torch
@require_vision
class _lowercase	(				unittest.TestCase		):




							@cached_property
							def 		lowerCamelCase_				(							self:				Optional[int]     ):
														return (
														    MobileNetVaImageProcessor.from_pretrained("""google/mobilenet_v1_1.0_224"""     ) if is_vision_available() else None
														)




							@slow
							def 		lowerCamelCase_				(							self:				Dict     ):
														lowerCamelCase__					:	List[Any]         =       MobileNetVaForImageClassification.from_pretrained("""google/mobilenet_v1_1.0_224"""     ).to(UpperCamelCase__     )

														lowerCamelCase__					:	Dict         =       self.default_image_processor
														lowerCamelCase__					:	int         =       prepare_img()
														lowerCamelCase__					:	List[Any]         =       image_processor(images=UpperCamelCase__			,							return_tensors="""pt"""     ).to(UpperCamelCase__     )

														# forward pass
														with torch.no_grad():
																					lowerCamelCase__					:	str         =       model(**UpperCamelCase__     )

														# verify the logits
														lowerCamelCase__					:	List[str]         =       torch.Size((1, 1_001)     )
														self.assertEqual(outputs.logits.shape			,							UpperCamelCase__     )

														lowerCamelCase__					:	List[str]         =       torch.tensor([-4.1_739, -1.1_233, 3.1_205]     ).to(UpperCamelCase__     )

														self.assertTrue(torch.allclose(outputs.logits[0, :3]			,							UpperCamelCase__			,							atol=1e-4     )     )

