





from __future__ import annotations






def 			SCREAMING_SNAKE_CASE__							(    UpperCamelCase__:							float | int   ,				UpperCamelCase__:							float | int   ,				UpperCamelCase__:							float | int   ,				):





		if not all((nth_term, start_term_a, common_ratio_r)      ):
				return []
		SCREAMING_SNAKE_CASE__								= []
		SCREAMING_SNAKE_CASE__								= 1
		SCREAMING_SNAKE_CASE__								= common_ratio_r
		for _ in range(int(SCREAMING_SNAKE_CASE_      )      ):
				if not series:
						series.append(SCREAMING_SNAKE_CASE_      )
				else:
						power += 1
						series.append(float(start_term_a * multiple      )      )
						SCREAMING_SNAKE_CASE__								= pow(float(SCREAMING_SNAKE_CASE_      )   ,				SCREAMING_SNAKE_CASE_      )
		return series


if __name__ == "__main__":
	import doctest

	doctest.testmod()

	_lowerCamelCase :    Dict							=     float(input('Enter the last number (n term) of the Geometric Series'))
	_lowerCamelCase :    int							=     float(input('Enter the starting term (a) of the Geometric Series'))
	_lowerCamelCase :    List[Any]							=     float(
	    input('Enter the common ratio between two terms (r) of the Geometric Series')
	)
	print('Formula of Geometric Series => a + ar + ar^2 ... +ar^n')
	print(geometric_series(nth_term, start_term_a, common_ratio_r))






import unittest

from transformers import (
    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    PreTrainedTokenizer,
    is_vision_available,
)
from transformers.pipelines import ImageClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_torch_or_tf,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
	from PIL import Image
else:




	class 					UpperCamelCase_    :


								@staticmethod
								def        _snake_case					(							*__A      :Optional[Any]							,			**__A      :Any						)				->				str:



										"""simple docstring"""



										pass




@is_pipeline_test
@require_torch_or_tf
@require_vision
class 					UpperCamelCase_    (       unittest.TestCase      ):
							lowerCamelCase_          =   MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
							lowerCamelCase_          =   TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING


							def        _snake_case					(							self      :Union[str, Any]							,			__A      :str							,			__A      :Dict							,			__A      :List[str]						)				->				Optional[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= ImageClassificationPipeline(model=__A							,			image_processor=__A							,			top_k=2						)
									SCREAMING_SNAKE_CASE__								= [
									    Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""						),
									    """http://images.cocodataset.org/val2017/000000039769.jpg""",
									]
									return image_classifier, examples


							def        _snake_case					(							self      :List[Any]							,			__A      :Any							,			__A      :List[str]						)				->				Any:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= image_classifier("""./tests/fixtures/tests_samples/COCO/000000039769.png"""						)

									self.assertEqual(
									    __A							,			[
									        {"""score""": ANY(__A						), """label""": ANY(__A						)},
									        {"""score""": ANY(__A						), """label""": ANY(__A						)},
									    ]							,			)

									import datasets

									SCREAMING_SNAKE_CASE__								= datasets.load_dataset("""hf-internal-testing/fixtures_image_utils"""							,			"""image"""							,			split="""test"""						)

									# Accepts URL + PIL.Image + lists
									SCREAMING_SNAKE_CASE__								= image_classifier(
									    [
									        Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""						),
									        """http://images.cocodataset.org/val2017/000000039769.jpg""",
									        # RGBA
									        dataset[0]["""file"""],
									        # LA
									        dataset[1]["""file"""],
									        # L
									        dataset[2]["""file"""],
									    ]						)
									self.assertEqual(
									    __A							,			[
									        [
									            {"""score""": ANY(__A						), """label""": ANY(__A						)},
									            {"""score""": ANY(__A						), """label""": ANY(__A						)},
									        ],
									        [
									            {"""score""": ANY(__A						), """label""": ANY(__A						)},
									            {"""score""": ANY(__A						), """label""": ANY(__A						)},
									        ],
									        [
									            {"""score""": ANY(__A						), """label""": ANY(__A						)},
									            {"""score""": ANY(__A						), """label""": ANY(__A						)},
									        ],
									        [
									            {"""score""": ANY(__A						), """label""": ANY(__A						)},
									            {"""score""": ANY(__A						), """label""": ANY(__A						)},
									        ],
									        [
									            {"""score""": ANY(__A						), """label""": ANY(__A						)},
									            {"""score""": ANY(__A						), """label""": ANY(__A						)},
									        ],
									    ]							,			)


							@require_torch
							def        _snake_case					(							self      :str						)				->				Optional[Any]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= """hf-internal-testing/tiny-random-vit"""
									SCREAMING_SNAKE_CASE__								= pipeline("""image-classification"""							,			model=__A						)

									SCREAMING_SNAKE_CASE__								= image_classifier("""http://images.cocodataset.org/val2017/000000039769.jpg"""						)
									self.assertEqual(
									    nested_simplify(__A							,			decimals=4						)							,			[{"""label""": """LABEL_1""", """score""": 0.5_7_4}, {"""label""": """LABEL_0""", """score""": 0.4_2_6}]							,			)

									SCREAMING_SNAKE_CASE__								= image_classifier(
									    [
									        """http://images.cocodataset.org/val2017/000000039769.jpg""",
									        """http://images.cocodataset.org/val2017/000000039769.jpg""",
									    ]							,			top_k=2							,			)
									self.assertEqual(
									    nested_simplify(__A							,			decimals=4						)							,			[
									        [{"""label""": """LABEL_1""", """score""": 0.5_7_4}, {"""label""": """LABEL_0""", """score""": 0.4_2_6}],
									        [{"""label""": """LABEL_1""", """score""": 0.5_7_4}, {"""label""": """LABEL_0""", """score""": 0.4_2_6}],
									    ]							,			)


							@require_tf
							def        _snake_case					(							self      :Any						)				->				Dict:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= """hf-internal-testing/tiny-random-vit"""
									SCREAMING_SNAKE_CASE__								= pipeline("""image-classification"""							,			model=__A							,			framework="""tf"""						)

									SCREAMING_SNAKE_CASE__								= image_classifier("""http://images.cocodataset.org/val2017/000000039769.jpg"""						)
									self.assertEqual(
									    nested_simplify(__A							,			decimals=4						)							,			[{"""label""": """LABEL_1""", """score""": 0.5_7_4}, {"""label""": """LABEL_0""", """score""": 0.4_2_6}]							,			)

									SCREAMING_SNAKE_CASE__								= image_classifier(
									    [
									        """http://images.cocodataset.org/val2017/000000039769.jpg""",
									        """http://images.cocodataset.org/val2017/000000039769.jpg""",
									    ]							,			top_k=2							,			)
									self.assertEqual(
									    nested_simplify(__A							,			decimals=4						)							,			[
									        [{"""label""": """LABEL_1""", """score""": 0.5_7_4}, {"""label""": """LABEL_0""", """score""": 0.4_2_6}],
									        [{"""label""": """LABEL_1""", """score""": 0.5_7_4}, {"""label""": """LABEL_0""", """score""": 0.4_2_6}],
									    ]							,			)


							def        _snake_case					(							self      :Dict						)				->				str:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= PreTrainedTokenizer()

									# Assert that the pipeline can be initialized with a feature extractor that is not in any mapping
									SCREAMING_SNAKE_CASE__								= pipeline(
									    """image-classification"""							,			model="""hf-internal-testing/tiny-random-vit"""							,			tokenizer=__A						)

									self.assertIs(image_classifier.tokenizer							,			__A						)


							@slow
							@require_torch
							def        _snake_case					(							self      :Optional[int]						)				->				Optional[int]:



									"""simple docstring"""



									SCREAMING_SNAKE_CASE__								= pipeline("""image-classification"""							,			model="""deepmind/vision-perceiver-conv"""						)
									SCREAMING_SNAKE_CASE__								= image_classifier("""http://images.cocodataset.org/val2017/000000039769.jpg"""						)
									self.assertEqual(
									    nested_simplify(__A							,			decimals=4						)							,			[
									        {"""score""": 0.4_3_8_5, """label""": """tabby, tabby cat"""},
									        {"""score""": 0.3_2_1, """label""": """tiger cat"""},
									        {"""score""": 0.0_5_0_2, """label""": """Egyptian cat"""},
									        {"""score""": 0.0_1_3_7, """label""": """crib, cot"""},
									        {"""score""": 0.0_0_7, """label""": """radiator"""},
									    ]							,			)

									SCREAMING_SNAKE_CASE__								= pipeline("""image-classification"""							,			model="""deepmind/vision-perceiver-fourier"""						)
									SCREAMING_SNAKE_CASE__								= image_classifier("""http://images.cocodataset.org/val2017/000000039769.jpg"""						)
									self.assertEqual(
									    nested_simplify(__A							,			decimals=4						)							,			[
									        {"""score""": 0.5_6_5_8, """label""": """tabby, tabby cat"""},
									        {"""score""": 0.1_3_0_9, """label""": """tiger cat"""},
									        {"""score""": 0.0_7_2_2, """label""": """Egyptian cat"""},
									        {"""score""": 0.0_7_0_7, """label""": """remote control, remote"""},
									        {"""score""": 0.0_0_8_2, """label""": """computer keyboard, keypad"""},
									    ]							,			)

									SCREAMING_SNAKE_CASE__								= pipeline("""image-classification"""							,			model="""deepmind/vision-perceiver-learned"""						)
									SCREAMING_SNAKE_CASE__								= image_classifier("""http://images.cocodataset.org/val2017/000000039769.jpg"""						)
									self.assertEqual(
									    nested_simplify(__A							,			decimals=4						)							,			[
									        {"""score""": 0.3_0_2_2, """label""": """tabby, tabby cat"""},
									        {"""score""": 0.2_3_6_2, """label""": """Egyptian cat"""},
									        {"""score""": 0.1_8_5_6, """label""": """tiger cat"""},
									        {"""score""": 0.0_3_2_4, """label""": """remote control, remote"""},
									        {"""score""": 0.0_0_9_6, """label""": """quilt, comforter, comfort, puff"""},
									    ]							,			)