





from math import pi, sqrt, tan




def      a__			(       lowercase__	):






   '''simple docstring'''






   if side_length < 0:
      raise ValueError("surface_area_cube() only accepts non-negative values"	)
   return 6 * side_length**2




def      a__			(       lowercase__	,     lowercase__	,     lowercase__	):






   '''simple docstring'''






   if length < 0 or breadth < 0 or height < 0:
      raise ValueError("surface_area_cuboid() only accepts non-negative values"	)
   return 2 * ((length * breadth) + (breadth * height) + (length * height))




def      a__			(       lowercase__	):






   '''simple docstring'''






   if radius < 0:
      raise ValueError("surface_area_sphere() only accepts non-negative values"	)
   return 4 * pi * radius**2




def      a__			(       lowercase__	):






   '''simple docstring'''






   if radius < 0:
      raise ValueError("surface_area_hemisphere() only accepts non-negative values"	)
   return 3 * pi * radius**2




def      a__			(       lowercase__	,     lowercase__	):






   '''simple docstring'''






   if radius < 0 or height < 0:
      raise ValueError("surface_area_cone() only accepts non-negative values"	)
   return pi * radius * (radius + (height**2 + radius**2) ** 0.5)




def      a__			(       lowercase__	,     lowercase__	,     lowercase__	):






   '''simple docstring'''






   if radius_a < 0 or radius_a < 0 or height < 0:
      raise ValueError(
          "surface_area_conical_frustum() only accepts non-negative values"	)
   UpperCAmelCase_           =(height**2 + (radius_a - radius_a) ** 2) ** 0.5
   return pi * ((slant_height * (radius_a + radius_a)) + radius_a**2 + radius_a**2)




def      a__			(       lowercase__	,     lowercase__	):






   '''simple docstring'''






   if radius < 0 or height < 0:
      raise ValueError("surface_area_cylinder() only accepts non-negative values"	)
   return 2 * pi * radius * (height + radius)




def      a__			(       lowercase__	,     lowercase__	):






   '''simple docstring'''






   if torus_radius < 0 or tube_radius < 0:
      raise ValueError("surface_area_torus() only accepts non-negative values"	)
   if torus_radius < tube_radius:
      raise ValueError(
          "surface_area_torus() does not support spindle or self intersecting tori"	)
   return 4 * pow(lowercase__	,     2	) * torus_radius * tube_radius




def      a__			(       lowercase__	,     lowercase__	):






   '''simple docstring'''






   if length < 0 or width < 0:
      raise ValueError("area_rectangle() only accepts non-negative values"	)
   return length * width




def      a__			(       lowercase__	):






   '''simple docstring'''






   if side_length < 0:
      raise ValueError("area_square() only accepts non-negative values"	)
   return side_length**2




def      a__			(       lowercase__	,     lowercase__	):






   '''simple docstring'''






   if base < 0 or height < 0:
      raise ValueError("area_triangle() only accepts non-negative values"	)
   return (base * height) / 2




def      a__			(       lowercase__	,     lowercase__	,     lowercase__	):






   '''simple docstring'''






   if sidea < 0 or sidea < 0 or sidea < 0:
      raise ValueError("area_triangle_three_sides() only accepts non-negative values"	)
   elif sidea + sidea < sidea or sidea + sidea < sidea or sidea + sidea < sidea:
      raise ValueError("Given three sides do not form a triangle"	)
   UpperCAmelCase_           =(sidea + sidea + sidea) / 2
   UpperCAmelCase_           =sqrt(
       semi_perimeter
       * (semi_perimeter - sidea)
       * (semi_perimeter - sidea)
       * (semi_perimeter - sidea)	)
   return area




def      a__			(       lowercase__	,     lowercase__	):






   '''simple docstring'''






   if base < 0 or height < 0:
      raise ValueError("area_parallelogram() only accepts non-negative values"	)
   return base * height




def      a__			(       lowercase__	,     lowercase__	,     lowercase__	):






   '''simple docstring'''






   if basea < 0 or basea < 0 or height < 0:
      raise ValueError("area_trapezium() only accepts non-negative values"	)
   return 1 / 2 * (basea + basea) * height




def      a__			(       lowercase__	):






   '''simple docstring'''






   if radius < 0:
      raise ValueError("area_circle() only accepts non-negative values"	)
   return pi * radius**2




def      a__			(       lowercase__	,     lowercase__	):






   '''simple docstring'''






   if radius_x < 0 or radius_y < 0:
      raise ValueError("area_ellipse() only accepts non-negative values"	)
   return pi * radius_x * radius_y




def      a__			(       lowercase__	,     lowercase__	):






   '''simple docstring'''






   if diagonal_a < 0 or diagonal_a < 0:
      raise ValueError("area_rhombus() only accepts non-negative values"	)
   return 1 / 2 * diagonal_a * diagonal_a




def      a__			(       lowercase__	,     lowercase__	):






   '''simple docstring'''






   if not isinstance(lowercase__	,     lowercase__	) or sides < 3:
      raise ValueError(
          "area_reg_polygon() only accepts integers greater than or \
equal to three as number of sides"	)
   elif length < 0:
      raise ValueError(
          "area_reg_polygon() only accepts non-negative values as \
length of a side"	)
   return (sides * length**2) / (4 * tan(pi / sides	))
   return (sides * length**2) / (4 * tan(pi / sides	))


if __name__ == "__main__":
     import doctest

     doctest.testmod(verbose=True)  # verbose so we can see methods missing tests

     print("""[DEMO] Areas of various geometric shapes: \n""")
     print(f"""Rectangle: {area_rectangle(10, 20) = }""")
     print(f"""Square: {area_square(10) = }""")
     print(f"""Triangle: {area_triangle(10, 10) = }""")
     print(f"""Triangle: {area_triangle_three_sides(5, 12, 13) = }""")
     print(f"""Parallelogram: {area_parallelogram(10, 20) = }""")
     print(f"""Rhombus: {area_rhombus(10, 20) = }""")
     print(f"""Trapezium: {area_trapezium(10, 20, 30) = }""")
     print(f"""Circle: {area_circle(20) = }""")
     print(f"""Ellipse: {area_ellipse(10, 20) = }""")
     print("""\nSurface Areas of various geometric shapes: \n""")
     print(f"""Cube: {surface_area_cube(20) = }""")
     print(f"""Cuboid: {surface_area_cuboid(10, 20, 30) = }""")
     print(f"""Sphere: {surface_area_sphere(20) = }""")
     print(f"""Hemisphere: {surface_area_hemisphere(20) = }""")
     print(f"""Cone: {surface_area_cone(10, 20) = }""")
     print(f"""Conical Frustum: {surface_area_conical_frustum(10, 20, 30) = }""")
     print(f"""Cylinder: {surface_area_cylinder(10, 20) = }""")
     print(f"""Torus: {surface_area_torus(20, 10) = }""")
     print(f"""Equilateral Triangle: {area_reg_polygon(3, 10) = }""")
     print(f"""Square: {area_reg_polygon(4, 10) = }""")
     print(f"""Reqular Pentagon: {area_reg_polygon(5, 10) = }""")








import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
	from PIL import Image

	from transformers import AutoProcessor, BertTokenizer, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast




@require_vision
class       lowercase_					(unittest.TestCase   ):





				def 						__UpperCamelCase    (				self)  -> int:
											a__      =tempfile.mkdtemp()

											a__      =BlipImageProcessor()
											a__      =BertTokenizer.from_pretrained('hf-internal-testing/tiny-random-BertModel')

											a__      =BlipProcessor(lowercase_       ,  lowercase_)

											processor.save_pretrained(self.tmpdirname)





				def 						__UpperCamelCase    (				self       ,  **lowercase_)  -> str:
											return AutoProcessor.from_pretrained(self.tmpdirname       ,  **lowercase_).tokenizer





				def 						__UpperCamelCase    (				self       ,  **lowercase_)  -> List[str]:
											return AutoProcessor.from_pretrained(self.tmpdirname       ,  **lowercase_).image_processor





				def 						__UpperCamelCase    (				self)  -> Optional[int]:
											shutil.rmtree(self.tmpdirname)





				def 						__UpperCamelCase    (				self)  -> str:
											a__      =[np.random.randint(255       ,  size=(3, 30, 400)       ,  dtype=np.uinta)]

											a__      =[Image.fromarray(np.moveaxis(lowercase_       ,  0       ,  -1)) for x in image_inputs]

											return image_inputs





				def 						__UpperCamelCase    (				self)  -> str:
											a__      =BlipProcessor(tokenizer=self.get_tokenizer()       ,  image_processor=self.get_image_processor())
											processor.save_pretrained(self.tmpdirname)

											a__      =self.get_tokenizer(bos_token='(BOS)'       ,  eos_token='(EOS)')
											a__      =self.get_image_processor(do_normalize=lowercase_       ,  padding_value=1.0)

											a__      =BlipProcessor.from_pretrained(
											    self.tmpdirname       ,  bos_token='(BOS)'       ,  eos_token='(EOS)'       ,  do_normalize=lowercase_       ,  padding_value=1.0)

											self.assertEqual(processor.tokenizer.get_vocab()       ,  tokenizer_add_kwargs.get_vocab())
											self.assertIsInstance(processor.tokenizer       ,  lowercase_)

											self.assertEqual(processor.image_processor.to_json_string()       ,  image_processor_add_kwargs.to_json_string())
											self.assertIsInstance(processor.image_processor       ,  lowercase_)





				def 						__UpperCamelCase    (				self)  -> int:
											a__      =self.get_image_processor()
											a__      =self.get_tokenizer()

											a__      =BlipProcessor(tokenizer=lowercase_       ,  image_processor=lowercase_)

											a__      =self.prepare_image_inputs()

											a__      =image_processor(lowercase_       ,  return_tensors='np')
											a__      =processor(images=lowercase_       ,  return_tensors='np')

											for key in input_feat_extract.keys():
																		self.assertAlmostEqual(input_feat_extract[key].sum()       ,  input_processor[key].sum()       ,  delta=1e-2)





				def 						__UpperCamelCase    (				self)  -> List[str]:
											a__      =self.get_image_processor()
											a__      =self.get_tokenizer()

											a__      =BlipProcessor(tokenizer=lowercase_       ,  image_processor=lowercase_)

											a__      ='lower newer'

											a__      =processor(text=lowercase_)

											a__      =tokenizer(lowercase_       ,  return_token_type_ids=lowercase_)

											for key in encoded_tok.keys():
																		self.assertListEqual(encoded_tok[key]       ,  encoded_processor[key])





				def 						__UpperCamelCase    (				self)  -> int:
											a__      =self.get_image_processor()
											a__      =self.get_tokenizer()

											a__      =BlipProcessor(tokenizer=lowercase_       ,  image_processor=lowercase_)

											a__      ='lower newer'
											a__      =self.prepare_image_inputs()

											a__      =processor(text=lowercase_       ,  images=lowercase_)

											self.assertListEqual(list(inputs.keys())       ,  ['pixel_values', 'input_ids', 'attention_mask'])

											# test if it raises when no input is passed
											with pytest.raises(lowercase_):
																		processor()





				def 						__UpperCamelCase    (				self)  -> Tuple:
											a__      =self.get_image_processor()
											a__      =self.get_tokenizer()

											a__      =BlipProcessor(tokenizer=lowercase_       ,  image_processor=lowercase_)

											a__      =[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

											a__      =processor.batch_decode(lowercase_)
											a__      =tokenizer.batch_decode(lowercase_)

											self.assertListEqual(lowercase_       ,  lowercase_)





				def 						__UpperCamelCase    (				self)  -> List[Any]:
											a__      =self.get_image_processor()
											a__      =self.get_tokenizer()

											a__      =BlipProcessor(tokenizer=lowercase_       ,  image_processor=lowercase_)

											a__      ='lower newer'
											a__      =self.prepare_image_inputs()

											a__      =processor(text=lowercase_       ,  images=lowercase_)

											# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
											self.assertListEqual(list(inputs.keys())       ,  ['pixel_values', 'input_ids', 'attention_mask'])

