





"""simple docstring"""


from pathlib import Path
from typing import List

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image


__magic_name__       :					List[str]						=			["""text""", """image""", """audio"""]

def      UpperCamelCase							(SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		str				   =			[]

						for input_type in input_types:
												if input_type == "text":
																		inputs.append("""Text input"""     )
												elif input_type == "image":
																		inputs.append(
																		    Image.open(Path(get_tests_dir("""fixtures/tests_samples/COCO"""     )     ) / """000000039769.png"""     ).resize((512, 512)     )     )
												elif input_type == "audio":
																		inputs.append(torch.ones(3000     )     )
												elif isinstance(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
																		inputs.append(create_inputs(SCREAMING_SNAKE_CASE     )     )
												else:
																		raise ValueError(f"""Invalid type requested: {input_type}"""     )

						return inputs

def      UpperCamelCase							(SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		List[Any]				   =			[]

						for output in outputs:
												if isinstance(SCREAMING_SNAKE_CASE							,     (str, AgentText)     ):
																		output_types.append("""text"""     )
												elif isinstance(SCREAMING_SNAKE_CASE							,     (Image.Image, AgentImage)     ):
																		output_types.append("""image"""     )
												elif isinstance(SCREAMING_SNAKE_CASE							,     (torch.Tensor, AgentAudio)     ):
																		output_types.append("""audio"""     )
												else:
																		raise ValueError(f"""Invalid output: {output}"""     )

						return output_types






@is_tool_test
class 							lowercase__			:

			"""simple docstring"""







			def   _a      (      self    ):






									'''simple docstring'''







									self.assertTrue(hasattr(self.tool						,   """inputs"""    )    )
									self.assertTrue(hasattr(self.tool						,   """outputs"""    )    )

									UpperCamelCase				:		int				   =			self.tool.inputs
									for _input in inputs:
															if isinstance(_input						,   _A    ):
																					for __input in _input:
																											self.assertTrue(__input in authorized_types    )
															else:
																					self.assertTrue(_input in authorized_types    )

									UpperCamelCase				:		Any				   =			self.tool.outputs
									for _output in outputs:
															self.assertTrue(_output in authorized_types    )







			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		Union[str, Any]				   =			create_inputs(self.tool.inputs    )
									UpperCamelCase				:		Optional[int]				   =			self.tool(*_A    )

									# There is a single output
									if len(self.tool.outputs    ) == 1:
															UpperCamelCase				:		Optional[Any]				   =			[outputs]

									self.assertListEqual(output_types(_A    )						,   self.tool.outputs    )







			def   _a      (      self    ):






									'''simple docstring'''







									self.assertTrue(hasattr(self.tool						,   """description"""    )    )
									self.assertTrue(hasattr(self.tool						,   """default_checkpoint"""    )    )
									self.assertTrue(self.tool.description.startswith("""This is a tool that"""    )    )







			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		Any				   =			create_inputs(self.tool.inputs    )
									UpperCamelCase				:		Optional[Any]				   =			self.tool(*_A    )

									if not isinstance(_A						,   _A    ):
															UpperCamelCase				:		Dict				   =			[outputs]

									self.assertEqual(len(_A    )						,   len(self.tool.outputs    )    )

									for output, output_type in zip(_A						,   self.tool.outputs    ):
															UpperCamelCase				:		List[Any]				   =			AGENT_TYPE_MAPPING[output_type]
															self.assertTrue(isinstance(_A						,   _A    )    )







			def   _a      (      self    ):






									'''simple docstring'''







									UpperCamelCase				:		List[str]				   =			create_inputs(self.tool.inputs    )

									UpperCamelCase				:		Optional[int]				   =			[]

									for _input, input_type in zip(_A						,   self.tool.inputs    ):
															if isinstance(_A						,   _A    ):
																					_inputs.append([AGENT_TYPE_MAPPING[_input_type](_input    ) for _input_type in input_type]    )
															else:
																					_inputs.append(AGENT_TYPE_MAPPING[input_type](_input    )    )

        # Should not raise an error
									UpperCamelCase				:		Dict				   =			self.tool(*_A    )

									if not isinstance(_A						,   _A    ):
															UpperCamelCase				:		Tuple				   =			[outputs]

									self.assertEqual(len(_A    )						,   len(self.tool.outputs    )    )










"""simple docstring"""


import colorsys

from PIL import Image  # type: ignore

def      UpperCamelCase							(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     ):
						UpperCamelCase				:		Optional[int]				   =			x
						UpperCamelCase				:		str				   =			y
						for step in range(SCREAMING_SNAKE_CASE     ):  # noqa: B007
												UpperCamelCase				:		str				   =			a * a - b * b + x
												UpperCamelCase				:		Any				   =			2 * a * b + y
												UpperCamelCase				:		List[str]				   =			a_new

												# divergence happens for all complex number with an absolute value
												# greater than 4
												if a * a + b * b > 4:
																		break
						return step / (max_step - 1)

def      UpperCamelCase							(SCREAMING_SNAKE_CASE     ):
						if distance == 1:
												return (0, 0, 0)
						else:
												return (255, 255, 255)

def      UpperCamelCase							(SCREAMING_SNAKE_CASE     ):
						if distance == 1:
												return (0, 0, 0)
						else:
												return tuple(round(i * 255     ) for i in colorsys.hsv_to_rgb(SCREAMING_SNAKE_CASE							,     1							,     1     )     )

def      UpperCamelCase							(SCREAMING_SNAKE_CASE = 800							,     SCREAMING_SNAKE_CASE = 600							,     SCREAMING_SNAKE_CASE = -0.6							,     SCREAMING_SNAKE_CASE = 0							,     SCREAMING_SNAKE_CASE = 3.2							,     SCREAMING_SNAKE_CASE = 50							,     SCREAMING_SNAKE_CASE = True							,     ):
						UpperCamelCase				:		Tuple				   =			Image.new("""RGB"""							,     (image_width, image_height)     )
						UpperCamelCase				:		Dict				   =			img.load()

						# loop through the image-coordinates
						for image_x in range(SCREAMING_SNAKE_CASE     ):
												for image_y in range(SCREAMING_SNAKE_CASE     ):
																		# determine the figure-coordinates based on the image-coordinates
																		UpperCamelCase				:		Dict				   =			figure_width / image_width * image_height
																		UpperCamelCase				:		Tuple				   =			figure_center_x + (image_x / image_width - 0.5) * figure_width
																		UpperCamelCase				:		Dict				   =			figure_center_y + (image_y / image_height - 0.5) * figure_height

																		UpperCamelCase				:		Union[str, Any]				   =			get_distance(SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE							,     SCREAMING_SNAKE_CASE     )

																		# color the corresponding pixel based on the selected coloring-function
																		if use_distance_color_coding:
																								UpperCamelCase				:		int				   =			get_color_coded_rgb(SCREAMING_SNAKE_CASE     )
																		else:
																								UpperCamelCase				:		Optional[int]				   =			get_black_and_white_rgb(SCREAMING_SNAKE_CASE     )

						return img


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		# colored version, full figure
		__magic_name__       :					int						=			get_image()

		# uncomment for colored version, different section, zoomed in
		# img = get_image(figure_center_x = -0.6, figure_center_y = -0.4,
		# figure_width = 0.8)

		# uncomment for black and white version, full figure
		# img = get_image(use_distance_color_coding = False)

		# uncomment to save the image
		# img.save("mandelbrot.png")

		img.show()



