

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


lowerCAmelCase__								=		["""text""", """image""", """audio"""]






def 					lowerCamelCase_     (							UpperCAmelCase_							:     List[str]	)						->				Optional[int]:




	'''simple docstring'''




	_UpperCamelCase   :		Dict			=						[]

	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((5_1_2, 5_1_2)	)	)
		elif input_type == "audio":
			inputs.append(torch.ones(3_0_0_0	)	)
		elif isinstance(UpperCAmelCase_							,       UpperCAmelCase_	):
			inputs.append(create_inputs(UpperCAmelCase_	)	)
		else:
			raise ValueError(F'''Invalid type requested: {input_type}'''	)

	return inputs






def 					lowerCamelCase_     (							UpperCAmelCase_							:     List	)						->				str:




	'''simple docstring'''




	_UpperCamelCase   :		Optional[Any]			=						[]

	for output in outputs:
		if isinstance(UpperCAmelCase_							,       (str, AgentText)	):
			output_types.append('text'	)
		elif isinstance(UpperCAmelCase_							,       (Image.Image, AgentImage)	):
			output_types.append('image'	)
		elif isinstance(UpperCAmelCase_							,       (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):
			self.assertTrue(hasattr(self.tool	,       'inputs'))
			self.assertTrue(hasattr(self.tool	,       'outputs'))

			_UpperCamelCase   :		str			=						self.tool.inputs
			for _input in inputs:
				if isinstance(_input	,       __snake_case):
					for __input in _input:
						self.assertTrue(__input in authorized_types)
				else:
					self.assertTrue(_input in authorized_types)

			_UpperCamelCase   :		Optional[int]			=						self.tool.outputs
			for _output in outputs:
				self.assertTrue(_output in authorized_types)


		def 		A__					(				self):
			_UpperCamelCase   :		int			=						create_inputs(self.tool.inputs)
			_UpperCamelCase   :		Any			=						self.tool(*__snake_case)

			# There is a single output
			if len(self.tool.outputs) == 1:
				_UpperCamelCase   :		Tuple			=						[outputs]

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


		def 		A__					(				self):
			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):
			_UpperCamelCase   :		str			=						create_inputs(self.tool.inputs)
			_UpperCamelCase   :		int			=						self.tool(*__snake_case)

			if not isinstance(__snake_case	,       __snake_case):
				_UpperCamelCase   :		Dict			=						[outputs]

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

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






		def 		A__					(				self):
			_UpperCamelCase   :		List[Any]			=						create_inputs(self.tool.inputs)

			_UpperCamelCase   :		List[str]			=						[]

			for _input, input_type in zip(__snake_case	,       self.tool.inputs):
				if isinstance(__snake_case	,       __snake_case):
					_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   :		Optional[int]			=						self.tool(*__snake_case)

			if not isinstance(__snake_case	,       __snake_case):
				_UpperCamelCase   :		str			=						[outputs]

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







from __future__ import annotations

import matplotlib.pyplot as plt  # type: ignore
import numpy

# initial triangle of Koch snowflake
lowerCAmelCase__								=		numpy.array([0, 0])
lowerCAmelCase__								=		numpy.array([0.5, 0.8_66_02_54])
lowerCAmelCase__								=		numpy.array([1, 0])
lowerCAmelCase__								=		[VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1]






def 					lowerCamelCase_     (							UpperCAmelCase_							:     list[numpy.ndarray]							,       UpperCAmelCase_							:     int	)						->				list[numpy.ndarray]:




	'''simple docstring'''




	_UpperCamelCase   :		Tuple			=						initial_vectors
	for _ in range(UpperCAmelCase_	):
		_UpperCamelCase   :		str			=						iteration_step(UpperCAmelCase_	)
	return vectors






def 					lowerCamelCase_     (							UpperCAmelCase_							:     list[numpy.ndarray]	)						->				list[numpy.ndarray]:




	'''simple docstring'''




	_UpperCamelCase   :		int			=						[]
	for i, start_vector in enumerate(vectors[:-1]	):
		_UpperCamelCase   :		Union[str, Any]			=						vectors[i + 1]
		new_vectors.append(UpperCAmelCase_	)
		_UpperCamelCase   :		Tuple			=						end_vector - start_vector
		new_vectors.append(start_vector + difference_vector / 3	)
		new_vectors.append(
		    start_vector + difference_vector / 3 + rotate(difference_vector / 3							,       6_0	)	)
		new_vectors.append(start_vector + difference_vector * 2 / 3	)
	new_vectors.append(vectors[-1]	)
	return new_vectors






def 					lowerCamelCase_     (							UpperCAmelCase_							:     numpy.ndarray							,       UpperCAmelCase_							:     float	)						->				numpy.ndarray:




	'''simple docstring'''




	_UpperCamelCase   :		str			=						numpy.radians(UpperCAmelCase_	)
	_UpperCamelCase	,		_UpperCamelCase   :		Optional[Any]			=						numpy.cos(UpperCAmelCase_	), numpy.sin(UpperCAmelCase_	)
	_UpperCamelCase   :		Any			=						numpy.array(((c, -s), (s, c))	)
	return numpy.dot(UpperCAmelCase_							,       UpperCAmelCase_	)






def 					lowerCamelCase_     (							UpperCAmelCase_							:     list[numpy.ndarray]	)						->				None:




	'''simple docstring'''




	_UpperCamelCase   :		str			=						plt.gca()
	axes.set_aspect('equal'	)

	# matplotlib.pyplot.plot takes a list of all x-coordinates and a list of all
	# y-coordinates as inputs, which are constructed from the vector-list using
	# zip()
	_UpperCamelCase	,		_UpperCamelCase   :		Dict			=						zip(*UpperCAmelCase_	)
	plt.plot(UpperCAmelCase_							,       UpperCAmelCase_	)
	plt.show()


if __name__ == "__main__":
							import doctest

							doctest.testmod()

							lowerCAmelCase__								=		iterate(INITIAL_VECTORS, 5)
							plot(processed_vectors)




