

import os
import tempfile
import unittest
import uuid
from pathlib import Path

from transformers.testing_utils import get_tests_dir, require_soundfile, require_torch, require_vision
from transformers.tools.agent_types import AgentAudio, AgentImage, AgentText
from transformers.utils import is_soundfile_availble, is_torch_available, is_vision_available


if is_torch_available():
			import torch

if is_soundfile_availble():
			import soundfile as sf

if is_vision_available():
			from PIL import Image



def   __UpperCAmelCase					(      __a :     Dict=""      )     ->	str:

					"""simple docstring"""

					_a      :       int   =						tempfile.mkdtemp()
					return os.path.join(__a						,str(uuid.uuida()      ) + suffix      )

@require_soundfile
@require_torch
class 							UpperCAmelCase_						(     unittest.TestCase  ):
							"""simple docstring"""







							def 							__lowercase							(     self			)       ->       Tuple:
												_a      :       List[Any]   =						torch.rand(1_2			,				dtype=torch.floataa			) - 0.5
												_a      :       Tuple   =						AgentAudio(_a			)
												_a      :       List[Any]   =						str(agent_type.to_string()			)

												# Ensure that the tensor and the agent_type's tensor are the same
												self.assertTrue(torch.allclose(_a			,				agent_type.to_raw()			,				atol=1e-4			)			)

												del agent_type

												# Ensure the path remains even after the object deletion
												self.assertTrue(os.path.exists(_a			)			)

												# Ensure that the file contains the same value as the original tensor
												_a ,   _a      :       Dict   =						sf.read(_a			)
												self.assertTrue(torch.allclose(_a			,				torch.tensor(_a			)			,				atol=1e-4			)			)




							def 							__lowercase							(     self			)       ->       Optional[int]:
												_a      :       Dict   =						torch.rand(1_2			,				dtype=torch.floataa			) - 0.5
												_a      :       str   =						get_new_path(suffix='''.wav'''			)
												sf.write(_a			,				_a			,				1_6_0_0_0			)

												_a      :       int   =						AgentAudio(_a			)

												self.assertTrue(torch.allclose(_a			,				agent_type.to_raw()			,				atol=1e-4			)			)
												self.assertEqual(agent_type.to_string()			,				_a			)



@require_vision
@require_torch
class 							UpperCAmelCase_						(     unittest.TestCase  ):
							"""simple docstring"""







							def 							__lowercase							(     self			)       ->       Optional[int]:
												_a      :       str   =						torch.randint(0			,				2_5_6			,				(6_4, 6_4, 3)			)
												_a      :       List[str]   =						AgentImage(_a			)
												_a      :       Union[str, Any]   =						str(agent_type.to_string()			)

												# Ensure that the tensor and the agent_type's tensor are the same
												self.assertTrue(torch.allclose(_a			,				agent_type._tensor			,				atol=1e-4			)			)

												self.assertIsInstance(agent_type.to_raw()			,				Image.Image			)

												# Ensure the path remains even after the object deletion
												del agent_type
												self.assertTrue(os.path.exists(_a			)			)


							def 							__lowercase							(     self			)       ->       Tuple:
												_a      :       Tuple   =						Path(get_tests_dir('''fixtures/tests_samples/COCO'''			)			) / '''000000039769.png'''
												_a      :       str   =						Image.open(_a			)
												_a      :       str   =						AgentImage(_a			)

												self.assertTrue(path.samefile(agent_type.to_string()			)			)
												self.assertTrue(image == agent_type.to_raw()			)

												# Ensure the path remains even after the object deletion
												del agent_type
												self.assertTrue(os.path.exists(_a			)			)




							def 							__lowercase							(     self			)       ->       List[Any]:
												_a      :       Optional[Any]   =						Path(get_tests_dir('''fixtures/tests_samples/COCO'''			)			) / '''000000039769.png'''
												_a      :       Union[str, Any]   =						Image.open(_a			)
												_a      :       Tuple   =						AgentImage(_a			)

												self.assertFalse(path.samefile(agent_type.to_string()			)			)
												self.assertTrue(image == agent_type.to_raw()			)

												# Ensure the path remains even after the object deletion
												del agent_type
												self.assertTrue(os.path.exists(_a			)			)



class 							UpperCAmelCase_						(     unittest.TestCase  ):
							"""simple docstring"""







							def 							__lowercase							(     self			)       ->       int:
												_a      :       List[Any]   =						'''Hey!'''
												_a      :       Dict   =						AgentText(_a			)

												self.assertEqual(_a			,				agent_type.to_string()			)
												self.assertEqual(_a			,				agent_type.to_raw()			)
												self.assertEqual(_a			,				_a			)






import random
import sys

import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap

a__												=						'''Usage of script: script_name <size_of_canvas:int>'''

a__												=						[0] * 100 + [1] * 10
random.shuffle(choice)



def   __UpperCAmelCase					(      __a :     int      )     ->	list[list[bool]]:

					"""simple docstring"""

					_a      :       int   =						[[False for i in range(__a      )] for j in range(__a      )]
					return canvas



def   __UpperCAmelCase					(      __a :     list[list[bool]]      )     ->	None:

					"""simple docstring"""

					for i, row in enumerate(__a      ):
										for j, _ in enumerate(__a      ):
															_a      :       Optional[int]   =						bool(random.getrandbits(1      )      )



def   __UpperCAmelCase					(      __a :     list[list[bool]]      )     ->	list[list[bool]]:

					"""simple docstring"""

					_a      :       Any   =						np.array(__a      )
					_a      :       Optional[int]   =						np.array(create_canvas(current_canvas.shape[0]      )      )
					for r, row in enumerate(__a      ):
										for c, pt in enumerate(__a      ):
															_a      :       Tuple   =						__judge_point(
															    __a						,current_canvas[r - 1 : r + 2, c - 1 : c + 2]      )

					_a      :       List[str]   =						next_gen_canvas
					del next_gen_canvas  # cleaning memory as we move on.
					_a      :       list[list[bool]]   =						current_canvas.tolist()
					return return_canvas



def   __UpperCAmelCase					(      __a :     bool						,__a :     list[list[bool]]      )     ->	bool:

					"""simple docstring"""

					_a      :       Optional[Any]   =						0
					_a      :       str   =						0
					# finding dead or alive neighbours count.
					for i in neighbours:
										for status in i:
															if status:
																				alive += 1
															else:
																				dead += 1

    # handling duplicate entry for focus pt.
					if pt:
										alive -= 1
					else:
										dead -= 1

					# running the rules of game here.
					_a      :       Optional[int]   =						pt
					if pt:
										if alive < 2:
															_a      :       Dict   =						False
										elif alive == 2 or alive == 3:
															_a      :       Optional[Any]   =						True
										elif alive > 3:
															_a      :       str   =						False
					else:
										if alive == 3:
															_a      :       int   =						True

					return state


if __name__ == "__main__":
			if len(sys.argv) != 2:
						raise Exception(usage_doc)

			a__												=						int(sys.argv[1])
			# main working structure of this module.
			a__												=						create_canvas(canvas_size)
			seed(c)
			a__						,     a__												=						plt.subplots()
			fig.show()
			a__												=						ListedColormap(['''w''', '''k'''])
			try:
						while True:
									a__												=						run(c)
									ax.matshow(c, cmap=cmap)
									fig.canvas.draw()
									ax.cla()
			except KeyboardInterrupt:
						# do nothing.
						pass



