






'''simple docstring'''




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       ( __magic_name__=""						)->    str:



			"""simple docstring"""



			snake_case_				:					Any   =       tempfile.mkdtemp()
			return os.path.join(__magic_name__     ,str(uuid.uuida()						) + suffix						)







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




							def 							_A	(		self			:List[str]				)				->  Union[str, Any]:


										'''simple docstring'''




										snake_case_				:					Optional[Any]   =       torch.rand(12       ,							dtype=torch.floataa				) - 0.5
										snake_case_				:					Dict   =       AgentAudio(lowerCAmelCase__				)
										snake_case_				:					Optional[int]   =       str(agent_type.to_string()				)

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

										del agent_type

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

										# Ensure that the file contains the same value as the original tensor
										snake_case_,	snake_case_				:					List[str]   =       sf.read(lowerCAmelCase__				)
										self.assertTrue(torch.allclose(lowerCAmelCase__       ,							torch.tensor(lowerCAmelCase__				)       ,							atol=1E-4				)				)






							def 							_A	(		self			:Union[str, Any]				)				->  Optional[Any]:


										'''simple docstring'''




										snake_case_				:					Any   =       torch.rand(12       ,							dtype=torch.floataa				) - 0.5
										snake_case_				:					Union[str, Any]   =       get_new_path(suffix=".wav"				)
										sf.write(lowerCAmelCase__       ,							lowerCAmelCase__       ,							16_000				)

										snake_case_				:					Dict   =       AgentAudio(lowerCAmelCase__				)

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







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




							def 							_A	(		self			:Optional[Any]				)				->  int:


										'''simple docstring'''




										snake_case_				:					str   =       torch.randint(0       ,							256       ,							(64, 64, 3)				)
										snake_case_				:					List[str]   =       AgentImage(lowerCAmelCase__				)
										snake_case_				:					List[Any]   =       str(agent_type.to_string()				)

										# Ensure that the tensor and the agent_type's tensor are the same
										self.assertTrue(torch.allclose(lowerCAmelCase__       ,							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(lowerCAmelCase__				)				)

							def 							_A	(		self			:Union[str, Any]				)				->  Tuple:


										'''simple docstring'''




										snake_case_				:					Any   =       Path(get_tests_dir("fixtures/tests_samples/COCO"				)				) / "000000039769.png"
										snake_case_				:					Union[str, Any]   =       Image.open(lowerCAmelCase__				)
										snake_case_				:					List[str]   =       AgentImage(lowerCAmelCase__				)

										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(lowerCAmelCase__				)				)






							def 							_A	(		self			:Any				)				->  Tuple:


										'''simple docstring'''




										snake_case_				:					List[str]   =       Path(get_tests_dir("fixtures/tests_samples/COCO"				)				) / "000000039769.png"
										snake_case_				:					List[Any]   =       Image.open(lowerCAmelCase__				)
										snake_case_				:					int   =       AgentImage(lowerCAmelCase__				)

										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(lowerCAmelCase__				)				)







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




							def 							_A	(		self			:int				)				->  Dict:


										'''simple docstring'''




										snake_case_				:					Dict   =       "Hey!"
										snake_case_				:					Dict   =       AgentText(lowerCAmelCase__				)

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












'''simple docstring'''




from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__						)->    float:



			"""simple docstring"""



			return math.sqrt(sum(pow(a - b     ,2						) for a, b in zip(__magic_name__     ,__magic_name__						)						)						)

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__						)->    list[list[list[float] | float]]:



			"""simple docstring"""



			if dataset.ndim != value_array.ndim:
						snake_case_				:					int   =       (
						    "Wrong input data's dimensions... "
						    F'''dataset : {dataset.ndim}, value_array : {value_array.ndim}'''
						)
						raise ValueError(__magic_name__						)

			try:
						if dataset.shape[1] != value_array.shape[1]:
									snake_case_				:					Dict   =       (
									    "Wrong input data's shape... "
									    F'''dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}'''
									)
									raise ValueError(__magic_name__						)
			except IndexError:
						if dataset.ndim != value_array.ndim:
									raise TypeError("Wrong shape"						)

			if dataset.dtype != value_array.dtype:
						snake_case_				:					Dict   =       (
						    "Input data have different datatype... "
						    F'''dataset : {dataset.dtype}, value_array : {value_array.dtype}'''
						)
						raise TypeError(__magic_name__						)

			snake_case_				:					Optional[int]   =       []

			for value in value_array:
						snake_case_				:					List[str]   =       euclidean(__magic_name__     ,dataset[0]						)
						snake_case_				:					int   =       dataset[0].tolist()

						for dataset_value in dataset[1:]:
									snake_case_				:					Optional[Any]   =       euclidean(__magic_name__     ,__magic_name__						)

									if dist > temp_dist:
												snake_case_				:					Tuple   =       temp_dist
												snake_case_				:					Optional[int]   =       dataset_value.tolist()

						answer.append([vector, dist]						)

			return answer

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__						)->    float:



			"""simple docstring"""



			return np.dot(__magic_name__     ,__magic_name__						) / (norm(__magic_name__						) * norm(__magic_name__						))


if __name__ == "__main__":
		import doctest

		doctest.testmod()




