


"""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__       (lowerCAmelCase_=""   ):




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE						   =				tempfile.mkdtemp()
					return os.path.join(lowerCAmelCase_  ,						str(uuid.uuida()   ) + suffix   )




@require_soundfile
@require_torch
class  UpperCamelCase_	( unittest.TestCase):

	"""simple docstring"""





	def   UpperCAmelCase_      ( self			:				Optional[int]   )       ->	Union[str, Any]:
						__SCREAMING_SNAKE_CASE						   =				torch.rand(1_2			,			dtype=torch.floataa   ) - 0.5
						__SCREAMING_SNAKE_CASE						   =				AgentAudio(UpperCAmelCase__   )
						__SCREAMING_SNAKE_CASE						   =				str(agent_type.to_string()   )

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

						del agent_type

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

						# Ensure that the file contains the same value as the original tensor
						__SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE						   =				sf.read(UpperCAmelCase__   )
						self.assertTrue(torch.allclose(UpperCAmelCase__			,			torch.tensor(UpperCAmelCase__   )			,			atol=1E-4   )   )




	def   UpperCAmelCase_      ( self			:				Tuple   )       ->	str:
						__SCREAMING_SNAKE_CASE						   =				torch.rand(1_2			,			dtype=torch.floataa   ) - 0.5
						__SCREAMING_SNAKE_CASE						   =				get_new_path(suffix=".wav"   )
						sf.write(UpperCAmelCase__			,			UpperCAmelCase__			,			1_6_0_0_0   )

						__SCREAMING_SNAKE_CASE						   =				AgentAudio(UpperCAmelCase__   )

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




@require_vision
@require_torch
class  UpperCamelCase_	( unittest.TestCase):

	"""simple docstring"""





	def   UpperCAmelCase_      ( self			:				Dict   )       ->	Union[str, Any]:
						__SCREAMING_SNAKE_CASE						   =				torch.randint(0			,			2_5_6			,			(6_4, 6_4, 3)   )
						__SCREAMING_SNAKE_CASE						   =				AgentImage(UpperCAmelCase__   )
						__SCREAMING_SNAKE_CASE						   =				str(agent_type.to_string()   )

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




	def   UpperCAmelCase_      ( self			:				Union[str, Any]   )       ->	Any:
						__SCREAMING_SNAKE_CASE						   =				Path(get_tests_dir("fixtures/tests_samples/COCO"   )   ) / "000000039769.png"
						__SCREAMING_SNAKE_CASE						   =				Image.open(UpperCAmelCase__   )
						__SCREAMING_SNAKE_CASE						   =				AgentImage(UpperCAmelCase__   )

						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(UpperCAmelCase__   )   )




	def   UpperCAmelCase_      ( self			:				Optional[int]   )       ->	List[str]:
						__SCREAMING_SNAKE_CASE						   =				Path(get_tests_dir("fixtures/tests_samples/COCO"   )   ) / "000000039769.png"
						__SCREAMING_SNAKE_CASE						   =				Image.open(UpperCAmelCase__   )
						__SCREAMING_SNAKE_CASE						   =				AgentImage(UpperCAmelCase__   )

						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(UpperCAmelCase__   )   )




class  UpperCamelCase_	( unittest.TestCase):

	"""simple docstring"""





	def   UpperCAmelCase_      ( self			:				int   )       ->	Tuple:
						__SCREAMING_SNAKE_CASE						   =				"Hey!"
						__SCREAMING_SNAKE_CASE						   =				AgentText(UpperCAmelCase__   )

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






"""simple docstring"""






from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm







def      _snake_case			(				lowercase__							,    lowercase__		):
				return math.sqrt(sum(pow(a - b							,    2		) for a, b in zip(lowercase__							,    lowercase__		)		)		)







def      _snake_case			(				lowercase__							,    lowercase__		):

				if dataset.ndim != value_array.ndim:
								_lowerCamelCase     :			Tuple    =    (
								    'Wrong input data\'s dimensions... '
								    f'''dataset : {dataset.ndim}, value_array : {value_array.ndim}'''
								)
								raise ValueError(lowercase__		)

				try:
								if dataset.shape[1] != value_array.shape[1]:
												_lowerCamelCase     :			Optional[int]    =    (
												    'Wrong input data\'s shape... '
												    f'''dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}'''
												)
												raise ValueError(lowercase__		)
				except IndexError:
								if dataset.ndim != value_array.ndim:
												raise TypeError('Wrong shape'		)

				if dataset.dtype != value_array.dtype:
								_lowerCamelCase     :			int    =    (
								    'Input data have different datatype... '
								    f'''dataset : {dataset.dtype}, value_array : {value_array.dtype}'''
								)
								raise TypeError(lowercase__		)

				_lowerCamelCase     :			Optional[int]    =    []

				for value in value_array:
								_lowerCamelCase     :			Tuple    =    euclidean(lowercase__							,    dataset[0]		)
								_lowerCamelCase     :			Union[str, Any]    =    dataset[0].tolist()

								for dataset_value in dataset[1:]:
												_lowerCamelCase     :			Optional[Any]    =    euclidean(lowercase__							,    lowercase__		)

												if dist > temp_dist:
																_lowerCamelCase     :			List[Any]    =    temp_dist
																_lowerCamelCase     :			List[str]    =    dataset_value.tolist()

								answer.append([vector, dist]		)

				return answer







def      _snake_case			(				lowercase__							,    lowercase__		):
				return np.dot(lowercase__							,    lowercase__		) / (norm(lowercase__		) * norm(lowercase__		))


if __name__ == "__main__":
			import doctest

			doctest.testmod()