



'''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    lowerCamelCase__ (		_A=""       ):
  a		:      Union[str, Any]       =  tempfile.mkdtemp()
  return os.path.join(_A				,    str(uuid.uuida()       ) + suffix       )

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







    def     lowercase_ (   self				:						Optional[Any]			):
      a		:      Optional[int]       =  torch.rand(12		,    dtype=torch.floataa			) - 0.5
      a		:      List[str]       =  AgentAudio(__snake_case			)
      a		:      str       =  str(agent_type.to_string()			)

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

      del agent_type

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

      # Ensure that the file contains the same value as the original tensor
      a     ,			a		:      Union[str, Any]       =  sf.read(__snake_case			)
      self.assertTrue(torch.allclose(__snake_case		,    torch.tensor(__snake_case			)		,    atol=1e-4			)			)







    def     lowercase_ (   self				:						Union[str, Any]			):
      a		:      int       =  torch.rand(12		,    dtype=torch.floataa			) - 0.5
      a		:      List[Any]       =  get_new_path(suffix='.wav'			)
      sf.write(__snake_case		,    __snake_case		,    1_60_00			)

      a		:      Any       =  AgentAudio(__snake_case			)

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

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







    def     lowercase_ (   self				:						Optional[int]			):
      a		:      int       =  torch.randint(0		,    2_56		,    (64, 64, 3)			)
      a		:      Dict       =  AgentImage(__snake_case			)
      a		:      Dict       =  str(agent_type.to_string()			)

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







    def     lowercase_ (   self				:						int			):
      a		:      Tuple       =  Path(get_tests_dir('fixtures/tests_samples/COCO'			)			) / '000000039769.png'
      a		:      int       =  Image.open(__snake_case			)
      a		:      Optional[Any]       =  AgentImage(__snake_case			)

      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(__snake_case			)			)







    def     lowercase_ (   self				:						str			):
      a		:      Dict       =  Path(get_tests_dir('fixtures/tests_samples/COCO'			)			) / '000000039769.png'
      a		:      List[Any]       =  Image.open(__snake_case			)
      a		:      Tuple       =  AgentImage(__snake_case			)

      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(__snake_case			)			)

class 							a__(      unittest.TestCase			):







    def     lowercase_ (   self				:						Any			):
      a		:      Tuple       =  'Hey!'
      a		:      Dict       =  AgentText(__snake_case			)

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




'''simple docstring'''






import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
def    lowerCamelCase__ (		_A       ):
  return (data["data"], data["target"])
def    lowerCamelCase__ (		_A				,    _A				,    _A       ):
  a		:      Any       =  XGBRegressor(verbosity=0				,    random_state=42       )
  xgb.fit(_A				,    _A       )
  # Predict target for test data
  a		:      Any       =  xgb.predict(_A       )
  a		:      Any       =  predictions.reshape(len(_A       )				,    1       )
  return predictions
def    lowerCamelCase__ (		):
  a		:      Optional[int]       =  fetch_california_housing()
  a     ,			a		:      Optional[Any]       =  data_handling(_A       )
  a     ,			a     ,			a     ,			a		:      Dict       =  train_test_split(
      _A				,    _A				,    test_size=0.25				,    random_state=1       )
  a		:      List[str]       =  xgboost(_A				,    _A				,    _A       )
  # Error printing
  print(f"""Mean Absolute Error : {mean_absolute_error(_A				,    _A       )}"""       )
  print(f"""Mean Square Error  : {mean_squared_error(_A				,    _A       )}"""       )


if __name__ == "__main__":
     import doctest

     doctest.testmod(verbose=True)
     main()