




"""simple docstring"""




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  snake_case(    __magic_name__   )			->							str:





     '''simple docstring'''


     lowercase :       Optional[Any]       =			[]

     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_12, 5_12)   )   )
          elif input_type == "audio":
               inputs.append(torch.ones(30_00   )   )
          elif isinstance(__magic_name__			,		__magic_name__   ):
               inputs.append(create_inputs(__magic_name__   )   )
          else:
               raise ValueError(F"""Invalid type requested: {input_type}"""   )

     return inputs






def  snake_case(    __magic_name__   )			->							Any:





     '''simple docstring'''


     lowercase :       Any       =			[]

     for output in outputs:
          if isinstance(__magic_name__			,		(str, AgentText)   ):
               output_types.append('''text'''   )
          elif isinstance(__magic_name__			,		(Image.Image, AgentImage)   ):
               output_types.append('''image'''   )
          elif isinstance(__magic_name__			,		(torch.Tensor, AgentAudio)   ):
               output_types.append('''audio'''   )
          else:
               raise ValueError(F"""Invalid output: {output}"""   )

     return output_types



@is_tool_test
class 						_A :






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






       """simple docstring"""







       self.assertTrue(hasattr(self.tool				,						'''inputs'''				)				)
       self.assertTrue(hasattr(self.tool				,						'''outputs'''				)				)

       lowercase :       List[Any]       =			self.tool.inputs
       for _input in inputs:
            if isinstance(_input				,						_A				):
                 for __input in _input:
                      self.assertTrue(__input in authorized_types				)
            else:
                 self.assertTrue(_input in authorized_types				)

       lowercase :       Tuple       =			self.tool.outputs
       for _output in outputs:
            self.assertTrue(_output in authorized_types				)






  def  __a      (  self		:     Dict				)		->   Any:






       """simple docstring"""







       lowercase :       Union[str, Any]       =			create_inputs(self.tool.inputs				)
       lowercase :       Dict       =			self.tool(*_A				)

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

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






  def  __a      (  self		:     List[str]				)		->   List[Any]:






       """simple docstring"""







       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		:     Any				)		->   Optional[int]:






       """simple docstring"""







       lowercase :       str       =			create_inputs(self.tool.inputs				)
       lowercase :       int       =			self.tool(*_A				)

       if not isinstance(_A				,						_A				):
            lowercase :       Optional[int]       =			[outputs]

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

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






  def  __a      (  self		:     List[str]				)		->   Any:






       """simple docstring"""







       lowercase :       int       =			create_inputs(self.tool.inputs				)

       lowercase :       Tuple       =			[]

       for _input, input_type in zip(_A				,						self.tool.inputs				):
            if isinstance(_A				,						_A				):
                 _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
       lowercase :       int       =			self.tool(*_A				)

       if not isinstance(_A				,						_A				):
            lowercase :       Union[str, Any]       =			[outputs]

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





from sklearn.metrics import mean_squared_error

import datasets


lowerCAmelCase_						=			'\\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n journal={Journal of Machine Learning Research},\n volume={12},\n pages={2825--2830},\n year={2011}\n}\n'

lowerCAmelCase_						=			'\\nMean Squared Error(MSE) is the average of the square of difference between the predicted\nand actual values.\n'


lowerCAmelCase_						=			'\nArgs:\n    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Estimated target values.\n    references: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Ground truth (correct) target values.\n    sample_weight: array-like of shape (n_samples,), default=None\n        Sample weights.\n    multioutput: {"raw_values", "uniform_average"} or array-like of shape (n_outputs,), default="uniform_average"\n        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.\n\n        "raw_values" : Returns a full set of errors in case of multioutput input.\n\n        "uniform_average" : Errors of all outputs are averaged with uniform weight.\n\n    squared : bool, default=True\n        If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.\n\nReturns:\n    mse : mean squared error.\nExamples:\n\n    >>> mse_metric = datasets.load_metric("mse")\n    >>> predictions = [2.5, 0.0, 2, 8]\n    >>> references = [3, -0.5, 2, 7]\n    >>> results = mse_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'mse\': 0.375}\n    >>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)\n    >>> print(rmse_result)\n    {\'mse\': 0.6123724356957945}\n\n    If you\'re using multi-dimensional lists, then set the config as follows :\n\n    >>> mse_metric = datasets.load_metric("mse", "multilist")\n    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]\n    >>> references = [[0, 2], [-1, 2], [8, -5]]\n    >>> results = mse_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'mse\': 0.7083333333333334}\n    >>> results = mse_metric.compute(predictions=predictions, references=references, multioutput=\'raw_values\')\n    >>> print(results) # doctest: +NORMALIZE_WHITESPACE\n    {\'mse\': array([0.41666667, 1. ])}\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION      ,       _KWARGS_DESCRIPTION				)
class 						_A (  datasets.Metric				):






  def  __a      (  self		:     List[Any]				)		->   Optional[Any]:






       """simple docstring"""







       return datasets.MetricInfo(
           description=_DESCRIPTION				,						citation=_CITATION				,						inputs_description=_KWARGS_DESCRIPTION				,						features=datasets.Features(self._get_feature_types()				)				,						reference_urls=[
               '''https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html'''
           ]				,						)






  def  __a      (  self		:     List[Any]				)		->   int:






       """simple docstring"""







       if self.config_name == "multilist":
            return {
                "predictions": datasets.Sequence(datasets.Value('''float'''				)				),
                "references": datasets.Sequence(datasets.Value('''float'''				)				),
            }
       else:
            return {
                "predictions": datasets.Value('''float'''				),
                "references": datasets.Value('''float'''				),
            }






  def  __a      (  self		:     Any				,						_A		:     Dict				,						_A		:     Any				,						_A		:     Any=None				,						_A		:     Any="uniform_average"				,						_A		:     Optional[Any]=True				)		->   Dict:






       """simple docstring"""







       lowercase :       Any       =			mean_squared_error(
           _A				,						_A				,						sample_weight=_A				,						multioutput=_A				,						squared=_A				)

       return {"mse": mse}