






"""simple docstring"""


import unittest

from transformers import load_tool
from transformers.tools.agent_types import AGENT_TYPE_MAPPING

from .test_tools_common import ToolTesterMixin, output_types


class    snake_case						(				unittest.TestCase  ,      _lowerCAmelCase		):


   '''simple docstring'''

   def 							_SCREAMING_SNAKE_CASE							(      self					:	Any    ):

     '''simple docstring'''
     __A		     =					load_tool('''translation'''    )
     self.tool.setup()
     __A		     =					load_tool('''translation''',			remote=_lowerCamelCase    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	int    ):

     '''simple docstring'''
     __A		     =					self.tool('''Hey, what\'s up?''',			src_lang='''English''',			tgt_lang='''French'''    )
     self.assertEqual(_lowerCamelCase,			'''- Hé, comment ça va?'''    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	int    ):

     '''simple docstring'''
     __A		     =					self.remote_tool('''Hey, what\'s up?''',			src_lang='''English''',			tgt_lang='''French'''    )
     self.assertEqual(_lowerCamelCase,			'''- Hé, comment ça va?'''    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Optional[int]    ):

     '''simple docstring'''
     __A		     =					self.tool(text='''Hey, what\'s up?''',			src_lang='''English''',			tgt_lang='''French'''    )
     self.assertEqual(_lowerCamelCase,			'''- Hé, comment ça va?'''    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Tuple    ):

     '''simple docstring'''
     __A		     =					self.remote_tool(text='''Hey, what\'s up?''',			src_lang='''English''',			tgt_lang='''French'''    )
     self.assertEqual(_lowerCamelCase,			'''- Hé, comment ça va?'''    )


   def 							_SCREAMING_SNAKE_CASE							(      self					:	List[Any]    ):

     '''simple docstring'''
     __A		     =					['''Hey, what\'s up?''', '''English''', '''Spanish''']
     __A		     =					self.tool(*_lowerCamelCase    )

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

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


   def 							_SCREAMING_SNAKE_CASE							(      self					:	Dict    ):

     '''simple docstring'''
     __A		     =					['''Hey, what\'s up?''', '''English''', '''Spanish''']
     __A		     =					self.tool(*_lowerCamelCase    )

     if not isinstance(_lowerCamelCase,			_lowerCamelCase    ):
       __A		     =					[outputs]

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

     for output, output_type in zip(_lowerCamelCase,			self.tool.outputs    ):
       __A		     =					AGENT_TYPE_MAPPING[output_type]
       self.assertTrue(isinstance(_lowerCamelCase,			_lowerCamelCase    )    )






   def 							_SCREAMING_SNAKE_CASE							(      self					:	List[str]    ):

     '''simple docstring'''
     __A		     =					['''Hey, what\'s up?''', '''English''', '''Spanish''']

     __A		     =					[]

     for _input, input_type in zip(_lowerCamelCase,			self.tool.inputs    ):
       if isinstance(_lowerCamelCase,			_lowerCamelCase    ):
         _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
     __A		     =					self.tool(*_lowerCamelCase    )

     if not isinstance(_lowerCamelCase,			_lowerCamelCase    ):
       __A		     =					[outputs]

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





"""simple docstring"""
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

# Load iris file
lowercase__   			=      load_iris()
iris.keys()


print(f"Target names: \n {iris.target_names} ")
print(f"\n Features: \n {iris.feature_names}")

# Train set e Test set
lowercase__			,   lowercase__			,   lowercase__			,   lowercase__   			=      train_test_split(
    iris['data'], iris['target'], random_state=4
)

# KNN

lowercase__   			=      KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)

# new array to test
lowercase__   			=      [[1, 2, 1, 4], [2, 3, 4, 5]]

lowercase__   			=      knn.predict(X_new)

print(
    f"\nNew array: \n {X_new}\n\nTarget Names Prediction: \n"
    f" {iris['target_names'][prediction]}"
)


