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  __lowerCAmelCase	(							unittest.TestCase,						a_    ):



 '''simple docstring'''

 def        __snake_case       (      self							:		Any	)			->				Optional[Any]:
        _UpperCamelCase  :							int	    =	load_tool('translation'	)
        self.tool.setup()
        _UpperCamelCase  :							str	    =	load_tool('translation'							,      remote=lowerCamelCase	)

 def        __snake_case       (      self							:		Dict	)			->				Any:
        _UpperCamelCase  :							Union[str, Any]	    =	self.tool('Hey, what\'s up?'							,      src_lang='English'							,      tgt_lang='French'	)
        self.assertEqual(lowerCamelCase							,      '- Hé, comment ça va?'	)

 def        __snake_case       (      self							:		Any	)			->				Tuple:
        _UpperCamelCase  :							str	    =	self.remote_tool('Hey, what\'s up?'							,      src_lang='English'							,      tgt_lang='French'	)
        self.assertEqual(lowerCamelCase							,      '- Hé, comment ça va?'	)

 def        __snake_case       (      self							:		List[Any]	)			->				List[str]:
        _UpperCamelCase  :							Optional[Any]	    =	self.tool(text='Hey, what\'s up?'							,      src_lang='English'							,      tgt_lang='French'	)
        self.assertEqual(lowerCamelCase							,      '- Hé, comment ça va?'	)

 def        __snake_case       (      self							:		List[Any]	)			->				str:
        _UpperCamelCase  :							str	    =	self.remote_tool(text='Hey, what\'s up?'							,      src_lang='English'							,      tgt_lang='French'	)
        self.assertEqual(lowerCamelCase							,      '- Hé, comment ça va?'	)

 def        __snake_case       (      self							:		Tuple	)			->				Dict:
        _UpperCamelCase  :							Any	    =	['Hey, what\'s up?', 'English', 'Spanish']
        _UpperCamelCase  :							Dict	    =	self.tool(*lowerCamelCase	)

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

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

 def        __snake_case       (      self							:		Union[str, Any]	)			->				int:
        _UpperCamelCase  :							str	    =	['Hey, what\'s up?', 'English', 'Spanish']
        _UpperCamelCase  :							str	    =	self.tool(*lowerCamelCase	)

        if not isinstance(lowerCamelCase							,      lowerCamelCase	):
               _UpperCamelCase  :							List[Any]	    =	[outputs]

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

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

 def        __snake_case       (      self							:		Any	)			->				List[str]:
        _UpperCamelCase  :							Optional[int]	    =	['Hey, what\'s up?', 'English', 'Spanish']

        _UpperCamelCase  :							List[str]	    =	[]

        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
        _UpperCamelCase  :							Any	    =	self.tool(*lowerCamelCase	)

        if not isinstance(lowerCamelCase							,      lowerCamelCase	):
               _UpperCamelCase  :							List[Any]	    =	[outputs]

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




import unittest
from dataclasses import dataclass
from typing import List, Union

import numpy as np
import PIL.Image

from diffusers.utils.outputs import BaseOutput




@dataclass
class  __lowerCAmelCase	(							a_    ):



 '''simple docstring'''
 _lowerCamelCase    :						Union[List[PIL.Image.Image], np.ndarray]







class  __lowerCAmelCase	(							unittest.TestCase    ):



 '''simple docstring'''

 def        __snake_case       (      self							:		int	)			->				Tuple:
        _UpperCamelCase  :							str	    =	CustomOutput(images=np.random.rand(1							,      3							,      4							,      4	)	)

        # check every way of getting the attribute
        assert isinstance(outputs.images							,      np.ndarray	)
        assert outputs.images.shape == (1, 3, 4, 4)
        assert isinstance(outputs['images']							,      np.ndarray	)
        assert outputs["images"].shape == (1, 3, 4, 4)
        assert isinstance(outputs[0]							,      np.ndarray	)
        assert outputs[0].shape == (1, 3, 4, 4)

        # test with a non-tensor attribute
        _UpperCamelCase  :							str	    =	CustomOutput(images=[PIL.Image.new('RGB'							,      (4, 4)	)]	)

        # check every way of getting the attribute
        assert isinstance(outputs.images							,      lowerCamelCase	)
        assert isinstance(outputs.images[0]							,      PIL.Image.Image	)
        assert isinstance(outputs['images']							,      lowerCamelCase	)
        assert isinstance(outputs['images'][0]							,      PIL.Image.Image	)
        assert isinstance(outputs[0]							,      lowerCamelCase	)
        assert isinstance(outputs[0][0]							,      PIL.Image.Image	)

 def        __snake_case       (      self							:		Any	)			->				Dict:
        # test output reinitialization with a `dict` for compatibility with `accelerate`
        _UpperCamelCase  :							Tuple	    =	CustomOutput({'images': np.random.rand(1							,      3							,      4							,      4	)}	)

        # check every way of getting the attribute
        assert isinstance(outputs.images							,      np.ndarray	)
        assert outputs.images.shape == (1, 3, 4, 4)
        assert isinstance(outputs['images']							,      np.ndarray	)
        assert outputs["images"].shape == (1, 3, 4, 4)
        assert isinstance(outputs[0]							,      np.ndarray	)
        assert outputs[0].shape == (1, 3, 4, 4)

        # test with a non-tensor attribute
        _UpperCamelCase  :							int	    =	CustomOutput({'images': [PIL.Image.new('RGB'							,      (4, 4)	)]}	)

        # check every way of getting the attribute
        assert isinstance(outputs.images							,      lowerCamelCase	)
        assert isinstance(outputs.images[0]							,      PIL.Image.Image	)
        assert isinstance(outputs['images']							,      lowerCamelCase	)
        assert isinstance(outputs['images'][0]							,      PIL.Image.Image	)
        assert isinstance(outputs[0]							,      lowerCamelCase	)
        assert isinstance(outputs[0][0]							,      PIL.Image.Image	)



