






"""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    )    )







import numpy as np
import PIL
import torch
import torchvision.transforms as T
import torchvision.transforms.functional as TF
from PIL import Image







def 						_snake_case		(     SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE=256					)							->   Optional[int]:

     """simple docstring"""







     _lowerCAmelCase :       Any	   =     min(img.size					)

     if s < target_image_size:
          raise ValueError(f'''min dim for image {s} < {target_image_size}'''					)

     _lowerCAmelCase :       Dict	   =     target_image_size / s
     _lowerCAmelCase :       Dict	   =     (round(r * img.size[1]					), round(r * img.size[0]					))
     _lowerCAmelCase :       Any	   =     TF.resize(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				interpolation=PIL.Image.LANCZOS					)
     _lowerCAmelCase :       str	   =     TF.center_crop(SCREAMING_SNAKE_CASE		,				output_size=2 * [target_image_size]					)
     _lowerCAmelCase :       int	   =     torch.unsqueeze(T.ToTensor()(SCREAMING_SNAKE_CASE					)		,				0					)
     return img







def 						_snake_case		(     SCREAMING_SNAKE_CASE					)							->   List[str]:

     """simple docstring"""







     _lowerCAmelCase :       List[Any]	   =     2.0 * x - 1.0
     return x







def 						_snake_case		(     SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE=True		,				SCREAMING_SNAKE_CASE="RGB"					)							->   int:

     """simple docstring"""







     _lowerCAmelCase :       Optional[int]	   =     x.detach().cpu()
     if process:
          _lowerCAmelCase :       List[Any]	   =     post_process_tensor(SCREAMING_SNAKE_CASE					)
     _lowerCAmelCase :       Optional[int]	   =     x.numpy()
     if process:
          _lowerCAmelCase :       List[Any]	   =     (255 * x).astype(np.uinta					)
     _lowerCAmelCase :       Optional[int]	   =     Image.fromarray(SCREAMING_SNAKE_CASE					)
     if not x.mode == mode:
          _lowerCAmelCase :       str	   =     x.convert(SCREAMING_SNAKE_CASE					)
     return x







def 						_snake_case		(     SCREAMING_SNAKE_CASE					)							->   Union[str, Any]:

     """simple docstring"""







     _lowerCAmelCase :       Any	   =     torch.clamp(SCREAMING_SNAKE_CASE		,				-1.0		,				1.0					)
     _lowerCAmelCase :       Any	   =     (x + 1.0) / 2.0
     _lowerCAmelCase :       List[Any]	   =     x.permute(1		,				2		,				0					)
     return x







def 						_snake_case		(     SCREAMING_SNAKE_CASE					)							->   Tuple:

     """simple docstring"""







     _lowerCAmelCase :       str	   =     post_process_tensor(x.squeeze()					)
     return x.permute(2		,				0		,				1					).unsqueeze(0					)






