
"""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 	UpperCamelCase      (			unittest.TestCase    , lowerCAmelCase_ ):

				"""simple docstring"""



				def 				A   (							self     )       ->       List[str]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      load_tool('translation'     )
									self.tool.setup()
									SCREAMING_SNAKE_CASE           =      load_tool('translation'						,      remote=lowerCamelCase_     )


				def 				A   (							self     )       ->       Optional[Any]:
									"""simple docstring"""







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


				def 				A   (							self     )       ->       Tuple:
									"""simple docstring"""







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


				def 				A   (							self     )       ->       Optional[Any]:
									"""simple docstring"""







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


				def 				A   (							self     )       ->       Any:
									"""simple docstring"""







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


				def 				A   (							self     )       ->       Any:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      ['Hey, what\'s up?', 'English', 'Spanish']
									SCREAMING_SNAKE_CASE           =      self.tool(*lowerCamelCase_     )

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

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


				def 				A   (							self     )       ->       int:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      ['Hey, what\'s up?', 'English', 'Spanish']
									SCREAMING_SNAKE_CASE           =      self.tool(*lowerCamelCase_     )

									if not isinstance(lowerCamelCase_						,      lowerCamelCase_     ):
														SCREAMING_SNAKE_CASE           =      [outputs]

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

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




				def 				A   (							self     )       ->       List[str]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE           =      ['Hey, what\'s up?', 'English', 'Spanish']

									SCREAMING_SNAKE_CASE           =      []

									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
									SCREAMING_SNAKE_CASE           =      self.tool(*lowerCamelCase_     )

									if not isinstance(lowerCamelCase_						,      lowerCamelCase_     ):
														SCREAMING_SNAKE_CASE           =      [outputs]

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


"""simple docstring"""







from typing import List

import PIL
import torch
from PIL import Image

from ...configuration_utils import ConfigMixin
from ...models.modeling_utils import ModelMixin
from ...utils import PIL_INTERPOLATION






class      __a      (       lowerCAmelCase_					,						lowerCAmelCase_      ):







       def __init__(       self		):



          '''simple docstring'''

          super().__init__()

          self.register_buffer("watermark_image"				,torch.zeros((62, 62, 4)		)		)
          _UpperCAmelCase 						=       None







       def  _snake_case      (       self				,lowerCamelCase_				,lowerCamelCase_=None		):



          '''simple docstring'''

          _UpperCAmelCase 						=       images[0].height
          _UpperCAmelCase 						=       images[0].width

          _UpperCAmelCase 						=       sample_size or h

          _UpperCAmelCase 						=       min(h / sample_size				,w / sample_size		)
          _UpperCAmelCase						,				_UpperCAmelCase 						=       (int(h / coef		), int(w / coef		)) if coef < 1 else (h, w)

          _UpperCAmelCase						,				_UpperCAmelCase 						=       1024**2, img_w * img_h
          _UpperCAmelCase 						=       (Sa / Sa) ** 0.5
          _UpperCAmelCase						,				_UpperCAmelCase						,				_UpperCAmelCase 						=       int(K * 62		), img_w - int(14 * K		), img_h - int(14 * K		)

          if self.watermark_image_as_pil is None:
             _UpperCAmelCase 						=       self.watermark_image.to(torch.uinta		).cpu().numpy()
             _UpperCAmelCase 						=       Image.fromarray(lowerCamelCase_				,mode="RGBA"		)
             _UpperCAmelCase 						=       watermark_image

          _UpperCAmelCase 						=       self.watermark_image_as_pil.resize(
              (wm_size, wm_size)				,PIL_INTERPOLATION["bicubic"]				,reducing_gap=lowerCamelCase_		)

          for pil_img in images:
             pil_img.paste(lowerCamelCase_				,box=(wm_x - wm_size, wm_y - wm_size, wm_x, wm_y)				,mask=wm_img.split()[-1]		)

          return images

