





import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image

     from transformers import PoolFormerImageProcessor



class 							_UpperCamelCase					(	unittest.TestCase     ):

       def __init__(		self: int					,   _SCREAMING_SNAKE_CASE: Any					,   _SCREAMING_SNAKE_CASE: Optional[Any]=7					,   _SCREAMING_SNAKE_CASE: Union[str, Any]=3					,   _SCREAMING_SNAKE_CASE: Dict=30					,   _SCREAMING_SNAKE_CASE: List[str]=400					,   _SCREAMING_SNAKE_CASE: List[str]=True					,   _SCREAMING_SNAKE_CASE: Tuple=None					,   _SCREAMING_SNAKE_CASE: Dict=0.9					,   _SCREAMING_SNAKE_CASE: int=None					,   _SCREAMING_SNAKE_CASE: Union[str, Any]=True					,   _SCREAMING_SNAKE_CASE: str=[0.5, 0.5, 0.5]					,   _SCREAMING_SNAKE_CASE: Tuple=[0.5, 0.5, 0.5]					,   )       -> List[Any]:




              """simple docstring"""


              UpperCamelCase_          =  size if size is not None else {"shortest_edge": 30}
              UpperCamelCase_          =  crop_size if crop_size is not None else {"height": 30, "width": 30}
              UpperCamelCase_          =  parent
              UpperCamelCase_          =  batch_size
              UpperCamelCase_          =  num_channels
              UpperCamelCase_          =  min_resolution
              UpperCamelCase_          =  max_resolution
              UpperCamelCase_          =  do_resize_and_center_crop
              UpperCamelCase_          =  size
              UpperCamelCase_          =  crop_pct
              UpperCamelCase_          =  crop_size
              UpperCamelCase_          =  do_normalize
              UpperCamelCase_          =  image_mean
              UpperCamelCase_          =  image_std



       def lowercase (		self: Dict		)       -> List[str]:




              """simple docstring"""


              return {
                  "size": self.size,
                  "do_resize_and_center_crop": self.do_resize_and_center_crop,
                  "crop_pct": self.crop_pct,
                  "crop_size": self.crop_size,
                  "do_normalize": self.do_normalize,
                  "image_mean": self.image_mean,
                  "image_std": self.image_std,
              }



@require_torch
@require_vision
class 							_UpperCamelCase					(	lowerCAmelCase_  ,						unittest.TestCase     ):
       _UpperCamelCase				:					int         =   PoolFormerImageProcessor if is_vision_available() else None

       def lowercase (		self: Any		)       -> List[Any]:




              """simple docstring"""


              UpperCamelCase_          =  PoolFormerImageProcessingTester(self		)

       @property
       def lowercase (		self: Any		)       -> int:




              """simple docstring"""


              return self.image_processor_tester.prepare_image_processor_dict()

       def lowercase (		self: Dict		)       -> int:




              """simple docstring"""


              UpperCamelCase_          =  self.image_processing_class(**self.image_processor_dict		)
              self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE					,   "do_resize_and_center_crop"		)		)
              self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE					,   "size"		)		)
              self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE					,   "crop_pct"		)		)
              self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE					,   "do_normalize"		)		)
              self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE					,   "image_mean"		)		)
              self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE					,   "image_std"		)		)

       def lowercase (		self: Any		)       -> Tuple:




              """simple docstring"""


              UpperCamelCase_          =  self.image_processing_class.from_dict(self.image_processor_dict		)
              self.assertEqual(image_processor.size					,   {"shortest_edge": 30}		)
              self.assertEqual(image_processor.crop_size					,   {"height": 30, "width": 30}		)

              UpperCamelCase_          =  self.image_processing_class.from_dict(self.image_processor_dict					,   size=42					,   crop_size=84		)
              self.assertEqual(image_processor.size					,   {"shortest_edge": 42}		)
              self.assertEqual(image_processor.crop_size					,   {"height": 84, "width": 84}		)

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




              """simple docstring"""


              pass

       def lowercase (		self: Union[str, Any]		)       -> Tuple:




              """simple docstring"""


              UpperCamelCase_          =  self.image_processing_class(**self.image_processor_dict		)
              # create random PIL images
              UpperCamelCase_          =  prepare_image_inputs(self.image_processor_tester					,   equal_resolution=_SCREAMING_SNAKE_CASE		)
              for image in image_inputs:
                     self.assertIsInstance(_SCREAMING_SNAKE_CASE					,   Image.Image		)

              # Test not batched input
              UpperCamelCase_          =  image_processing(image_inputs[0]					,   return_tensors="pt"		).pixel_values

              self.assertEqual(
                  encoded_images.shape					,   (
                      1,
                      self.image_processor_tester.num_channels,
                      self.image_processor_tester.crop_size["height"],
                      self.image_processor_tester.crop_size["width"],
                  )					,   )

              # Test batched
              UpperCamelCase_          =  image_processing(_SCREAMING_SNAKE_CASE					,   return_tensors="pt"		).pixel_values
              self.assertEqual(
                  encoded_images.shape					,   (
                      self.image_processor_tester.batch_size,
                      self.image_processor_tester.num_channels,
                      self.image_processor_tester.crop_size["height"],
                      self.image_processor_tester.crop_size["width"],
                  )					,   )

       def lowercase (		self: Any		)       -> List[Any]:




              """simple docstring"""


              UpperCamelCase_          =  self.image_processing_class(**self.image_processor_dict		)
              # create random numpy tensors
              UpperCamelCase_          =  prepare_image_inputs(self.image_processor_tester					,   equal_resolution=_SCREAMING_SNAKE_CASE					,   numpify=_SCREAMING_SNAKE_CASE		)
              for image in image_inputs:
                     self.assertIsInstance(_SCREAMING_SNAKE_CASE					,   np.ndarray		)

              # Test not batched input
              UpperCamelCase_          =  image_processing(image_inputs[0]					,   return_tensors="pt"		).pixel_values
              self.assertEqual(
                  encoded_images.shape					,   (
                      1,
                      self.image_processor_tester.num_channels,
                      self.image_processor_tester.crop_size["height"],
                      self.image_processor_tester.crop_size["width"],
                  )					,   )

              # Test batched
              UpperCamelCase_          =  image_processing(_SCREAMING_SNAKE_CASE					,   return_tensors="pt"		).pixel_values
              self.assertEqual(
                  encoded_images.shape					,   (
                      self.image_processor_tester.batch_size,
                      self.image_processor_tester.num_channels,
                      self.image_processor_tester.crop_size["height"],
                      self.image_processor_tester.crop_size["width"],
                  )					,   )



       def lowercase (		self: List[Any]		)       -> Union[str, Any]:




              """simple docstring"""


              UpperCamelCase_          =  self.image_processing_class(**self.image_processor_dict		)
              # create random PyTorch tensors
              UpperCamelCase_          =  prepare_image_inputs(self.image_processor_tester					,   equal_resolution=_SCREAMING_SNAKE_CASE					,   torchify=_SCREAMING_SNAKE_CASE		)
              for image in image_inputs:
                     self.assertIsInstance(_SCREAMING_SNAKE_CASE					,   torch.Tensor		)

              # Test not batched input
              UpperCamelCase_          =  image_processing(image_inputs[0]					,   return_tensors="pt"		).pixel_values
              self.assertEqual(
                  encoded_images.shape					,   (
                      1,
                      self.image_processor_tester.num_channels,
                      self.image_processor_tester.crop_size["height"],
                      self.image_processor_tester.crop_size["width"],
                  )					,   )

              # Test batched
              UpperCamelCase_          =  image_processing(_SCREAMING_SNAKE_CASE					,   return_tensors="pt"		).pixel_values
              self.assertEqual(
                  encoded_images.shape					,   (
                      self.image_processor_tester.batch_size,
                      self.image_processor_tester.num_channels,
                      self.image_processor_tester.crop_size["height"],
                      self.image_processor_tester.crop_size["width"],
                  )					,   )








import unittest

from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate




def  lowerCAmelCase_ (							UpperCamelCase_  )     ->		Optional[int]:
       return x + 2







class 							_UpperCamelCase					(	unittest.TestCase     ):

       def lowercase (		self: Optional[int]		)       -> str:




              """simple docstring"""


              UpperCamelCase_          =  "x = 3"
              UpperCamelCase_          =  {}
              UpperCamelCase_          =  evaluate(_SCREAMING_SNAKE_CASE					,   {}					,   state=_SCREAMING_SNAKE_CASE		)
              assert result == 3
              self.assertDictEqual(_SCREAMING_SNAKE_CASE					,   {"x": 3}		)

              UpperCamelCase_          =  "x = y"
              UpperCamelCase_          =  {"y": 5}
              UpperCamelCase_          =  evaluate(_SCREAMING_SNAKE_CASE					,   {}					,   state=_SCREAMING_SNAKE_CASE		)
              # evaluate returns the value of the last assignment.
              assert result == 5
              self.assertDictEqual(_SCREAMING_SNAKE_CASE					,   {"x": 5, "y": 5}		)

       def lowercase (		self: List[str]		)       -> Dict:




              """simple docstring"""


              UpperCamelCase_          =  "y = add_two(x)"
              UpperCamelCase_          =  {"x": 3}
              UpperCamelCase_          =  evaluate(_SCREAMING_SNAKE_CASE					,   {"add_two": add_two}					,   state=_SCREAMING_SNAKE_CASE		)
              assert result == 5
              self.assertDictEqual(_SCREAMING_SNAKE_CASE					,   {"x": 3, "y": 5}		)

              # Won't work without the tool
              with CaptureStdout() as out:
                     UpperCamelCase_          =  evaluate(_SCREAMING_SNAKE_CASE					,   {}					,   state=_SCREAMING_SNAKE_CASE		)
              assert result is None
              assert "tried to execute add_two" in out.out

       def lowercase (		self: Union[str, Any]		)       -> str:




              """simple docstring"""


              UpperCamelCase_          =  "x = 3"
              UpperCamelCase_          =  {}
              UpperCamelCase_          =  evaluate(_SCREAMING_SNAKE_CASE					,   {}					,   state=_SCREAMING_SNAKE_CASE		)
              assert result == 3
              self.assertDictEqual(_SCREAMING_SNAKE_CASE					,   {"x": 3}		)

       def lowercase (		self: Dict		)       -> Any:




              """simple docstring"""


              UpperCamelCase_          =  "test_dict = {'x': x, 'y': add_two(x)}"
              UpperCamelCase_          =  {"x": 3}
              UpperCamelCase_          =  evaluate(_SCREAMING_SNAKE_CASE					,   {"add_two": add_two}					,   state=_SCREAMING_SNAKE_CASE		)
              self.assertDictEqual(_SCREAMING_SNAKE_CASE					,   {"x": 3, "y": 5}		)
              self.assertDictEqual(_SCREAMING_SNAKE_CASE					,   {"x": 3, "test_dict": {"x": 3, "y": 5}}		)

       def lowercase (		self: List[str]		)       -> Union[str, Any]:




              """simple docstring"""


              UpperCamelCase_          =  "x = 3\ny = 5"
              UpperCamelCase_          =  {}
              UpperCamelCase_          =  evaluate(_SCREAMING_SNAKE_CASE					,   {}					,   state=_SCREAMING_SNAKE_CASE		)
              # evaluate returns the value of the last assignment.
              assert result == 5
              self.assertDictEqual(_SCREAMING_SNAKE_CASE					,   {"x": 3, "y": 5}		)

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




              """simple docstring"""


              UpperCamelCase_          =  "text = f'This is x: {x}.'"
              UpperCamelCase_          =  {"x": 3}
              UpperCamelCase_          =  evaluate(_SCREAMING_SNAKE_CASE					,   {}					,   state=_SCREAMING_SNAKE_CASE		)
              # evaluate returns the value of the last assignment.
              assert result == "This is x: 3."
              self.assertDictEqual(_SCREAMING_SNAKE_CASE					,   {"x": 3, "text": "This is x: 3."}		)

       def lowercase (		self: int		)       -> Union[str, Any]:




              """simple docstring"""


              UpperCamelCase_          =  "if x <= 3:\n    y = 2\nelse:\n    y = 5"
              UpperCamelCase_          =  {"x": 3}
              UpperCamelCase_          =  evaluate(_SCREAMING_SNAKE_CASE					,   {}					,   state=_SCREAMING_SNAKE_CASE		)
              # evaluate returns the value of the last assignment.
              assert result == 2
              self.assertDictEqual(_SCREAMING_SNAKE_CASE					,   {"x": 3, "y": 2}		)

              UpperCamelCase_          =  {"x": 8}
              UpperCamelCase_          =  evaluate(_SCREAMING_SNAKE_CASE					,   {}					,   state=_SCREAMING_SNAKE_CASE		)
              # evaluate returns the value of the last assignment.
              assert result == 5
              self.assertDictEqual(_SCREAMING_SNAKE_CASE					,   {"x": 8, "y": 5}		)

       def lowercase (		self: str		)       -> Optional[Any]:




              """simple docstring"""


              UpperCamelCase_          =  "test_list = [x, add_two(x)]"
              UpperCamelCase_          =  {"x": 3}
              UpperCamelCase_          =  evaluate(_SCREAMING_SNAKE_CASE					,   {"add_two": add_two}					,   state=_SCREAMING_SNAKE_CASE		)
              self.assertListEqual(_SCREAMING_SNAKE_CASE					,   [3, 5]		)
              self.assertDictEqual(_SCREAMING_SNAKE_CASE					,   {"x": 3, "test_list": [3, 5]}		)

       def lowercase (		self: List[str]		)       -> int:




              """simple docstring"""


              UpperCamelCase_          =  "y = x"
              UpperCamelCase_          =  {"x": 3}
              UpperCamelCase_          =  evaluate(_SCREAMING_SNAKE_CASE					,   {}					,   state=_SCREAMING_SNAKE_CASE		)
              assert result == 3
              self.assertDictEqual(_SCREAMING_SNAKE_CASE					,   {"x": 3, "y": 3}		)

       def lowercase (		self: str		)       -> int:




              """simple docstring"""


              UpperCamelCase_          =  "test_list = [x, add_two(x)]\ntest_list[1]"
              UpperCamelCase_          =  {"x": 3}
              UpperCamelCase_          =  evaluate(_SCREAMING_SNAKE_CASE					,   {"add_two": add_two}					,   state=_SCREAMING_SNAKE_CASE		)
              assert result == 5
              self.assertDictEqual(_SCREAMING_SNAKE_CASE					,   {"x": 3, "test_list": [3, 5]}		)

              UpperCamelCase_          =  "test_dict = {'x': x, 'y': add_two(x)}\ntest_dict['y']"
              UpperCamelCase_          =  {"x": 3}
              UpperCamelCase_          =  evaluate(_SCREAMING_SNAKE_CASE					,   {"add_two": add_two}					,   state=_SCREAMING_SNAKE_CASE		)
              assert result == 5
              self.assertDictEqual(_SCREAMING_SNAKE_CASE					,   {"x": 3, "test_dict": {"x": 3, "y": 5}}		)



       def lowercase (		self: Dict		)       -> Optional[int]:




              """simple docstring"""


              UpperCamelCase_          =  "x = 0\nfor i in range(3):\n    x = i"
              UpperCamelCase_          =  {}
              UpperCamelCase_          =  evaluate(_SCREAMING_SNAKE_CASE					,   {"range": range}					,   state=_SCREAMING_SNAKE_CASE		)
              assert result == 2
              self.assertDictEqual(_SCREAMING_SNAKE_CASE					,   {"x": 2, "i": 2}		)

