

# This is the module that test_patching.py uses to test patch_submodule()

import os  # noqa: this is just for tests
import os as renamed_os  # noqa: this is just for tests
from os import path  # noqa: this is just for tests
from os import path as renamed_path  # noqa: this is just for tests
from os.path import join  # noqa: this is just for tests
from os.path import join as renamed_join  # noqa: this is just for tests


lowerCAmelCase_			      =      open  # noqa: we just need to have a builtin inside this module to test it properly





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     __a(	unittest.TestCase  ):






    """simple docstring"""





    def __init__( self  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE=7  ,_SCREAMING_SNAKE_CASE=3  ,_SCREAMING_SNAKE_CASE=30  ,_SCREAMING_SNAKE_CASE=400  ,_SCREAMING_SNAKE_CASE=True  ,_SCREAMING_SNAKE_CASE=None  ,_SCREAMING_SNAKE_CASE=0.9  ,_SCREAMING_SNAKE_CASE=None  ,_SCREAMING_SNAKE_CASE=True  ,_SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]  ,_SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]  ,)					->						Optional[int]:
         UpperCAmelCase_     :				int  =     size if size is not None else {'''shortest_edge''': 30}
         UpperCAmelCase_     :				List[str]  =     crop_size if crop_size is not None else {'''height''': 30, '''width''': 30}
         UpperCAmelCase_     :				Dict  =     parent
         UpperCAmelCase_     :				int  =     batch_size
         UpperCAmelCase_     :				int  =     num_channels
         UpperCAmelCase_     :				Any  =     min_resolution
         UpperCAmelCase_     :				Tuple  =     max_resolution
         UpperCAmelCase_     :				Optional[int]  =     do_resize_and_center_crop
         UpperCAmelCase_     :				Tuple  =     size
         UpperCAmelCase_     :				List[str]  =     crop_pct
         UpperCAmelCase_     :				List[str]  =     crop_size
         UpperCAmelCase_     :				Any  =     do_normalize
         UpperCAmelCase_     :				str  =     image_mean
         UpperCAmelCase_     :				List[Any]  =     image_std





    def 				a__ ( self    )					->						str:
         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     __a(	_a							,		unittest.TestCase  ):






    """simple docstring"""





    lowerCAmelCase         = PoolFormerImageProcessor if is_vision_available() else None





    def 				a__ ( self    )					->						Dict:
         UpperCAmelCase_     :				str  =     PoolFormerImageProcessingTester(self    )





    @property
    def 				a__ ( self    )					->						Optional[int]:
         return self.image_processor_tester.prepare_image_processor_dict()





    def 				a__ ( self    )					->						Optional[int]:
         UpperCAmelCase_     :				List[Any]  =     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 				a__ ( self    )					->						Union[str, Any]:
         UpperCAmelCase_     :				Any  =     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_     :				Optional[Any]  =     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 				a__ ( self    )					->						Optional[int]:
         pass





    def 				a__ ( self    )					->						Dict:
         # Initialize image_processing
         UpperCAmelCase_     :				str  =     self.image_processing_class(**self.image_processor_dict    )
         # create random PIL images
         UpperCAmelCase_     :				int  =     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_     :				Optional[int]  =     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_     :				Union[str, Any]  =     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 				a__ ( self    )					->						List[Any]:
         # Initialize image_processing
         UpperCAmelCase_     :				Any  =     self.image_processing_class(**self.image_processor_dict    )
         # create random numpy tensors
         UpperCAmelCase_     :				List[str]  =     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_     :				Tuple  =     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_     :				List[Any]  =     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 				a__ ( self    )					->						Union[str, Any]:
         # Initialize image_processing
         UpperCAmelCase_     :				List[Any]  =     self.image_processing_class(**self.image_processor_dict    )
         # create random PyTorch tensors
         UpperCAmelCase_     :				Optional[Any]  =     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_     :				Tuple  =     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_     :				List[Any]  =     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'''],
             )  ,)