


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     ):


   '''simple docstring'''







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




   def 		UpperCamelCase  (     self		):
        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					(		_UpperCAmelCase						,      unittest.TestCase     ):


   '''simple docstring'''







   lowercase					:	Dict									=PoolFormerImageProcessor if is_vision_available() else None
   def 		UpperCamelCase  (     self		):
        lowercase_       :Union[str, Any]     =       PoolFormerImageProcessingTester(self		)
   @property
   def 		UpperCamelCase  (     self		):
        return self.image_processor_tester.prepare_image_processor_dict()
   def 		UpperCamelCase  (     self		):
        lowercase_       :int     =       self.image_processing_class(**self.image_processor_dict		)
        self.assertTrue(hasattr(lowercase_   ,							'''do_resize_and_center_crop'''		)		)
        self.assertTrue(hasattr(lowercase_   ,							'''size'''		)		)
        self.assertTrue(hasattr(lowercase_   ,							'''crop_pct'''		)		)
        self.assertTrue(hasattr(lowercase_   ,							'''do_normalize'''		)		)
        self.assertTrue(hasattr(lowercase_   ,							'''image_mean'''		)		)
        self.assertTrue(hasattr(lowercase_   ,							'''image_std'''		)		)
   def 		UpperCamelCase  (     self		):
        lowercase_       :List[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}		)

        lowercase_       :Optional[int]     =       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 		UpperCamelCase  (     self		):
        pass
   def 		UpperCamelCase  (     self		):
        # Initialize image_processing
        lowercase_       :int     =       self.image_processing_class(**self.image_processor_dict		)
        # create random PIL images
        lowercase_       :Union[str, Any]     =       prepare_image_inputs(self.image_processor_tester   ,							equal_resolution=lowercase_		)
        for image in image_inputs:
             self.assertIsInstance(lowercase_   ,							Image.Image		)

        # Test not batched input
        lowercase_       :Any     =       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
        lowercase_       :Union[str, Any]     =       image_processing(lowercase_   ,							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 		UpperCamelCase  (     self		):
        # Initialize image_processing
        lowercase_       :Union[str, Any]     =       self.image_processing_class(**self.image_processor_dict		)
        # create random numpy tensors
        lowercase_       :Any     =       prepare_image_inputs(self.image_processor_tester   ,							equal_resolution=lowercase_   ,							numpify=lowercase_		)
        for image in image_inputs:
             self.assertIsInstance(lowercase_   ,							np.ndarray		)

        # Test not batched input
        lowercase_       :str     =       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
        lowercase_       :Union[str, Any]     =       image_processing(lowercase_   ,							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 		UpperCamelCase  (     self		):
        # Initialize image_processing
        lowercase_       :Any     =       self.image_processing_class(**self.image_processor_dict		)
        # create random PyTorch tensors
        lowercase_       :int     =       prepare_image_inputs(self.image_processor_tester   ,							equal_resolution=lowercase_   ,							torchify=lowercase_		)
        for image in image_inputs:
             self.assertIsInstance(lowercase_   ,							torch.Tensor		)

        # Test not batched input
        lowercase_       :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
        lowercase_       :Dict     =       image_processing(lowercase_   ,							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'''],
            )   ,							)





from torch import nn




def     UpperCamelCase    (      _a		)					->							str:
     '''simple docstring'''





     if act_fn in ["swish", "silu"]:
          return nn.SiLU()
     elif act_fn == "mish":
          return nn.Mish()
     elif act_fn == "gelu":
          return nn.GELU()
     else:
          raise ValueError(f"Unsupported activation function: {act_fn}"		)

