




'''simple docstring'''







import numpy as np
from PIL import Image





def 		snake_case_				(_a      :				np.array	):
    UpperCAmelCase			,	UpperCAmelCase			,	UpperCAmelCase     			=						rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
    return 0.2989 * r + 0.5870 * g + 0.1140 * b





def 		snake_case_				(_a      :				np.array	):
    return (gray > 1_2_7) & (gray <= 2_5_5)





def 		snake_case_				(_a      :				np.array       ,  _a      :				np.array	):
    UpperCAmelCase     			=						np.zeros_like(_a	)
    UpperCAmelCase     			=						np.zeros(
        (image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1)	)

    # Copy image to padded image
    UpperCAmelCase     			=						image

    # Iterate over image & apply kernel
    for x in range(image.shape[1]	):
        for y in range(image.shape[0]	):
            UpperCAmelCase     			=						(
                kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
            ).sum()
            UpperCAmelCase     			=						int(summation == 5	)
    return output


# kernel to be applied
A					       =np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])

if __name__ == "__main__":
   # read original image
   A					       =np.array(Image.open(r'..\image_data\lena.jpg'))
   # Apply erosion operation to a binary image
   A					       =erosion(grayabinary(rgbagray(image)), structuring_element)
   # Save the output image
   A					       =Image.fromarray(output).convert('RGB')
   pil_img.save('result_erosion.png')










'''simple docstring'''







import inspect
import unittest

from transformers import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import MODEL_MAPPING, PoolFormerConfig, PoolFormerForImageClassification, PoolFormerModel
   from transformers.models.poolformer.modeling_poolformer import POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
   from PIL import Image

   from transformers import PoolFormerImageProcessor


class      _a    (      __a					):
       def        A (      self		:      Tuple							):



           '''simple docstring'''

           UpperCAmelCase     			=						self.config_class(**self.inputs_dict							)
           self.parent.assertTrue(hasattr(lowercase  ,							'''hidden_sizes'''							)							)
           self.parent.assertTrue(hasattr(lowercase  ,							'''num_encoder_blocks'''							)							)






class      _a    :
       def __init__(      self		:      Any  ,							lowercase		:      Any  ,							lowercase		:      Optional[Any]=13  ,							lowercase		:      List[str]=64  ,							lowercase		:      Optional[Any]=3  ,							lowercase		:      List[str]=4  ,							lowercase		:      Optional[int]=[2, 2, 2, 2]  ,							lowercase		:      Tuple=[8, 4, 2, 1]  ,							lowercase		:      Optional[int]=[16, 32, 64, 128]  ,							lowercase		:      Tuple=[1, 4, 8, 16]  ,							lowercase		:      List[Any]=False  ,							lowercase		:      List[Any]=True  ,							lowercase		:      List[str]="gelu"  ,							lowercase		:      Any=0.1  ,							lowercase		:      Dict=0.02  ,							lowercase		:      str=3  ,							lowercase		:      Optional[Any]=None  ,							):



           '''simple docstring'''

           UpperCAmelCase     			=						parent
           UpperCAmelCase     			=						batch_size
           UpperCAmelCase     			=						image_size
           UpperCAmelCase     			=						num_channels
           UpperCAmelCase     			=						num_encoder_blocks
           UpperCAmelCase     			=						sr_ratios
           UpperCAmelCase     			=						depths
           UpperCAmelCase     			=						hidden_sizes
           UpperCAmelCase     			=						downsampling_rates
           UpperCAmelCase     			=						is_training
           UpperCAmelCase     			=						use_labels
           UpperCAmelCase     			=						hidden_act
           UpperCAmelCase     			=						hidden_dropout_prob
           UpperCAmelCase     			=						initializer_range
           UpperCAmelCase     			=						num_labels
           UpperCAmelCase     			=						scope
       def        A (      self		:      Union[str, Any]							):



           '''simple docstring'''

           UpperCAmelCase     			=						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

           UpperCAmelCase     			=						None
           if self.use_labels:
               UpperCAmelCase     			=						ids_tensor([self.batch_size, self.image_size, self.image_size]  ,							self.num_labels							)

           UpperCAmelCase     			=						PoolFormerConfig(
               image_size=self.image_size  ,							num_channels=self.num_channels  ,							num_encoder_blocks=self.num_encoder_blocks  ,							depths=self.depths  ,							hidden_sizes=self.hidden_sizes  ,							hidden_act=self.hidden_act  ,							hidden_dropout_prob=self.hidden_dropout_prob  ,							initializer_range=self.initializer_range  ,							)

           return config, pixel_values, labels
       def        A (      self		:      Optional[Any]  ,							lowercase		:      List[Any]  ,							lowercase		:      Dict  ,							lowercase		:      Union[str, Any]							):



           '''simple docstring'''

           UpperCAmelCase     			=						PoolFormerModel(config=lowercase							)
           model.to(lowercase							)
           model.eval()
           UpperCAmelCase     			=						model(lowercase							)
           UpperCAmelCase     			=						UpperCAmelCase     			=						self.image_size // 32.0
           self.parent.assertEqual(
               result.last_hidden_state.shape  ,							(self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)							)







       def        A (      self		:      Union[str, Any]							):



           '''simple docstring'''

           UpperCAmelCase     			=						self.prepare_config_and_inputs()
           UpperCAmelCase			,	UpperCAmelCase			,	UpperCAmelCase     			=						config_and_inputs
           UpperCAmelCase     			=						{'''pixel_values''': pixel_values}
           return config, inputs_dict






@require_torch
class      _a    (      __a				,				__a				,				unittest.TestCase					):
       __a							:	Optional[int]  							=   (PoolFormerModel, PoolFormerForImageClassification) if is_torch_available() else ()
       __a							:	Dict  							=   (
           {"""feature-extraction""": PoolFormerModel, """image-classification""": PoolFormerForImageClassification}
           if is_torch_available()
           else {}
       )

       __a							:	str  							=   False
       __a							:	Optional[int]  							=   False
       __a							:	Tuple  							=   False
       __a							:	Union[str, Any]  							=   False
       __a							:	Dict  							=   False
       def        A (      self		:      Any							):



           '''simple docstring'''

           UpperCAmelCase     			=						PoolFormerModelTester(self							)
           UpperCAmelCase     			=						PoolFormerConfigTester(self  ,							config_class=lowercase							)
       def        A (      self		:      Any							):



           '''simple docstring'''

           self.config_tester.run_common_tests()
       def        A (      self		:      Optional[int]							):



           '''simple docstring'''

           UpperCAmelCase     			=						self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*lowercase							)
       @unittest.skip('''PoolFormer does not use inputs_embeds'''							)
       def        A (      self		:      Dict							):



           '''simple docstring'''

           pass
       @unittest.skip('''PoolFormer does not have get_input_embeddings method and get_output_embeddings methods'''							)
       def        A (      self		:      Union[str, Any]							):



           '''simple docstring'''

           pass
       def        A (      self		:      Dict							):



           '''simple docstring'''

           def check_hidden_states_output(lowercase		:      int  ,							lowercase		:      Any  ,							lowercase		:      Union[str, Any]							):
               UpperCAmelCase     			=						model_class(lowercase							)
               model.to(lowercase							)
               model.eval()

               with torch.no_grad():
                   UpperCAmelCase     			=						model(**self._prepare_for_class(lowercase  ,							lowercase							)							)

               UpperCAmelCase     			=						outputs.hidden_states

               UpperCAmelCase     			=						self.model_tester.num_encoder_blocks
               self.assertEqual(len(lowercase							)  ,							lowercase							)

               # verify the first hidden states (first block)
               self.assertListEqual(
                   list(hidden_states[0].shape[-3:]							)  ,							[
                       self.model_tester.hidden_sizes[0],
                       self.model_tester.image_size // 4,
                       self.model_tester.image_size // 4,
                   ]  ,							)

           UpperCAmelCase			,	UpperCAmelCase     			=						self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
               UpperCAmelCase     			=						True
               check_hidden_states_output(lowercase  ,							lowercase  ,							lowercase							)

               # check that output_hidden_states also work using config
               del inputs_dict["output_hidden_states"]
               UpperCAmelCase     			=						True

               check_hidden_states_output(lowercase  ,							lowercase  ,							lowercase							)
       def        A (      self		:      Optional[Any]							):



           '''simple docstring'''

           if not self.model_tester.is_training:
               return

           UpperCAmelCase			,	UpperCAmelCase     			=						self.model_tester.prepare_config_and_inputs_for_common()
           UpperCAmelCase     			=						True

           for model_class in self.all_model_classes:
               if model_class in get_values(lowercase							):
                   continue
               UpperCAmelCase     			=						model_class(lowercase							)
               model.to(lowercase							)
               model.train()
               UpperCAmelCase     			=						self._prepare_for_class(lowercase  ,							lowercase  ,							return_labels=lowercase							)
               UpperCAmelCase     			=						model(**lowercase							).loss
               loss.backward()
       def        A (      self		:      str							):



           '''simple docstring'''

           UpperCAmelCase			,	UpperCAmelCase     			=						self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
               UpperCAmelCase     			=						model_class(lowercase							)
               UpperCAmelCase     			=						inspect.signature(model.forward							)
               # signature.parameters is an OrderedDict => so arg_names order is deterministic
               UpperCAmelCase     			=						[*signature.parameters.keys()]

               UpperCAmelCase     			=						['''pixel_values''']
               self.assertListEqual(arg_names[:1]  ,							lowercase							)







       @slow
       def        A (      self		:      Union[str, Any]							):



           '''simple docstring'''

           for model_name in POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               UpperCAmelCase     			=						PoolFormerModel.from_pretrained(lowercase							)
               self.assertIsNotNone(lowercase							)






def 		snake_case_				():
    UpperCAmelCase     			=						Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''	)
    return image







@require_torch
class      _a    (      unittest.TestCase					):
       @slow
       def        A (      self		:      str							):



           '''simple docstring'''

           UpperCAmelCase     			=						PoolFormerImageProcessor()
           UpperCAmelCase     			=						PoolFormerForImageClassification.from_pretrained('''sail/poolformer_s12'''							).to(lowercase							)

           UpperCAmelCase     			=						image_processor(images=prepare_img()  ,							return_tensors='''pt'''							).to(lowercase							)

           # forward pass
           with torch.no_grad():
               UpperCAmelCase     			=						model(**lowercase							)

           # verify the logits
           UpperCAmelCase     			=						torch.Size((1, 1_000)							)
           self.assertEqual(outputs.logits.shape  ,							lowercase							)

           UpperCAmelCase     			=						torch.tensor([-0.6113, 0.1685, -0.0492]							).to(lowercase							)
           self.assertTrue(torch.allclose(outputs.logits[0, :3]  ,							lowercase  ,							atol=1E-4							)							)




