


'''simple docstring'''

import copy
import inspect
import unittest

from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
    require_torch,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available

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 torch import nn

 from transformers import SwiftFormerForImageClassification, SwiftFormerModel
 from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import ViTImageProcessor
class   __lowerCAmelCase						:





      '''simple docstring'''






      def __init__(self    :    Optional[int]   ,						UpperCamelCase    :    Union[str, Any]   ,						UpperCamelCase    :    int=13   ,						UpperCamelCase    :    List[Any]=3   ,						UpperCamelCase    :    Union[str, Any]=True   ,						UpperCamelCase    :    Any=True   ,						UpperCamelCase    :    Optional[int]=0.1   ,						UpperCamelCase    :    List[Any]=0.1   ,						UpperCamelCase    :    Optional[Any]=224   ,						UpperCamelCase    :    int=1000   ,						UpperCamelCase    :    Dict=[3, 3, 6, 4]   ,						UpperCamelCase    :    str=[48, 56, 112, 220]   ,						):

             '''simple docstring'''







             lowercase__      			= parent
             lowercase__      			= batch_size
             lowercase__      			= num_channels
             lowercase__      			= is_training
             lowercase__      			= use_labels
             lowercase__      			= hidden_dropout_prob
             lowercase__      			= attention_probs_dropout_prob
             lowercase__      			= num_labels
             lowercase__      			= image_size
             lowercase__      			= layer_depths
             lowercase__      			= embed_dims



      def       UpperCamelCase__		(self    :    str ):

             '''simple docstring'''







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

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

             lowercase__      			= self.get_config()

             return config, pixel_values, labels



      def       UpperCamelCase__		(self    :    Optional[Any] ):

             '''simple docstring'''







             return SwiftFormerConfig(
                 depths=self.layer_depths   ,						embed_dims=self.embed_dims   ,						mlp_ratio=4   ,						downsamples=[True, True, True, True]   ,						hidden_act='''gelu'''   ,						num_labels=self.num_labels   ,						down_patch_size=3   ,						down_stride=2   ,						down_pad=1   ,						drop_rate=0.0   ,						drop_path_rate=0.0   ,						use_layer_scale=UpperCamelCase   ,						layer_scale_init_value=1E-5   ,						)



      def       UpperCamelCase__		(self    :    Tuple   ,						UpperCamelCase    :    Any   ,						UpperCamelCase    :    Optional[int]   ,						UpperCamelCase    :    Optional[int] ):

             '''simple docstring'''







             lowercase__      			= SwiftFormerModel(config=UpperCamelCase )
             model.to(UpperCamelCase )
             model.eval()
             lowercase__      			= model(UpperCamelCase )
             self.parent.assertEqual(result.last_hidden_state.shape   ,						(self.batch_size, self.embed_dims[-1], 7, 7) )



      def       UpperCamelCase__		(self    :    str   ,						UpperCamelCase    :    Optional[Any]   ,						UpperCamelCase    :    List[Any]   ,						UpperCamelCase    :    Union[str, Any] ):

             '''simple docstring'''







             lowercase__      			= self.num_labels
             lowercase__      			= SwiftFormerForImageClassification(UpperCamelCase )
             model.to(UpperCamelCase )
             model.eval()
             lowercase__      			= model(UpperCamelCase   ,						labels=UpperCamelCase )
             self.parent.assertEqual(result.logits.shape   ,						(self.batch_size, self.num_labels) )

             lowercase__      			= SwiftFormerForImageClassification(UpperCamelCase )
             model.to(UpperCamelCase )
             model.eval()

             lowercase__      			= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
             lowercase__      			= model(UpperCamelCase )
             self.parent.assertEqual(result.logits.shape   ,						(self.batch_size, self.num_labels) )






      def       UpperCamelCase__		(self    :    List[Any] ):

             '''simple docstring'''







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






@require_torch
class   __lowerCAmelCase						(lowercase_  ,     lowercase_  ,     unittest.TestCase     ):





      '''simple docstring'''






      lowerCAmelCase__					:     int  					=							(SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
      lowerCAmelCase__					:     int  					=							(
          {"""feature-extraction""": SwiftFormerModel, """image-classification""": SwiftFormerForImageClassification}
          if is_torch_available()
          else {}
      )

      lowerCAmelCase__					:     str  					=							False
      lowerCAmelCase__					:     Dict  					=							False
      lowerCAmelCase__					:     str  					=							False
      lowerCAmelCase__					:     Tuple  					=							False
      lowerCAmelCase__					:     List[str]  					=							False



      def       UpperCamelCase__		(self    :    Any ):

             '''simple docstring'''







             lowercase__      			= SwiftFormerModelTester(self )
             lowercase__      			= ConfigTester(
                 self   ,						config_class=UpperCamelCase   ,						has_text_modality=UpperCamelCase   ,						hidden_size=37   ,						num_attention_heads=12   ,						num_hidden_layers=12   ,						)



      def       UpperCamelCase__		(self    :    List[str] ):

             '''simple docstring'''







             self.config_tester.run_common_tests()



      @unittest.skip(reason='''SwiftFormer does not use inputs_embeds''' )
      def       UpperCamelCase__		(self    :    List[Any] ):

             '''simple docstring'''







             pass



      def       UpperCamelCase__		(self    :    Optional[Any] ):

             '''simple docstring'''







             lowercase__			,lowercase__      			= self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                    lowercase__      			= model_class(UpperCamelCase )
                    lowercase__      			= model.get_output_embeddings()
                    self.assertTrue(x is None or isinstance(UpperCamelCase   ,						nn.Linear ) )



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

             '''simple docstring'''







             lowercase__			,lowercase__      			= self.model_tester.prepare_config_and_inputs_for_common()

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

                    lowercase__      			= ['''pixel_values''']
                    self.assertListEqual(arg_names[:1]   ,						UpperCamelCase )



      def       UpperCamelCase__		(self    :    Dict ):

             '''simple docstring'''







             lowercase__      			= self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*UpperCamelCase )



      def       UpperCamelCase__		(self    :    Any ):

             '''simple docstring'''







             lowercase__      			= self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_image_classification(*UpperCamelCase )



      @slow
      def       UpperCamelCase__		(self    :    Dict ):

             '''simple docstring'''







             for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                    lowercase__      			= SwiftFormerModel.from_pretrained(UpperCamelCase )
                    self.assertIsNotNone(UpperCamelCase )



      @unittest.skip(reason='''SwiftFormer does not output attentions''' )
      def       UpperCamelCase__		(self    :    Dict ):

             '''simple docstring'''







             pass



      def       UpperCamelCase__		(self    :    int ):

             '''simple docstring'''







             def check_hidden_states_output(UpperCamelCase    :    str   ,						UpperCamelCase    :    Optional[Any]   ,						UpperCamelCase    :    str ):
                    lowercase__      			= model_class(UpperCamelCase )
                    model.to(UpperCamelCase )
                    model.eval()

                    with torch.no_grad():
                           lowercase__      			= model(**self._prepare_for_class(UpperCamelCase   ,						UpperCamelCase ) )

                    lowercase__      			= outputs.hidden_states

                    lowercase__      			= 8
                    self.assertEqual(len(UpperCamelCase )   ,						UpperCamelCase )  # TODO

                    # SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
                    # with the width and height being successively divided by 2, after every 2 blocks
                    for i in range(len(UpperCamelCase ) ):
                           self.assertEqual(
                               hidden_states[i].shape   ,						torch.Size(
                                   [
                                       self.model_tester.batch_size,
                                       self.model_tester.embed_dims[i // 2],
                                       (self.model_tester.image_size // 4) // 2 ** (i // 2),
                                       (self.model_tester.image_size // 4) // 2 ** (i // 2),
                                   ] )   ,						)

             lowercase__			,lowercase__      			= self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                    lowercase__      			= True
                    check_hidden_states_output(UpperCamelCase   ,						UpperCamelCase   ,						UpperCamelCase )

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

                    check_hidden_states_output(UpperCamelCase   ,						UpperCamelCase   ,						UpperCamelCase )



      def       UpperCamelCase__		(self    :    Optional[int] ):

             '''simple docstring'''







             def _config_zero_init(UpperCamelCase    :    int ):
                    lowercase__      			= copy.deepcopy(UpperCamelCase )
                    for key in configs_no_init.__dict__.keys():
                           if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
                                  setattr(UpperCamelCase   ,						UpperCamelCase   ,						1E-10 )
                           if isinstance(getattr(UpperCamelCase   ,						UpperCamelCase   ,						UpperCamelCase )   ,						UpperCamelCase ):
                                  lowercase__      			= _config_zero_init(getattr(UpperCamelCase   ,						UpperCamelCase ) )
                                  setattr(UpperCamelCase   ,						UpperCamelCase   ,						UpperCamelCase )
                    return configs_no_init

             lowercase__			,lowercase__      			= self.model_tester.prepare_config_and_inputs_for_common()

             lowercase__      			= _config_zero_init(UpperCamelCase )
             for model_class in self.all_model_classes:
                    lowercase__      			= model_class(config=UpperCamelCase )
                    for name, param in model.named_parameters():
                           if param.requires_grad:
                                  self.assertIn(
                                      ((param.data.mean() * 1E9) / 1E9).round().item()   ,						[0.0, 1.0]   ,						msg=f"Parameter {name} of model {model_class} seems not properly initialized"   ,						)






      @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
      def       UpperCamelCase__		(self    :    Dict ):

             '''simple docstring'''







             pass






def _SCREAMING_SNAKE_CASE      ()       ->       Tuple:






       """simple docstring"""







       lowercase__      			= Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''       )
       return image

@require_torch
@require_vision
class   __lowerCAmelCase						(unittest.TestCase     ):





      '''simple docstring'''






      @cached_property
      def       UpperCamelCase__		(self    :    Dict ):

             '''simple docstring'''







             return ViTImageProcessor.from_pretrained('''MBZUAI/swiftformer-xs''' ) if is_vision_available() else None






      @slow
      def       UpperCamelCase__		(self    :    Any ):

             '''simple docstring'''







             lowercase__      			= SwiftFormerForImageClassification.from_pretrained('''MBZUAI/swiftformer-xs''' ).to(UpperCamelCase )

             lowercase__      			= self.default_image_processor
             lowercase__      			= prepare_img()
             lowercase__      			= image_processor(images=UpperCamelCase   ,						return_tensors='''pt''' ).to(UpperCamelCase )

             # forward pass
             with torch.no_grad():
                    lowercase__      			= model(**UpperCamelCase )

             # verify the logits
             lowercase__      			= torch.Size((1, 1000) )
             self.assertEqual(outputs.logits.shape   ,						UpperCamelCase )

             lowercase__      			= torch.tensor([[-2.17_03E00, 2.11_07E00, -2.08_11E00]] ).to(UpperCamelCase )
             self.assertTrue(torch.allclose(outputs.logits[0, :3]   ,						UpperCamelCase   ,						atol=1E-4 ) )








'''simple docstring'''

import numpy as np

# Importing the Keras libraries and packages
import tensorflow as tf
from tensorflow.keras import layers, models

if __name__ == "__main__":
 # Initialising the CNN
 # (Sequential- Building the model layer by layer)
 lowerCamelCase							:   Any  						=	models.Sequential()

 # Step 1 - Convolution
 # Here 64,64 is the length & breadth of dataset images and 3 is for the RGB channel
 # (3,3) is the kernel size (filter matrix)
 classifier.add(
     layers.ConvaD(32, (3, 3), input_shape=(64, 64, 3), activation='relu')
 )

 # Step 2 - Pooling
 classifier.add(layers.MaxPoolingaD(pool_size=(2, 2)))

 # Adding a second convolutional layer
 classifier.add(layers.ConvaD(32, (3, 3), activation='relu'))
 classifier.add(layers.MaxPoolingaD(pool_size=(2, 2)))

 # Step 3 - Flattening
 classifier.add(layers.Flatten())

 # Step 4 - Full connection
 classifier.add(layers.Dense(units=128, activation='relu'))
 classifier.add(layers.Dense(units=1, activation='sigmoid'))

 # Compiling the CNN
 classifier.compile(
     optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']
 )

 # Part 2 - Fitting the CNN to the images

 # Load Trained model weights

 # from keras.models import load_model
 # regressor=load_model('cnn.h5')

 lowerCamelCase							:   Optional[Any]  						=	tf.keras.preprocessing.image.ImageDataGenerator(
     rescale=1.0 / 255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True
 )

 lowerCamelCase							:   Any  						=	tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 255)

 lowerCamelCase							:   List[Any]  						=	train_datagen.flow_from_directory(
     'dataset/training_set', target_size=(64, 64), batch_size=32, class_mode='binary'
 )

 lowerCamelCase							:   List[str]  						=	test_datagen.flow_from_directory(
     'dataset/test_set', target_size=(64, 64), batch_size=32, class_mode='binary'
 )

 classifier.fit_generator(
     training_set, steps_per_epoch=5, epochs=30, validation_data=test_set
 )

 classifier.save('cnn.h5')

 # Part 3 - Making new predictions

 lowerCamelCase							:   List[str]  						=	tf.keras.preprocessing.image.load_img(
     'dataset/single_prediction/image.png', target_size=(64, 64)
 )
 lowerCamelCase							:   Optional[int]  						=	tf.keras.preprocessing.image.img_to_array(test_image)
 lowerCamelCase							:   str  						=	np.expand_dims(test_image, axis=0)
 lowerCamelCase							:   List[str]  						=	classifier.predict(test_image)
 # training_set.class_indices
 if result[0][0] == 0:
  lowerCamelCase							:   Any  						=	'Normal'
 if result[0][0] == 1:
  lowerCamelCase							:   Any  						=	'Abnormality detected'




