





'''simple docstring'''





import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path

import timm
import torch
from huggingface_hub import hf_hub_download

from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
_UpperCAmelCase        =				logging.get_logger()




def  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_ = True  )     ->		Optional[int]:
       print(F'''Converting {name}...'''  )

       with torch.no_grad():
              if hidden_sizes == 128:
                     if name[-1] == "S":
                            UpperCamelCase_          =  timm.create_model("levit_128s"					,		pretrained=UpperCamelCase_  )
                     else:
                            UpperCamelCase_          =  timm.create_model("levit_128"					,		pretrained=UpperCamelCase_  )
              if hidden_sizes == 192:
                     UpperCamelCase_          =  timm.create_model("levit_192"					,		pretrained=UpperCamelCase_  )
              if hidden_sizes == 256:
                     UpperCamelCase_          =  timm.create_model("levit_256"					,		pretrained=UpperCamelCase_  )
              if hidden_sizes == 384:
                     UpperCamelCase_          =  timm.create_model("levit_384"					,		pretrained=UpperCamelCase_  )

              from_model.eval()
              UpperCamelCase_          =  LevitForImageClassificationWithTeacher(UpperCamelCase_  ).eval()
              UpperCamelCase_          =  OrderedDict()

              UpperCamelCase_          =  from_model.state_dict()
              UpperCamelCase_          =  list(from_model.state_dict().keys()  )
              UpperCamelCase_          =  list(our_model.state_dict().keys()  )
              print(len(UpperCamelCase_  )					,		len(UpperCamelCase_  )  )
              for i in range(len(UpperCamelCase_  )  ):
                     UpperCamelCase_          =  weights[og_keys[i]]
              our_model.load_state_dict(UpperCamelCase_  )

              UpperCamelCase_          =  torch.randn((2, 3, 224, 224)  )
              UpperCamelCase_          =  from_model(UpperCamelCase_  )
              UpperCamelCase_          =  our_model(UpperCamelCase_  ).logits

       assert torch.allclose(UpperCamelCase_					,		UpperCamelCase_  ), "The model logits don't match the original one."

       UpperCamelCase_          =  name
       print(UpperCamelCase_  )

       if push_to_hub:
              our_model.save_pretrained(save_directory / checkpoint_name  )
              UpperCamelCase_          =  LevitImageProcessor()
              image_processor.save_pretrained(save_directory / checkpoint_name  )

              print(F'''Pushed {checkpoint_name}'''  )




def  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_ = None					,		UpperCamelCase_ = True  )     ->		List[str]:
       UpperCamelCase_          =  "imagenet-1k-id2label.json"
       UpperCamelCase_          =  1000
       UpperCamelCase_          =  (1, num_labels)

       UpperCamelCase_          =  "huggingface/label-files"
       UpperCamelCase_          =  num_labels
       UpperCamelCase_          =  json.load(open(hf_hub_download(UpperCamelCase_					,		UpperCamelCase_					,		repo_type="dataset"  )					,		"r"  )  )
       UpperCamelCase_          =  {int(UpperCamelCase_  ): v for k, v in idalabel.items()}

       UpperCamelCase_          =  idalabel
       UpperCamelCase_          =  {v: k for k, v in idalabel.items()}

       UpperCamelCase_          =  partial(UpperCamelCase_					,		num_labels=UpperCamelCase_					,		idalabel=UpperCamelCase_					,		labelaid=UpperCamelCase_  )

       UpperCamelCase_          =  {
           "levit-128S": 128,
           "levit-128": 128,
           "levit-192": 192,
           "levit-256": 256,
           "levit-384": 384,
       }

       UpperCamelCase_          =  {
           "levit-128S": ImageNetPreTrainedConfig(
               hidden_sizes=[128, 256, 384]					,		num_attention_heads=[4, 6, 8]					,		depths=[2, 3, 4]					,		key_dim=[16, 16, 16]					,		drop_path_rate=0					,		),
           "levit-128": ImageNetPreTrainedConfig(
               hidden_sizes=[128, 256, 384]					,		num_attention_heads=[4, 8, 12]					,		depths=[4, 4, 4]					,		key_dim=[16, 16, 16]					,		drop_path_rate=0					,		),
           "levit-192": ImageNetPreTrainedConfig(
               hidden_sizes=[192, 288, 384]					,		num_attention_heads=[3, 5, 6]					,		depths=[4, 4, 4]					,		key_dim=[32, 32, 32]					,		drop_path_rate=0					,		),
           "levit-256": ImageNetPreTrainedConfig(
               hidden_sizes=[256, 384, 512]					,		num_attention_heads=[4, 6, 8]					,		depths=[4, 4, 4]					,		key_dim=[32, 32, 32]					,		drop_path_rate=0					,		),
           "levit-384": ImageNetPreTrainedConfig(
               hidden_sizes=[384, 512, 768]					,		num_attention_heads=[6, 9, 12]					,		depths=[4, 4, 4]					,		key_dim=[32, 32, 32]					,		drop_path_rate=0.1					,		),
       }

       if model_name:
              convert_weight_and_push(
                  names_to_hidden_sizes[model_name]					,		UpperCamelCase_					,		names_to_config[model_name]					,		UpperCamelCase_					,		UpperCamelCase_  )
       else:
              for model_name, config in names_to_config.items():
                     convert_weight_and_push(names_to_hidden_sizes[model_name]					,		UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_  )
       return config, expected_shape


if __name__ == "__main__":
     _UpperCAmelCase        =				argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--model_name',
         default=None,
         type=str,
         help='The name of the model you wish to convert, it must be one of the supported Levit* architecture,',
     )
     parser.add_argument(
         '--pytorch_dump_folder_path',
         default='levit-dump-folder/',
         type=Path,
         required=False,
         help='Path to the output PyTorch model directory.',
     )
     parser.add_argument('--push_to_hub', action='store_true', help='Push model and image processor to the hub')
     parser.add_argument(
         '--no-push_to_hub',
         dest='push_to_hub',
         action='store_false',
         help='Do not push model and image processor to the hub',
     )

     _UpperCAmelCase        =				parser.parse_args()
     _UpperCAmelCase        =				args.pytorch_dump_folder_path
     pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
     convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)








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)
     _UpperCAmelCase        =				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(3_2, (3, 3), input_shape=(6_4, 6_4, 3), activation='relu')
     )

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

     # Adding a second convolutional layer
     classifier.add(layers.ConvaD(3_2, (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=1_2_8, 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')

     _UpperCAmelCase        =				tf.keras.preprocessing.image.ImageDataGenerator(
         rescale=1.0 / 2_5_5, shear_range=0.2, zoom_range=0.2, horizontal_flip=True
     )

     _UpperCAmelCase        =				tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 2_5_5)

     _UpperCAmelCase        =				train_datagen.flow_from_directory(
         'dataset/training_set', target_size=(6_4, 6_4), batch_size=3_2, class_mode='binary'
     )

     _UpperCAmelCase        =				test_datagen.flow_from_directory(
         'dataset/test_set', target_size=(6_4, 6_4), batch_size=3_2, class_mode='binary'
     )

     classifier.fit_generator(
         training_set, steps_per_epoch=5, epochs=3_0, validation_data=test_set
     )

     classifier.save('cnn.h5')

     # Part 3 - Making new predictions

     _UpperCAmelCase        =				tf.keras.preprocessing.image.load_img(
         'dataset/single_prediction/image.png', target_size=(6_4, 6_4)
     )
     _UpperCAmelCase        =				tf.keras.preprocessing.image.img_to_array(test_image)
     _UpperCAmelCase        =				np.expand_dims(test_image, axis=0)
     _UpperCAmelCase        =				classifier.predict(test_image)
     # training_set.class_indices
     if result[0][0] == 0:
          _UpperCAmelCase        =				'Normal'
     if result[0][0] == 1:
          _UpperCAmelCase        =				'Abnormality detected'

