






'''simple docstring'''



import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging


logging.set_verbosity_info()
__UpperCamelCase   :							List[Any]         =   logging.get_logger()
@dataclass
class 				__SCREAMING_SNAKE_CASE							:
      __a	       =42
      __a	       =field(default_factory=_lowerCAmelCase  )
      __a	       =field(default_factory=_lowerCAmelCase  )




      def 			__UpperCamelCase				(	self			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase   )				->Union[str, Any]:



         '''simple docstring'''
         __a					  =     len(list(m.modules()   )   ) == 1 or isinstance(lowerCamelCase			, nn.Convad   ) or isinstance(lowerCamelCase			, nn.BatchNormad   )
         if has_not_submodules:
            self.traced.append(lowerCamelCase   )




      def __call__(	self			, lowerCamelCase   )				->Union[str, Any]:



         '''simple docstring'''
         for m in self.module.modules():
            self.handles.append(m.register_forward_hook(self._forward_hook   )   )
         self.module(lowerCamelCase   )
         [x.remove() for x in self.handles]
         return self






      @property
      def 			__UpperCamelCase				(	self   )				->Any:



         '''simple docstring'''
         # check the len of the state_dict keys to see if we have learnable params
         return list(filter(lambda lowerCamelCase   : len(list(x.state_dict().keys()   )   ) > 0			, self.traced   )   )







@dataclass
class 				__SCREAMING_SNAKE_CASE							:
      __a	       =42
      __a	       =42
      __a	       =0
      __a	       =field(default_factory=_lowerCAmelCase  )
      __a	       =field(default_factory=_lowerCAmelCase  )




      def __call__(	self			, lowerCamelCase   )				->Tuple:



         '''simple docstring'''
         __a					  =     Tracker(self.dest   )(lowerCamelCase   ).parametrized
         __a					  =     Tracker(self.src   )(lowerCamelCase   ).parametrized

         __a					  =     list(filter(lambda lowerCamelCase   : type(lowerCamelCase   ) not in self.src_skip			, lowerCamelCase   )   )
         __a					  =     list(filter(lambda lowerCamelCase   : type(lowerCamelCase   ) not in self.dest_skip			, lowerCamelCase   )   )

         if len(lowerCamelCase   ) != len(lowerCamelCase   ):
            raise Exception(
                F"""Numbers of operations are different. Source module has {len(lowerCamelCase   )} operations while"""
                F""" destination module has {len(lowerCamelCase   )}."""   )

         for dest_m, src_m in zip(lowerCamelCase			, lowerCamelCase   ):
            dest_m.load_state_dict(src_m.state_dict()   )
            if self.verbose == 1:
               print(F"""Transfered from={src_m} to={dest_m}"""   )







def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						str,       SCREAMING_SNAKE_CASE__:						ResNetConfig,       SCREAMING_SNAKE_CASE__:						Path,       SCREAMING_SNAKE_CASE__:						bool = True     )	->      List[Any]:

   """simple docstring"""
   print(f"""Converting {name}..."""     )
   with torch.no_grad():
      __a					  =     timm.create_model(SCREAMING_SNAKE_CASE__,       pretrained=SCREAMING_SNAKE_CASE__     ).eval()
      __a					  =     ResNetForImageClassification(SCREAMING_SNAKE_CASE__     ).eval()
      __a					  =     ModuleTransfer(src=SCREAMING_SNAKE_CASE__,       dest=SCREAMING_SNAKE_CASE__     )
      __a					  =     torch.randn((1, 3, 224, 224)     )
      module_transfer(SCREAMING_SNAKE_CASE__     )

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

   __a					  =     f"""resnet{"-".join(name.split("resnet"     )     )}"""
   print(SCREAMING_SNAKE_CASE__     )

   if push_to_hub:
      our_model.push_to_hub(
          repo_path_or_name=save_directory / checkpoint_name,       commit_message='Add model',       use_temp_dir=SCREAMING_SNAKE_CASE__,       )

      # we can use the convnext one
      __a					  =     AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'     )
      image_processor.push_to_hub(
          repo_path_or_name=save_directory / checkpoint_name,       commit_message='Add image processor',       use_temp_dir=SCREAMING_SNAKE_CASE__,       )

      print(f"""Pushed {checkpoint_name}"""     )



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						Path,       SCREAMING_SNAKE_CASE__:						str = None,       SCREAMING_SNAKE_CASE__:						bool = True     )	->      int:

   """simple docstring"""
   __a					  =     'imagenet-1k-id2label.json'
   __a					  =     1000
   __a					  =     (1, num_labels)

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

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

   __a					  =     partial(SCREAMING_SNAKE_CASE__,       num_labels=SCREAMING_SNAKE_CASE__,       idalabel=SCREAMING_SNAKE_CASE__,       labelaid=SCREAMING_SNAKE_CASE__     )

   __a					  =     {
       'resnet18': ImageNetPreTrainedConfig(
           depths=[2, 2, 2, 2],       hidden_sizes=[64, 128, 256, 512],       layer_type='basic'     ),
       'resnet26': ImageNetPreTrainedConfig(
           depths=[2, 2, 2, 2],       hidden_sizes=[256, 512, 1024, 2048],       layer_type='bottleneck'     ),
       'resnet34': ImageNetPreTrainedConfig(
           depths=[3, 4, 6, 3],       hidden_sizes=[64, 128, 256, 512],       layer_type='basic'     ),
       'resnet50': ImageNetPreTrainedConfig(
           depths=[3, 4, 6, 3],       hidden_sizes=[256, 512, 1024, 2048],       layer_type='bottleneck'     ),
       'resnet101': ImageNetPreTrainedConfig(
           depths=[3, 4, 23, 3],       hidden_sizes=[256, 512, 1024, 2048],       layer_type='bottleneck'     ),
       'resnet152': ImageNetPreTrainedConfig(
           depths=[3, 8, 36, 3],       hidden_sizes=[256, 512, 1024, 2048],       layer_type='bottleneck'     ),
   }

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


if __name__ == "__main__":
 __UpperCamelCase   :							Optional[int]         =   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 resnet* architecture,"""
         """ currently: resnet18,26,34,50,101,152. If `None`, all of them will the converted."""
     ),
 )
 parser.add_argument(
     """--pytorch_dump_folder_path""",
     default=None,
     type=Path,
     required=True,
     help="""Path to the output PyTorch model directory.""",
 )
 parser.add_argument(
     """--push_to_hub""",
     default=True,
     type=bool,
     required=False,
     help="""If True, push model and image processor to the hub.""",
 )

 __UpperCamelCase   :							Union[str, Any]         =   parser.parse_args()
 __UpperCamelCase   :							Path         =   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)







'''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)
 __UpperCamelCase   :							Union[str, 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')

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

 __UpperCamelCase   :							Union[str, Any]         =   tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 255)

 __UpperCamelCase   :							Any         =   train_datagen.flow_from_directory(
     """dataset/training_set""", target_size=(64, 64), batch_size=32, class_mode="""binary"""
 )

 __UpperCamelCase   :							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

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