


"""simple docstring"""





import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


logging.set_verbosity_info()
snake_case__						:   Any			 = logging.get_logger(__name__)


def    _snake_case  (  _snake_case :	List[Any]       ,      _snake_case :	Tuple=False	):
      lowerCAmelCase    :		List[str]				=      []
      for i in range(config.num_hidden_layers	):
            # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
            rename_keys.append((f'''blocks.{i}.norm1.weight''', f'''vit.encoder.layer.{i}.layernorm_before.weight''')	)
            rename_keys.append((f'''blocks.{i}.norm1.bias''', f'''vit.encoder.layer.{i}.layernorm_before.bias''')	)
            rename_keys.append((f'''blocks.{i}.attn.proj.weight''', f'''vit.encoder.layer.{i}.attention.output.dense.weight''')	)
            rename_keys.append((f'''blocks.{i}.attn.proj.bias''', f'''vit.encoder.layer.{i}.attention.output.dense.bias''')	)
            rename_keys.append((f'''blocks.{i}.norm2.weight''', f'''vit.encoder.layer.{i}.layernorm_after.weight''')	)
            rename_keys.append((f'''blocks.{i}.norm2.bias''', f'''vit.encoder.layer.{i}.layernorm_after.bias''')	)
            rename_keys.append((f'''blocks.{i}.mlp.fc1.weight''', f'''vit.encoder.layer.{i}.intermediate.dense.weight''')	)
            rename_keys.append((f'''blocks.{i}.mlp.fc1.bias''', f'''vit.encoder.layer.{i}.intermediate.dense.bias''')	)
            rename_keys.append((f'''blocks.{i}.mlp.fc2.weight''', f'''vit.encoder.layer.{i}.output.dense.weight''')	)
            rename_keys.append((f'''blocks.{i}.mlp.fc2.bias''', f'''vit.encoder.layer.{i}.output.dense.bias''')	)

      # projection layer + position embeddings
      rename_keys.extend(
          [
              ('''cls_token''', '''vit.embeddings.cls_token'''),
              ('''patch_embed.proj.weight''', '''vit.embeddings.patch_embeddings.projection.weight'''),
              ('''patch_embed.proj.bias''', '''vit.embeddings.patch_embeddings.projection.bias'''),
              ('''pos_embed''', '''vit.embeddings.position_embeddings'''),
          ]	)

      if base_model:
            # layernorm + pooler
            rename_keys.extend(
                [
                    ('''norm.weight''', '''layernorm.weight'''),
                    ('''norm.bias''', '''layernorm.bias'''),
                    ('''pre_logits.fc.weight''', '''pooler.dense.weight'''),
                    ('''pre_logits.fc.bias''', '''pooler.dense.bias'''),
                ]	)

            # if just the base model, we should remove "vit" from all keys that start with "vit"
            lowerCAmelCase    :		Union[str, Any]				=      [(pair[0], pair[1][4:]) if pair[1].startswith('''vit'''	) else pair for pair in rename_keys]
      else:
            # layernorm + classification head
            rename_keys.extend(
                [
                    ('''norm.weight''', '''vit.layernorm.weight'''),
                    ('''norm.bias''', '''vit.layernorm.bias'''),
                    ('''head.weight''', '''classifier.weight'''),
                    ('''head.bias''', '''classifier.bias'''),
                ]	)

      return rename_keys


def    _snake_case  (  _snake_case :	Tuple       ,      _snake_case :	List[Any]       ,      _snake_case :	Tuple=False	):
      for i in range(config.num_hidden_layers	):
            if base_model:
                  lowerCAmelCase    :		Optional[int]				=      ''''''
            else:
                  lowerCAmelCase    :		Union[str, Any]				=      '''vit.'''
            # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
            lowerCAmelCase    :		List[Any]				=      state_dict.pop(f'''blocks.{i}.attn.qkv.weight'''	)
            lowerCAmelCase    :		Tuple				=      state_dict.pop(f'''blocks.{i}.attn.qkv.bias'''	)
            # next, add query, keys and values (in that order) to the state dict
            lowerCAmelCase    :		Optional[Any]				=      in_proj_weight[
                : config.hidden_size, :
            ]
            lowerCAmelCase    :		Tuple				=      in_proj_bias[: config.hidden_size]
            lowerCAmelCase    :		Tuple				=      in_proj_weight[
                config.hidden_size : config.hidden_size * 2, :
            ]
            lowerCAmelCase    :		Tuple				=      in_proj_bias[
                config.hidden_size : config.hidden_size * 2
            ]
            lowerCAmelCase    :		Union[str, Any]				=      in_proj_weight[
                -config.hidden_size :, :
            ]
            lowerCAmelCase    :		List[Any]				=      in_proj_bias[-config.hidden_size :]


def    _snake_case  (  _snake_case :	Tuple	):
      lowerCAmelCase    :		List[Any]				=      ['''head.weight''', '''head.bias''']
      for k in ignore_keys:
            state_dict.pop(_snake_case       ,      _snake_case	)


def    _snake_case  (  _snake_case :	Union[str, Any]       ,      _snake_case :	Any       ,      _snake_case :	List[Any]	):
      lowerCAmelCase    :		Optional[int]				=      dct.pop(_snake_case	)
      lowerCAmelCase    :		Union[str, Any]				=      val


def    _snake_case  (  ):
      lowerCAmelCase    :		Any				=      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
      lowerCAmelCase    :		Any				=      Image.open(requests.get(_snake_case       ,      stream=_snake_case	).raw	)
      return im


@torch.no_grad()
def    _snake_case  (  _snake_case :	Optional[int]       ,      _snake_case :	Optional[Any]	):
      lowerCAmelCase    :		Any				=      ViTConfig()
      lowerCAmelCase    :		Any				=      False
      # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
      if vit_name[-5:] == "in21k":
            lowerCAmelCase    :		List[str]				=      True
            lowerCAmelCase    :		int				=      int(vit_name[-12:-10]	)
            lowerCAmelCase    :		List[Any]				=      int(vit_name[-9:-6]	)
      else:
            lowerCAmelCase    :		str				=      1000
            lowerCAmelCase    :		Optional[int]				=      '''huggingface/label-files'''
            lowerCAmelCase    :		Any				=      '''imagenet-1k-id2label.json'''
            lowerCAmelCase    :		Optional[Any]				=      json.load(open(hf_hub_download(_snake_case       ,      _snake_case       ,      repo_type='''dataset'''	)       ,      '''r'''	)	)
            lowerCAmelCase    :		Optional[Any]				=      {int(_snake_case	): v for k, v in idalabel.items()}
            lowerCAmelCase    :		Dict				=      idalabel
            lowerCAmelCase    :		List[Any]				=      {v: k for k, v in idalabel.items()}
            lowerCAmelCase    :		List[str]				=      int(vit_name[-6:-4]	)
            lowerCAmelCase    :		int				=      int(vit_name[-3:]	)
      # size of the architecture
      if "deit" in vit_name:
            if vit_name[9:].startswith('''tiny'''	):
                  lowerCAmelCase    :		str				=      192
                  lowerCAmelCase    :		int				=      768
                  lowerCAmelCase    :		List[str]				=      12
                  lowerCAmelCase    :		str				=      3
            elif vit_name[9:].startswith('''small'''	):
                  lowerCAmelCase    :		List[str]				=      384
                  lowerCAmelCase    :		Optional[int]				=      1536
                  lowerCAmelCase    :		int				=      12
                  lowerCAmelCase    :		str				=      6
            else:
                  pass
      else:
            if vit_name[4:].startswith('''small'''	):
                  lowerCAmelCase    :		List[str]				=      768
                  lowerCAmelCase    :		Dict				=      2304
                  lowerCAmelCase    :		Dict				=      8
                  lowerCAmelCase    :		Tuple				=      8
            elif vit_name[4:].startswith('''base'''	):
                  pass
            elif vit_name[4:].startswith('''large'''	):
                  lowerCAmelCase    :		Union[str, Any]				=      1024
                  lowerCAmelCase    :		List[Any]				=      4096
                  lowerCAmelCase    :		Union[str, Any]				=      24
                  lowerCAmelCase    :		Any				=      16
            elif vit_name[4:].startswith('''huge'''	):
                  lowerCAmelCase    :		Any				=      1280
                  lowerCAmelCase    :		str				=      5120
                  lowerCAmelCase    :		Tuple				=      32
                  lowerCAmelCase    :		Tuple				=      16

    # load original model from timm
      lowerCAmelCase    :		Any				=      timm.create_model(_snake_case       ,      pretrained=_snake_case	)
      timm_model.eval()

      # load state_dict of original model, remove and rename some keys
      lowerCAmelCase    :		int				=      timm_model.state_dict()
      if base_model:
            remove_classification_head_(_snake_case	)
      lowerCAmelCase    :		Optional[Any]				=      create_rename_keys(_snake_case       ,      _snake_case	)
      for src, dest in rename_keys:
            rename_key(_snake_case       ,      _snake_case       ,      _snake_case	)
      read_in_q_k_v(_snake_case       ,      _snake_case       ,      _snake_case	)

      # load HuggingFace model
      if vit_name[-5:] == "in21k":
            lowerCAmelCase    :		Any				=      ViTModel(_snake_case	).eval()
      else:
            lowerCAmelCase    :		Any				=      ViTForImageClassification(_snake_case	).eval()
      model.load_state_dict(_snake_case	)

      # Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
      if "deit" in vit_name:
            lowerCAmelCase    :		Dict				=      DeiTImageProcessor(size=config.image_size	)
      else:
            lowerCAmelCase    :		Union[str, Any]				=      ViTImageProcessor(size=config.image_size	)
      lowerCAmelCase    :		Union[str, Any]				=      image_processor(images=prepare_img()       ,      return_tensors='''pt'''	)
      lowerCAmelCase    :		Dict				=      encoding['''pixel_values''']
      lowerCAmelCase    :		List[Any]				=      model(_snake_case	)

      if base_model:
            lowerCAmelCase    :		Dict				=      timm_model.forward_features(_snake_case	)
            assert timm_pooled_output.shape == outputs.pooler_output.shape
            assert torch.allclose(_snake_case       ,      outputs.pooler_output       ,      atol=1E-3	)
      else:
            lowerCAmelCase    :		Dict				=      timm_model(_snake_case	)
            assert timm_logits.shape == outputs.logits.shape
            assert torch.allclose(_snake_case       ,      outputs.logits       ,      atol=1E-3	)

      Path(_snake_case	).mkdir(exist_ok=_snake_case	)
      print(f'''Saving model {vit_name} to {pytorch_dump_folder_path}'''	)
      model.save_pretrained(_snake_case	)
      print(f'''Saving image processor to {pytorch_dump_folder_path}'''	)
      image_processor.save_pretrained(_snake_case	)


if __name__ == "__main__":
 snake_case__						:   Union[str, Any]			 = argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--vit_name''',
     default='''vit_base_patch16_224''',
     type=str,
     help='''Name of the ViT timm model you\'d like to convert.''',
 )
 parser.add_argument(
     '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
 )

 snake_case__						:   int			 = parser.parse_args()
 convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)






'''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'




