






"""simple docstring"""







from cva import destroyAllWindows, imread, imshow, waitKey






def       _lowerCAmelCase       (      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase    ,       UpperCAmelCase	    =			img.shape[0], img.shape[1]

       # converting each pixel's color to its negative
       for i in range(lowerCAmelCase		):
              for j in range(lowerCAmelCase		):
                     UpperCAmelCase	    =			[255, 255, 255] - img[i][j]

       return img


if __name__ == "__main__":
 # read original image
 lowerCAmelCase_	:    Optional[Any]            =      imread('''image_data/lena.jpg''', 1)

 # convert to its negative
 lowerCAmelCase_	:    Union[str, Any]            =      convert_to_negative(img)

 # show result image
 imshow('''negative of original image''', img)
 waitKey(0)
 destroyAllWindows()








"""simple docstring"""







import argparse
import json
import os

import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image

from transformers import (
    EfficientNetConfig,
    EfficientNetForImageClassification,
    EfficientNetImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase_	:    List[Any]            =      logging.get_logger(__name__)

lowerCAmelCase_	:    Tuple            =      {
    '''b0''': efficientnet.EfficientNetBa,
    '''b1''': efficientnet.EfficientNetBa,
    '''b2''': efficientnet.EfficientNetBa,
    '''b3''': efficientnet.EfficientNetBa,
    '''b4''': efficientnet.EfficientNetBa,
    '''b5''': efficientnet.EfficientNetBa,
    '''b6''': efficientnet.EfficientNetBa,
    '''b7''': efficientnet.EfficientNetBa,
}

lowerCAmelCase_	:    int            =      {
    '''b0''': {
        '''hidden_dim''': 1_2_8_0,
        '''width_coef''': 1.0,
        '''depth_coef''': 1.0,
        '''image_size''': 2_2_4,
        '''dropout_rate''': 0.2,
        '''dw_padding''': [],
    },
    '''b1''': {
        '''hidden_dim''': 1_2_8_0,
        '''width_coef''': 1.0,
        '''depth_coef''': 1.1,
        '''image_size''': 2_4_0,
        '''dropout_rate''': 0.2,
        '''dw_padding''': [1_6],
    },
    '''b2''': {
        '''hidden_dim''': 1_4_0_8,
        '''width_coef''': 1.1,
        '''depth_coef''': 1.2,
        '''image_size''': 2_6_0,
        '''dropout_rate''': 0.3,
        '''dw_padding''': [5, 8, 1_6],
    },
    '''b3''': {
        '''hidden_dim''': 1_5_3_6,
        '''width_coef''': 1.2,
        '''depth_coef''': 1.4,
        '''image_size''': 3_0_0,
        '''dropout_rate''': 0.3,
        '''dw_padding''': [5, 1_8],
    },
    '''b4''': {
        '''hidden_dim''': 1_7_9_2,
        '''width_coef''': 1.4,
        '''depth_coef''': 1.8,
        '''image_size''': 3_8_0,
        '''dropout_rate''': 0.4,
        '''dw_padding''': [6],
    },
    '''b5''': {
        '''hidden_dim''': 2_0_4_8,
        '''width_coef''': 1.6,
        '''depth_coef''': 2.2,
        '''image_size''': 4_5_6,
        '''dropout_rate''': 0.4,
        '''dw_padding''': [1_3, 2_7],
    },
    '''b6''': {
        '''hidden_dim''': 2_3_0_4,
        '''width_coef''': 1.8,
        '''depth_coef''': 2.6,
        '''image_size''': 5_2_8,
        '''dropout_rate''': 0.5,
        '''dw_padding''': [3_1],
    },
    '''b7''': {
        '''hidden_dim''': 2_5_6_0,
        '''width_coef''': 2.0,
        '''depth_coef''': 3.1,
        '''image_size''': 6_0_0,
        '''dropout_rate''': 0.5,
        '''dw_padding''': [1_8],
    },
}






def       _lowerCAmelCase       (      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			EfficientNetConfig()
       UpperCAmelCase	    =			CONFIG_MAP[model_name]["""hidden_dim"""]
       UpperCAmelCase	    =			CONFIG_MAP[model_name]["""width_coef"""]
       UpperCAmelCase	    =			CONFIG_MAP[model_name]["""depth_coef"""]
       UpperCAmelCase	    =			CONFIG_MAP[model_name]["""image_size"""]
       UpperCAmelCase	    =			CONFIG_MAP[model_name]["""dropout_rate"""]
       UpperCAmelCase	    =			CONFIG_MAP[model_name]["""dw_padding"""]

       UpperCAmelCase	    =			"""huggingface/label-files"""
       UpperCAmelCase	    =			"""imagenet-1k-id2label.json"""
       UpperCAmelCase	    =			1000
       UpperCAmelCase	    =			json.load(open(hf_hub_download(lowerCAmelCase	,      lowerCAmelCase	,      repo_type="""dataset"""		)	,      """r"""		)		)
       UpperCAmelCase	    =			{int(lowerCAmelCase		): v for k, v in idalabel.items()}

       UpperCAmelCase	    =			idalabel
       UpperCAmelCase	    =			{v: k for k, v in idalabel.items()}
       return config






def       _lowerCAmelCase       (      ):







       '''simple docstring'''
       UpperCAmelCase	    =			"""http://images.cocodataset.org/val2017/000000039769.jpg"""
       UpperCAmelCase	    =			Image.open(requests.get(lowerCAmelCase	,      stream=lowerCAmelCase		).raw		)
       return im






def       _lowerCAmelCase       (      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			CONFIG_MAP[model_name]["""image_size"""]
       UpperCAmelCase	    =			EfficientNetImageProcessor(
           size={"""height""": size, """width""": size}	,      image_mean=[0.4_85, 0.4_56, 0.4_06]	,      image_std=[0.47_85_39_44, 0.4_73_28_64, 0.47_43_41_63]	,      do_center_crop=lowerCAmelCase	,      )
       return preprocessor






def       _lowerCAmelCase       (      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			[v.split("""_"""		)[0].split("""block"""		)[1] for v in original_param_names if v.startswith("""block"""		)]
       UpperCAmelCase	    =			sorted(set(lowerCAmelCase		)		)
       UpperCAmelCase	    =			len(lowerCAmelCase		)
       UpperCAmelCase	    =			{b: str(lowerCAmelCase		) for b, i in zip(lowerCAmelCase	,      range(lowerCAmelCase		)		)}

       UpperCAmelCase	    =			[]
       rename_keys.append(("""stem_conv/kernel:0""", """embeddings.convolution.weight""")		)
       rename_keys.append(("""stem_bn/gamma:0""", """embeddings.batchnorm.weight""")		)
       rename_keys.append(("""stem_bn/beta:0""", """embeddings.batchnorm.bias""")		)
       rename_keys.append(("""stem_bn/moving_mean:0""", """embeddings.batchnorm.running_mean""")		)
       rename_keys.append(("""stem_bn/moving_variance:0""", """embeddings.batchnorm.running_var""")		)

       for b in block_names:
              UpperCAmelCase	    =			block_name_mapping[b]
              rename_keys.append((F'''block{b}_expand_conv/kernel:0''', F'''encoder.blocks.{hf_b}.expansion.expand_conv.weight''')		)
              rename_keys.append((F'''block{b}_expand_bn/gamma:0''', F'''encoder.blocks.{hf_b}.expansion.expand_bn.weight''')		)
              rename_keys.append((F'''block{b}_expand_bn/beta:0''', F'''encoder.blocks.{hf_b}.expansion.expand_bn.bias''')		)
              rename_keys.append(
                  (F'''block{b}_expand_bn/moving_mean:0''', F'''encoder.blocks.{hf_b}.expansion.expand_bn.running_mean''')		)
              rename_keys.append(
                  (F'''block{b}_expand_bn/moving_variance:0''', F'''encoder.blocks.{hf_b}.expansion.expand_bn.running_var''')		)
              rename_keys.append(
                  (F'''block{b}_dwconv/depthwise_kernel:0''', F'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight''')		)
              rename_keys.append((F'''block{b}_bn/gamma:0''', F'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight''')		)
              rename_keys.append((F'''block{b}_bn/beta:0''', F'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias''')		)
              rename_keys.append(
                  (F'''block{b}_bn/moving_mean:0''', F'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean''')		)
              rename_keys.append(
                  (F'''block{b}_bn/moving_variance:0''', F'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var''')		)

              rename_keys.append((F'''block{b}_se_reduce/kernel:0''', F'''encoder.blocks.{hf_b}.squeeze_excite.reduce.weight''')		)
              rename_keys.append((F'''block{b}_se_reduce/bias:0''', F'''encoder.blocks.{hf_b}.squeeze_excite.reduce.bias''')		)
              rename_keys.append((F'''block{b}_se_expand/kernel:0''', F'''encoder.blocks.{hf_b}.squeeze_excite.expand.weight''')		)
              rename_keys.append((F'''block{b}_se_expand/bias:0''', F'''encoder.blocks.{hf_b}.squeeze_excite.expand.bias''')		)
              rename_keys.append(
                  (F'''block{b}_project_conv/kernel:0''', F'''encoder.blocks.{hf_b}.projection.project_conv.weight''')		)
              rename_keys.append((F'''block{b}_project_bn/gamma:0''', F'''encoder.blocks.{hf_b}.projection.project_bn.weight''')		)
              rename_keys.append((F'''block{b}_project_bn/beta:0''', F'''encoder.blocks.{hf_b}.projection.project_bn.bias''')		)
              rename_keys.append(
                  (F'''block{b}_project_bn/moving_mean:0''', F'''encoder.blocks.{hf_b}.projection.project_bn.running_mean''')		)
              rename_keys.append(
                  (F'''block{b}_project_bn/moving_variance:0''', F'''encoder.blocks.{hf_b}.projection.project_bn.running_var''')		)

       rename_keys.append(("""top_conv/kernel:0""", """encoder.top_conv.weight""")		)
       rename_keys.append(("""top_bn/gamma:0""", """encoder.top_bn.weight""")		)
       rename_keys.append(("""top_bn/beta:0""", """encoder.top_bn.bias""")		)
       rename_keys.append(("""top_bn/moving_mean:0""", """encoder.top_bn.running_mean""")		)
       rename_keys.append(("""top_bn/moving_variance:0""", """encoder.top_bn.running_var""")		)

       UpperCAmelCase	    =			{}
       for item in rename_keys:
              if item[0] in original_param_names:
                     UpperCAmelCase	    =			"""efficientnet.""" + item[1]

       UpperCAmelCase	    =			"""classifier.weight"""
       UpperCAmelCase	    =			"""classifier.bias"""
       return key_mapping






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       for key, value in tf_params.items():
              if "normalization" in key:
                     continue

              UpperCAmelCase	    =			key_mapping[key]
              if "_conv" in key and "kernel" in key:
                     UpperCAmelCase	    =			torch.from_numpy(lowerCAmelCase		).permute(3	,      2	,      0	,      1		)
              elif "depthwise_kernel" in key:
                     UpperCAmelCase	    =			torch.from_numpy(lowerCAmelCase		).permute(2	,      3	,      0	,      1		)
              elif "kernel" in key:
                     UpperCAmelCase	    =			torch.from_numpy(np.transpose(lowerCAmelCase		)		)
              else:
                     UpperCAmelCase	    =			torch.from_numpy(lowerCAmelCase		)

              # Replace HF parameters with original TF model parameters
              assert hf_params[hf_key].shape == new_hf_value.shape
              hf_params[hf_key].copy_(lowerCAmelCase		)






@torch.no_grad()
def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			model_classes[model_name](
           include_top=lowerCAmelCase	,      weights="""imagenet"""	,      input_tensor=lowerCAmelCase	,      input_shape=lowerCAmelCase	,      pooling=lowerCAmelCase	,      classes=1000	,      classifier_activation="""softmax"""	,      )

       UpperCAmelCase	    =			original_model.trainable_variables
       UpperCAmelCase	    =			original_model.non_trainable_variables
       UpperCAmelCase	    =			{param.name: param.numpy() for param in tf_params}
       for param in tf_non_train_params:
              UpperCAmelCase	    =			param.numpy()
       UpperCAmelCase	    =			list(tf_params.keys()		)

       # Load HuggingFace model
       UpperCAmelCase	    =			get_efficientnet_config(lowerCAmelCase		)
       UpperCAmelCase	    =			EfficientNetForImageClassification(lowerCAmelCase		).eval()
       UpperCAmelCase	    =			hf_model.state_dict()

       # Create src-to-dst parameter name mapping dictionary
       print("""Converting parameters..."""		)
       UpperCAmelCase	    =			rename_keys(lowerCAmelCase		)
       replace_params(lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		)

       # Initialize preprocessor and preprocess input image
       UpperCAmelCase	    =			convert_image_processor(lowerCAmelCase		)
       UpperCAmelCase	    =			preprocessor(images=prepare_img()	,      return_tensors="""pt"""		)

       # HF model inference
       hf_model.eval()
       with torch.no_grad():
              UpperCAmelCase	    =			hf_model(**lowerCAmelCase		)
       UpperCAmelCase	    =			outputs.logits.detach().numpy()

       # Original model inference
       UpperCAmelCase	    =			False
       UpperCAmelCase	    =			CONFIG_MAP[model_name]["""image_size"""]
       UpperCAmelCase	    =			prepare_img().resize((image_size, image_size)	,      resample=PIL.Image.NEAREST		)
       UpperCAmelCase	    =			image.img_to_array(lowerCAmelCase		)
       UpperCAmelCase	    =			np.expand_dims(lowerCAmelCase	,      axis=0		)
       UpperCAmelCase	    =			original_model.predict(lowerCAmelCase		)

       # Check whether original and HF model outputs match  -> np.allclose
       assert np.allclose(lowerCAmelCase	,      lowerCAmelCase	,      atol=1e-3		), "The predicted logits are not the same."
       print("""Model outputs match!"""		)

       if save_model:
              # Create folder to save model
              if not os.path.isdir(lowerCAmelCase		):
                     os.mkdir(lowerCAmelCase		)
              # Save converted model and image processor
              hf_model.save_pretrained(lowerCAmelCase		)
              preprocessor.save_pretrained(lowerCAmelCase		)

       if push_to_hub:
              # Push model and image processor to hub
              print(F'''Pushing converted {model_name} to the hub...'''		)
              UpperCAmelCase	    =			F'''efficientnet-{model_name}'''
              preprocessor.push_to_hub(lowerCAmelCase		)
              hf_model.push_to_hub(lowerCAmelCase		)


if __name__ == "__main__":
 lowerCAmelCase_	:    int            =      argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '''--model_name''',
     default='''b0''',
     type=str,
     help='''Version name of the EfficientNet model you want to convert, select from [b0, b1, b2, b3, b4, b5, b6, b7].''',
 )
 parser.add_argument(
     '''--pytorch_dump_folder_path''',
     default='''hf_model''',
     type=str,
     help='''Path to the output PyTorch model directory.''',
 )
 parser.add_argument('''--save_model''', action='''store_true''', help='''Save model to local''')
 parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Push model and image processor to the hub''')

 lowerCAmelCase_	:    Optional[Any]            =      parser.parse_args()
 convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)
