


'''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()
A				:					List[str]    						=			logging.get_logger(__name__)

A				:					Union[str, Any]    						=			{
    '''b0''': efficientnet.EfficientNetBa,
    '''b1''': efficientnet.EfficientNetBa,
    '''b2''': efficientnet.EfficientNetBa,
    '''b3''': efficientnet.EfficientNetBa,
    '''b4''': efficientnet.EfficientNetBa,
    '''b5''': efficientnet.EfficientNetBa,
    '''b6''': efficientnet.EfficientNetBa,
    '''b7''': efficientnet.EfficientNetBa,
}

A				:					int    						=			{
    '''b0''': {
        '''hidden_dim''': 1280,
        '''width_coef''': 1.0,
        '''depth_coef''': 1.0,
        '''image_size''': 224,
        '''dropout_rate''': 0.2,
        '''dw_padding''': [],
    },
    '''b1''': {
        '''hidden_dim''': 1280,
        '''width_coef''': 1.0,
        '''depth_coef''': 1.1,
        '''image_size''': 240,
        '''dropout_rate''': 0.2,
        '''dw_padding''': [16],
    },
    '''b2''': {
        '''hidden_dim''': 1408,
        '''width_coef''': 1.1,
        '''depth_coef''': 1.2,
        '''image_size''': 260,
        '''dropout_rate''': 0.3,
        '''dw_padding''': [5, 8, 16],
    },
    '''b3''': {
        '''hidden_dim''': 1536,
        '''width_coef''': 1.2,
        '''depth_coef''': 1.4,
        '''image_size''': 300,
        '''dropout_rate''': 0.3,
        '''dw_padding''': [5, 18],
    },
    '''b4''': {
        '''hidden_dim''': 1792,
        '''width_coef''': 1.4,
        '''depth_coef''': 1.8,
        '''image_size''': 380,
        '''dropout_rate''': 0.4,
        '''dw_padding''': [6],
    },
    '''b5''': {
        '''hidden_dim''': 2048,
        '''width_coef''': 1.6,
        '''depth_coef''': 2.2,
        '''image_size''': 456,
        '''dropout_rate''': 0.4,
        '''dw_padding''': [13, 27],
    },
    '''b6''': {
        '''hidden_dim''': 2304,
        '''width_coef''': 1.8,
        '''depth_coef''': 2.6,
        '''image_size''': 528,
        '''dropout_rate''': 0.5,
        '''dw_padding''': [31],
    },
    '''b7''': {
        '''hidden_dim''': 2560,
        '''width_coef''': 2.0,
        '''depth_coef''': 3.1,
        '''image_size''': 600,
        '''dropout_rate''': 0.5,
        '''dw_padding''': [18],
    },
}






def 				lowerCAmelCase__	(    lowerCamelCase					:      Any       ):
       _A					:     Any     = EfficientNetConfig()
       _A					:     Optional[int]     = CONFIG_MAP[model_name]['hidden_dim']
       _A					:     List[Any]     = CONFIG_MAP[model_name]['width_coef']
       _A					:     str     = CONFIG_MAP[model_name]['depth_coef']
       _A					:     Dict     = CONFIG_MAP[model_name]['image_size']
       _A					:     Dict     = CONFIG_MAP[model_name]['dropout_rate']
       _A					:     List[Any]     = CONFIG_MAP[model_name]['dw_padding']

       _A					:     Dict     = 'huggingface/label-files'
       _A					:     Union[str, Any]     = 'imagenet-1k-id2label.json'
       _A					:     Union[str, Any]     = 1000
       _A					:     Tuple     = json.load(open(hf_hub_download(lowerCamelCase  ,lowerCamelCase  ,repo_type='dataset'       )  ,'r'       )       )
       _A					:     Any     = {int(lowerCamelCase       ): v for k, v in idalabel.items()}

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






def 				lowerCAmelCase__	(    ):
       _A					:     Union[str, Any]     = 'http://images.cocodataset.org/val2017/000000039769.jpg'
       _A					:     int     = Image.open(requests.get(lowerCamelCase  ,stream=lowerCamelCase       ).raw       )
       return im






def 				lowerCAmelCase__	(    lowerCamelCase					:      Optional[int]       ):
       _A					:     Tuple     = CONFIG_MAP[model_name]['image_size']
       _A					:     Any     = EfficientNetImageProcessor(
           size={'height': size, 'width': size}  ,image_mean=[0.485, 0.456, 0.406]  ,image_std=[0.47_853_944, 0.4_732_864, 0.47_434_163]  ,do_center_crop=lowerCamelCase  ,)
       return preprocessor






def 				lowerCAmelCase__	(    lowerCamelCase					:      Dict       ):
       _A					:     Union[str, Any]     = [v.split('_'       )[0].split('block'       )[1] for v in original_param_names if v.startswith('block'       )]
       _A					:     str     = sorted(set(lowerCamelCase       )       )
       _A					:     List[Any]     = len(lowerCamelCase       )
       _A					:     int     = {b: str(lowerCamelCase       ) for b, i in zip(lowerCamelCase  ,range(lowerCamelCase       )       )}

       _A					:     List[Any]     = []
       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:
              _A					:     Tuple     = 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')       )

       _A					:     Any     = {}
       for item in rename_keys:
              if item[0] in original_param_names:
                     _A					:     List[str]     = 'efficientnet.' + item[1]

       _A					:     Optional[int]     = 'classifier.weight'
       _A					:     List[str]     = 'classifier.bias'
       return key_mapping






def 				lowerCAmelCase__	(    lowerCamelCase					:      Tuple  ,lowerCamelCase					:      Dict  ,lowerCamelCase					:      Optional[Any]       ):
       for key, value in tf_params.items():
              if "normalization" in key:
                     continue

              _A					:     Dict     = key_mapping[key]
              if "_conv" in key and "kernel" in key:
                     _A					:     List[Any]     = torch.from_numpy(lowerCamelCase       ).permute(3  ,2  ,0  ,1       )
              elif "depthwise_kernel" in key:
                     _A					:     Any     = torch.from_numpy(lowerCamelCase       ).permute(2  ,3  ,0  ,1       )
              elif "kernel" in key:
                     _A					:     Optional[Any]     = torch.from_numpy(np.transpose(lowerCamelCase       )       )
              else:
                     _A					:     str     = 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					:      Dict  ,lowerCamelCase					:      Tuple  ,lowerCamelCase					:      Union[str, Any]  ,lowerCamelCase					:      int       ):
       _A					:     List[Any]     = model_classes[model_name](
           include_top=lowerCamelCase  ,weights='imagenet'  ,input_tensor=lowerCamelCase  ,input_shape=lowerCamelCase  ,pooling=lowerCamelCase  ,classes=1000  ,classifier_activation='softmax'  ,)

       _A					:     List[str]     = original_model.trainable_variables
       _A					:     str     = original_model.non_trainable_variables
       _A					:     List[Any]     = {param.name: param.numpy() for param in tf_params}
       for param in tf_non_train_params:
              _A					:     Optional[Any]     = param.numpy()
       _A					:     List[str]     = list(tf_params.keys()       )

       # Load HuggingFace model
       _A					:     Tuple     = get_efficientnet_config(lowerCamelCase       )
       _A					:     List[str]     = EfficientNetForImageClassification(lowerCamelCase       ).eval()
       _A					:     Optional[Any]     = hf_model.state_dict()

       # Create src-to-dst parameter name mapping dictionary
       print('Converting parameters...'       )
       _A					:     Optional[Any]     = rename_keys(lowerCamelCase       )
       replace_params(lowerCamelCase  ,lowerCamelCase  ,lowerCamelCase       )

       # Initialize preprocessor and preprocess input image
       _A					:     Dict     = convert_image_processor(lowerCamelCase       )
       _A					:     Tuple     = preprocessor(images=prepare_img()  ,return_tensors='pt'       )

       # HF model inference
       hf_model.eval()
       with torch.no_grad():
              _A					:     int     = hf_model(**lowerCamelCase       )
       _A					:     Optional[int]     = outputs.logits.detach().numpy()

       # Original model inference
       _A					:     List[Any]     = False
       _A					:     List[Any]     = CONFIG_MAP[model_name]['image_size']
       _A					:     Dict     = prepare_img().resize((image_size, image_size)  ,resample=PIL.Image.NEAREST       )
       _A					:     List[str]     = image.img_to_array(lowerCamelCase       )
       _A					:     Any     = np.expand_dims(lowerCamelCase  ,axis=0       )
       _A					:     Any     = 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...'       )
              _A					:     Tuple     = F'efficientnet-{model_name}'
              preprocessor.push_to_hub(lowerCamelCase       )
              hf_model.push_to_hub(lowerCamelCase       )


if __name__ == "__main__":
   A				:					Tuple    						=			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''')

   A				:					List[str]    						=			parser.parse_args()
   convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)










'''simple docstring'''







from __future__ import annotations


class 			__lowerCamelCase			:
    """simple docstring"""


    def __init__(  self      :							Optional[Any]				,							SCREAMING_SNAKE_CASE      :							Tuple=None):
           _A					:     Any     = data
           _A					:     Optional[Any]     = None


    def __repr__(  self      :							List[str]):
           _A					:     List[Any]     = []
           _A					:     Any     = self
           while temp:
                  string_rep.append(F'{temp.data}')
                  _A					:     List[Any]     = temp.next
           return "->".join(SCREAMING_SNAKE_CASE)






def 				lowerCAmelCase__	(    lowerCamelCase					:      list       ):
       if not elements_list:
              raise Exception('The Elements List is empty'       )

       _A					:     Union[str, Any]     = Node(elements_list[0]       )
       for i in range(1  ,len(lowerCamelCase       )       ):
              _A					:     Dict     = Node(elements_list[i]       )
              _A					:     int     = current.next
       return head






def 				lowerCAmelCase__	(    lowerCamelCase					:      Node       ):
       if head_node is not None and isinstance(lowerCamelCase  ,lowerCamelCase       ):
              print_reverse(head_node.next       )
              print(head_node.data       )






def 				lowerCAmelCase__	(    ):
       from doctest import testmod

       testmod()

       _A					:     List[str]     = make_linked_list([14, 52, 14, 12, 43]       )
       print('Linked List:'       )
       print(lowerCamelCase       )
       print('Elements in Reverse:'       )
       print_reverse(lowerCamelCase       )


if __name__ == "__main__":
   main()






