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

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

_UpperCAmelCase    :     str         =       {
    '''b0''': {
        '''hidden_dim''': 12_80,
        '''width_coef''': 1.0,
        '''depth_coef''': 1.0,
        '''image_size''': 2_24,
        '''dropout_rate''': 0.2,
        '''dw_padding''': [],
    },
    '''b1''': {
        '''hidden_dim''': 12_80,
        '''width_coef''': 1.0,
        '''depth_coef''': 1.1,
        '''image_size''': 2_40,
        '''dropout_rate''': 0.2,
        '''dw_padding''': [16],
    },
    '''b2''': {
        '''hidden_dim''': 14_08,
        '''width_coef''': 1.1,
        '''depth_coef''': 1.2,
        '''image_size''': 2_60,
        '''dropout_rate''': 0.3,
        '''dw_padding''': [5, 8, 16],
    },
    '''b3''': {
        '''hidden_dim''': 15_36,
        '''width_coef''': 1.2,
        '''depth_coef''': 1.4,
        '''image_size''': 3_00,
        '''dropout_rate''': 0.3,
        '''dw_padding''': [5, 18],
    },
    '''b4''': {
        '''hidden_dim''': 17_92,
        '''width_coef''': 1.4,
        '''depth_coef''': 1.8,
        '''image_size''': 3_80,
        '''dropout_rate''': 0.4,
        '''dw_padding''': [6],
    },
    '''b5''': {
        '''hidden_dim''': 20_48,
        '''width_coef''': 1.6,
        '''depth_coef''': 2.2,
        '''image_size''': 4_56,
        '''dropout_rate''': 0.4,
        '''dw_padding''': [13, 27],
    },
    '''b6''': {
        '''hidden_dim''': 23_04,
        '''width_coef''': 1.8,
        '''depth_coef''': 2.6,
        '''image_size''': 5_28,
        '''dropout_rate''': 0.5,
        '''dw_padding''': [31],
    },
    '''b7''': {
        '''hidden_dim''': 25_60,
        '''width_coef''': 2.0,
        '''depth_coef''': 3.1,
        '''image_size''': 6_00,
        '''dropout_rate''': 0.5,
        '''dw_padding''': [18],
    },
}





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			Any				):
     _A    		=  EfficientNetConfig()
     _A    		=  CONFIG_MAP[model_name]['hidden_dim']
     _A    		=  CONFIG_MAP[model_name]['width_coef']
     _A    		=  CONFIG_MAP[model_name]['depth_coef']
     _A    		=  CONFIG_MAP[model_name]['image_size']
     _A    		=  CONFIG_MAP[model_name]['dropout_rate']
     _A    		=  CONFIG_MAP[model_name]['dw_padding']

     _A    		=  'huggingface/label-files'
     _A    		=  'imagenet-1k-id2label.json'
     _A    		=  1_0_0_0
     _A    		=  json.load(open(hf_hub_download(__snake_case   ,		__snake_case   ,		repo_type='dataset'				)   ,		'r'				)				)
     _A    		=  {int(__snake_case				): v for k, v in idalabel.items()}

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





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





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			Optional[int]				):
     _A    		=  CONFIG_MAP[model_name]['image_size']
     _A    		=  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=__snake_case   ,		)
     return preprocessor





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			Optional[int]				):
     _A    		=  [v.split('_'				)[0].split('block'				)[1] for v in original_param_names if v.startswith('block'				)]
     _A    		=  sorted(set(__snake_case				)				)
     _A    		=  len(__snake_case				)
     _A    		=  {b: str(__snake_case				) for b, i in zip(__snake_case   ,		range(__snake_case				)				)}

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

     _A    		=  'classifier.weight'
     _A    		=  'classifier.bias'
     return key_mapping





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			int   ,		__snake_case							:			List[Any]   ,		__snake_case							:			str				):
     for key, value in tf_params.items():
          if "normalization" in key:
               continue

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

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





@torch.no_grad()
def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			str   ,		__snake_case							:			Dict   ,		__snake_case							:			List[Any]   ,		__snake_case							:			Optional[int]				):
     _A    		=  model_classes[model_name](
         include_top=__snake_case   ,		weights='imagenet'   ,		input_tensor=__snake_case   ,		input_shape=__snake_case   ,		pooling=__snake_case   ,		classes=1_0_0_0   ,		classifier_activation='softmax'   ,		)

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

     # Load HuggingFace model
     _A    		=  get_efficientnet_config(__snake_case				)
     _A    		=  EfficientNetForImageClassification(__snake_case				).eval()
     _A    		=  hf_model.state_dict()

     # Create src-to-dst parameter name mapping dictionary
     print('Converting parameters...'				)
     _A    		=  rename_keys(__snake_case				)
     replace_params(__snake_case   ,		__snake_case   ,		__snake_case				)

     # Initialize preprocessor and preprocess input image
     _A    		=  convert_image_processor(__snake_case				)
     _A    		=  preprocessor(images=prepare_img()   ,		return_tensors='pt'				)

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

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

     # Check whether original and HF model outputs match  -> np.allclose
     assert np.allclose(__snake_case   ,		__snake_case   ,		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(__snake_case				):
               os.mkdir(__snake_case				)
          # Save converted model and image processor
          hf_model.save_pretrained(__snake_case				)
          preprocessor.save_pretrained(__snake_case				)

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


if __name__ == "__main__":
   _UpperCAmelCase    :     List[Any]         =       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''')

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




'''simple docstring'''






import math





def  _SCREAMING_SNAKE_CASE		(      __snake_case							:			int				):
     _A    		=  []
     _A    		=  2
     _A    		=  int(math.sqrt(__snake_case				)				)  # Size of every segment
     _A    		=  [True] * (end + 1)
     _A    		=  []

     while start <= end:
          if temp[start] is True:
               in_prime.append(__snake_case				)
               for i in range(start * start   ,		end + 1   ,		__snake_case				):
                    _A    		=  False
          start += 1
     prime += in_prime

     _A    		=  end + 1
     _A    		=  min(2 * end   ,		__snake_case				)

     while low <= n:
          _A    		=  [True] * (high - low + 1)
          for each in in_prime:
               _A    		=  math.floor(low / each				) * each
               if t < low:
                    t += each

               for j in range(__snake_case   ,		high + 1   ,		__snake_case				):
                    _A    		=  False

          for j in range(len(__snake_case				)				):
               if temp[j] is True:
                    prime.append(j + low				)

          _A    		=  high + 1
          _A    		=  min(high + end   ,		__snake_case				)

     return prime
print(sieve(10**6))



