"""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			   = logging.get_logger(__name__)

__A			   = {
    "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			   = {
    "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 SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase )  ->  str:
   lowercase__:   str					=      EfficientNetConfig()
   lowercase__:   int					=      CONFIG_MAP[model_name]['''hidden_dim''']
   lowercase__:   List[Any]					=      CONFIG_MAP[model_name]['''width_coef''']
   lowercase__:   Any					=      CONFIG_MAP[model_name]['''depth_coef''']
   lowercase__:   Dict					=      CONFIG_MAP[model_name]['''image_size''']
   lowercase__:   Union[str, Any]					=      CONFIG_MAP[model_name]['''dropout_rate''']
   lowercase__:   Optional[int]					=      CONFIG_MAP[model_name]['''dw_padding''']

   lowercase__:   Dict					=      '''huggingface/label-files'''
   lowercase__:   int					=      '''imagenet-1k-id2label.json'''
   lowercase__:   Optional[int]					=      1_0_0_0
   lowercase__:   Tuple					=      json.load(open(hf_hub_download(__UpperCAmelCase       ,    __UpperCAmelCase       ,    repo_type='''dataset''' )       ,    '''r''' ) )
   lowercase__:   List[Any]					=      {int(__UpperCAmelCase ): v for k, v in idalabel.items()}

   lowercase__:   Any					=      idalabel
   lowercase__:   Union[str, Any]					=      {v: k for k, v in idalabel.items()}
   return config



def SCREAMING_SNAKE_CASE__			(   )  ->  Union[str, Any]:
   lowercase__:   str					=      '''http://images.cocodataset.org/val2017/000000039769.jpg'''
   lowercase__:   List[Any]					=      Image.open(requests.get(__UpperCAmelCase       ,    stream=__UpperCAmelCase ).raw )
   return im



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase )  ->  Union[str, Any]:
   lowercase__:   Optional[Any]					=      CONFIG_MAP[model_name]['''image_size''']
   lowercase__:   str					=      EfficientNetImageProcessor(
       size={'''height''': size, '''width''': size}       ,    image_mean=[0.4_8_5, 0.4_5_6, 0.4_0_6]       ,    image_std=[0.4_7_8_5_3_9_4_4, 0.4_7_3_2_8_6_4, 0.4_7_4_3_4_1_6_3]       ,    do_center_crop=__UpperCAmelCase       ,    )
   return preprocessor



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase )  ->  Tuple:
   lowercase__:   str					=      [v.split('''_''' )[0].split('''block''' )[1] for v in original_param_names if v.startswith('''block''' )]
   lowercase__:   str					=      sorted(set(__UpperCAmelCase ) )
   lowercase__:   Dict					=      len(__UpperCAmelCase )
   lowercase__:   List[Any]					=      {b: str(__UpperCAmelCase ) for b, i in zip(__UpperCAmelCase       ,    range(__UpperCAmelCase ) )}

   lowercase__:   Optional[int]					=      []
   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:
      lowercase__:   int					=      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''') )

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

   lowercase__:   Any					=      '''classifier.weight'''
   lowercase__:   Optional[Any]					=      '''classifier.bias'''
   return key_mapping



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase )  ->  Any:
   for key, value in tf_params.items():
      if "normalization" in key:
         continue

      lowercase__:   Any					=      key_mapping[key]
      if "_conv" in key and "kernel" in key:
         lowercase__:   Optional[int]					=      torch.from_numpy(__UpperCAmelCase ).permute(3       ,    2       ,    0       ,    1 )
      elif "depthwise_kernel" in key:
         lowercase__:   Optional[Any]					=      torch.from_numpy(__UpperCAmelCase ).permute(2       ,    3       ,    0       ,    1 )
      elif "kernel" in key:
         lowercase__:   List[str]					=      torch.from_numpy(np.transpose(__UpperCAmelCase ) )
      else:
         lowercase__:   Any					=      torch.from_numpy(__UpperCAmelCase )

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



@torch.no_grad()
def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase )  ->  List[Any]:
   lowercase__:   Optional[Any]					=      model_classes[model_name](
       include_top=__UpperCAmelCase       ,    weights='''imagenet'''       ,    input_tensor=__UpperCAmelCase       ,    input_shape=__UpperCAmelCase       ,    pooling=__UpperCAmelCase       ,    classes=1_0_0_0       ,    classifier_activation='''softmax'''       ,    )

   lowercase__:   Union[str, Any]					=      original_model.trainable_variables
   lowercase__:   Union[str, Any]					=      original_model.non_trainable_variables
   lowercase__:   Union[str, Any]					=      {param.name: param.numpy() for param in tf_params}
   for param in tf_non_train_params:
      lowercase__:   Any					=      param.numpy()
   lowercase__:   str					=      list(tf_params.keys() )

   # Load HuggingFace model
   lowercase__:   int					=      get_efficientnet_config(__UpperCAmelCase )
   lowercase__:   Optional[int]					=      EfficientNetForImageClassification(__UpperCAmelCase ).eval()
   lowercase__:   Any					=      hf_model.state_dict()

   # Create src-to-dst parameter name mapping dictionary
   print('''Converting parameters...''' )
   lowercase__:   List[str]					=      rename_keys(__UpperCAmelCase )
   replace_params(__UpperCAmelCase       ,    __UpperCAmelCase       ,    __UpperCAmelCase )

   # Initialize preprocessor and preprocess input image
   lowercase__:   Union[str, Any]					=      convert_image_processor(__UpperCAmelCase )
   lowercase__:   List[Any]					=      preprocessor(images=prepare_img()       ,    return_tensors='''pt''' )

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

   # Original model inference
   lowercase__:   Tuple					=      False
   lowercase__:   Optional[Any]					=      CONFIG_MAP[model_name]['''image_size''']
   lowercase__:   List[str]					=      prepare_img().resize((image_size, image_size)       ,    resample=PIL.Image.NEAREST )
   lowercase__:   str					=      image.img_to_array(__UpperCAmelCase )
   lowercase__:   List[Any]					=      np.expand_dims(__UpperCAmelCase       ,    axis=0 )
   lowercase__:   str					=      original_model.predict(__UpperCAmelCase )

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

   if push_to_hub:
      # Push model and image processor to hub
      print(F"""Pushing converted {model_name} to the hub...""" )
      lowercase__:   List[Any]					=      F"""efficientnet-{model_name}"""
      preprocessor.push_to_hub(__UpperCAmelCase )
      hf_model.push_to_hub(__UpperCAmelCase )


if __name__ == "__main__":
       __A			   = 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			   = parser.parse_args()
       convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)

"""simple docstring"""






from collections.abc import Sequence



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase = False )  ->  float:
   if not arr:
      return 0

   lowercase__:   Any					=      0 if allow_empty_subarrays else float('''-inf''' )
   lowercase__:   Union[str, Any]					=      0.0
   for num in arr:
      lowercase__:   List[str]					=      max(0 if allow_empty_subarrays else num       ,    curr_sum + num )
      lowercase__:   int					=      max(__UpperCAmelCase       ,    __UpperCAmelCase )

   return max_sum


if __name__ == "__main__":
       from doctest import testmod

       testmod()

       __A			   = [-2, 1, -3, 4, -1, 2, 1, -5, 4]
       print(f'''{max_subarray_sum(nums) = }''')
