
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							=			logging.get_logger(__name__)

__UpperCAmelCase							=			{
    "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							=			{
    "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       A__      (    __lowerCamelCase		):
   SCREAMING_SNAKE_CASE_     =	EfficientNetConfig()
   SCREAMING_SNAKE_CASE_     =	CONFIG_MAP[model_name]['hidden_dim']
   SCREAMING_SNAKE_CASE_     =	CONFIG_MAP[model_name]['width_coef']
   SCREAMING_SNAKE_CASE_     =	CONFIG_MAP[model_name]['depth_coef']
   SCREAMING_SNAKE_CASE_     =	CONFIG_MAP[model_name]['image_size']
   SCREAMING_SNAKE_CASE_     =	CONFIG_MAP[model_name]['dropout_rate']
   SCREAMING_SNAKE_CASE_     =	CONFIG_MAP[model_name]['dw_padding']

   SCREAMING_SNAKE_CASE_     =	'huggingface/label-files'
   SCREAMING_SNAKE_CASE_     =	'imagenet-1k-id2label.json'
   SCREAMING_SNAKE_CASE_     =	10_00
   SCREAMING_SNAKE_CASE_     =	json.load(open(hf_hub_download(snake_case__,   snake_case__,   repo_type='''dataset'''		),   '''r'''		)		)
   SCREAMING_SNAKE_CASE_     =	{int(snake_case__		): v for k, v in idalabel.items()}

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


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


def       A__      (    __lowerCamelCase		):
   SCREAMING_SNAKE_CASE_     =	CONFIG_MAP[model_name]['image_size']
   SCREAMING_SNAKE_CASE_     =	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       A__      (    __lowerCamelCase		):
   SCREAMING_SNAKE_CASE_     =	[v.split('''_'''		)[0].split('''block'''		)[1] for v in original_param_names if v.startswith('''block'''		)]
   SCREAMING_SNAKE_CASE_     =	sorted(set(snake_case__		)		)
   SCREAMING_SNAKE_CASE_     =	len(snake_case__		)
   SCREAMING_SNAKE_CASE_     =	{b: str(snake_case__		) for b, i in zip(snake_case__,   range(snake_case__		)		)}

   SCREAMING_SNAKE_CASE_     =	[]
   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:
      SCREAMING_SNAKE_CASE_     =	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''')		)

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

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


def       A__      (    __lowerCamelCase,   __lowerCamelCase,   __lowerCamelCase		):
   for key, value in tf_params.items():
      if "normalization" in key:
         continue

      SCREAMING_SNAKE_CASE_     =	key_mapping[key]
      if "_conv" in key and "kernel" in key:
         SCREAMING_SNAKE_CASE_     =	torch.from_numpy(snake_case__		).permute(3,   2,   0,   1		)
      elif "depthwise_kernel" in key:
         SCREAMING_SNAKE_CASE_     =	torch.from_numpy(snake_case__		).permute(2,   3,   0,   1		)
      elif "kernel" in key:
         SCREAMING_SNAKE_CASE_     =	torch.from_numpy(np.transpose(snake_case__		)		)
      else:
         SCREAMING_SNAKE_CASE_     =	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       A__      (    __lowerCamelCase,   __lowerCamelCase,   __lowerCamelCase,   __lowerCamelCase		):
   SCREAMING_SNAKE_CASE_     =	model_classes[model_name](
       include_top=snake_case__,   weights='''imagenet''',   input_tensor=snake_case__,   input_shape=snake_case__,   pooling=snake_case__,   classes=10_00,   classifier_activation='''softmax''',   )

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

   # Load HuggingFace model
   SCREAMING_SNAKE_CASE_     =	get_efficientnet_config(snake_case__		)
   SCREAMING_SNAKE_CASE_     =	EfficientNetForImageClassification(snake_case__		).eval()
   SCREAMING_SNAKE_CASE_     =	hf_model.state_dict()

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

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

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

   # Original model inference
   SCREAMING_SNAKE_CASE_     =	False
   SCREAMING_SNAKE_CASE_     =	CONFIG_MAP[model_name]['image_size']
   SCREAMING_SNAKE_CASE_     =	prepare_img().resize((image_size, image_size),   resample=PIL.Image.NEAREST		)
   SCREAMING_SNAKE_CASE_     =	image.img_to_array(snake_case__		)
   SCREAMING_SNAKE_CASE_     =	np.expand_dims(snake_case__,   axis=0		)
   SCREAMING_SNAKE_CASE_     =	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...'''		)
      SCREAMING_SNAKE_CASE_     =	F'''efficientnet-{model_name}'''
      preprocessor.push_to_hub(snake_case__		)
      hf_model.push_to_hub(snake_case__		)


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







import functools
from typing import Any
def 		UpperCamelCase	(   snake_case__       :    str       ,     snake_case__       :    list[str]	)	->     bool:

   # Validation
   if not isinstance(snake_case__       ,     snake_case__	) or len(snake_case__	) == 0:
      raise ValueError('the string should be not empty string'	)

   if not isinstance(snake_case__       ,     snake_case__	) or not all(
       isinstance(snake_case__       ,     snake_case__	) and len(snake_case__	) > 0 for item in words	):
      raise ValueError('the words should be a list of non-empty strings'	)

   # Build trie
   UpperCamelCase    :  dict[str, Any]							=	{}
   UpperCamelCase    :  List[str]							=	'WORD_KEEPER'

   for word in words:
      UpperCamelCase    :  List[str]							=	trie
      for c in word:
         if c not in trie_node:
            UpperCamelCase    :  int							=	{}

         UpperCamelCase    :  str							=	trie_node[c]

      UpperCamelCase    :  Tuple							=	True

   UpperCamelCase    :  List[Any]							=	len(snake_case__	)

   # Dynamic programming method
   @functools.cache
   def is_breakable(snake_case__       :    int	) -> bool:
      if index == len_string:
         return True

      UpperCamelCase    :  Dict							=	trie
      for i in range(snake_case__       ,     snake_case__	):
         UpperCamelCase    :  List[Any]							=	trie_node.get(string[i]       ,     snake_case__	)

         if trie_node is None:
            return False

         if trie_node.get(snake_case__       ,     snake_case__	) and is_breakable(i + 1	):
            return True

      return False

   return is_breakable(0	)


if __name__ == "__main__":
    import doctest

    doctest.testmod()



