



'''simple docstring'''






from __future__ import annotations



class 							lowercase       :


 """simple docstring"""


 def __init__(							self  ,a_ = 0							)		->   Dict:
        _UpperCAmelCase  :						Dict			     = key







 def _snake_case (							self  ,a_  ,a_							)		->   list[str]:
        assert isinstance(a_  ,a_							) and isinstance(a_  ,a_							)

        _UpperCAmelCase  :						List[Any]			     = key or self.__key or 1

        # make sure key is an appropriate size
        key %= 255

        return [chr(ord(a_							) ^ key							) for ch in content]







 def _snake_case (							self  ,a_  ,a_							)		->   list[str]:
        assert isinstance(a_  ,a_							) and isinstance(a_  ,a_							)

        _UpperCAmelCase  :						Optional[Any]			     = key or self.__key or 1

        # make sure key is an appropriate size
        key %= 255

        return [chr(ord(a_							) ^ key							) for ch in content]







 def _snake_case (							self  ,a_  ,a_ = 0							)		->   str:
        assert isinstance(a_  ,a_							) and isinstance(a_  ,a_							)

        _UpperCAmelCase  :						List[Any]			     = key or self.__key or 1

        # make sure key can be any size
        while key > 255:
               key -= 255

        # This will be returned
        _UpperCAmelCase  :						str			     = """"""

        for ch in content:
               ans += chr(ord(a_							) ^ key							)

        return ans







 def _snake_case (							self  ,a_  ,a_ = 0							)		->   str:
        assert isinstance(a_  ,a_							) and isinstance(a_  ,a_							)

        _UpperCAmelCase  :						List[Any]			     = key or self.__key or 1

        # make sure key can be any size
        while key > 255:
               key -= 255

        # This will be returned
        _UpperCAmelCase  :						str			     = """"""

        for ch in content:
               ans += chr(ord(a_							) ^ key							)

        return ans







 def _snake_case (							self  ,a_  ,a_ = 0							)		->   bool:
        assert isinstance(a_  ,a_							) and isinstance(a_  ,a_							)

        try:
               with open(a_							) as fin, open("""encrypt.out"""  ,"""w+"""							) as fout:
                      # actual encrypt-process
                      for line in fin:
                             fout.write(self.encrypt_string(a_  ,a_							)							)

        except OSError:
               return False

        return True







 def _snake_case (							self  ,a_  ,a_							)		->   bool:
        assert isinstance(a_  ,a_							) and isinstance(a_  ,a_							)

        try:
               with open(a_							) as fin, open("""decrypt.out"""  ,"""w+"""							) as fout:
                      # actual encrypt-process
                      for line in fin:
                             fout.write(self.decrypt_string(a_  ,a_							)							)

        except OSError:
               return False

        return True


# Tests
# crypt = XORCipher()
# key = 67

# # test encrypt
# print(crypt.encrypt("hallo welt",key))
# # test decrypt
# print(crypt.decrypt(crypt.encrypt("hallo welt",key), key))

# # test encrypt_string
# print(crypt.encrypt_string("hallo welt",key))

# # test decrypt_string
# print(crypt.decrypt_string(crypt.encrypt_string("hallo welt",key),key))

# if (crypt.encrypt_file("test.txt",key)):
#       print("encrypt successful")
# else:
#       print("encrypt unsuccessful")

# if (crypt.decrypt_file("encrypt.out",key)):
#       print("decrypt successful")
# else:
#       print("decrypt unsuccessful")








'''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_							: Optional[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_							: Dict						 =	{
    """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     snake_case_					(     lowerCAmelCase_		)->		int:





       '''simple docstring'''


       _UpperCAmelCase  :						Union[str, Any]			     = EfficientNetConfig()
       _UpperCAmelCase  :						List[Any]			     = CONFIG_MAP[model_name]["""hidden_dim"""]
       _UpperCAmelCase  :						str			     = CONFIG_MAP[model_name]["""width_coef"""]
       _UpperCAmelCase  :						int			     = CONFIG_MAP[model_name]["""depth_coef"""]
       _UpperCAmelCase  :						Optional[Any]			     = CONFIG_MAP[model_name]["""image_size"""]
       _UpperCAmelCase  :						List[str]			     = CONFIG_MAP[model_name]["""dropout_rate"""]
       _UpperCAmelCase  :						Optional[int]			     = CONFIG_MAP[model_name]["""dw_padding"""]

       _UpperCAmelCase  :						List[str]			     = """huggingface/label-files"""
       _UpperCAmelCase  :						Optional[Any]			     = """imagenet-1k-id2label.json"""
       _UpperCAmelCase  :						List[str]			     = 1000
       _UpperCAmelCase  :						Optional[int]			     = json.load(open(hf_hub_download(lowerCAmelCase_       ,   lowerCAmelCase_       ,   repo_type="""dataset"""		)       ,   """r"""		)		)
       _UpperCAmelCase  :						Dict			     = {int(lowerCAmelCase_		): v for k, v in idalabel.items()}

       _UpperCAmelCase  :						List[str]			     = idalabel
       _UpperCAmelCase  :						str			     = {v: k for k, v in idalabel.items()}
       return config




def     snake_case_					(     )->		Tuple:





       '''simple docstring'''


       _UpperCAmelCase  :						List[str]			     = """http://images.cocodataset.org/val2017/000000039769.jpg"""
       _UpperCAmelCase  :						Tuple			     = Image.open(requests.get(lowerCAmelCase_       ,   stream=lowerCAmelCase_		).raw		)
       return im




def     snake_case_					(     lowerCAmelCase_		)->		Tuple:





       '''simple docstring'''


       _UpperCAmelCase  :						str			     = CONFIG_MAP[model_name]["""image_size"""]
       _UpperCAmelCase  :						Tuple			     = 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=lowerCAmelCase_       ,   )
       return preprocessor




def     snake_case_					(     lowerCAmelCase_		)->		List[str]:





       '''simple docstring'''


       _UpperCAmelCase  :						int			     = [v.split("""_"""		)[0].split("""block"""		)[1] for v in original_param_names if v.startswith("""block"""		)]
       _UpperCAmelCase  :						Optional[int]			     = sorted(set(lowerCAmelCase_		)		)
       _UpperCAmelCase  :						str			     = len(lowerCAmelCase_		)
       _UpperCAmelCase  :						Optional[Any]			     = {b: str(lowerCAmelCase_		) for b, i in zip(lowerCAmelCase_       ,   range(lowerCAmelCase_		)		)}

       _UpperCAmelCase  :						List[str]			     = []
       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  :						Any			     = 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  :						Union[str, Any]			     = {}
       for item in rename_keys:
              if item[0] in original_param_names:
                     _UpperCAmelCase  :						str			     = """efficientnet.""" + item[1]

       _UpperCAmelCase  :						Optional[Any]			     = """classifier.weight"""
       _UpperCAmelCase  :						Optional[Any]			     = """classifier.bias"""
       return key_mapping




def     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_		)->		Any:





       '''simple docstring'''


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

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





       '''simple docstring'''


       _UpperCAmelCase  :						Union[str, Any]			     = model_classes[model_name](
           include_top=lowerCAmelCase_       ,   weights="""imagenet"""       ,   input_tensor=lowerCAmelCase_       ,   input_shape=lowerCAmelCase_       ,   pooling=lowerCAmelCase_       ,   classes=1000       ,   classifier_activation="""softmax"""       ,   )

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

       # Load HuggingFace model
       _UpperCAmelCase  :						List[Any]			     = get_efficientnet_config(lowerCAmelCase_		)
       _UpperCAmelCase  :						Optional[Any]			     = EfficientNetForImageClassification(lowerCAmelCase_		).eval()
       _UpperCAmelCase  :						int			     = hf_model.state_dict()

       # Create src-to-dst parameter name mapping dictionary
       print("""Converting parameters..."""		)
       _UpperCAmelCase  :						Optional[int]			     = rename_keys(lowerCAmelCase_		)
       replace_params(lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_		)

       # Initialize preprocessor and preprocess input image
       _UpperCAmelCase  :						str			     = convert_image_processor(lowerCAmelCase_		)
       _UpperCAmelCase  :						List[str]			     = preprocessor(images=prepare_img()       ,   return_tensors="""pt"""		)

       # HF model inference
       hf_model.eval()
       with torch.no_grad():
              _UpperCAmelCase  :						List[str]			     = hf_model(**lowerCAmelCase_		)
       _UpperCAmelCase  :						Any			     = outputs.logits.detach().numpy()

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


if __name__ == "__main__":
   A_							: Dict						 =	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_							: Optional[Any]						 =	parser.parse_args()
   convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)



