






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()
lowerCamelCase__							=     logging.get_logger(__name__)

lowerCamelCase__							=     {
    """b0""": efficientnet.EfficientNetBa,
    """b1""": efficientnet.EfficientNetBa,
    """b2""": efficientnet.EfficientNetBa,
    """b3""": efficientnet.EfficientNetBa,
    """b4""": efficientnet.EfficientNetBa,
    """b5""": efficientnet.EfficientNetBa,
    """b6""": efficientnet.EfficientNetBa,
    """b7""": efficientnet.EfficientNetBa,
}

lowerCamelCase__							=     {
    """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   UpperCamelCase  (       snake_case__     :   Union[str, Any]  ):




     '''simple docstring'''






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

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

     __snake_case			:List[Any]          =						idalabel
     __snake_case			:Optional[int]          =						{v: k for k, v in idalabel.items()}
     return config






def   UpperCamelCase  (       ):




     '''simple docstring'''






     __snake_case			:Any          =						"""http://images.cocodataset.org/val2017/000000039769.jpg"""
     __snake_case			:Optional[int]          =						Image.open(requests.get(snake_case__	,stream=snake_case__  ).raw  )
     return im






def   UpperCamelCase  (       snake_case__     :   Optional[Any]  ):




     '''simple docstring'''






     __snake_case			:Dict          =						CONFIG_MAP[model_name]["""image_size"""]
     __snake_case			:Optional[int]          =						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=snake_case__	,)
     return preprocessor






def   UpperCamelCase  (       snake_case__     :   List[str]  ):




     '''simple docstring'''






     __snake_case			:Union[str, Any]          =						[v.split("""_"""  )[0].split("""block"""  )[1] for v in original_param_names if v.startswith("""block"""  )]
     __snake_case			:Tuple          =						sorted(set(snake_case__  )  )
     __snake_case			:str          =						len(snake_case__  )
     __snake_case			:Any          =						{b: str(snake_case__  ) for b, i in zip(snake_case__	,range(snake_case__  )  )}

     __snake_case			: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:
          __snake_case			: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""")  )

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

     __snake_case			:Optional[int]          =						"""classifier.weight"""
     __snake_case			:List[str]          =						"""classifier.bias"""
     return key_mapping






def   UpperCamelCase  (       snake_case__     :   int	,snake_case__     :   List[str]	,snake_case__     :   Union[str, Any]  ):




     '''simple docstring'''






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

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




     '''simple docstring'''






     __snake_case			:Any          =						model_classes[model_name](
         include_top=snake_case__	,weights="""imagenet"""	,input_tensor=snake_case__	,input_shape=snake_case__	,pooling=snake_case__	,classes=1000	,classifier_activation="""softmax"""	,)

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

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

     # Create src-to-dst parameter name mapping dictionary
     print("""Converting parameters..."""  )
     __snake_case			:Dict          =						rename_keys(snake_case__  )
     replace_params(snake_case__	,snake_case__	,snake_case__  )

     # Initialize preprocessor and preprocess input image
     __snake_case			:Dict          =						convert_image_processor(snake_case__  )
     __snake_case			:List[Any]          =						preprocessor(images=prepare_img()	,return_tensors="""pt"""  )

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

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


if __name__ == "__main__":
     lowerCamelCase__							=     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""")

     lowerCamelCase__							=     parser.parse_args()
     convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)










def   UpperCamelCase  (       snake_case__     :   str	,snake_case__     :   int  ):




     '''simple docstring'''






     __snake_case			:list[list[str]]          =						[[] for _ in range(snake_case__  )]
     __snake_case			:Union[str, Any]          =						key - 1

     if key <= 0:
          raise ValueError("""Height of grid can't be 0 or negative"""  )
     if key == 1 or len(snake_case__  ) <= key:
          return input_string

     for position, character in enumerate(snake_case__  ):
          __snake_case			:Any          =						position % (lowest * 2)  # puts it in bounds
          __snake_case			:Optional[int]          =						min(snake_case__	,lowest * 2 - num  )  # creates zigzag pattern
          temp_grid[num].append(snake_case__  )
     __snake_case			:List[Any]          =						["""""".join(snake_case__  ) for row in temp_grid]
     __snake_case			:Any          =						"""""".join(snake_case__  )

     return output_string






def   UpperCamelCase  (       snake_case__     :   str	,snake_case__     :   int  ):




     '''simple docstring'''






     __snake_case			:Union[str, Any]          =						[]
     __snake_case			:Optional[int]          =						key - 1

     if key <= 0:
          raise ValueError("""Height of grid can't be 0 or negative"""  )
     if key == 1:
          return input_string

     __snake_case			:list[list[str]]          =						[[] for _ in range(snake_case__  )]  # generates template
     for position in range(len(snake_case__  )  ):
          __snake_case			:List[str]          =						position % (lowest * 2)  # puts it in bounds
          __snake_case			:int          =						min(snake_case__	,lowest * 2 - num  )  # creates zigzag pattern
          temp_grid[num].append("""*"""  )

     __snake_case			:str          =						0
     for row in temp_grid:  # fills in the characters
          __snake_case			:str          =						input_string[counter : counter + len(snake_case__  )]
          grid.append(list(snake_case__  )  )
          counter += len(snake_case__  )

     __snake_case			:Any          =						""""""  # reads as zigzag
     for position in range(len(snake_case__  )  ):
          __snake_case			:Optional[int]          =						position % (lowest * 2)  # puts it in bounds
          __snake_case			:int          =						min(snake_case__	,lowest * 2 - num  )  # creates zigzag pattern
          output_string += grid[num][0]
          grid[num].pop(0  )
     return output_string






def   UpperCamelCase  (       snake_case__     :   str  ):




     '''simple docstring'''






     __snake_case			:Optional[Any]          =						{}
     for key_guess in range(1	,len(snake_case__  )  ):  # tries every key
          __snake_case			:Dict          =						decrypt(snake_case__	,snake_case__  )
     return results


if __name__ == "__main__":
     import doctest

     doctest.testmod()


