'''simple docstring'''

import argparse
import os
import re

import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor

from accelerate import Accelerator







def __UpperCamelCase(   _A					:				List[str]    ):
   '''simple docstring'''
   UpperCAmelCase__							:				List[str]   				=  fname.split(os.path.sep    )[-1]
   return re.search(R'''^(.*)_\d+\.jpg$'''  ,			_A    ).groups()[0]


class 				_lowercase	(      lowerCAmelCase   ):
      '''simple docstring'''


      def __init__(				self					,lowerCamelCase_					,lowerCamelCase_=None					,lowerCamelCase_=None							)			->			List[str]:



         '''simple docstring'''



         UpperCAmelCase__							:				Tuple   				=  file_names
         UpperCAmelCase__							:				int   				=  image_transform
         UpperCAmelCase__							:				List[str]   				=  label_to_id




      def __len__(				self							)			->			Optional[int]:



         '''simple docstring'''



         return len(self.file_names							)




      def __getitem__(				self					,lowerCamelCase_							)			->			Optional[int]:



         '''simple docstring'''



         UpperCAmelCase__							:				Union[str, Any]   				=  self.file_names[idx]
         UpperCAmelCase__							:				Tuple   				=  PIL.Image.open(lowerCamelCase_							)
         UpperCAmelCase__							:				Optional[Any]   				=  raw_image.convert('''RGB'''							)
         if self.image_transform is not None:
            UpperCAmelCase__							:				List[str]   				=  self.image_transform(lowerCamelCase_							)
         UpperCAmelCase__							:				Optional[Any]   				=  extract_label(lowerCamelCase_							)
         if self.label_to_id is not None:
            UpperCAmelCase__							:				Tuple   				=  self.label_to_id[label]
         return {"image": image, "label": label}







def __UpperCamelCase(   _A					:				Tuple  ,			_A					:				Optional[int]    ):
   '''simple docstring'''
   # Initialize accelerator
   if args.with_tracking:
      UpperCAmelCase__							:				List[Any]   				=  Accelerator(
          cpu=args.cpu  ,			mixed_precision=args.mixed_precision  ,			log_with='''all'''  ,			project_dir=args.project_dir    )
   else:
      UpperCAmelCase__							:				Any   				=  Accelerator(cpu=args.cpu  ,			mixed_precision=args.mixed_precision    )

   # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
   UpperCAmelCase__							:				Dict   				=  config['''lr''']
   UpperCAmelCase__							:				str   				=  int(config['''num_epochs''']    )
   UpperCAmelCase__							:				Union[str, Any]   				=  int(config['''seed''']    )
   UpperCAmelCase__							:				Tuple   				=  int(config['''batch_size''']    )
   UpperCAmelCase__							:				Union[str, Any]   				=  config['''image_size''']
   if not isinstance(_A  ,			(list, tuple)    ):
      UpperCAmelCase__							:				str   				=  (image_size, image_size)

   # Parse out whether we are saving every epoch or after a certain number of batches
   if hasattr(args.checkpointing_steps  ,			'''isdigit'''    ):
      if args.checkpointing_steps == "epoch":
         UpperCAmelCase__							:				Optional[int]   				=  args.checkpointing_steps
      elif args.checkpointing_steps.isdigit():
         UpperCAmelCase__							:				Optional[Any]   				=  int(args.checkpointing_steps    )
      else:
         raise ValueError(
             F'''Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'''    )
   else:
      UpperCAmelCase__							:				List[Any]   				=  None

   # We need to initialize the trackers we use, and also store our configuration
   if args.with_tracking:
      UpperCAmelCase__							:				str   				=  os.path.split(_A    )[-1].split('''.'''    )[0]
      accelerator.init_trackers(_A  ,			_A    )

   # Grab all the image filenames
   UpperCAmelCase__							:				Dict   				=  [os.path.join(args.data_dir  ,			_A    ) for fname in os.listdir(args.data_dir    ) if fname.endswith('''.jpg'''    )]

   # Build the label correspondences
   UpperCAmelCase__							:				Tuple   				=  [extract_label(_A    ) for fname in file_names]
   UpperCAmelCase__							:				Optional[Any]   				=  list(set(_A    )    )
   id_to_label.sort()
   UpperCAmelCase__							:				str   				=  {lbl: i for i, lbl in enumerate(_A    )}

   # Set the seed before splitting the data.
   np.random.seed(_A    )
   torch.manual_seed(_A    )
   torch.cuda.manual_seed_all(_A    )

   # Split our filenames between train and validation
   UpperCAmelCase__							:				List[Any]   				=  np.random.permutation(len(_A    )    )
   UpperCAmelCase__							:				int   				=  int(0.8 * len(_A    )    )
   UpperCAmelCase__							:				str   				=  random_perm[:cut]
   UpperCAmelCase__							:				int   				=  random_perm[cut:]

   # For training we use a simple RandomResizedCrop
   UpperCAmelCase__							:				Optional[int]   				=  Compose([RandomResizedCrop(_A  ,			scale=(0.5, 1.0)    ), ToTensor()]    )
   UpperCAmelCase__							:				Union[str, Any]   				=  PetsDataset(
       [file_names[i] for i in train_split]  ,			image_transform=_A  ,			label_to_id=_A    )

   # For evaluation, we use a deterministic Resize
   UpperCAmelCase__							:				List[Any]   				=  Compose([Resize(_A    ), ToTensor()]    )
   UpperCAmelCase__							:				List[str]   				=  PetsDataset([file_names[i] for i in eval_split]  ,			image_transform=_A  ,			label_to_id=_A    )

   # Instantiate dataloaders.
   UpperCAmelCase__							:				List[Any]   				=  DataLoader(_A  ,			shuffle=_A  ,			batch_size=_A  ,			num_workers=4    )
   UpperCAmelCase__							:				Dict   				=  DataLoader(_A  ,			shuffle=_A  ,			batch_size=_A  ,			num_workers=4    )

   # Instantiate the model (we build the model here so that the seed also control new weights initialization)
   UpperCAmelCase__							:				int   				=  create_model('''resnet50d'''  ,			pretrained=_A  ,			num_classes=len(_A    )    )

   # We could avoid this line since the accelerator is set with `device_placement=True` (default value).
   # Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
   # creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
   UpperCAmelCase__							:				Optional[Any]   				=  model.to(accelerator.device    )

   # Freezing the base model
   for param in model.parameters():
      UpperCAmelCase__							:				Union[str, Any]   				=  False
   for param in model.get_classifier().parameters():
      UpperCAmelCase__							:				List[str]   				=  True

   # We normalize the batches of images to be a bit faster.
   UpperCAmelCase__							:				Dict   				=  torch.tensor(model.default_cfg['''mean''']    )[None, :, None, None].to(accelerator.device    )
   UpperCAmelCase__							:				Any   				=  torch.tensor(model.default_cfg['''std''']    )[None, :, None, None].to(accelerator.device    )

   # Instantiate optimizer
   UpperCAmelCase__							:				str   				=  torch.optim.Adam(params=model.parameters()  ,			lr=lr / 25    )

   # Instantiate learning rate scheduler
   UpperCAmelCase__							:				List[str]   				=  OneCycleLR(optimizer=_A  ,			max_lr=_A  ,			epochs=_A  ,			steps_per_epoch=len(_A    )    )

   # Prepare everything
   # There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
   # prepare method.
   UpperCAmelCase__				,      UpperCAmelCase__				,      UpperCAmelCase__				,      UpperCAmelCase__				,      UpperCAmelCase__							:				Optional[int]   				=  accelerator.prepare(
       _A  ,			_A  ,			_A  ,			_A  ,			_A    )
   # We need to keep track of how many total steps we have iterated over
   UpperCAmelCase__							:				Tuple   				=  0
   # We also need to keep track of the starting epoch so files are named properly
   UpperCAmelCase__							:				List[Any]   				=  0

   # Potentially load in the weights and states from a previous save
   if args.resume_from_checkpoint:
      if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
         accelerator.print(F'''Resumed from checkpoint: {args.resume_from_checkpoint}'''    )
         accelerator.load_state(args.resume_from_checkpoint    )
         UpperCAmelCase__							:				Optional[Any]   				=  os.path.basename(args.resume_from_checkpoint    )
      else:
         # Get the most recent checkpoint
         UpperCAmelCase__							:				Any   				=  [f.name for f in os.scandir(os.getcwd()    ) if f.is_dir()]
         dirs.sort(key=os.path.getctime    )
         UpperCAmelCase__							:				Optional[int]   				=  dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
      # Extract `epoch_{i}` or `step_{i}`
      UpperCAmelCase__							:				Any   				=  os.path.splitext(_A    )[0]

      if "epoch" in training_difference:
         UpperCAmelCase__							:				Optional[Any]   				=  int(training_difference.replace('''epoch_'''  ,			''''''    )    ) + 1
         UpperCAmelCase__							:				Optional[Any]   				=  None
      else:
         UpperCAmelCase__							:				List[str]   				=  int(training_difference.replace('''step_'''  ,			''''''    )    )
         UpperCAmelCase__							:				Tuple   				=  resume_step // len(_A    )
         resume_step -= starting_epoch * len(_A    )

    # Now we train the model
   for epoch in range(_A  ,			_A    ):
      model.train()
      if args.with_tracking:
         UpperCAmelCase__							:				Union[str, Any]   				=  0
      if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
         # We need to skip steps until we reach the resumed step
         UpperCAmelCase__							:				str   				=  accelerator.skip_first_batches(_A  ,			_A    )
         overall_step += resume_step
      else:
         # After the first iteration though, we need to go back to the original dataloader
         UpperCAmelCase__							:				Any   				=  train_dataloader
      for batch in active_dataloader:
         # We could avoid this line since we set the accelerator with `device_placement=True`.
         UpperCAmelCase__							:				Optional[Any]   				=  {k: v.to(accelerator.device    ) for k, v in batch.items()}
         UpperCAmelCase__							:				List[str]   				=  (batch['''image'''] - mean) / std
         UpperCAmelCase__							:				int   				=  model(_A    )
         UpperCAmelCase__							:				Optional[int]   				=  torch.nn.functional.cross_entropy(_A  ,			batch['''label''']    )
         # We keep track of the loss at each epoch
         if args.with_tracking:
            total_loss += loss.detach().float()
         accelerator.backward(_A    )
         optimizer.step()
         lr_scheduler.step()
         optimizer.zero_grad()
         overall_step += 1
         if isinstance(_A  ,			_A    ):
            UpperCAmelCase__							:				str   				=  F'''step_{overall_step}'''
            if overall_step % checkpointing_steps == 0:
               if args.output_dir is not None:
                  UpperCAmelCase__							:				Union[str, Any]   				=  os.path.join(args.output_dir  ,			_A    )
               accelerator.save_state(_A    )
      model.eval()
      UpperCAmelCase__							:				int   				=  0
      UpperCAmelCase__							:				List[str]   				=  0
      for step, batch in enumerate(_A    ):
         # We could avoid this line since we set the accelerator with `device_placement=True`.
         UpperCAmelCase__							:				List[Any]   				=  {k: v.to(accelerator.device    ) for k, v in batch.items()}
         UpperCAmelCase__							:				Union[str, Any]   				=  (batch['''image'''] - mean) / std
         with torch.no_grad():
            UpperCAmelCase__							:				int   				=  model(_A    )
         UpperCAmelCase__							:				Dict   				=  outputs.argmax(dim=-1    )
         UpperCAmelCase__				,      UpperCAmelCase__							:				int   				=  accelerator.gather_for_metrics((predictions, batch['''label'''])    )
         UpperCAmelCase__							:				Tuple   				=  predictions == references
         num_elems += accurate_preds.shape[0]
         accurate += accurate_preds.long().sum()

      UpperCAmelCase__							:				Optional[int]   				=  accurate.item() / num_elems
      # Use accelerator.print to print only on the main process.
      accelerator.print(F'''epoch {epoch}: {1_00 * eval_metric:.2f}'''    )
      if args.with_tracking:
         accelerator.log(
             {
                 '''accuracy''': 1_00 * eval_metric,
                 '''train_loss''': total_loss.item() / len(_A    ),
                 '''epoch''': epoch,
             }  ,			step=_A  ,			)
      if checkpointing_steps == "epoch":
         UpperCAmelCase__							:				List[str]   				=  F'''epoch_{epoch}'''
         if args.output_dir is not None:
            UpperCAmelCase__							:				List[str]   				=  os.path.join(args.output_dir  ,			_A    )
         accelerator.save_state(_A    )

   if args.with_tracking:
      accelerator.end_training()







def __UpperCamelCase(   ):
   '''simple docstring'''
   UpperCAmelCase__							:				str   				=  argparse.ArgumentParser(description='''Simple example of training script.'''    )
   parser.add_argument('''--data_dir'''  ,			required=_A  ,			help='''The data folder on disk.'''    )
   parser.add_argument('''--fp16'''  ,			action='''store_true'''  ,			help='''If passed, will use FP16 training.'''    )
   parser.add_argument(
       '''--mixed_precision'''  ,			type=_A  ,			default=_A  ,			choices=['''no''', '''fp16''', '''bf16''', '''fp8''']  ,			help='''Whether to use mixed precision. Choose'''
       '''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
       '''and an Nvidia Ampere GPU.'''  ,			)
   parser.add_argument('''--cpu'''  ,			action='''store_true'''  ,			help='''If passed, will train on the CPU.'''    )
   parser.add_argument(
       '''--checkpointing_steps'''  ,			type=_A  ,			default=_A  ,			help='''Whether the various states should be saved at the end of every n steps, or \'epoch\' for each epoch.'''  ,			)
   parser.add_argument(
       '''--output_dir'''  ,			type=_A  ,			default='''.'''  ,			help='''Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'''  ,			)
   parser.add_argument(
       '''--resume_from_checkpoint'''  ,			type=_A  ,			default=_A  ,			help='''If the training should continue from a checkpoint folder.'''  ,			)
   parser.add_argument(
       '''--with_tracking'''  ,			action='''store_true'''  ,			help='''Whether to load in all available experiment trackers from the environment and use them for logging.'''  ,			)
   parser.add_argument(
       '''--project_dir'''  ,			type=_A  ,			default='''logs'''  ,			help='''Location on where to store experiment tracking logs` and relevent project information'''  ,			)
   UpperCAmelCase__							:				Any   				=  parser.parse_args()
   UpperCAmelCase__							:				Any   				=  {'''lr''': 3e-2, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 64, '''image_size''': 2_24}
   training_function(_A  ,			_A    )


if __name__ == "__main__":
  main()





'''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__     :	Optional[Any]				=			logging.get_logger(__name__)

UpperCamelCase__     :	Tuple				=			{
    '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__     :	int				=			{
    'b0': {
        'hidden_dim': 1_280,
        'width_coef': 1.0,
        'depth_coef': 1.0,
        'image_size': 224,
        'dropout_rate': 0.2,
        'dw_padding': [],
    },
    'b1': {
        'hidden_dim': 1_280,
        'width_coef': 1.0,
        'depth_coef': 1.1,
        'image_size': 240,
        'dropout_rate': 0.2,
        'dw_padding': [16],
    },
    'b2': {
        'hidden_dim': 1_408,
        'width_coef': 1.1,
        'depth_coef': 1.2,
        'image_size': 260,
        'dropout_rate': 0.3,
        'dw_padding': [5, 8, 16],
    },
    'b3': {
        'hidden_dim': 1_536,
        'width_coef': 1.2,
        'depth_coef': 1.4,
        'image_size': 300,
        'dropout_rate': 0.3,
        'dw_padding': [5, 18],
    },
    'b4': {
        'hidden_dim': 1_792,
        'width_coef': 1.4,
        'depth_coef': 1.8,
        'image_size': 380,
        'dropout_rate': 0.4,
        'dw_padding': [6],
    },
    'b5': {
        'hidden_dim': 2_048,
        'width_coef': 1.6,
        'depth_coef': 2.2,
        'image_size': 456,
        'dropout_rate': 0.4,
        'dw_padding': [13, 27],
    },
    'b6': {
        'hidden_dim': 2_304,
        'width_coef': 1.8,
        'depth_coef': 2.6,
        'image_size': 528,
        'dropout_rate': 0.5,
        'dw_padding': [31],
    },
    'b7': {
        'hidden_dim': 2_560,
        'width_coef': 2.0,
        'depth_coef': 3.1,
        'image_size': 600,
        'dropout_rate': 0.5,
        'dw_padding': [18],
    },
}







def __UpperCamelCase(   _A					:				List[Any]    ):
   '''simple docstring'''
   UpperCAmelCase__							:				Dict   				=  EfficientNetConfig()
   UpperCAmelCase__							:				int   				=  CONFIG_MAP[model_name]['''hidden_dim''']
   UpperCAmelCase__							:				Optional[Any]   				=  CONFIG_MAP[model_name]['''width_coef''']
   UpperCAmelCase__							:				int   				=  CONFIG_MAP[model_name]['''depth_coef''']
   UpperCAmelCase__							:				Optional[int]   				=  CONFIG_MAP[model_name]['''image_size''']
   UpperCAmelCase__							:				int   				=  CONFIG_MAP[model_name]['''dropout_rate''']
   UpperCAmelCase__							:				Any   				=  CONFIG_MAP[model_name]['''dw_padding''']

   UpperCAmelCase__							:				Tuple   				=  '''huggingface/label-files'''
   UpperCAmelCase__							:				Dict   				=  '''imagenet-1k-id2label.json'''
   UpperCAmelCase__							:				List[Any]   				=  10_00
   UpperCAmelCase__							:				Union[str, Any]   				=  json.load(open(hf_hub_download(_A  ,			_A  ,			repo_type='''dataset'''    )  ,			'''r'''    )    )
   UpperCAmelCase__							:				Union[str, Any]   				=  {int(_A    ): v for k, v in idalabel.items()}

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







def __UpperCamelCase(   ):
   '''simple docstring'''
   UpperCAmelCase__							:				Union[str, Any]   				=  '''http://images.cocodataset.org/val2017/000000039769.jpg'''
   UpperCAmelCase__							:				List[str]   				=  Image.open(requests.get(_A  ,			stream=_A    ).raw    )
   return im







def __UpperCamelCase(   _A					:				Tuple    ):
   '''simple docstring'''
   UpperCAmelCase__							:				Tuple   				=  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=_A  ,			)
   return preprocessor







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

   UpperCAmelCase__							:				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:
      UpperCAmelCase__							:				Tuple   				=  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__							:				List[str]   				=  {}
   for item in rename_keys:
      if item[0] in original_param_names:
         UpperCAmelCase__							:				str   				=  '''efficientnet.''' + item[1]

   UpperCAmelCase__							:				List[str]   				=  '''classifier.weight'''
   UpperCAmelCase__							:				Union[str, Any]   				=  '''classifier.bias'''
   return key_mapping







def __UpperCamelCase(   _A					:				Optional[Any]  ,			_A					:				List[Any]  ,			_A					:				Optional[Any]    ):
   '''simple docstring'''
   for key, value in tf_params.items():
      if "normalization" in key:
         continue

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

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







@torch.no_grad()
def __UpperCamelCase(   _A					:				Tuple  ,			_A					:				str  ,			_A					:				Union[str, Any]  ,			_A					:				List[str]    ):
   '''simple docstring'''
   UpperCAmelCase__							:				int   				=  model_classes[model_name](
       include_top=_A  ,			weights='''imagenet'''  ,			input_tensor=_A  ,			input_shape=_A  ,			pooling=_A  ,			classes=10_00  ,			classifier_activation='''softmax'''  ,			)

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

   # Load HuggingFace model
   UpperCAmelCase__							:				Union[str, Any]   				=  get_efficientnet_config(_A    )
   UpperCAmelCase__							:				Any   				=  EfficientNetForImageClassification(_A    ).eval()
   UpperCAmelCase__							:				Tuple   				=  hf_model.state_dict()

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

   # Initialize preprocessor and preprocess input image
   UpperCAmelCase__							:				List[Any]   				=  convert_image_processor(_A    )
   UpperCAmelCase__							:				List[Any]   				=  preprocessor(images=prepare_img()  ,			return_tensors='''pt'''    )

   # HF model inference
   hf_model.eval()
   with torch.no_grad():
      UpperCAmelCase__							:				Optional[Any]   				=  hf_model(**_A    )
   UpperCAmelCase__							:				Union[str, Any]   				=  outputs.logits.detach().numpy()

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

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

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


if __name__ == "__main__":
  UpperCamelCase__     :	int				=			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__     :	Union[str, Any]				=			parser.parse_args()
  convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)




