




import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.local_sgd import LocalSGD


########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to gradient accumulation.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


SCREAMING_SNAKE_CASE__       :      List[str]  =   16
SCREAMING_SNAKE_CASE__       :      Union[str, Any]  =   32

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = 16    )	-> Union[str, Any]:
       lowerCamelCase   :      Tuple			 =					AutoTokenizer.from_pretrained("bert-base-cased"    )
       lowerCamelCase   :      Tuple			 =					load_dataset("glue" ,"mrpc"    )

       def tokenize_function(_SCREAMING_SNAKE_CASE    ):
              # max_length=None => use the model max length (it's actually the default)
              lowerCamelCase   :      Dict			 =					tokenizer(examples["sentence1"] ,examples["sentence2"] ,truncation=_SCREAMING_SNAKE_CASE ,max_length=_SCREAMING_SNAKE_CASE    )
              return outputs

       # Apply the method we just defined to all the examples in all the splits of the dataset
       # starting with the main process first:
       with accelerator.main_process_first():
              lowerCamelCase   :      str			 =					datasets.map(
                  _SCREAMING_SNAKE_CASE ,batched=_SCREAMING_SNAKE_CASE ,remove_columns=["idx", "sentence1", "sentence2"] ,)

       # We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
       # transformers library
       lowerCamelCase   :      Tuple			 =					tokenized_datasets.rename_column("label" ,"labels"    )

       def collate_fn(_SCREAMING_SNAKE_CASE    ):
              # On TPU it's best to pad everything to the same length or training will be very slow.
              lowerCamelCase   :      int			 =					128 if accelerator.distributed_type == DistributedType.TPU else None
              # When using mixed precision we want round multiples of 8/16
              if accelerator.mixed_precision == "fp8":
                     lowerCamelCase   :      Optional[Any]			 =					16
              elif accelerator.mixed_precision != "no":
                     lowerCamelCase   :      Tuple			 =					8
              else:
                     lowerCamelCase   :      Dict			 =					None

              return tokenizer.pad(
                  _SCREAMING_SNAKE_CASE ,padding="longest" ,max_length=_SCREAMING_SNAKE_CASE ,pad_to_multiple_of=_SCREAMING_SNAKE_CASE ,return_tensors="pt" ,)

       # Instantiate dataloaders.
       lowerCamelCase   :      int			 =					DataLoader(
           tokenized_datasets["train"] ,shuffle=_SCREAMING_SNAKE_CASE ,collate_fn=_SCREAMING_SNAKE_CASE ,batch_size=_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      str			 =					DataLoader(
           tokenized_datasets["validation"] ,shuffle=_SCREAMING_SNAKE_CASE ,collate_fn=_SCREAMING_SNAKE_CASE ,batch_size=_SCREAMING_SNAKE_CASE    )

       return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
       from accelerate.test_utils.training import mocked_dataloaders

       SCREAMING_SNAKE_CASE__       :      Union[str, Any]  =   mocked_dataloaders  # noqa: F811

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )	-> str:
       # For testing only
       if os.environ.get("TESTING_MOCKED_DATALOADERS" ,_SCREAMING_SNAKE_CASE    ) == "1":
              lowerCamelCase   :      Any			 =					2
       # New Code #
       lowerCamelCase   :      List[Any]			 =					int(args.gradient_accumulation_steps    )
       lowerCamelCase   :      List[str]			 =					int(args.local_sgd_steps    )
       # Initialize accelerator
       lowerCamelCase   :      Any			 =					Accelerator(
           cpu=args.cpu ,mixed_precision=args.mixed_precision ,gradient_accumulation_steps=_SCREAMING_SNAKE_CASE    )
       if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
              raise NotImplementedError("LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)"    )
       # Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
       lowerCamelCase   :      List[str]			 =					config["lr"]
       lowerCamelCase   :      int			 =					int(config["num_epochs"]    )
       lowerCamelCase   :      Any			 =					int(config["seed"]    )
       lowerCamelCase   :      int			 =					int(config["batch_size"]    )

       lowerCamelCase   :      Any			 =					evaluate.load("glue" ,"mrpc"    )

       set_seed(_SCREAMING_SNAKE_CASE    )
       lowerCamelCase ,      lowerCamelCase   :      Dict			 =					get_dataloaders(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )
       # Instantiate the model (we build the model here so that the seed also control new weights initialization)
       lowerCamelCase   :      Optional[int]			 =					AutoModelForSequenceClassification.from_pretrained("bert-base-cased" ,return_dict=_SCREAMING_SNAKE_CASE    )

       # 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).
       lowerCamelCase   :      Dict			 =					model.to(accelerator.device    )

       # Instantiate optimizer
       lowerCamelCase   :      Dict			 =					AdamW(params=model.parameters() ,lr=_SCREAMING_SNAKE_CASE    )

       # Instantiate scheduler
       lowerCamelCase   :      Any			 =					get_linear_schedule_with_warmup(
           optimizer=_SCREAMING_SNAKE_CASE ,num_warmup_steps=100 ,num_training_steps=(len(_SCREAMING_SNAKE_CASE    ) * num_epochs) ,)

       # 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.
       lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase ,      lowerCamelCase   :      Union[str, Any]			 =					accelerator.prepare(
           _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )

       # Now we train the model
       for epoch in range(_SCREAMING_SNAKE_CASE    ):
              model.train()
              with LocalSGD(
                  accelerator=_SCREAMING_SNAKE_CASE ,model=_SCREAMING_SNAKE_CASE ,local_sgd_steps=_SCREAMING_SNAKE_CASE ,enabled=local_sgd_steps is not None    ) as local_sgd:
                     for step, batch in enumerate(_SCREAMING_SNAKE_CASE    ):
                            # We could avoid this line since we set the accelerator with `device_placement=True`.
                            batch.to(accelerator.device    )
                            # New code #
                            # We use the new `accumulate` context manager to perform gradient accumulation
                            # We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
                            with accelerator.accumulate(_SCREAMING_SNAKE_CASE    ):
                                   lowerCamelCase   :      Union[str, Any]			 =					model(**_SCREAMING_SNAKE_CASE    )
                                   lowerCamelCase   :      Union[str, Any]			 =					output.loss
                                   accelerator.backward(_SCREAMING_SNAKE_CASE    )
                                   optimizer.step()
                                   lr_scheduler.step()
                                   optimizer.zero_grad()
                                   # LocalSGD-specific line
                                   local_sgd.step()

              model.eval()
              for step, batch in enumerate(_SCREAMING_SNAKE_CASE    ):
                     # We could avoid this line since we set the accelerator with `device_placement=True`.
                     batch.to(accelerator.device    )
                     with torch.no_grad():
                            lowerCamelCase   :      Union[str, Any]			 =					model(**_SCREAMING_SNAKE_CASE    )
                     lowerCamelCase   :      int			 =					outputs.logits.argmax(dim=-1    )
                     lowerCamelCase ,      lowerCamelCase   :      str			 =					accelerator.gather_for_metrics((predictions, batch["labels"])    )
                     metric.add_batch(
                         predictions=_SCREAMING_SNAKE_CASE ,references=_SCREAMING_SNAKE_CASE ,)

              lowerCamelCase   :      List[str]			 =					metric.compute()
              # Use accelerator.print to print only on the main process.
              accelerator.print(f'''epoch {epoch}:''' ,_SCREAMING_SNAKE_CASE    )

def      A      (      )	-> Optional[int]:
       lowerCamelCase   :      str			 =					argparse.ArgumentParser(description="Simple example of training script."    )
       parser.add_argument(
           "--mixed_precision" ,type=_SCREAMING_SNAKE_CASE ,default=_SCREAMING_SNAKE_CASE ,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." ,)
       # New Code #
       parser.add_argument(
           "--gradient_accumulation_steps" ,type=_SCREAMING_SNAKE_CASE ,default=1 ,help="The number of minibatches to be ran before gradients are accumulated." ,)
       parser.add_argument(
           "--local_sgd_steps" ,type=_SCREAMING_SNAKE_CASE ,default=8 ,help="Number of local SGD steps or None to disable local SGD"    )
       parser.add_argument("--cpu" ,action="store_true" ,help="If passed, will train on the CPU."    )
       lowerCamelCase   :      Optional[int]			 =					parser.parse_args()
       lowerCamelCase   :      Optional[Any]			 =					{"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
       training_function(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )


if __name__ == "__main__":
       main()












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()
SCREAMING_SNAKE_CASE__       :      List[str]  =   logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__       :      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,
}

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

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

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

def      A      (      )	-> int:
       lowerCamelCase   :      str			 =					"http://images.cocodataset.org/val2017/000000039769.jpg"
       lowerCamelCase   :      Tuple			 =					Image.open(requests.get(_SCREAMING_SNAKE_CASE ,stream=_SCREAMING_SNAKE_CASE    ).raw    )
       return im

def      A      (      _SCREAMING_SNAKE_CASE    )	-> str:
       lowerCamelCase   :      List[Any]			 =					CONFIG_MAP[model_name]["image_size"]
       lowerCamelCase   :      str			 =					EfficientNetImageProcessor(
           size={"height": size, "width": size} ,image_mean=[0.485, 0.456, 0.406] ,image_std=[0.47853944, 0.4732864, 0.47434163] ,do_center_crop=_SCREAMING_SNAKE_CASE ,)
       return preprocessor

def      A      (      _SCREAMING_SNAKE_CASE    )	-> Union[str, Any]:
       lowerCamelCase   :      Any			 =					[v.split("_"    )[0].split("block"    )[1] for v in original_param_names if v.startswith("block"    )]
       lowerCamelCase   :      Any			 =					sorted(set(_SCREAMING_SNAKE_CASE    )    )
       lowerCamelCase   :      Dict			 =					len(_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      List[Any]			 =					{b: str(_SCREAMING_SNAKE_CASE    ) for b, i in zip(_SCREAMING_SNAKE_CASE ,range(_SCREAMING_SNAKE_CASE    )    )}

       lowerCamelCase   :      List[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:
              lowerCamelCase   :      Dict			 =					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")    )

       lowerCamelCase   :      Optional[int]			 =					{}
       for item in rename_keys:
              if item[0] in original_param_names:
                     lowerCamelCase   :      List[str]			 =					"efficientnet." + item[1]

       lowerCamelCase   :      int			 =					"classifier.weight"
       lowerCamelCase   :      Union[str, Any]			 =					"classifier.bias"
       return key_mapping

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )	-> Dict:
       for key, value in tf_params.items():
              if "normalization" in key:
                     continue

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

@torch.no_grad()
def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )	-> Optional[int]:
       lowerCamelCase   :      Optional[int]			 =					model_classes[model_name](
           include_top=_SCREAMING_SNAKE_CASE ,weights="imagenet" ,input_tensor=_SCREAMING_SNAKE_CASE ,input_shape=_SCREAMING_SNAKE_CASE ,pooling=_SCREAMING_SNAKE_CASE ,classes=1000 ,classifier_activation="softmax" ,)

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

       # Load HuggingFace model
       lowerCamelCase   :      Union[str, Any]			 =					get_efficientnet_config(_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      Optional[int]			 =					EfficientNetForImageClassification(_SCREAMING_SNAKE_CASE    ).eval()
       lowerCamelCase   :      Tuple			 =					hf_model.state_dict()

       # Create src-to-dst parameter name mapping dictionary
       print("Converting parameters..."    )
       lowerCamelCase   :      Union[str, Any]			 =					rename_keys(_SCREAMING_SNAKE_CASE    )
       replace_params(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )

       # Initialize preprocessor and preprocess input image
       lowerCamelCase   :      int			 =					convert_image_processor(_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      int			 =					preprocessor(images=prepare_img() ,return_tensors="pt"    )

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

       # Original model inference
       lowerCamelCase   :      Optional[Any]			 =					False
       lowerCamelCase   :      Any			 =					CONFIG_MAP[model_name]["image_size"]
       lowerCamelCase   :      Optional[int]			 =					prepare_img().resize((image_size, image_size) ,resample=PIL.Image.NEAREST    )
       lowerCamelCase   :      Union[str, Any]			 =					image.img_to_array(_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      str			 =					np.expand_dims(_SCREAMING_SNAKE_CASE ,axis=0    )
       lowerCamelCase   :      Dict			 =					original_model.predict(_SCREAMING_SNAKE_CASE    )

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

       if push_to_hub:
              # Push model and image processor to hub
              print(f'''Pushing converted {model_name} to the hub...'''    )
              lowerCamelCase   :      int			 =					f'''efficientnet-{model_name}'''
              preprocessor.push_to_hub(_SCREAMING_SNAKE_CASE    )
              hf_model.push_to_hub(_SCREAMING_SNAKE_CASE    )


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__       :      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')

       SCREAMING_SNAKE_CASE__       :      Tuple  =   parser.parse_args()
       convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)






