






"""simple docstring"""







import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple

import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
from utils_ner import Split, TokenClassificationDataset, TokenClassificationTask

import transformers
from transformers import (
    AutoConfig,
    AutoModelForTokenClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


_lowerCAmelCase            =	logging.getLogger(__name__)


@dataclass
class      UpperCamelCase		:
						_SCREAMING_SNAKE_CASE  : str												=		field(
						    metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}     )
						_SCREAMING_SNAKE_CASE  : Optional[str]												=		field(
						    default=__snake_case						,						metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}     )
						_SCREAMING_SNAKE_CASE  : Optional[str]												=		field(
						    default="""NER"""						,						metadata={"""help""": """Task type to fine tune in training (e.g. NER, POS, etc)"""}     )
						_SCREAMING_SNAKE_CASE  : Optional[str]												=		field(
						    default=__snake_case						,						metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}     )
						_SCREAMING_SNAKE_CASE  : bool												=		field(default=__snake_case						,						metadata={"""help""": """Set this flag to use fast tokenization."""}     )
						# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
						# or just modify its tokenizer_config.json.
						_SCREAMING_SNAKE_CASE  : Optional[str]												=		field(
						    default=__snake_case						,						metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}						,						)


@dataclass
class      UpperCamelCase		:
						_SCREAMING_SNAKE_CASE  : str												=		field(
						    metadata={"""help""": """The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task."""}     )
						_SCREAMING_SNAKE_CASE  : Optional[str]												=		field(
						    default=__snake_case						,						metadata={"""help""": """Path to a file containing all labels. If not specified, CoNLL-2003 labels are used."""}						,						)
						_SCREAMING_SNAKE_CASE  : int												=		field(
						    default=128						,						metadata={
						        """help""": (
						            """The maximum total input sequence length after tokenization. Sequences longer """
						            """than this will be truncated, sequences shorter will be padded."""
						        )
						    }						,						)
						_SCREAMING_SNAKE_CASE  : bool												=		field(
						    default=__snake_case						,						metadata={"""help""": """Overwrite the cached training and evaluation sets"""}     )


def      UpperCamelCase       ( )     ->       Dict:
				# See all possible arguments in src/transformers/training_args.py
				# or by passing the --help flag to this script.
				# We now keep distinct sets of args, for a cleaner separation of concerns.

				lowercase    :      List[Any]       = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
				if len(sys.argv ) == 2 and sys.argv[1].endswith(""".json""" ):
								# If we pass only one argument to the script and it's the path to a json file,
								# let's parse it to get our arguments.
								lowercase      ,      lowercase      ,      lowercase    :      str       = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
				else:
								lowercase      ,      lowercase      ,      lowercase    :      List[Any]       = parser.parse_args_into_dataclasses()

				if (
				    os.path.exists(training_args.output_dir )
				    and os.listdir(training_args.output_dir )
				    and training_args.do_train
				    and not training_args.overwrite_output_dir
				):
								raise ValueError(
								    F"""Output directory ({training_args.output_dir}) already exists and is not empty. Use"""
								    """ --overwrite_output_dir to overcome.""" )

				lowercase    :      List[Any]       = import_module("""tasks""" )
				try:
								lowercase    :      List[Any]       = getattr(_A  ,				model_args.task_type )
								lowercase    :      TokenClassificationTask       = token_classification_task_clazz()
				except AttributeError:
								raise ValueError(
								    F"""Task {model_args.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. """
								    F"""Available tasks classes are: {TokenClassificationTask.__subclasses__()}""" )

				# Setup logging
				logging.basicConfig(
				    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""  ,				datefmt="""%m/%d/%Y %H:%M:%S"""  ,				level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN  ,				)
				logger.warning(
				    """Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s"""  ,				training_args.local_rank  ,				training_args.device  ,				training_args.n_gpu  ,				bool(training_args.local_rank != -1 )  ,				training_args.fpaa  ,				)
				# Set the verbosity to info of the Transformers logger (on main process only):
				if is_main_process(training_args.local_rank ):
								transformers.utils.logging.set_verbosity_info()
								transformers.utils.logging.enable_default_handler()
								transformers.utils.logging.enable_explicit_format()
				logger.info("""Training/evaluation parameters %s"""  ,				_A )

				# Set seed
				set_seed(training_args.seed )

				# Prepare CONLL-2003 task
				lowercase    :      Optional[int]       = token_classification_task.get_labels(data_args.labels )
				lowercase    :      Dict[int, str]       = dict(enumerate(_A ) )
				lowercase    :      List[str]       = len(_A )

				# Load pretrained model and tokenizer
				#
				# Distributed training:
				# The .from_pretrained methods guarantee that only one local process can concurrently
				# download model & vocab.

				lowercase    :      Any       = AutoConfig.from_pretrained(
				    model_args.config_name if model_args.config_name else model_args.model_name_or_path  ,				num_labels=_A  ,				idalabel=_A  ,				labelaid={label: i for i, label in enumerate(_A )}  ,				cache_dir=model_args.cache_dir  ,				)
				lowercase    :      List[Any]       = AutoTokenizer.from_pretrained(
				    model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path  ,				cache_dir=model_args.cache_dir  ,				use_fast=model_args.use_fast  ,				)
				lowercase    :      List[Any]       = AutoModelForTokenClassification.from_pretrained(
				    model_args.model_name_or_path  ,				from_tf=bool(""".ckpt""" in model_args.model_name_or_path )  ,				config=_A  ,				cache_dir=model_args.cache_dir  ,				)

				# Get datasets
				lowercase    :      Tuple       = (
				    TokenClassificationDataset(
				        token_classification_task=_A  ,				data_dir=data_args.data_dir  ,				tokenizer=_A  ,				labels=_A  ,				model_type=config.model_type  ,				max_seq_length=data_args.max_seq_length  ,				overwrite_cache=data_args.overwrite_cache  ,				mode=Split.train  ,				)
				    if training_args.do_train
				    else None
				)
				lowercase    :      List[str]       = (
				    TokenClassificationDataset(
				        token_classification_task=_A  ,				data_dir=data_args.data_dir  ,				tokenizer=_A  ,				labels=_A  ,				model_type=config.model_type  ,				max_seq_length=data_args.max_seq_length  ,				overwrite_cache=data_args.overwrite_cache  ,				mode=Split.dev  ,				)
				    if training_args.do_eval
				    else None
				)

				def align_predictions(_A  ,				_A ) -> Tuple[List[int], List[int]]:
								lowercase    :      List[str]       = np.argmax(_A  ,				axis=2 )

								lowercase      ,      lowercase    :      int       = preds.shape

								lowercase    :      str       = [[] for _ in range(_A )]
								lowercase    :      int       = [[] for _ in range(_A )]

								for i in range(_A ):
												for j in range(_A ):
																if label_ids[i, j] != nn.CrossEntropyLoss().ignore_index:
																				out_label_list[i].append(label_map[label_ids[i][j]] )
																				preds_list[i].append(label_map[preds[i][j]] )

								return preds_list, out_label_list

				def compute_metrics(_A ) -> Dict:
								lowercase      ,      lowercase    :      Tuple       = align_predictions(p.predictions  ,				p.label_ids )
								return {
								    "accuracy_score": accuracy_score(_A  ,				_A ),
								    "precision": precision_score(_A  ,				_A ),
								    "recall": recall_score(_A  ,				_A ),
								    "f1": fa_score(_A  ,				_A ),
								}

				# Data collator
				lowercase    :      Tuple       = DataCollatorWithPadding(_A  ,				pad_to_multiple_of=8 ) if training_args.fpaa else None

				# Initialize our Trainer
				lowercase    :      Union[str, Any]       = Trainer(
				    model=_A  ,				args=_A  ,				train_dataset=_A  ,				eval_dataset=_A  ,				compute_metrics=_A  ,				data_collator=_A  ,				)

				# Training
				if training_args.do_train:
								trainer.train(
								    model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
								trainer.save_model()
								# For convenience, we also re-save the tokenizer to the same directory,
								# so that you can share your model easily on huggingface.co/models =)
								if trainer.is_world_process_zero():
												tokenizer.save_pretrained(training_args.output_dir )

    # Evaluation
				lowercase    :      Tuple       = {}
				if training_args.do_eval:
								logger.info("""*** Evaluate ***""" )

								lowercase    :      str       = trainer.evaluate()

								lowercase    :      List[Any]       = os.path.join(training_args.output_dir  ,				"""eval_results.txt""" )
								if trainer.is_world_process_zero():
												with open(_A  ,				"""w""" ) as writer:
																logger.info("""***** Eval results *****""" )
																for key, value in result.items():
																				logger.info("""  %s = %s"""  ,				_A  ,				_A )
																				writer.write("""%s = %s\n""" % (key, value) )

												results.update(_A )

    # Predict
				if training_args.do_predict:
								lowercase    :      Dict       = TokenClassificationDataset(
								    token_classification_task=_A  ,				data_dir=data_args.data_dir  ,				tokenizer=_A  ,				labels=_A  ,				model_type=config.model_type  ,				max_seq_length=data_args.max_seq_length  ,				overwrite_cache=data_args.overwrite_cache  ,				mode=Split.test  ,				)

								lowercase      ,      lowercase      ,      lowercase    :      Union[str, Any]       = trainer.predict(_A )
								lowercase      ,      lowercase    :      Dict       = align_predictions(_A  ,				_A )

								lowercase    :      Optional[int]       = os.path.join(training_args.output_dir  ,				"""test_results.txt""" )
								if trainer.is_world_process_zero():
												with open(_A  ,				"""w""" ) as writer:
																for key, value in metrics.items():
																				logger.info("""  %s = %s"""  ,				_A  ,				_A )
																				writer.write("""%s = %s\n""" % (key, value) )

        # Save predictions
								lowercase    :      List[Any]       = os.path.join(training_args.output_dir  ,				"""test_predictions.txt""" )
								if trainer.is_world_process_zero():
												with open(_A  ,				"""w""" ) as writer:
																with open(os.path.join(data_args.data_dir  ,				"""test.txt""" )  ,				"""r""" ) as f:
																				token_classification_task.write_predictions_to_file(_A  ,				_A  ,				_A )

				return results


def      UpperCamelCase       ( _A )     ->       str:
				# For xla_spawn (TPUs)
				main()


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()
_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': 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      UpperCamelCase       ( _A )     ->       List[str]:
				lowercase    :      List[str]       = EfficientNetConfig()
				lowercase    :      Any       = CONFIG_MAP[model_name]["""hidden_dim"""]
				lowercase    :      List[str]       = CONFIG_MAP[model_name]["""width_coef"""]
				lowercase    :      str       = CONFIG_MAP[model_name]["""depth_coef"""]
				lowercase    :      int       = CONFIG_MAP[model_name]["""image_size"""]
				lowercase    :      List[Any]       = CONFIG_MAP[model_name]["""dropout_rate"""]
				lowercase    :      int       = CONFIG_MAP[model_name]["""dw_padding"""]

				lowercase    :      Optional[int]       = """huggingface/label-files"""
				lowercase    :      int       = """imagenet-1k-id2label.json"""
				lowercase    :      Any       = 1_000
				lowercase    :      Any       = json.load(open(hf_hub_download(_A  ,				_A  ,				repo_type="""dataset""" )  ,				"""r""" ) )
				lowercase    :      Optional[int]       = {int(_A ): v for k, v in idalabel.items()}

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


def      UpperCamelCase       ( )     ->       Tuple:
				lowercase    :      Any       = """http://images.cocodataset.org/val2017/000000039769.jpg"""
				lowercase    :      Optional[int]       = Image.open(requests.get(_A  ,				stream=_A ).raw )
				return im


def      UpperCamelCase       ( _A )     ->       Optional[Any]:
				lowercase    :      str       = CONFIG_MAP[model_name]["""image_size"""]
				lowercase    :      Optional[int]       = EfficientNetImageProcessor(
				    size={"""height""": size, """width""": size}  ,				image_mean=[0.485, 0.456, 0.406]  ,				image_std=[0.4785_3944, 0.473_2864, 0.4743_4163]  ,				do_center_crop=_A  ,				)
				return preprocessor


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

				lowercase    :      Union[str, 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:
								lowercase    :      str       = 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""") )

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

				lowercase    :      Any       = """classifier.weight"""
				lowercase    :      Tuple       = """classifier.bias"""
				return key_mapping


def      UpperCamelCase       ( _A  ,				_A  ,				_A )     ->       Optional[Any]:
				for key, value in tf_params.items():
								if "normalization" in key:
												continue

								lowercase    :      List[Any]       = key_mapping[key]
								if "_conv" in key and "kernel" in key:
												lowercase    :      str       = torch.from_numpy(_A ).permute(3  ,				2  ,				0  ,				1 )
								elif "depthwise_kernel" in key:
												lowercase    :      Optional[int]       = torch.from_numpy(_A ).permute(2  ,				3  ,				0  ,				1 )
								elif "kernel" in key:
												lowercase    :      List[Any]       = torch.from_numpy(np.transpose(_A ) )
								else:
												lowercase    :      Optional[int]       = 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  ,				_A  ,				_A  ,				_A )     ->       str:
				lowercase    :      Any       = model_classes[model_name](
				    include_top=_A  ,				weights="""imagenet"""  ,				input_tensor=_A  ,				input_shape=_A  ,				pooling=_A  ,				classes=1_000  ,				classifier_activation="""softmax"""  ,				)

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

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

				# Create src-to-dst parameter name mapping dictionary
				print("""Converting parameters...""" )
				lowercase    :      int       = rename_keys(_A )
				replace_params(_A  ,				_A  ,				_A )

				# Initialize preprocessor and preprocess input image
				lowercase    :      Optional[int]       = convert_image_processor(_A )
				lowercase    :      Any       = preprocessor(images=prepare_img()  ,				return_tensors="""pt""" )

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

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


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)
