



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

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

UpperCamelCase__:     List[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 				snake_case_  (      _lowerCAmelCase						:		Optional[int]						)   ->     Union[str, Any]:
							UpperCAmelCase				: Optional[int]											=	EfficientNetConfig()
							UpperCAmelCase				: str											=	CONFIG_MAP[model_name]['''hidden_dim''']
							UpperCAmelCase				: Union[str, Any]											=	CONFIG_MAP[model_name]['''width_coef''']
							UpperCAmelCase				: Any											=	CONFIG_MAP[model_name]['''depth_coef''']
							UpperCAmelCase				: Optional[Any]											=	CONFIG_MAP[model_name]['''image_size''']
							UpperCAmelCase				: Tuple											=	CONFIG_MAP[model_name]['''dropout_rate''']
							UpperCAmelCase				: Union[str, Any]											=	CONFIG_MAP[model_name]['''dw_padding''']

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

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


def 				snake_case_  (      )   ->     Any:
							UpperCAmelCase				: Any											=	'''http://images.cocodataset.org/val2017/000000039769.jpg'''
							UpperCAmelCase				: Tuple											=	Image.open(requests.get(_lowerCAmelCase     ,   stream=_lowerCAmelCase						).raw						)
							return im


def 				snake_case_  (      _lowerCAmelCase						:		List[Any]						)   ->     Optional[int]:
							UpperCAmelCase				: List[Any]											=	CONFIG_MAP[model_name]['''image_size''']
							UpperCAmelCase				: Any											=	EfficientNetImageProcessor(
							    size={'''height''': size, '''width''': size}     ,   image_mean=[0.4_8_5, 0.4_5_6, 0.4_0_6]     ,   image_std=[0.4_7_8_5_3_9_4_4, 0.4_7_3_2_8_6_4, 0.4_7_4_3_4_1_6_3]     ,   do_center_crop=_lowerCAmelCase     ,   )
							return preprocessor


def 				snake_case_  (      _lowerCAmelCase						:		Optional[Any]						)   ->     Optional[int]:
							UpperCAmelCase				: Optional[int]											=	[v.split('''_'''						)[0].split('''block'''						)[1] for v in original_param_names if v.startswith('''block'''						)]
							UpperCAmelCase				: List[str]											=	sorted(set(_lowerCAmelCase						)						)
							UpperCAmelCase				: List[str]											=	len(_lowerCAmelCase						)
							UpperCAmelCase				: List[str]											=	{b: str(_lowerCAmelCase						) for b, i in zip(_lowerCAmelCase     ,   range(_lowerCAmelCase						)						)}

							UpperCAmelCase				: 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:
														UpperCAmelCase				: Optional[Any]											=	block_name_mapping[b]
														rename_keys.append((f"""block{b}_expand_conv/kernel:0""", f"""encoder.blocks.{hf_b}.expansion.expand_conv.weight""")						)
														rename_keys.append((f"""block{b}_expand_bn/gamma:0""", f"""encoder.blocks.{hf_b}.expansion.expand_bn.weight""")						)
														rename_keys.append((f"""block{b}_expand_bn/beta:0""", f"""encoder.blocks.{hf_b}.expansion.expand_bn.bias""")						)
														rename_keys.append(
														    (f"""block{b}_expand_bn/moving_mean:0""", f"""encoder.blocks.{hf_b}.expansion.expand_bn.running_mean""")						)
														rename_keys.append(
														    (f"""block{b}_expand_bn/moving_variance:0""", f"""encoder.blocks.{hf_b}.expansion.expand_bn.running_var""")						)
														rename_keys.append(
														    (f"""block{b}_dwconv/depthwise_kernel:0""", f"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight""")						)
														rename_keys.append((f"""block{b}_bn/gamma:0""", f"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight""")						)
														rename_keys.append((f"""block{b}_bn/beta:0""", f"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias""")						)
														rename_keys.append(
														    (f"""block{b}_bn/moving_mean:0""", f"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean""")						)
														rename_keys.append(
														    (f"""block{b}_bn/moving_variance:0""", f"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var""")						)

														rename_keys.append((f"""block{b}_se_reduce/kernel:0""", f"""encoder.blocks.{hf_b}.squeeze_excite.reduce.weight""")						)
														rename_keys.append((f"""block{b}_se_reduce/bias:0""", f"""encoder.blocks.{hf_b}.squeeze_excite.reduce.bias""")						)
														rename_keys.append((f"""block{b}_se_expand/kernel:0""", f"""encoder.blocks.{hf_b}.squeeze_excite.expand.weight""")						)
														rename_keys.append((f"""block{b}_se_expand/bias:0""", f"""encoder.blocks.{hf_b}.squeeze_excite.expand.bias""")						)
														rename_keys.append(
														    (f"""block{b}_project_conv/kernel:0""", f"""encoder.blocks.{hf_b}.projection.project_conv.weight""")						)
														rename_keys.append((f"""block{b}_project_bn/gamma:0""", f"""encoder.blocks.{hf_b}.projection.project_bn.weight""")						)
														rename_keys.append((f"""block{b}_project_bn/beta:0""", f"""encoder.blocks.{hf_b}.projection.project_bn.bias""")						)
														rename_keys.append(
														    (f"""block{b}_project_bn/moving_mean:0""", f"""encoder.blocks.{hf_b}.projection.project_bn.running_mean""")						)
														rename_keys.append(
														    (f"""block{b}_project_bn/moving_variance:0""", f"""encoder.blocks.{hf_b}.projection.project_bn.running_var""")						)

							rename_keys.append(('''top_conv/kernel:0''', '''encoder.top_conv.weight''')						)
							rename_keys.append(('''top_bn/gamma:0''', '''encoder.top_bn.weight''')						)
							rename_keys.append(('''top_bn/beta:0''', '''encoder.top_bn.bias''')						)
							rename_keys.append(('''top_bn/moving_mean:0''', '''encoder.top_bn.running_mean''')						)
							rename_keys.append(('''top_bn/moving_variance:0''', '''encoder.top_bn.running_var''')						)

							UpperCAmelCase				: Dict											=	{}
							for item in rename_keys:
														if item[0] in original_param_names:
																					UpperCAmelCase				: Union[str, Any]											=	'''efficientnet.''' + item[1]

							UpperCAmelCase				: int											=	'''classifier.weight'''
							UpperCAmelCase				: List[str]											=	'''classifier.bias'''
							return key_mapping


def 				snake_case_  (      _lowerCAmelCase						:		Optional[int]     ,   _lowerCAmelCase						:		List[str]     ,   _lowerCAmelCase						:		Optional[Any]						)   ->     Optional[Any]:
							for key, value in tf_params.items():
														if "normalization" in key:
																					continue

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

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


@torch.no_grad()
def 				snake_case_  (      _lowerCAmelCase						:		int     ,   _lowerCAmelCase						:		Optional[int]     ,   _lowerCAmelCase						:		Union[str, Any]     ,   _lowerCAmelCase						:		Dict						)   ->     int:
							UpperCAmelCase				: Union[str, Any]											=	model_classes[model_name](
							    include_top=_lowerCAmelCase     ,   weights='''imagenet'''     ,   input_tensor=_lowerCAmelCase     ,   input_shape=_lowerCAmelCase     ,   pooling=_lowerCAmelCase     ,   classes=1000     ,   classifier_activation='''softmax'''     ,   )

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

							# Load HuggingFace model
							UpperCAmelCase				: str											=	get_efficientnet_config(_lowerCAmelCase						)
							UpperCAmelCase				: Tuple											=	EfficientNetForImageClassification(_lowerCAmelCase						).eval()
							UpperCAmelCase				: List[str]											=	hf_model.state_dict()

							# Create src-to-dst parameter name mapping dictionary
							print('''Converting parameters...'''						)
							UpperCAmelCase				: List[Any]											=	rename_keys(_lowerCAmelCase						)
							replace_params(_lowerCAmelCase     ,   _lowerCAmelCase     ,   _lowerCAmelCase						)

							# Initialize preprocessor and preprocess input image
							UpperCAmelCase				: List[Any]											=	convert_image_processor(_lowerCAmelCase						)
							UpperCAmelCase				: Union[str, Any]											=	preprocessor(images=prepare_img()     ,   return_tensors='''pt'''						)

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

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

							# Check whether original and HF model outputs match  -> np.allclose
							assert np.allclose(_lowerCAmelCase     ,   _lowerCAmelCase     ,   atol=1e-3						), "The predicted logits are not the same."
							print('''Model outputs match!'''						)

							if save_model:
														# Create folder to save model
														if not os.path.isdir(_lowerCAmelCase						):
																					os.mkdir(_lowerCAmelCase						)
														# Save converted model and image processor
														hf_model.save_pretrained(_lowerCAmelCase						)
														preprocessor.save_pretrained(_lowerCAmelCase						)

							if push_to_hub:
														# Push model and image processor to hub
														print(f"""Pushing converted {model_name} to the hub..."""						)
														UpperCAmelCase				: List[Any]											=	f"""efficientnet-{model_name}"""
														preprocessor.push_to_hub(_lowerCAmelCase						)
														hf_model.push_to_hub(_lowerCAmelCase						)


if __name__ == "__main__":
						UpperCamelCase__:     Any										=    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__:     Dict										=    parser.parse_args()
						convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)










'''simple docstring'''






import argparse
from copy import deepcopy

import numpy as np
from datasets import ClassLabel, DatasetDict, load_dataset
from evaluate import load

from transformers import (
    AutoModelForSequenceClassification,
    AutoTokenizer,
    DataCollatorWithPadding,
    Trainer,
    TrainerCallback,
    TrainingArguments,
    set_seed,
)


def 				snake_case_  (      )   ->     Optional[int]:
							UpperCAmelCase				: Any											=	argparse.ArgumentParser()
							parser.add_argument('''--model_ckpt'''     ,   type=_lowerCAmelCase     ,   default='''microsoft/unixcoder-base-nine'''						)
							parser.add_argument('''--num_epochs'''     ,   type=_lowerCAmelCase     ,   default=5						)
							parser.add_argument('''--batch_size'''     ,   type=_lowerCAmelCase     ,   default=6						)
							parser.add_argument('''--gradient_accumulation_steps'''     ,   type=_lowerCAmelCase     ,   default=1						)
							parser.add_argument('''--freeze'''     ,   type=_lowerCAmelCase     ,   default=_lowerCAmelCase						)
							parser.add_argument('''--learning_rate'''     ,   type=_lowerCAmelCase     ,   default=5e-4						)
							parser.add_argument('''--seed'''     ,   type=_lowerCAmelCase     ,   default=0						)
							parser.add_argument('''--lr_scheduler_type'''     ,   type=_lowerCAmelCase     ,   default='''cosine'''						)
							parser.add_argument('''--num_warmup_steps'''     ,   type=_lowerCAmelCase     ,   default=10						)
							parser.add_argument('''--weight_decay'''     ,   type=_lowerCAmelCase     ,   default=0.0_1						)
							parser.add_argument('''--output_dir'''     ,   type=_lowerCAmelCase     ,   default='''./results'''						)
							return parser.parse_args()


UpperCamelCase__:     Any										=    load("accuracy")


def 				snake_case_  (      _lowerCAmelCase						:		int						)   ->     int:
							UpperCAmelCase							,	UpperCAmelCase				: Dict											=	eval_pred
							UpperCAmelCase				: Optional[Any]											=	np.argmax(_lowerCAmelCase     ,   axis=1						)
							return metric.compute(predictions=_lowerCAmelCase     ,   references=_lowerCAmelCase						)

class        SCREAMING_SNAKE_CASE(							A__    ):







	"""simple docstring"""






	def __init__(		self    :  List[Any]	,    __snake_case    :  Optional[int]	)    ->	None:
								super().__init__()
								UpperCAmelCase				: Dict											=	trainer







	def   A			(		self    :  str	,    __snake_case    :  Union[str, Any]	,    __snake_case    :  int	,    __snake_case    :  Any	,    **__snake_case    :  List[Any]	)    ->	Dict:
								if control.should_evaluate:
															UpperCAmelCase				: Optional[int]											=	deepcopy(__snake_case	)
															self._trainer.evaluate(eval_dataset=self._trainer.train_dataset	,    metric_key_prefix='''train'''	)
															return control_copy


def 				snake_case_  (      )   ->     List[Any]:
							UpperCAmelCase				: Tuple											=	get_args()
							set_seed(args.seed						)

							UpperCAmelCase				: List[str]											=	load_dataset('''codeparrot/codecomplex'''     ,   split='''train'''						)
							UpperCAmelCase				: Dict											=	dataset.train_test_split(test_size=0.2						)
							UpperCAmelCase				: Dict											=	train_test['''test'''].train_test_split(test_size=0.5						)
							UpperCAmelCase				: List[Any]											=	DatasetDict(
							    {
							        '''train''': train_test['''train'''],
							        '''test''': test_validation['''train'''],
							        '''valid''': test_validation['''test'''],
							    }						)

							print('''Loading tokenizer and model'''						)
							UpperCAmelCase				: Tuple											=	AutoTokenizer.from_pretrained(args.model_ckpt						)
							UpperCAmelCase				: Optional[int]											=	tokenizer.eos_token
							UpperCAmelCase				: Optional[int]											=	AutoModelForSequenceClassification.from_pretrained(args.model_ckpt     ,   num_labels=7						)
							UpperCAmelCase				: List[Any]											=	model.config.eos_token_id

							if args.freeze:
														for param in model.roberta.parameters():
																					UpperCAmelCase				: Any											=	False

							UpperCAmelCase				: Optional[Any]											=	ClassLabel(num_classes=7     ,   names=list(set(train_test_validation['''train''']['''complexity''']						)						)						)

							def tokenize(_lowerCAmelCase						:		Any						):
														UpperCAmelCase				: List[Any]											=	tokenizer(example['''src''']     ,   truncation=_lowerCAmelCase     ,   max_length=1024						)
														UpperCAmelCase				: Optional[Any]											=	labels.straint(example['''complexity''']						)
														return {
														    "input_ids": inputs["input_ids"],
														    "attention_mask": inputs["attention_mask"],
														    "label": label,
														}

							UpperCAmelCase				: List[str]											=	train_test_validation.map(
							    _lowerCAmelCase     ,   batched=_lowerCAmelCase     ,   remove_columns=train_test_validation['''train'''].column_names     ,   )
							UpperCAmelCase				: int											=	DataCollatorWithPadding(tokenizer=_lowerCAmelCase						)

							UpperCAmelCase				: Tuple											=	TrainingArguments(
							    output_dir=args.output_dir     ,   learning_rate=args.learning_rate     ,   lr_scheduler_type=args.lr_scheduler_type     ,   evaluation_strategy='''epoch'''     ,   save_strategy='''epoch'''     ,   logging_strategy='''epoch'''     ,   per_device_train_batch_size=args.batch_size     ,   per_device_eval_batch_size=args.batch_size     ,   num_train_epochs=args.num_epochs     ,   gradient_accumulation_steps=args.gradient_accumulation_steps     ,   weight_decay=0.0_1     ,   metric_for_best_model='''accuracy'''     ,   run_name='''complexity-java'''     ,   report_to='''wandb'''     ,   )

							UpperCAmelCase				: int											=	Trainer(
							    model=_lowerCAmelCase     ,   args=_lowerCAmelCase     ,   train_dataset=tokenized_datasets['''train''']     ,   eval_dataset=tokenized_datasets['''valid''']     ,   tokenizer=_lowerCAmelCase     ,   data_collator=_lowerCAmelCase     ,   compute_metrics=_lowerCAmelCase     ,   )

							print('''Training...'''						)
							trainer.add_callback(CustomCallback(_lowerCAmelCase						)						)
							trainer.train()


if __name__ == "__main__":
						main()





