

from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


lowerCAmelCase							:  Optional[int]            =						"""\
@article{wang2019superglue,
  title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},
  author={Wang, Alex and Pruksachatkun, Yada and Nangia, Nikita and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R},
  journal={arXiv preprint arXiv:1905.00537},
  year={2019}
}
"""

lowerCAmelCase							:  str            =						"""\
SuperGLUE (https://super.gluebenchmark.com/) is a new benchmark styled after
GLUE with a new set of more difficult language understanding tasks, improved
resources, and a new public leaderboard.
"""

lowerCAmelCase							:  Optional[int]            =						"""
Compute SuperGLUE evaluation metric associated to each SuperGLUE dataset.
Args:
    predictions: list of predictions to score. Depending on the SuperGlUE subset:
        - for 'record': list of question-answer dictionaries with the following keys:
            - 'idx': index of the question as specified by the dataset
            - 'prediction_text': the predicted answer text
        - for 'multirc': list of question-answer dictionaries with the following keys:
            - 'idx': index of the question-answer pair as specified by the dataset
            - 'prediction': the predicted answer label
        - otherwise: list of predicted labels
    references: list of reference labels. Depending on the SuperGLUE subset:
        - for 'record': list of question-answers dictionaries with the following keys:
            - 'idx': index of the question as specified by the dataset
            - 'answers': list of possible answers
        - otherwise: list of reference labels
Returns: depending on the SuperGLUE subset:
    - for 'record':
        - 'exact_match': Exact match between answer and gold answer
        - 'f1': F1 score
    - for 'multirc':
        - 'exact_match': Exact match between answer and gold answer
        - 'f1_m': Per-question macro-F1 score
        - 'f1_a': Average F1 score over all answers
    - for 'axb':
        'matthews_correlation': Matthew Correlation
    - for 'cb':
        - 'accuracy': Accuracy
        - 'f1': F1 score
    - for all others:
        - 'accuracy': Accuracy
Examples:

    >>> super_glue_metric = datasets.load_metric('super_glue', 'copa')  # any of [\"copa\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"boolq\", \"axg\"]
    >>> predictions = [0, 1]
    >>> references = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0}

    >>> super_glue_metric = datasets.load_metric('super_glue', 'cb')
    >>> predictions = [0, 1]
    >>> references = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0, 'f1': 1.0}

    >>> super_glue_metric = datasets.load_metric('super_glue', 'record')
    >>> predictions = [{'idx': {'passage': 0, 'query': 0}, 'prediction_text': 'answer'}]
    >>> references = [{'idx': {'passage': 0, 'query': 0}, 'answers': ['answer', 'another_answer']}]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'exact_match': 1.0, 'f1': 1.0}

    >>> super_glue_metric = datasets.load_metric('super_glue', 'multirc')
    >>> predictions = [{'idx': {'answer': 0, 'paragraph': 0, 'question': 0}, 'prediction': 0}, {'idx': {'answer': 1, 'paragraph': 2, 'question': 3}, 'prediction': 1}]
    >>> references = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'exact_match': 1.0, 'f1_m': 1.0, 'f1_a': 1.0}

    >>> super_glue_metric = datasets.load_metric('super_glue', 'axb')
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'matthews_correlation': 1.0}
"""







def       A_				(   _UpperCAmelCase    ,     _UpperCAmelCase   ):
						return float((preds == labels).mean()   )







def       A_				(   _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase="binary"   ):
						SCREAMING_SNAKE_CASE_:			List[Any]  		=	simple_accuracy(_UpperCAmelCase    ,     _UpperCAmelCase   )
						SCREAMING_SNAKE_CASE_:			str  		=	float(fa_score(y_true=_UpperCAmelCase    ,     y_pred=_UpperCAmelCase    ,     average=_UpperCAmelCase   )   )
						return {
						    "accuracy": acc,
						    "f1": fa,
						}







def       A_				(   _UpperCAmelCase    ,     _UpperCAmelCase   ):
						SCREAMING_SNAKE_CASE_:			str  		=	{}
						for id_pred, label in zip(_UpperCAmelCase    ,     _UpperCAmelCase   ):
												SCREAMING_SNAKE_CASE_:			Any  		=	f"{id_pred['idx']['paragraph']}-{id_pred['idx']['question']}"
												SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	id_pred["prediction"]
												if question_id in question_map:
																		question_map[question_id].append((pred, label)   )
												else:
																		SCREAMING_SNAKE_CASE_:			int  		=	[(pred, label)]
						SCREAMING_SNAKE_CASE_:			Optional[int]  		=	[], []
						for question, preds_labels in question_map.items():
												SCREAMING_SNAKE_CASE_:			int  		=	zip(*_UpperCAmelCase   )
												SCREAMING_SNAKE_CASE_:			int  		=	fa_score(y_true=_UpperCAmelCase    ,     y_pred=_UpperCAmelCase    ,     average="macro"   )
												fas.append(_UpperCAmelCase   )
												SCREAMING_SNAKE_CASE_:			List[Any]  		=	int(sum(pred == label for pred, label in preds_labels   ) == len(_UpperCAmelCase   )   )
												ems.append(_UpperCAmelCase   )
						SCREAMING_SNAKE_CASE_:			int  		=	float(sum(_UpperCAmelCase   ) / len(_UpperCAmelCase   )   )
						SCREAMING_SNAKE_CASE_:			Dict  		=	sum(_UpperCAmelCase   ) / len(_UpperCAmelCase   )
						SCREAMING_SNAKE_CASE_:			Tuple  		=	float(fa_score(y_true=_UpperCAmelCase    ,     y_pred=[id_pred["prediction"] for id_pred in ids_preds]   )   )
						return {"exact_match": em, "f1_m": fa_m, "f1_a": fa_a}



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION		,	_KWARGS_DESCRIPTION     )
class   __lowercase    (			datasets.Metric     ):




			"""simple docstring"""







			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[Any]):
									if self.config_name not in [
									    "boolq",
									    "cb",
									    "copa",
									    "multirc",
									    "record",
									    "rte",
									    "wic",
									    "wsc",
									    "wsc.fixed",
									    "axb",
									    "axg",
									]:
															raise KeyError(
															    "You should supply a configuration name selected in "
															    "[\"boolq\", \"cb\", \"copa\", \"multirc\", \"record\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"axb\", \"axg\",]")
									return datasets.MetricInfo(
									    description=_DESCRIPTION       ,						citation=_CITATION       ,						inputs_description=_KWARGS_DESCRIPTION       ,						features=datasets.Features(self._get_feature_types())       ,						codebase_urls=[]       ,						reference_urls=[]       ,						format="numpy" if not self.config_name == "record" and not self.config_name == "multirc" else None       ,						)



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[Any]):
									if self.config_name == "record":
															return {
															    "predictions": {
															        "idx": {
															            "passage": datasets.Value("int64"),
															            "query": datasets.Value("int64"),
															        },
															        "prediction_text": datasets.Value("string"),
															    },
															    "references": {
															        "idx": {
															            "passage": datasets.Value("int64"),
															            "query": datasets.Value("int64"),
															        },
															        "answers": datasets.Sequence(datasets.Value("string")),
															    },
															}
									elif self.config_name == "multirc":
															return {
															    "predictions": {
															        "idx": {
															            "answer": datasets.Value("int64"),
															            "paragraph": datasets.Value("int64"),
															            "question": datasets.Value("int64"),
															        },
															        "prediction": datasets.Value("int64"),
															    },
															    "references": datasets.Value("int64"),
															}
									else:
															return {
															    "predictions": datasets.Value("int64"),
															    "references": datasets.Value("int64"),
															}




			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[Any]       ,						lowerCAmelCase__  :					List[Any]       ,						lowerCAmelCase__  :					Optional[int]):
									if self.config_name == "axb":
															return {"matthews_correlation": matthews_corrcoef(lowerCAmelCase__       ,						lowerCAmelCase__)}
									elif self.config_name == "cb":
															return acc_and_fa(lowerCAmelCase__       ,						lowerCAmelCase__       ,						fa_avg="macro")
									elif self.config_name == "record":
															SCREAMING_SNAKE_CASE_:			Dict  		=	[
															    {
															        "qas": [
															            {"id": ref["idx"]["query"], "answers": [{"text": ans} for ans in ref["answers"]]}
															            for ref in references
															        ]
															    }
															]
															SCREAMING_SNAKE_CASE_:			Tuple  		=	{pred["idx"]["query"]: pred["prediction_text"] for pred in predictions}
															return evaluate_record(lowerCAmelCase__       ,						lowerCAmelCase__)[0]
									elif self.config_name == "multirc":
															return evaluate_multirc(lowerCAmelCase__       ,						lowerCAmelCase__)
									elif self.config_name in ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]:
															return {"accuracy": simple_accuracy(lowerCAmelCase__       ,						lowerCAmelCase__)}
									else:
															raise KeyError(
															    "You should supply a configuration name selected in "
															    "[\"boolq\", \"cb\", \"copa\", \"multirc\", \"record\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"axb\", \"axg\",]")



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							:  Tuple            =						logging.get_logger(__name__)

lowerCAmelCase							:  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,
}

lowerCAmelCase							:  str            =						{
    """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_				(   _UpperCAmelCase   ):
						SCREAMING_SNAKE_CASE_:			Dict  		=	EfficientNetConfig()
						SCREAMING_SNAKE_CASE_:			Any  		=	CONFIG_MAP[model_name]["hidden_dim"]
						SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	CONFIG_MAP[model_name]["width_coef"]
						SCREAMING_SNAKE_CASE_:			List[Any]  		=	CONFIG_MAP[model_name]["depth_coef"]
						SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	CONFIG_MAP[model_name]["image_size"]
						SCREAMING_SNAKE_CASE_:			Optional[int]  		=	CONFIG_MAP[model_name]["dropout_rate"]
						SCREAMING_SNAKE_CASE_:			Dict  		=	CONFIG_MAP[model_name]["dw_padding"]

						SCREAMING_SNAKE_CASE_:			str  		=	"huggingface/label-files"
						SCREAMING_SNAKE_CASE_:			str  		=	"imagenet-1k-id2label.json"
						SCREAMING_SNAKE_CASE_:			int  		=	10_00
						SCREAMING_SNAKE_CASE_:			int  		=	json.load(open(hf_hub_download(_UpperCAmelCase    ,     _UpperCAmelCase    ,     repo_type="dataset"   )    ,     "r"   )   )
						SCREAMING_SNAKE_CASE_:			int  		=	{int(_UpperCAmelCase   ): v for k, v in idalabel.items()}

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







def       A_				(   ):
						SCREAMING_SNAKE_CASE_:			Tuple  		=	"http://images.cocodataset.org/val2017/000000039769.jpg"
						SCREAMING_SNAKE_CASE_:			int  		=	Image.open(requests.get(_UpperCAmelCase    ,     stream=_UpperCAmelCase   ).raw   )
						return im







def       A_				(   _UpperCAmelCase   ):
						SCREAMING_SNAKE_CASE_:			int  		=	CONFIG_MAP[model_name]["image_size"]
						SCREAMING_SNAKE_CASE_:			Optional[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=_UpperCAmelCase    ,     )
						return preprocessor







def       A_				(   _UpperCAmelCase   ):
						SCREAMING_SNAKE_CASE_:			Optional[int]  		=	[v.split("_"   )[0].split("block"   )[1] for v in original_param_names if v.startswith("block"   )]
						SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	sorted(set(_UpperCAmelCase   )   )
						SCREAMING_SNAKE_CASE_:			int  		=	len(_UpperCAmelCase   )
						SCREAMING_SNAKE_CASE_:			Optional[int]  		=	{b: str(_UpperCAmelCase   ) for b, i in zip(_UpperCAmelCase    ,     range(_UpperCAmelCase   )   )}

						SCREAMING_SNAKE_CASE_:			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:
												SCREAMING_SNAKE_CASE_:			List[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")   )

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

						SCREAMING_SNAKE_CASE_:			List[str]  		=	"classifier.weight"
						SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	"classifier.bias"
						return key_mapping







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

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

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







@torch.no_grad()
def       A_				(   _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase   ):
						SCREAMING_SNAKE_CASE_:			Any  		=	model_classes[model_name](
						    include_top=_UpperCAmelCase    ,     weights="imagenet"    ,     input_tensor=_UpperCAmelCase    ,     input_shape=_UpperCAmelCase    ,     pooling=_UpperCAmelCase    ,     classes=10_00    ,     classifier_activation="softmax"    ,     )

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

						# Load HuggingFace model
						SCREAMING_SNAKE_CASE_:			Any  		=	get_efficientnet_config(_UpperCAmelCase   )
						SCREAMING_SNAKE_CASE_:			List[str]  		=	EfficientNetForImageClassification(_UpperCAmelCase   ).eval()
						SCREAMING_SNAKE_CASE_:			str  		=	hf_model.state_dict()

						# Create src-to-dst parameter name mapping dictionary
						print("Converting parameters..."   )
						SCREAMING_SNAKE_CASE_:			Tuple  		=	rename_keys(_UpperCAmelCase   )
						replace_params(_UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase   )

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

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

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

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

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


if __name__ == "__main__":
						lowerCAmelCase							:  List[str]            =						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							:  Any            =						parser.parse_args()
						convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)






