

import random
import unittest

import numpy as np
import torch

from diffusers import (
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionUpscalePipeline,
    PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
						import onnxruntime as ort



class   __lowercase    (			UpperCAmelCase_		,	unittest.TestCase     ):




			"""simple docstring"""







			_UpperCAmelCase       :							int      							=					'''ssube/stable-diffusion-x4-upscaler-onnx'''



			def 	_SCREAMING_SNAKE_CASE      (			self  :					str       ,						lowerCAmelCase__  :					str=0):
									SCREAMING_SNAKE_CASE_:			str  		=	floats_tensor((1, 3, 128, 128)       ,						rng=random.Random(lowerCAmelCase__))
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	torch.manual_seed(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_:			str  		=	{
									    "prompt": "A painting of a squirrel eating a burger",
									    "image": image,
									    "generator": generator,
									    "num_inference_steps": 3,
									    "guidance_scale": 7.5,
									    "output_type": "numpy",
									}
									return inputs



			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[Any]):
									SCREAMING_SNAKE_CASE_:			Any  		=	OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint       ,						provider="CPUExecutionProvider")
									pipe.set_progress_bar_config(disable=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			List[str]  		=	self.get_dummy_inputs()
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	pipe(**lowerCAmelCase__).images
									SCREAMING_SNAKE_CASE_:			Any  		=	image[0, -3:, -3:, -1].flatten()

									# started as 128, should now be 512
									assert image.shape == (1, 512, 512, 3)
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	np.array(
									    [0.697_4782, 0.6890_2093, 0.7013_5885, 0.758_3618, 0.780_4545, 0.785_4912, 0.7866_7426, 0.7874_3863, 0.7807_0223])
									assert np.abs(image_slice - expected_slice).max() < 1E-1



			def 	_SCREAMING_SNAKE_CASE      (			self  :					List[str]):
									SCREAMING_SNAKE_CASE_:			Dict  		=	OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint       ,						provider="CPUExecutionProvider")
									SCREAMING_SNAKE_CASE_:			Dict  		=	PNDMScheduler.from_config(pipe.scheduler.config       ,						skip_prk_steps=lowerCAmelCase__)
									pipe.set_progress_bar_config(disable=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			Dict  		=	self.get_dummy_inputs()
									SCREAMING_SNAKE_CASE_:			Tuple  		=	pipe(**lowerCAmelCase__).images
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	image[0, -3:, -3:, -1]

									assert image.shape == (1, 512, 512, 3)
									SCREAMING_SNAKE_CASE_:			List[str]  		=	np.array(
									    [0.689_8892, 0.5924_0556, 0.5249_9527, 0.5886_6215, 0.5225_8235, 0.5257_2715, 0.6241_4473, 0.617_4387, 0.621_4964])
									assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-1



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[int]):
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint       ,						provider="CPUExecutionProvider")
									SCREAMING_SNAKE_CASE_:			Dict  		=	DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
									pipe.set_progress_bar_config(disable=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			Tuple  		=	self.get_dummy_inputs()
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	pipe(**lowerCAmelCase__).images
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	image[0, -3:, -3:, -1]

									assert image.shape == (1, 512, 512, 3)
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	np.array(
									    [0.765_9278, 0.7643_7664, 0.7557_9107, 0.769_1116, 0.7766_6986, 0.772_7672, 0.775_8664, 0.781_2226, 0.7694_2515])

									assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-1



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Any):
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint       ,						provider="CPUExecutionProvider")
									SCREAMING_SNAKE_CASE_:			str  		=	EulerDiscreteScheduler.from_config(pipe.scheduler.config)
									pipe.set_progress_bar_config(disable=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	self.get_dummy_inputs()
									SCREAMING_SNAKE_CASE_:			List[str]  		=	pipe(**lowerCAmelCase__).images
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	image[0, -3:, -3:, -1]

									assert image.shape == (1, 512, 512, 3)
									SCREAMING_SNAKE_CASE_:			List[str]  		=	np.array(
									    [0.697_4782, 0.6890_2093, 0.7013_5885, 0.758_3618, 0.780_4545, 0.785_4912, 0.7866_7426, 0.7874_3863, 0.7807_0223])
									assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-1




			def 	_SCREAMING_SNAKE_CASE      (			self  :					Union[str, Any]):
									SCREAMING_SNAKE_CASE_:			Dict  		=	OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint       ,						provider="CPUExecutionProvider")
									SCREAMING_SNAKE_CASE_:			Tuple  		=	EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
									pipe.set_progress_bar_config(disable=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			Tuple  		=	self.get_dummy_inputs()
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	pipe(**lowerCAmelCase__).images
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	image[0, -3:, -3:, -1]

									assert image.shape == (1, 512, 512, 3)
									SCREAMING_SNAKE_CASE_:			Dict  		=	np.array(
									    [0.7742_4496, 0.77_3601, 0.764_5288, 0.776_9598, 0.777_2739, 0.773_8688, 0.7818_7233, 0.7787_9584, 0.76_7043])

									assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-1







@nightly
@require_onnxruntime
@require_torch_gpu
class   __lowercase    (			unittest.TestCase     ):




			"""simple docstring"""







			@property
			def 	_SCREAMING_SNAKE_CASE      (			self  :					Union[str, Any]):
									return (
									    "CUDAExecutionProvider",
									    {
									        "gpu_mem_limit": "15000000000",  # 15GB
									        "arena_extend_strategy": "kSameAsRequested",
									    },
									)



			@property
			def 	_SCREAMING_SNAKE_CASE      (			self  :					Dict):
									SCREAMING_SNAKE_CASE_:			str  		=	ort.SessionOptions()
									SCREAMING_SNAKE_CASE_:			str  		=	False
									return options



			def 	_SCREAMING_SNAKE_CASE      (			self  :					Optional[Any]):
									SCREAMING_SNAKE_CASE_:			Optional[int]  		=	load_image(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
									    "/img2img/sketch-mountains-input.jpg")
									SCREAMING_SNAKE_CASE_:			List[Any]  		=	init_image.resize((128, 128))
									# using the PNDM scheduler by default
									SCREAMING_SNAKE_CASE_:			Tuple  		=	OnnxStableDiffusionUpscalePipeline.from_pretrained(
									    "ssube/stable-diffusion-x4-upscaler-onnx"       ,						provider=self.gpu_provider       ,						sess_options=self.gpu_options       ,						)
									pipe.set_progress_bar_config(disable=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			List[Any]  		=	"A fantasy landscape, trending on artstation"

									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	torch.manual_seed(0)
									SCREAMING_SNAKE_CASE_:			str  		=	pipe(
									    prompt=lowerCAmelCase__       ,						image=lowerCAmelCase__       ,						guidance_scale=7.5       ,						num_inference_steps=10       ,						generator=lowerCAmelCase__       ,						output_type="np"       ,						)
									SCREAMING_SNAKE_CASE_:			Dict  		=	output.images
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	images[0, 255:258, 383:386, -1]

									assert images.shape == (1, 512, 512, 3)
									SCREAMING_SNAKE_CASE_:			List[str]  		=	np.array([0.4883, 0.4947, 0.4980, 0.4975, 0.4982, 0.4980, 0.5000, 0.5006, 0.4972])
									# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

									assert np.abs(image_slice.flatten() - expected_slice).max() < 2E-2




			def 	_SCREAMING_SNAKE_CASE      (			self  :					Union[str, Any]):
									SCREAMING_SNAKE_CASE_:			Dict  		=	load_image(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
									    "/img2img/sketch-mountains-input.jpg")
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	init_image.resize((128, 128))
									SCREAMING_SNAKE_CASE_:			Any  		=	LMSDiscreteScheduler.from_pretrained(
									    "ssube/stable-diffusion-x4-upscaler-onnx"       ,						subfolder="scheduler")
									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	OnnxStableDiffusionUpscalePipeline.from_pretrained(
									    "ssube/stable-diffusion-x4-upscaler-onnx"       ,						scheduler=lowerCAmelCase__       ,						provider=self.gpu_provider       ,						sess_options=self.gpu_options       ,						)
									pipe.set_progress_bar_config(disable=lowerCAmelCase__)

									SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	"A fantasy landscape, trending on artstation"

									SCREAMING_SNAKE_CASE_:			Tuple  		=	torch.manual_seed(0)
									SCREAMING_SNAKE_CASE_:			Tuple  		=	pipe(
									    prompt=lowerCAmelCase__       ,						image=lowerCAmelCase__       ,						guidance_scale=7.5       ,						num_inference_steps=20       ,						generator=lowerCAmelCase__       ,						output_type="np"       ,						)
									SCREAMING_SNAKE_CASE_:			Dict  		=	output.images
									SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	images[0, 255:258, 383:386, -1]

									assert images.shape == (1, 512, 512, 3)
									SCREAMING_SNAKE_CASE_:			Any  		=	np.array(
									    [0.5017_3753, 0.5022_3356, 0.50_2039, 0.5023_3036, 0.502_3725, 0.502_2601, 0.501_8758, 0.5023_4085, 0.5024_1566])
									# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

									assert np.abs(image_slice.flatten() - expected_slice).max() < 2E-2









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)






