



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()
__snake_case     :str			       =logging.get_logger(__name__)

__snake_case     :List[str]			       ={
    'b0': efficientnet.EfficientNetBa,
    'b1': efficientnet.EfficientNetBa,
    'b2': efficientnet.EfficientNetBa,
    'b3': efficientnet.EfficientNetBa,
    'b4': efficientnet.EfficientNetBa,
    'b5': efficientnet.EfficientNetBa,
    'b6': efficientnet.EfficientNetBa,
    'b7': efficientnet.EfficientNetBa,
}

__snake_case     :Union[str, 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  lowerCamelCase_	(      lowerCAmelCase__	:	Optional[int]				)       ->							List[str]:

							'''simple docstring'''






							A      						=    EfficientNetConfig()
							A      						=    CONFIG_MAP[model_name]['hidden_dim']
							A      						=    CONFIG_MAP[model_name]['width_coef']
							A      						=    CONFIG_MAP[model_name]['depth_coef']
							A      						=    CONFIG_MAP[model_name]['image_size']
							A      						=    CONFIG_MAP[model_name]['dropout_rate']
							A      						=    CONFIG_MAP[model_name]['dw_padding']

							A      						=    'huggingface/label-files'
							A      						=    'imagenet-1k-id2label.json'
							A      						=    1000
							A      						=    json.load(open(hf_hub_download(lowerCAmelCase__			,	lowerCAmelCase__			,	repo_type='dataset'				)			,	'r'				)				)
							A      						=    {int(lowerCAmelCase__				): v for k, v in idalabel.items()}

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






def  lowerCamelCase_	(      )       ->							Optional[Any]:

							'''simple docstring'''






							A      						=    'http://images.cocodataset.org/val2017/000000039769.jpg'
							A      						=    Image.open(requests.get(lowerCAmelCase__			,	stream=lowerCAmelCase__				).raw				)
							return im






def  lowerCamelCase_	(      lowerCAmelCase__	:	Dict				)       ->							Any:

							'''simple docstring'''






							A      						=    CONFIG_MAP[model_name]['image_size']
							A      						=    EfficientNetImageProcessor(
							    size={'height': size, 'width': size}			,	image_mean=[0.485, 0.456, 0.406]			,	image_std=[0.47853944, 0.4732864, 0.47434163]			,	do_center_crop=lowerCAmelCase__			,	)
							return preprocessor






def  lowerCamelCase_	(      lowerCAmelCase__	:	str				)       ->							Dict:

							'''simple docstring'''






							A      						=    [v.split('_'				)[0].split('block'				)[1] for v in original_param_names if v.startswith('block'				)]
							A      						=    sorted(set(lowerCAmelCase__				)				)
							A      						=    len(lowerCAmelCase__				)
							A      						=    {b: str(lowerCAmelCase__				) for b, i in zip(lowerCAmelCase__			,	range(lowerCAmelCase__				)				)}

							A      						=    []
							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:
														A      						=    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')				)

							A      						=    {}
							for item in rename_keys:
														if item[0] in original_param_names:
																					A      						=    'efficientnet.' + item[1]

							A      						=    'classifier.weight'
							A      						=    'classifier.bias'
							return key_mapping






def  lowerCamelCase_	(      lowerCAmelCase__	:	Tuple			,	lowerCAmelCase__	:	Tuple			,	lowerCAmelCase__	:	Dict				)       ->							int:

							'''simple docstring'''






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

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

							'''simple docstring'''






							A      						=    model_classes[model_name](
							    include_top=lowerCAmelCase__			,	weights='imagenet'			,	input_tensor=lowerCAmelCase__			,	input_shape=lowerCAmelCase__			,	pooling=lowerCAmelCase__			,	classes=1000			,	classifier_activation='softmax'			,	)

							A      						=    original_model.trainable_variables
							A      						=    original_model.non_trainable_variables
							A      						=    {param.name: param.numpy() for param in tf_params}
							for param in tf_non_train_params:
														A      						=    param.numpy()
							A      						=    list(tf_params.keys()				)

							# Load HuggingFace model
							A      						=    get_efficientnet_config(lowerCAmelCase__				)
							A      						=    EfficientNetForImageClassification(lowerCAmelCase__				).eval()
							A      						=    hf_model.state_dict()

							# Create src-to-dst parameter name mapping dictionary
							print('Converting parameters...'				)
							A      						=    rename_keys(lowerCAmelCase__				)
							replace_params(lowerCAmelCase__			,	lowerCAmelCase__			,	lowerCAmelCase__				)

							# Initialize preprocessor and preprocess input image
							A      						=    convert_image_processor(lowerCAmelCase__				)
							A      						=    preprocessor(images=prepare_img()			,	return_tensors='pt'				)

							# HF model inference
							hf_model.eval()
							with torch.no_grad():
														A      						=    hf_model(**lowerCAmelCase__				)
							A      						=    outputs.logits.detach().numpy()

							# Original model inference
							A      						=    False
							A      						=    CONFIG_MAP[model_name]['image_size']
							A      						=    prepare_img().resize((image_size, image_size)			,	resample=PIL.Image.NEAREST				)
							A      						=    image.img_to_array(lowerCAmelCase__				)
							A      						=    np.expand_dims(lowerCAmelCase__			,	axis=0				)
							A      						=    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...'''				)
														A      						=    F'''efficientnet-{model_name}'''
														preprocessor.push_to_hub(lowerCAmelCase__				)
														hf_model.push_to_hub(lowerCAmelCase__				)


if __name__ == "__main__":
				__snake_case     :int			       =argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '--model_name',
				    default='b0',
				    type=str,
				    help='Version name of the EfficientNet model you want to convert, select from [b0, b1, b2, b3, b4, b5, b6, b7].',
				)
				parser.add_argument(
				    '--pytorch_dump_folder_path',
				    default='hf_model',
				    type=str,
				    help='Path to the output PyTorch model directory.',
				)
				parser.add_argument('--save_model', action='store_true', help='Save model to local')
				parser.add_argument('--push_to_hub', action='store_true', help='Push model and image processor to the hub')

				__snake_case     :List[Any]			       =parser.parse_args()
				convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)




import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


__snake_case     :str			       =False



class        lowerCAmelCase__					(  unittest.TestCase       ):
				pass





@nightly
@require_torch_gpu
class        lowerCAmelCase__					(  unittest.TestCase       ):


				def __UpperCamelCase						(							self      :  Dict       )		->     List[Any]:
											# clean up the VRAM after each test
											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()


				def __UpperCamelCase						(							self      :  Union[str, Any]       )		->     List[Any]:
											A      						=    VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion'						, torch_dtype=torch.floataa       )
											pipe.to(__UpperCamelCase       )
											pipe.set_progress_bar_config(disable=__UpperCamelCase       )

											A      						=    load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'       )

											A      						=    torch.manual_seed(0       )
											A      						=    pipe.dual_guided(
											    prompt='first prompt'						, image=__UpperCamelCase						, text_to_image_strength=0.7_5						, generator=__UpperCamelCase						, guidance_scale=7.5						, num_inference_steps=2						, output_type='numpy'						, ).images

											with tempfile.TemporaryDirectory() as tmpdirname:
																		pipe.save_pretrained(__UpperCamelCase       )
																		A      						=    VersatileDiffusionPipeline.from_pretrained(__UpperCamelCase						, torch_dtype=torch.floataa       )
											pipe.to(__UpperCamelCase       )
											pipe.set_progress_bar_config(disable=__UpperCamelCase       )

											A      						=    generator.manual_seed(0       )
											A      						=    pipe.dual_guided(
											    prompt='first prompt'						, image=__UpperCamelCase						, text_to_image_strength=0.7_5						, generator=__UpperCamelCase						, guidance_scale=7.5						, num_inference_steps=2						, output_type='numpy'						, ).images

											assert np.abs(image - new_image       ).sum() < 1e-5, "Models don't have the same forward pass"


				def __UpperCamelCase						(							self      :  Tuple       )		->     List[str]:
											A      						=    VersatileDiffusionPipeline.from_pretrained('shi-labs/versatile-diffusion'						, torch_dtype=torch.floataa       )
											pipe.to(__UpperCamelCase       )
											pipe.set_progress_bar_config(disable=__UpperCamelCase       )

											A      						=    'cyberpunk 2077'
											A      						=    load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'       )
											A      						=    torch.manual_seed(0       )
											A      						=    pipe.dual_guided(
											    prompt=__UpperCamelCase						, image=__UpperCamelCase						, text_to_image_strength=0.7_5						, generator=__UpperCamelCase						, guidance_scale=7.5						, num_inference_steps=50						, output_type='numpy'						, ).images

											A      						=    image[0, 253:256, 253:256, -1]

											assert image.shape == (1, 512, 512, 3)
											A      						=    np.array([0.1_4_4_8, 0.1_6_1_9, 0.1_7_4_1, 0.1_0_8_6, 0.1_1_4_7, 0.1_1_2_8, 0.1_1_9_9, 0.1_1_6_5, 0.1_0_0_1]       )

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

											A      						=    'A painting of a squirrel eating a burger '
											A      						=    torch.manual_seed(0       )
											A      						=    pipe.text_to_image(
											    prompt=__UpperCamelCase						, generator=__UpperCamelCase						, guidance_scale=7.5						, num_inference_steps=50						, output_type='numpy'       ).images

											A      						=    image[0, 253:256, 253:256, -1]

											assert image.shape == (1, 512, 512, 3)
											A      						=    np.array([0.3_3_6_7, 0.3_1_6_9, 0.2_6_5_6, 0.3_8_7_0, 0.4_7_9_0, 0.3_7_9_6, 0.4_0_0_9, 0.4_8_7_8, 0.4_7_7_8]       )

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

											A      						=    pipe.image_variation(__UpperCamelCase						, generator=__UpperCamelCase						, output_type='numpy'       ).images

											A      						=    image[0, 253:256, 253:256, -1]

											assert image.shape == (1, 512, 512, 3)
											A      						=    np.array([0.3_0_7_6, 0.3_1_2_3, 0.3_2_8_4, 0.3_7_8_2, 0.3_7_7_0, 0.3_8_9_4, 0.4_2_9_7, 0.4_3_3_1, 0.4_4_5_6]       )

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