'''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()
SCREAMING_SNAKE_CASE_:     Union[str, Any]  =logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:     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,
}

SCREAMING_SNAKE_CASE_:     Optional[Any]  ={
    '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 	lowerCAmelCase_				(					snake_case_       : Union[str, Any]			)    ->      List[Any]:


     '''simple docstring'''




     UpperCAmelCase_           =   EfficientNetConfig()
     UpperCAmelCase_           =   CONFIG_MAP[model_name]["hidden_dim"]
     UpperCAmelCase_           =   CONFIG_MAP[model_name]["width_coef"]
     UpperCAmelCase_           =   CONFIG_MAP[model_name]["depth_coef"]
     UpperCAmelCase_           =   CONFIG_MAP[model_name]["image_size"]
     UpperCAmelCase_           =   CONFIG_MAP[model_name]["dropout_rate"]
     UpperCAmelCase_           =   CONFIG_MAP[model_name]["dw_padding"]

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

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





def 	lowerCAmelCase_				(					)    ->      List[Any]:


     '''simple docstring'''




     UpperCAmelCase_           =   "http://images.cocodataset.org/val2017/000000039769.jpg"
     UpperCAmelCase_           =   Image.open(requests.get(__lowerCamelCase					,    stream=__lowerCamelCase			).raw			)
     return im





def 	lowerCAmelCase_				(					snake_case_       : List[str]			)    ->      Any:


     '''simple docstring'''




     UpperCAmelCase_           =   CONFIG_MAP[model_name]["image_size"]
     UpperCAmelCase_           =   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=__lowerCamelCase					,    )
     return preprocessor





def 	lowerCAmelCase_				(					snake_case_       : Any			)    ->      List[str]:


     '''simple docstring'''




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

     UpperCAmelCase_           =   []
     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_           =   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_           =   {}
     for item in rename_keys:
          if item[0] in original_param_names:
               UpperCAmelCase_           =   "efficientnet." + item[1]

     UpperCAmelCase_           =   "classifier.weight"
     UpperCAmelCase_           =   "classifier.bias"
     return key_mapping





def 	lowerCAmelCase_				(					snake_case_       : Optional[Any]					,    snake_case_       : Optional[Any]					,    snake_case_       : Optional[int]			)    ->      Optional[int]:


     '''simple docstring'''




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

          UpperCAmelCase_           =   key_mapping[key]
          if "_conv" in key and "kernel" in key:
               UpperCAmelCase_           =   torch.from_numpy(__lowerCamelCase			).permute(3					,    2					,    0					,    1			)
          elif "depthwise_kernel" in key:
               UpperCAmelCase_           =   torch.from_numpy(__lowerCamelCase			).permute(2					,    3					,    0					,    1			)
          elif "kernel" in key:
               UpperCAmelCase_           =   torch.from_numpy(np.transpose(__lowerCamelCase			)			)
          else:
               UpperCAmelCase_           =   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_				(					snake_case_       : Optional[Any]					,    snake_case_       : str					,    snake_case_       : List[str]					,    snake_case_       : Union[str, Any]			)    ->      List[Any]:


     '''simple docstring'''




     UpperCAmelCase_           =   model_classes[model_name](
         include_top=__lowerCamelCase					,    weights="imagenet"					,    input_tensor=__lowerCamelCase					,    input_shape=__lowerCamelCase					,    pooling=__lowerCamelCase					,    classes=10_00					,    classifier_activation="softmax"					,    )

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

     # Load HuggingFace model
     UpperCAmelCase_           =   get_efficientnet_config(__lowerCamelCase			)
     UpperCAmelCase_           =   EfficientNetForImageClassification(__lowerCamelCase			).eval()
     UpperCAmelCase_           =   hf_model.state_dict()

     # Create src-to-dst parameter name mapping dictionary
     print("Converting parameters..."			)
     UpperCAmelCase_           =   rename_keys(__lowerCamelCase			)
     replace_params(__lowerCamelCase					,    __lowerCamelCase					,    __lowerCamelCase			)

     # Initialize preprocessor and preprocess input image
     UpperCAmelCase_           =   convert_image_processor(__lowerCamelCase			)
     UpperCAmelCase_           =   preprocessor(images=prepare_img()					,    return_tensors="pt"			)

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

     # Original model inference
     UpperCAmelCase_           =   False
     UpperCAmelCase_           =   CONFIG_MAP[model_name]["image_size"]
     UpperCAmelCase_           =   prepare_img().resize((image_size, image_size)					,    resample=PIL.Image.NEAREST			)
     UpperCAmelCase_           =   image.img_to_array(__lowerCamelCase			)
     UpperCAmelCase_           =   np.expand_dims(__lowerCamelCase					,    axis=0			)
     UpperCAmelCase_           =   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_           =   f"""efficientnet-{model_name}"""
          preprocessor.push_to_hub(__lowerCamelCase			)
          hf_model.push_to_hub(__lowerCamelCase			)


if __name__ == "__main__":
    SCREAMING_SNAKE_CASE_:     Optional[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')

    SCREAMING_SNAKE_CASE_:     str  =parser.parse_args()
    convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)






"""simple docstring"""



import os
import time

import numpy as np
import onnxruntime as ort


UpperCAmelCase__          = '1'
UpperCAmelCase__          = '0'
UpperCAmelCase__          = '1'

UpperCAmelCase__          = ort.SessionOptions()
UpperCAmelCase__          = ort.GraphOptimizationLevel.ORT_DISABLE_ALL
print('Create inference session...')
UpperCAmelCase__          = ['TensorrtExecutionProvider', 'CUDAExecutionProvider']
UpperCAmelCase__          = ort.InferenceSession('model.onnx', sess_options=sess_opt, providers=execution_provider)
UpperCAmelCase__          = ort.RunOptions()

UpperCAmelCase__          = 128
UpperCAmelCase__          = 1
UpperCAmelCase__          = np.ones((batch, sequence), dtype=np.intaa)
UpperCAmelCase__          = np.ones((batch, sequence), dtype=np.intaa)
UpperCAmelCase__          = np.ones((batch, sequence), dtype=np.intaa)

print('Warm up phase...')
sess.run(
    None,
    {
        sess.get_inputs()[0].name: input_ids,
        sess.get_inputs()[1].name: attention_mask,
        sess.get_inputs()[2].name: token_type_ids,
    },
    run_options=run_opt,
)

print('Start inference...')
UpperCAmelCase__          = time.time()
UpperCAmelCase__          = 2000
UpperCAmelCase__          = {}
for iter in range(max_iters):
 UpperCAmelCase__          = sess.run(
     None,
     {
         sess.get_inputs()[0].name: input_ids,
         sess.get_inputs()[1].name: attention_mask,
         sess.get_inputs()[2].name: token_type_ids,
     },
     run_options=run_opt,
 )
print('Average Inference Time = {:.3f} ms'.format((time.time() - start_time) * 1000 / max_iters))



