



import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transformers.utils import logging


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






def  lowerCamelCase_	(      lowerCAmelCase__	:	List[Any]			,	lowerCAmelCase__	:	Any			,	lowerCAmelCase__	:	str			,	lowerCAmelCase__	:	Tuple				)       ->							Optional[Any]:

							'''simple docstring'''






							A      						=    original_name.split('.'				)[0]
							A      						=    key.split('.'				)
							A      						=    int(key_list[key_list.index(lowerCAmelCase__				) - 2]				)
							A      						=    int(key_list[key_list.index(lowerCAmelCase__				) - 1]				)
							A      						=    orig_block_num - offset

							A      						=    key.replace(F'''{orig_block_num}.{layer_num}.{original_name}'''			,	F'''block.{new_block_num}.{layer_num}.{new_name}'''				)
							return key






def  lowerCamelCase_	(      lowerCAmelCase__	:	Optional[int]				)       ->							Tuple:

							'''simple docstring'''






							A      						=    OrderedDict()
							A						,   A      						=    0, 0
							for key, value in state_dict.items():
														if key.startswith('network'				):
																					A      						=    key.replace('network'			,	'poolformer.encoder'				)
														if "proj" in key:
																					# Works for the first embedding as well as the internal embedding layers
																					if key.endswith('bias'				) and "patch_embed" not in key:
																												patch_emb_offset += 1
																					A      						=    key[: key.find('proj'				)]
																					A      						=    key.replace(lowerCAmelCase__			,	F'''patch_embeddings.{total_embed_found}.'''				)
																					A      						=    key.replace('proj'			,	'projection'				)
																					if key.endswith('bias'				):
																												total_embed_found += 1
														if "patch_embeddings" in key:
																					A      						=    'poolformer.encoder.' + key
														if "mlp.fc1" in key:
																					A      						=    replace_key_with_offset(lowerCAmelCase__			,	lowerCAmelCase__			,	'mlp.fc1'			,	'output.conv1'				)
														if "mlp.fc2" in key:
																					A      						=    replace_key_with_offset(lowerCAmelCase__			,	lowerCAmelCase__			,	'mlp.fc2'			,	'output.conv2'				)
														if "norm1" in key:
																					A      						=    replace_key_with_offset(lowerCAmelCase__			,	lowerCAmelCase__			,	'norm1'			,	'before_norm'				)
														if "norm2" in key:
																					A      						=    replace_key_with_offset(lowerCAmelCase__			,	lowerCAmelCase__			,	'norm2'			,	'after_norm'				)
														if "layer_scale_1" in key:
																					A      						=    replace_key_with_offset(lowerCAmelCase__			,	lowerCAmelCase__			,	'layer_scale_1'			,	'layer_scale_1'				)
														if "layer_scale_2" in key:
																					A      						=    replace_key_with_offset(lowerCAmelCase__			,	lowerCAmelCase__			,	'layer_scale_2'			,	'layer_scale_2'				)
														if "head" in key:
																					A      						=    key.replace('head'			,	'classifier'				)
														A      						=    value
							return new_state_dict






def  lowerCamelCase_	(      )       ->							Any:

							'''simple docstring'''






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

							return image






@torch.no_grad()
def  lowerCamelCase_	(      lowerCAmelCase__	:	Optional[int]			,	lowerCAmelCase__	:	Dict			,	lowerCAmelCase__	:	Optional[int]				)       ->							Union[str, Any]:

							'''simple docstring'''






							A      						=    PoolFormerConfig()

							# set attributes based on model_name
							A      						=    'huggingface/label-files'
							A      						=    model_name[-3:]
							A      						=    1000
							A      						=    'imagenet-1k-id2label.json'
							A      						=    (1, 1000)

							# set config attributes
							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()}
							if size == "s12":
														A      						=    [2, 2, 6, 2]
														A      						=    [64, 128, 320, 512]
														A      						=    4.0
														A      						=    0.9
							elif size == "s24":
														A      						=    [4, 4, 12, 4]
														A      						=    [64, 128, 320, 512]
														A      						=    4.0
														A      						=    0.9
							elif size == "s36":
														A      						=    [6, 6, 18, 6]
														A      						=    [64, 128, 320, 512]
														A      						=    4.0
														A      						=    1E-6
														A      						=    0.9
							elif size == "m36":
														A      						=    [6, 6, 18, 6]
														A      						=    [96, 192, 384, 768]
														A      						=    4.0
														A      						=    1E-6
														A      						=    0.95
							elif size == "m48":
														A      						=    [8, 8, 24, 8]
														A      						=    [96, 192, 384, 768]
														A      						=    4.0
														A      						=    1E-6
														A      						=    0.95
							else:
														raise ValueError(F'''Size {size} not supported'''				)

							# load image processor
							A      						=    PoolFormerImageProcessor(crop_pct=lowerCAmelCase__				)

							# Prepare image
							A      						=    prepare_img()
							A      						=    image_processor(images=lowerCAmelCase__			,	return_tensors='pt'				).pixel_values

							logger.info(F'''Converting model {model_name}...'''				)

							# load original state dict
							A      						=    torch.load(lowerCAmelCase__			,	map_location=torch.device('cpu'				)				)

							# rename keys
							A      						=    rename_keys(lowerCAmelCase__				)

							# create HuggingFace model and load state dict
							A      						=    PoolFormerForImageClassification(lowerCAmelCase__				)
							model.load_state_dict(lowerCAmelCase__				)
							model.eval()

							# Define image processor
							A      						=    PoolFormerImageProcessor(crop_pct=lowerCAmelCase__				)
							A      						=    image_processor(images=prepare_img()			,	return_tensors='pt'				).pixel_values

							# forward pass
							A      						=    model(lowerCAmelCase__				)
							A      						=    outputs.logits

							# define expected logit slices for different models
							if size == "s12":
														A      						=    torch.tensor([-0.3045, -0.6758, -0.4869]				)
							elif size == "s24":
														A      						=    torch.tensor([0.4402, -0.1374, -0.8045]				)
							elif size == "s36":
														A      						=    torch.tensor([-0.6080, -0.5133, -0.5898]				)
							elif size == "m36":
														A      						=    torch.tensor([0.3952, 0.2263, -1.2668]				)
							elif size == "m48":
														A      						=    torch.tensor([0.1167, -0.0656, -0.3423]				)
							else:
														raise ValueError(F'''Size {size} not supported'''				)

							# verify logits
							assert logits.shape == expected_shape
							assert torch.allclose(logits[0, :3]			,	lowerCAmelCase__			,	atol=1E-2				)

							# finally, save model and image processor
							logger.info(F'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'''				)
							Path(lowerCAmelCase__				).mkdir(exist_ok=lowerCAmelCase__				)
							model.save_pretrained(lowerCAmelCase__				)
							print(F'''Saving image processor to {pytorch_dump_folder_path}'''				)
							image_processor.save_pretrained(lowerCAmelCase__				)


if __name__ == "__main__":
				__snake_case     :Any			       =argparse.ArgumentParser()

				parser.add_argument(
				    '--model_name',
				    default='poolformer_s12',
				    type=str,
				    help='Name of the model you\'d like to convert.',
				)
				parser.add_argument(
				    '--checkpoint_path', default=None, type=str, help='Path to the original PyTorch checkpoint (.pth file).'
				)
				parser.add_argument(
				    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
				)
				__snake_case     :Any			       =parser.parse_args()
				convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)




import os
import time

import numpy as np
import onnxruntime as ort


__snake_case     :Any			       ='1'
__snake_case     :List[str]			       ='0'
__snake_case     :Union[str, Any]			       ='1'

__snake_case     :Optional[Any]			       =ort.SessionOptions()
__snake_case     :List[str]			       =ort.GraphOptimizationLevel.ORT_DISABLE_ALL
print('Create inference session...')
__snake_case     :int			       =['TensorrtExecutionProvider', 'CUDAExecutionProvider']
__snake_case     :Optional[Any]			       =ort.InferenceSession('model.onnx', sess_options=sess_opt, providers=execution_provider)
__snake_case     :int			       =ort.RunOptions()

__snake_case     :Tuple			       =128
__snake_case     :List[Any]			       =1
__snake_case     :Union[str, Any]			       =np.ones((batch, sequence), dtype=np.intaa)
__snake_case     :Optional[Any]			       =np.ones((batch, sequence), dtype=np.intaa)
__snake_case     :Optional[int]			       =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...')
__snake_case     :Union[str, Any]			       =time.time()
__snake_case     :int			       =2000
__snake_case     :Optional[int]			       ={}
for iter in range(max_iters):
				__snake_case     :Optional[Any]			       =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))