




import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor




def 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	str:

    """simple docstring"""


    __UpperCamelCase  		=						SwinConfig(image_size=1_92      )

    if "base" in model_name:
        __UpperCamelCase  		=						6
        __UpperCamelCase  		=						1_28
        __UpperCamelCase  		=						(2, 2, 18, 2)
        __UpperCamelCase  		=						(4, 8, 16, 32)
    elif "large" in model_name:
        __UpperCamelCase  		=						12
        __UpperCamelCase  		=						1_92
        __UpperCamelCase  		=						(2, 2, 18, 2)
        __UpperCamelCase  		=						(6, 12, 24, 48)
    else:
        raise ValueError('''Model not supported, only supports base and large variants'''      )

    __UpperCamelCase  		=						window_size
    __UpperCamelCase  		=						embed_dim
    __UpperCamelCase  		=						depths
    __UpperCamelCase  		=						num_heads

    return config




def 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	Any:

    """simple docstring"""


    if "encoder.mask_token" in name:
        __UpperCamelCase  		=						name.replace('''encoder.mask_token'''					,     '''embeddings.mask_token'''      )
    if "encoder.patch_embed.proj" in name:
        __UpperCamelCase  		=						name.replace('''encoder.patch_embed.proj'''					,     '''embeddings.patch_embeddings.projection'''      )
    if "encoder.patch_embed.norm" in name:
        __UpperCamelCase  		=						name.replace('''encoder.patch_embed.norm'''					,     '''embeddings.norm'''      )
    if "attn.proj" in name:
        __UpperCamelCase  		=						name.replace('''attn.proj'''					,     '''attention.output.dense'''      )
    if "attn" in name:
        __UpperCamelCase  		=						name.replace('''attn'''					,     '''attention.self'''      )
    if "norm1" in name:
        __UpperCamelCase  		=						name.replace('''norm1'''					,     '''layernorm_before'''      )
    if "norm2" in name:
        __UpperCamelCase  		=						name.replace('''norm2'''					,     '''layernorm_after'''      )
    if "mlp.fc1" in name:
        __UpperCamelCase  		=						name.replace('''mlp.fc1'''					,     '''intermediate.dense'''      )
    if "mlp.fc2" in name:
        __UpperCamelCase  		=						name.replace('''mlp.fc2'''					,     '''output.dense'''      )

    if name == "encoder.norm.weight":
        __UpperCamelCase  		=						'''layernorm.weight'''
    if name == "encoder.norm.bias":
        __UpperCamelCase  		=						'''layernorm.bias'''

    if "decoder" in name:
        pass
    else:
        __UpperCamelCase  		=						'''swin.''' + name

    return name




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_      )       ->	Dict:

    """simple docstring"""


    for key in orig_state_dict.copy().keys():
        __UpperCamelCase  		=						orig_state_dict.pop(lowercase_      )

        if "attn_mask" in key:
            pass
        elif "qkv" in key:
            __UpperCamelCase  		=						key.split('''.'''      )
            __UpperCamelCase  		=						int(key_split[2]      )
            __UpperCamelCase  		=						int(key_split[4]      )
            __UpperCamelCase  		=						model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

            if "weight" in key:
                __UpperCamelCase  		=						val[:dim, :]
                __UpperCamelCase  		=						val[
                    dim : dim * 2, :
                ]
                __UpperCamelCase  		=						val[-dim:, :]
            else:
                __UpperCamelCase  		=						val[
                    :dim
                ]
                __UpperCamelCase  		=						val[
                    dim : dim * 2
                ]
                __UpperCamelCase  		=						val[
                    -dim:
                ]
        else:
            __UpperCamelCase  		=						val

    return orig_state_dict




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_					,     lowercase_      )       ->	int:

    """simple docstring"""


    __UpperCamelCase  		=						torch.load(lowercase_					,     map_location='''cpu'''      )['''model''']

    __UpperCamelCase  		=						get_swin_config(lowercase_      )
    __UpperCamelCase  		=						SwinForMaskedImageModeling(lowercase_      )
    model.eval()

    __UpperCamelCase  		=						convert_state_dict(lowercase_					,     lowercase_      )
    model.load_state_dict(lowercase_      )

    __UpperCamelCase  		=						'''http://images.cocodataset.org/val2017/000000039769.jpg'''

    __UpperCamelCase  		=						ViTImageProcessor(size={'''height''': 1_92, '''width''': 1_92}      )
    __UpperCamelCase  		=						Image.open(requests.get(lowercase_					,     stream=lowercase_      ).raw      )
    __UpperCamelCase  		=						image_processor(images=lowercase_					,     return_tensors='''pt'''      )

    with torch.no_grad():
        __UpperCamelCase  		=						model(**lowercase_      ).logits

    print(outputs.keys()      )
    print('''Looks ok!'''      )

    if pytorch_dump_folder_path is not None:
        print(F"Saving model {model_name} to {pytorch_dump_folder_path}"      )
        model.save_pretrained(lowercase_      )

        print(F"Saving image processor to {pytorch_dump_folder_path}"      )
        image_processor.save_pretrained(lowercase_      )

    if push_to_hub:
        print(F"Pushing model and image processor for {model_name} to hub"      )
        model.push_to_hub(F"microsoft/{model_name}"      )
        image_processor.push_to_hub(F"microsoft/{model_name}"      )


if __name__ == "__main__":
 a_   		=     argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--model_name",
     default="swin-base-simmim-window6-192",
     type=str,
     choices=["swin-base-simmim-window6-192", "swin-large-simmim-window12-192"],
     help="Name of the Swin SimMIM model you'd like to convert.",
 )
 parser.add_argument(
     "--checkpoint_path",
     default="/Users/nielsrogge/Documents/SwinSimMIM/simmim_pretrain__swin_base__img192_window6__100ep.pth",
     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 output PyTorch model directory."
 )
 parser.add_argument(
     "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
 )

 a_   		=     parser.parse_args()
 convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)












import os
import time

import numpy as np
import onnxruntime as ort


a_   		=     "1"
a_   		=     "0"
a_   		=     "1"

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

a_   		=     128
a_   		=     1
a_   		=     np.ones((batch, sequence), dtype=np.intaa)
a_   		=     np.ones((batch, sequence), dtype=np.intaa)
a_   		=     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...")
a_   		=     time.time()
a_   		=     2000
a_   		=     {}
for iter in range(max_iters):
 a_   		=     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))






