

import argparse
import json

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

from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification





def 	__a     (      A__			:  Any       ):
    SCREAMING_SNAKE_CASE										=   SwinConfig()
    SCREAMING_SNAKE_CASE										=   swin_name.split("_"       )

    SCREAMING_SNAKE_CASE										=   name_split[1]
    SCREAMING_SNAKE_CASE										=   int(name_split[4]       )
    SCREAMING_SNAKE_CASE										=   int(name_split[3][-1]       )

    if model_size == "tiny":
        SCREAMING_SNAKE_CASE										=   96
        SCREAMING_SNAKE_CASE										=   (2, 2, 6, 2)
        SCREAMING_SNAKE_CASE										=   (3, 6, 12, 24)
    elif model_size == "small":
        SCREAMING_SNAKE_CASE										=   96
        SCREAMING_SNAKE_CASE										=   (2, 2, 18, 2)
        SCREAMING_SNAKE_CASE										=   (3, 6, 12, 24)
    elif model_size == "base":
        SCREAMING_SNAKE_CASE										=   128
        SCREAMING_SNAKE_CASE										=   (2, 2, 18, 2)
        SCREAMING_SNAKE_CASE										=   (4, 8, 16, 32)
    else:
        SCREAMING_SNAKE_CASE										=   192
        SCREAMING_SNAKE_CASE										=   (2, 2, 18, 2)
        SCREAMING_SNAKE_CASE										=   (6, 12, 24, 48)

    if "in22k" in swin_name:
        SCREAMING_SNAKE_CASE										=   21841
    else:
        SCREAMING_SNAKE_CASE										=   1000
        SCREAMING_SNAKE_CASE										=   "huggingface/label-files"
        SCREAMING_SNAKE_CASE										=   "imagenet-1k-id2label.json"
        SCREAMING_SNAKE_CASE										=   json.load(open(hf_hub_download(A__			,  A__			,  repo_type="dataset"       )			,  "r"       )       )
        SCREAMING_SNAKE_CASE										=   {int(A__       ): v for k, v in idalabel.items()}
        SCREAMING_SNAKE_CASE										=   idalabel
        SCREAMING_SNAKE_CASE										=   {v: k for k, v in idalabel.items()}

    SCREAMING_SNAKE_CASE										=   img_size
    SCREAMING_SNAKE_CASE										=   num_classes
    SCREAMING_SNAKE_CASE										=   embed_dim
    SCREAMING_SNAKE_CASE										=   depths
    SCREAMING_SNAKE_CASE										=   num_heads
    SCREAMING_SNAKE_CASE										=   window_size

    return config





def 	__a     (      A__			:  List[str]       ):
    if "patch_embed.proj" in name:
        SCREAMING_SNAKE_CASE										=   name.replace("patch_embed.proj"			,  "embeddings.patch_embeddings.projection"       )
    if "patch_embed.norm" in name:
        SCREAMING_SNAKE_CASE										=   name.replace("patch_embed.norm"			,  "embeddings.norm"       )
    if "layers" in name:
        SCREAMING_SNAKE_CASE										=   "encoder." + name
    if "attn.proj" in name:
        SCREAMING_SNAKE_CASE										=   name.replace("attn.proj"			,  "attention.output.dense"       )
    if "attn" in name:
        SCREAMING_SNAKE_CASE										=   name.replace("attn"			,  "attention.self"       )
    if "norm1" in name:
        SCREAMING_SNAKE_CASE										=   name.replace("norm1"			,  "layernorm_before"       )
    if "norm2" in name:
        SCREAMING_SNAKE_CASE										=   name.replace("norm2"			,  "layernorm_after"       )
    if "mlp.fc1" in name:
        SCREAMING_SNAKE_CASE										=   name.replace("mlp.fc1"			,  "intermediate.dense"       )
    if "mlp.fc2" in name:
        SCREAMING_SNAKE_CASE										=   name.replace("mlp.fc2"			,  "output.dense"       )

    if name == "norm.weight":
        SCREAMING_SNAKE_CASE										=   "layernorm.weight"
    if name == "norm.bias":
        SCREAMING_SNAKE_CASE										=   "layernorm.bias"

    if "head" in name:
        SCREAMING_SNAKE_CASE										=   name.replace("head"			,  "classifier"       )
    else:
        SCREAMING_SNAKE_CASE										=   "swin." + name

    return name





def 	__a     (      A__			:  Optional[Any]			,  A__			:  Any       ):
    for key in orig_state_dict.copy().keys():
        SCREAMING_SNAKE_CASE										=   orig_state_dict.pop(A__       )

        if "mask" in key:
            continue
        elif "qkv" in key:
            SCREAMING_SNAKE_CASE										=   key.split("."       )
            SCREAMING_SNAKE_CASE										=   int(key_split[1]       )
            SCREAMING_SNAKE_CASE										=   int(key_split[3]       )
            SCREAMING_SNAKE_CASE										=   model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

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

    return orig_state_dict





def 	__a     (      A__			:  Optional[Any]			,  A__			:  Any       ):
    SCREAMING_SNAKE_CASE										=   timm.create_model(A__			,  pretrained=A__       )
    timm_model.eval()

    SCREAMING_SNAKE_CASE										=   get_swin_config(A__       )
    SCREAMING_SNAKE_CASE										=   SwinForImageClassification(A__       )
    model.eval()

    SCREAMING_SNAKE_CASE										=   convert_state_dict(timm_model.state_dict()			,  A__       )
    model.load_state_dict(A__       )

    SCREAMING_SNAKE_CASE										=   "http://images.cocodataset.org/val2017/000000039769.jpg"

    SCREAMING_SNAKE_CASE										=   AutoImageProcessor.from_pretrained("microsoft/{}".format(swin_name.replace("_"			,  "-"       )       )       )
    SCREAMING_SNAKE_CASE										=   Image.open(requests.get(A__			,  stream=A__       ).raw       )
    SCREAMING_SNAKE_CASE										=   image_processor(images=A__			,  return_tensors="pt"       )

    SCREAMING_SNAKE_CASE										=   timm_model(inputs["pixel_values"]       )
    SCREAMING_SNAKE_CASE										=   model(**A__       ).logits

    assert torch.allclose(A__			,  A__			,  atol=1E-3       )

    print(F"Saving model {swin_name} to {pytorch_dump_folder_path}"       )
    model.save_pretrained(A__       )

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


if __name__ == "__main__":
    __A			:					Any						=			argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '--swin_name',
        default='swin_tiny_patch4_window7_224',
        type=str,
        help='Name of the Swin timm model you\'d like to convert.',
    )
    parser.add_argument(
        '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
    )

    __A			:					List[Any]						=			parser.parse_args()
    convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)


from collections.abc import Callable

import numpy as np





def 	__a     (      A__			:  Callable			,  A__			:  float			,  A__			:  float			,  A__			:  float			,  A__			:  float       ):
    SCREAMING_SNAKE_CASE										=   int(np.ceil((x_end - xa) / step_size       )       )
    SCREAMING_SNAKE_CASE										=   np.zeros((n + 1,)       )
    SCREAMING_SNAKE_CASE										=   ya
    SCREAMING_SNAKE_CASE										=   xa

    for k in range(A__       ):
        SCREAMING_SNAKE_CASE										=   y[k] + step_size * ode_func(A__			,  y[k]       )
        SCREAMING_SNAKE_CASE										=   y[k] + (
            (step_size / 2) * (ode_func(A__			,  y[k]       ) + ode_func(x + step_size			,  A__       ))
        )
        x += step_size

    return y


if __name__ == "__main__":
    import doctest

    doctest.testmod()