





'''simple docstring'''

import argparse
import json
from pathlib import Path

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

from transformers import (
    SwiftFormerConfig,
    SwiftFormerForImageClassification,
    ViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
__lowerCAmelCase				:      Dict						      =logging.get_logger(__name__)

__lowerCAmelCase				:      str						      =torch.device("cpu")





def 		UpperCamelCase							(				):
    A__							=     "http://images.cocodataset.org/val2017/000000039769.jpg"
    A__							=     Image.open(requests.get(_lowerCamelCase		,			stream=_lowerCamelCase			).raw			)
    return im





def 		UpperCamelCase							(				_lowerCamelCase   :   str			):
    if swiftformer_name == "swiftformer_xs":
        return torch.tensor([-2.1703e00, 2.1107e00, -2.0811e00, 8.8685e-01, 2.4360e-01]			)

    elif swiftformer_name == "swiftformer_s":
        return torch.tensor([3.9636e-01, 2.3478e-01, -1.6963e00, -1.7381e00, -8.6337e-01]			)

    elif swiftformer_name == "swiftformer_l1":
        return torch.tensor([-4.2768e-01, -4.7429e-01, -1.0897e00, -1.0248e00, 3.5523e-02]			)

    elif swiftformer_name == "swiftformer_l3":
        return torch.tensor([-2.5330e-01, 2.4211e-01, -6.0185e-01, -8.2789e-01, -6.0446e-02]			)





def 		UpperCamelCase							(				_lowerCamelCase   :   Dict		,			_lowerCamelCase   :   Optional[int]		,			_lowerCamelCase   :   Optional[int]			):
    A__							=     dct.pop(_lowerCamelCase			)
    A__							=     val





def 		UpperCamelCase							(				_lowerCamelCase   :   List[Any]			):
    A__							=     []
    for k in state_dict.keys():
        A__							=     k
        if ".pwconv" in k:
            A__							=     k_new.replace(".pwconv"		,			".point_wise_conv"			)
        if ".dwconv" in k:
            A__							=     k_new.replace(".dwconv"		,			".depth_wise_conv"			)
        if ".Proj." in k:
            A__							=     k_new.replace(".Proj."		,			".proj."			)
        if "patch_embed" in k_new:
            A__							=     k_new.replace("patch_embed"		,			"swiftformer.patch_embed.patch_embedding"			)
        if "network" in k_new:
            A__							=     k_new.split("."			)
            if ls[2].isdigit():
                A__							=     "swiftformer.encoder.network." + ls[1] + ".blocks." + ls[2] + "." + ".".join(ls[3:]			)
            else:
                A__							=     k_new.replace("network"		,			"swiftformer.encoder.network"			)
        rename_keys.append((k, k_new)			)
    return rename_keys





@torch.no_grad()
def 		UpperCamelCase							(				_lowerCamelCase   :   str		,			_lowerCamelCase   :   List[str]		,			_lowerCamelCase   :   str			):
    A__							=     SwiftFormerConfig()

    # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
    A__							=     10_00
    A__							=     "huggingface/label-files"
    A__							=     "imagenet-1k-id2label.json"
    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()}

    # size of the architecture
    if swiftformer_name == "swiftformer_xs":
        A__							=     [3, 3, 6, 4]
        A__							=     [48, 56, 1_12, 2_20]

    elif swiftformer_name == "swiftformer_s":
        A__							=     [3, 3, 9, 6]
        A__							=     [48, 64, 1_68, 2_24]

    elif swiftformer_name == "swiftformer_l1":
        A__							=     [4, 3, 10, 5]
        A__							=     [48, 96, 1_92, 3_84]

    elif swiftformer_name == "swiftformer_l3":
        A__							=     [4, 4, 12, 6]
        A__							=     [64, 1_28, 3_20, 5_12]

    # load state_dict of original model, remove and rename some keys
    if original_ckpt:
        if original_ckpt.startswith("https"			):
            A__							=     torch.hub.load_state_dict_from_url(_lowerCamelCase		,			map_location="cpu"		,			check_hash=_lowerCamelCase			)
        else:
            A__							=     torch.load(_lowerCamelCase		,			map_location="cpu"			)
    A__							=     checkpoint

    A__							=     create_rename_keys(_lowerCamelCase			)
    for rename_key_src, rename_key_dest in rename_keys:
        rename_key(_lowerCamelCase		,			_lowerCamelCase		,			_lowerCamelCase			)

    # load HuggingFace model
    A__							=     SwiftFormerForImageClassification(_lowerCamelCase			).eval()
    hf_model.load_state_dict(_lowerCamelCase			)

    # prepare test inputs
    A__							=     prepare_img()
    A__							=     ViTImageProcessor.from_pretrained("preprocessor_config"			)
    A__							=     processor(images=_lowerCamelCase		,			return_tensors="pt"			)

    # compare outputs from both models
    A__							=     get_expected_output(_lowerCamelCase			)
    A__							=     hf_model(inputs["pixel_values"]			).logits

    assert hf_logits.shape == torch.Size([1, 10_00]			)
    assert torch.allclose(hf_logits[0, 0:5]		,			_lowerCamelCase		,			atol=1e-3			)

    Path(_lowerCamelCase			).mkdir(exist_ok=_lowerCamelCase			)
    print(F"Saving model {swiftformer_name} to {pytorch_dump_folder_path}"			)
    hf_model.save_pretrained(_lowerCamelCase			)


if __name__ == "__main__":
  __lowerCAmelCase				:      Tuple						      =argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--swiftformer_name",
      default="swiftformer_xs",
      choices=["swiftformer_xs", "swiftformer_s", "swiftformer_l1", "swiftformer_l3"],
      type=str,
      help="Name of the SwiftFormer model you'd like to convert.",
  )
  parser.add_argument(
      "--pytorch_dump_folder_path",
      default="./converted_outputs/",
      type=str,
      help="Path to the output PyTorch model directory.",
  )
  parser.add_argument("--original_ckpt", default=None, type=str, help="Path to the original model checkpoint.")

  __lowerCAmelCase				:      Dict						      =parser.parse_args()
  convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)










'''simple docstring'''

# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__lowerCAmelCase				:      Optional[int]						      ={
    "configuration_vivit": ["VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "VivitConfig"],
}
try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __lowerCAmelCase				:      int						      =["VivitImageProcessor"]


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __lowerCAmelCase				:      int						      =[
      "VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
      "VivitModel",
      "VivitPreTrainedModel",
      "VivitForVideoClassification",
  ]


if TYPE_CHECKING:
  from .configuration_vivit import VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, VivitConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .image_processing_vivit import VivitImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_vivit import (
        VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
        VivitForVideoClassification,
        VivitModel,
        VivitPreTrainedModel,
    )


else:
  import sys

  __lowerCAmelCase				:      List[Any]						      =_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



