from ...configuration_utils import PretrainedConfig
from ...utils import logging


A     :  Any					     =       logging.get_logger(__name__)

A     :  Any					     =       {
    "google/canine-s": "https://huggingface.co/google/canine-s/resolve/main/config.json",
    # See all CANINE models at https://huggingface.co/models?filter=canine
}


class   lowerCamelCase					(SCREAMING_SNAKE_CASE__					):
  """simple docstring"""







  lowerCamelCase__     =						'''canine'''







  def __init__(							self :       List[Any]  ,     __magic_name__ :       Optional[Any]=768  ,     __magic_name__ :       Union[str, Any]=12  ,     __magic_name__ :       str=12  ,     __magic_name__ :       Union[str, Any]=3_072  ,     __magic_name__ :       Union[str, Any]="gelu"  ,     __magic_name__ :       List[str]=0.1  ,     __magic_name__ :       List[str]=0.1  ,     __magic_name__ :       Optional[Any]=16_384  ,     __magic_name__ :       Optional[Any]=16  ,     __magic_name__ :       Optional[int]=0.02  ,     __magic_name__ :       Optional[Any]=1e-12  ,     __magic_name__ :       str=0  ,     __magic_name__ :       Any=0XE_0_0_0  ,     __magic_name__ :       Optional[int]=0XE_0_0_1  ,     __magic_name__ :       Union[str, Any]=4  ,     __magic_name__ :       str=4  ,     __magic_name__ :       Dict=8  ,     __magic_name__ :       Optional[int]=16_384  ,     __magic_name__ :       Optional[Any]=128  ,     **__magic_name__ :       Optional[int]  ,     )  ->	Optional[int]:
        super().__init__(pad_token_id=__magic_name__  ,     bos_token_id=__magic_name__  ,     eos_token_id=__magic_name__  ,     **__magic_name__   )

        SCREAMING_SNAKE_CASE_          =  max_position_embeddings
        SCREAMING_SNAKE_CASE_          =  hidden_size
        SCREAMING_SNAKE_CASE_          =  num_hidden_layers
        SCREAMING_SNAKE_CASE_          =  num_attention_heads
        SCREAMING_SNAKE_CASE_          =  intermediate_size
        SCREAMING_SNAKE_CASE_          =  hidden_act
        SCREAMING_SNAKE_CASE_          =  hidden_dropout_prob
        SCREAMING_SNAKE_CASE_          =  attention_probs_dropout_prob
        SCREAMING_SNAKE_CASE_          =  initializer_range
        SCREAMING_SNAKE_CASE_          =  type_vocab_size
        SCREAMING_SNAKE_CASE_          =  layer_norm_eps

        # Character config:
        SCREAMING_SNAKE_CASE_          =  downsampling_rate
        SCREAMING_SNAKE_CASE_          =  upsampling_kernel_size
        SCREAMING_SNAKE_CASE_          =  num_hash_functions
        SCREAMING_SNAKE_CASE_          =  num_hash_buckets
        SCREAMING_SNAKE_CASE_          =  local_transformer_stride



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()
A     :  Optional[Any]					     =       logging.get_logger(__name__)

A     :  List[Any]					     =       torch.device("cpu")


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


def      a__ (		__UpperCamelCase				):
      if swiftformer_name == "swiftformer_xs":
            return torch.tensor([-2.1_703E00, 2.1_107E00, -2.0_811E00, 8.8_685E-01, 2.4_360E-01]				)

      elif swiftformer_name == "swiftformer_s":
            return torch.tensor([3.9_636E-01, 2.3_478E-01, -1.6_963E00, -1.7_381E00, -8.6_337E-01]				)

      elif swiftformer_name == "swiftformer_l1":
            return torch.tensor([-4.2_768E-01, -4.7_429E-01, -1.0_897E00, -1.0_248E00, 3.5_523E-02]				)

      elif swiftformer_name == "swiftformer_l3":
            return torch.tensor([-2.5_330E-01, 2.4_211E-01, -6.0_185E-01, -8.2_789E-01, -6.0_446E-02]				)


def      a__ (		__UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase				):
      SCREAMING_SNAKE_CASE_          =  dct.pop(__UpperCamelCase				)
      SCREAMING_SNAKE_CASE_          =  val


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


@torch.no_grad()
def      a__ (		__UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase				):
      SCREAMING_SNAKE_CASE_          =  SwiftFormerConfig()

      # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
      SCREAMING_SNAKE_CASE_          =  1_0_0_0
      SCREAMING_SNAKE_CASE_          =  "huggingface/label-files"
      SCREAMING_SNAKE_CASE_          =  "imagenet-1k-id2label.json"
      SCREAMING_SNAKE_CASE_          =  json.load(open(hf_hub_download(__UpperCamelCase     ,      __UpperCamelCase     ,      repo_type="dataset"				)     ,      "r"				)				)
      SCREAMING_SNAKE_CASE_          =  {int(__UpperCamelCase				): v for k, v in idalabel.items()}
      SCREAMING_SNAKE_CASE_          =  idalabel
      SCREAMING_SNAKE_CASE_          =  {v: k for k, v in idalabel.items()}

      # size of the architecture
      if swiftformer_name == "swiftformer_xs":
            SCREAMING_SNAKE_CASE_          =  [3, 3, 6, 4]
            SCREAMING_SNAKE_CASE_          =  [4_8, 5_6, 1_1_2, 2_2_0]

      elif swiftformer_name == "swiftformer_s":
            SCREAMING_SNAKE_CASE_          =  [3, 3, 9, 6]
            SCREAMING_SNAKE_CASE_          =  [4_8, 6_4, 1_6_8, 2_2_4]

      elif swiftformer_name == "swiftformer_l1":
            SCREAMING_SNAKE_CASE_          =  [4, 3, 1_0, 5]
            SCREAMING_SNAKE_CASE_          =  [4_8, 9_6, 1_9_2, 3_8_4]

      elif swiftformer_name == "swiftformer_l3":
            SCREAMING_SNAKE_CASE_          =  [4, 4, 1_2, 6]
            SCREAMING_SNAKE_CASE_          =  [6_4, 1_2_8, 3_2_0, 5_1_2]

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

      SCREAMING_SNAKE_CASE_          =  create_rename_keys(__UpperCamelCase				)
      for rename_key_src, rename_key_dest in rename_keys:
            rename_key(__UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase				)

      # load HuggingFace model
      SCREAMING_SNAKE_CASE_          =  SwiftFormerForImageClassification(__UpperCamelCase				).eval()
      hf_model.load_state_dict(__UpperCamelCase				)

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

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

      assert hf_logits.shape == torch.Size([1, 1_0_0_0]				)
      assert torch.allclose(hf_logits[0, 0:5]     ,      __UpperCamelCase     ,      atol=1E-3				)

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


if __name__ == "__main__":
      A     :  Optional[Any]					     =       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.")

      A     :  List[Any]					     =       parser.parse_args()
      convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)


