import argparse

import requests
import torch
from PIL import Image

from transformers import SwinConfig, SwinForMaskedImageModeling, ViTImageProcessor


def      a__ (		__UpperCamelCase				):
      SCREAMING_SNAKE_CASE_          =  SwinConfig(image_size=1_9_2				)

      if "base" in model_name:
            SCREAMING_SNAKE_CASE_          =  6
            SCREAMING_SNAKE_CASE_          =  1_2_8
            SCREAMING_SNAKE_CASE_          =  (2, 2, 1_8, 2)
            SCREAMING_SNAKE_CASE_          =  (4, 8, 1_6, 3_2)
      elif "large" in model_name:
            SCREAMING_SNAKE_CASE_          =  1_2
            SCREAMING_SNAKE_CASE_          =  1_9_2
            SCREAMING_SNAKE_CASE_          =  (2, 2, 1_8, 2)
            SCREAMING_SNAKE_CASE_          =  (6, 1_2, 2_4, 4_8)
      else:
            raise ValueError("Model not supported, only supports base and large variants"				)

      SCREAMING_SNAKE_CASE_          =  window_size
      SCREAMING_SNAKE_CASE_          =  embed_dim
      SCREAMING_SNAKE_CASE_          =  depths
      SCREAMING_SNAKE_CASE_          =  num_heads

      return config


def      a__ (		__UpperCamelCase				):
      if "encoder.mask_token" in name:
            SCREAMING_SNAKE_CASE_          =  name.replace("encoder.mask_token"     ,      "embeddings.mask_token"				)
      if "encoder.patch_embed.proj" in name:
            SCREAMING_SNAKE_CASE_          =  name.replace("encoder.patch_embed.proj"     ,      "embeddings.patch_embeddings.projection"				)
      if "encoder.patch_embed.norm" in name:
            SCREAMING_SNAKE_CASE_          =  name.replace("encoder.patch_embed.norm"     ,      "embeddings.norm"				)
      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 == "encoder.norm.weight":
            SCREAMING_SNAKE_CASE_          =  "layernorm.weight"
      if name == "encoder.norm.bias":
            SCREAMING_SNAKE_CASE_          =  "layernorm.bias"

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

      return name


def      a__ (		__UpperCamelCase     ,      __UpperCamelCase				):
      for key in orig_state_dict.copy().keys():
            SCREAMING_SNAKE_CASE_          =  orig_state_dict.pop(__UpperCamelCase				)

            if "attn_mask" in key:
                  pass
            elif "qkv" in key:
                  SCREAMING_SNAKE_CASE_          =  key.split("."				)
                  SCREAMING_SNAKE_CASE_          =  int(key_split[2]				)
                  SCREAMING_SNAKE_CASE_          =  int(key_split[4]				)
                  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__ (		__UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase     ,      __UpperCamelCase				):
      SCREAMING_SNAKE_CASE_          =  torch.load(__UpperCamelCase     ,      map_location="cpu"				)["model"]

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

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

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

      SCREAMING_SNAKE_CASE_          =  ViTImageProcessor(size={"height": 1_9_2, "width": 1_9_2}				)
      SCREAMING_SNAKE_CASE_          =  Image.open(requests.get(__UpperCamelCase     ,      stream=__UpperCamelCase				).raw				)
      SCREAMING_SNAKE_CASE_          =  image_processor(images=__UpperCamelCase     ,      return_tensors="pt"				)

      with torch.no_grad():
            SCREAMING_SNAKE_CASE_          =  model(**__UpperCamelCase				).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(__UpperCamelCase				)

            print(F'''Saving image processor to {pytorch_dump_folder_path}'''				)
            image_processor.save_pretrained(__UpperCamelCase				)

      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     :  Union[str, Any]					     =       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     :  List[Any]					     =       parser.parse_args()
      convert_swin_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)



from collections import Counter
from pathlib import Path
from typing import Optional, Tuple

import yaml


class   lowerCamelCase					(yaml.SafeLoader					):
  """simple docstring"""







  def    __A     (							self :       str  ,     __magic_name__ :       str   )  ->	str:
        SCREAMING_SNAKE_CASE_          =  [self.constructed_objects[key_node] for key_node, _ in node.value]
        SCREAMING_SNAKE_CASE_          =  [tuple(__magic_name__   ) if isinstance(__magic_name__  ,     __magic_name__   ) else key for key in keys]
        SCREAMING_SNAKE_CASE_          =  Counter(__magic_name__   )
        SCREAMING_SNAKE_CASE_          =  [key for key in counter if counter[key] > 1]
        if duplicate_keys:
              raise TypeError(F'''Got duplicate yaml keys: {duplicate_keys}'''   )







  def    __A     (							self :       int  ,     __magic_name__ :       int  ,     __magic_name__ :       List[str]=False   )  ->	Optional[Any]:
        SCREAMING_SNAKE_CASE_          =  super().construct_mapping(__magic_name__  ,     deep=__magic_name__   )
        self._check_no_duplicates_on_constructed_node(__magic_name__   )
        return mapping



def      a__ (		__UpperCamelCase				):
      SCREAMING_SNAKE_CASE_          =  list(readme_content.splitlines()				)
      if full_content and full_content[0] == "---" and "---" in full_content[1:]:
            SCREAMING_SNAKE_CASE_          =  full_content[1:].index("---"				) + 1
            SCREAMING_SNAKE_CASE_          =  "\n".join(full_content[1:sep_idx]				)
            return yamlblock, "\n".join(full_content[sep_idx + 1 :]				)

      return None, "\n".join(__UpperCamelCase				)


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







  lowerCamelCase__     =						{'''train_eval_index'''}  # train-eval-index in the YAML metadata







  @classmethod
  def    __A     (							cls :       Dict  ,     __magic_name__ :       Path   )  ->	"DatasetMetadata":
        with open(__magic_name__  ,     encoding="utf-8"   ) as readme_file:
              SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  _split_yaml_from_readme(readme_file.read()   )
        if yaml_string is not None:
              return cls.from_yaml_string(__magic_name__   )
        else:
              return cls()







  def    __A     (							self :       str  ,     __magic_name__ :       Path   )  ->	List[str]:
        if path.exists():
              with open(__magic_name__  ,     encoding="utf-8"   ) as readme_file:
                    SCREAMING_SNAKE_CASE_          =  readme_file.read()
        else:
              SCREAMING_SNAKE_CASE_          =  None
        SCREAMING_SNAKE_CASE_          =  self._to_readme(__magic_name__   )
        with open(__magic_name__  ,     "w"  ,     encoding="utf-8"   ) as readme_file:
              readme_file.write(__magic_name__   )







  def    __A     (							self :       Any  ,     __magic_name__ :       Optional[str] = None   )  ->	str:
        if readme_content is not None:
              SCREAMING_SNAKE_CASE_     , SCREAMING_SNAKE_CASE_          =  _split_yaml_from_readme(__magic_name__   )
              SCREAMING_SNAKE_CASE_          =  "---\n" + self.to_yaml_string() + "---\n" + content
        else:
              SCREAMING_SNAKE_CASE_          =  "---\n" + self.to_yaml_string() + "---\n"
        return full_content







  @classmethod
  def    __A     (							cls :       List[Any]  ,     __magic_name__ :       str   )  ->	"DatasetMetadata":
        SCREAMING_SNAKE_CASE_          =  yaml.load(__magic_name__  ,     Loader=_NoDuplicateSafeLoader   ) or {}

        # Convert the YAML keys to DatasetMetadata fields
        SCREAMING_SNAKE_CASE_          =  {
            (key.replace("-"  ,     "_"   ) if key.replace("-"  ,     "_"   ) in cls._FIELDS_WITH_DASHES else key): value
            for key, value in metadata_dict.items()
        }
        return cls(**__magic_name__   )







  def    __A     (							self :       Optional[Any]   )  ->	str:
        return yaml.safe_dump(
            {
                (key.replace("_"  ,     "-"   ) if key in self._FIELDS_WITH_DASHES else key): value
                for key, value in self.items()
            }  ,     sort_keys=__magic_name__  ,     allow_unicode=__magic_name__  ,     encoding="utf-8"  ,     ).decode("utf-8"   )



A     :  List[Any]					     =       {
    "image-classification": [],
    "translation": [],
    "image-segmentation": [],
    "fill-mask": [],
    "automatic-speech-recognition": [],
    "token-classification": [],
    "sentence-similarity": [],
    "audio-classification": [],
    "question-answering": [],
    "summarization": [],
    "zero-shot-classification": [],
    "table-to-text": [],
    "feature-extraction": [],
    "other": [],
    "multiple-choice": [],
    "text-classification": [],
    "text-to-image": [],
    "text2text-generation": [],
    "zero-shot-image-classification": [],
    "tabular-classification": [],
    "tabular-regression": [],
    "image-to-image": [],
    "tabular-to-text": [],
    "unconditional-image-generation": [],
    "text-retrieval": [],
    "text-to-speech": [],
    "object-detection": [],
    "audio-to-audio": [],
    "text-generation": [],
    "conversational": [],
    "table-question-answering": [],
    "visual-question-answering": [],
    "image-to-text": [],
    "reinforcement-learning": [],
    "voice-activity-detection": [],
    "time-series-forecasting": [],
    "document-question-answering": [],
}


if __name__ == "__main__":
      from argparse import ArgumentParser

      A     :  Optional[Any]					     =       ArgumentParser(usage="Validate the yaml metadata block of a README.md file.")
      ap.add_argument("readme_filepath")
      A     :  Union[str, Any]					     =       ap.parse_args()

      A     :  Union[str, Any]					     =       Path(args.readme_filepath)
      A     :  List[Any]					     =       DatasetMetadata.from_readme(readme_filepath)
      print(dataset_metadata)
      dataset_metadata.to_readme(readme_filepath)


