





import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel





def      lowerCAmelCase_ (       __a		)     ->		int:







  """simple docstring"""





  if "img_encoder.pos_embed" in name:
    lowerCamelCase__:      List[Any]			  =name.replace("img_encoder.pos_embed"	,    "vision_model.embeddings.position_embeddings"		)
  if "img_encoder.patch_embed.proj" in name:
    lowerCamelCase__:      int			  =name.replace("img_encoder.patch_embed.proj"	,    "vision_model.embeddings.patch_embeddings.projection"		)
  if "img_encoder.patch_embed.norm" in name:
    lowerCamelCase__:      int			  =name.replace("img_encoder.patch_embed.norm"	,    "vision_model.embeddings.layernorm"		)
  if "img_encoder.layers" in name:
    lowerCamelCase__:      int			  =name.replace("img_encoder.layers"	,    "vision_model.encoder.stages"		)
  if "blocks" in name and "res" not in name:
    lowerCamelCase__:      Tuple			  =name.replace("blocks"	,    "layers"		)
  if "attn" in name and "pre_assign" not in name:
    lowerCamelCase__:      Optional[Any]			  =name.replace("attn"	,    "self_attn"		)
  if "proj" in name and "self_attn" in name and "text" not in name:
    lowerCamelCase__:      Dict			  =name.replace("proj"	,    "out_proj"		)
  if "pre_assign_attn.attn.proj" in name:
    lowerCamelCase__:      Optional[int]			  =name.replace("pre_assign_attn.attn.proj"	,    "pre_assign_attn.attn.out_proj"		)
  if "norm1" in name:
    lowerCamelCase__:      List[str]			  =name.replace("norm1"	,    "layer_norm1"		)
  if "norm2" in name and "pre_assign" not in name:
    lowerCamelCase__:      Union[str, Any]			  =name.replace("norm2"	,    "layer_norm2"		)
  if "img_encoder.norm" in name:
    lowerCamelCase__:      List[Any]			  =name.replace("img_encoder.norm"	,    "vision_model.layernorm"		)
  # text encoder
  if "text_encoder.token_embedding" in name:
    lowerCamelCase__:      Union[str, Any]			  =name.replace("text_encoder.token_embedding"	,    "text_model.embeddings.token_embedding"		)
  if "text_encoder.positional_embedding" in name:
    lowerCamelCase__:      Any			  =name.replace("text_encoder.positional_embedding"	,    "text_model.embeddings.position_embedding.weight"		)
  if "text_encoder.transformer.resblocks." in name:
    lowerCamelCase__:      str			  =name.replace("text_encoder.transformer.resblocks."	,    "text_model.encoder.layers."		)
  if "ln_1" in name:
    lowerCamelCase__:      Tuple			  =name.replace("ln_1"	,    "layer_norm1"		)
  if "ln_2" in name:
    lowerCamelCase__:      Union[str, Any]			  =name.replace("ln_2"	,    "layer_norm2"		)
  if "c_fc" in name:
    lowerCamelCase__:      Optional[Any]			  =name.replace("c_fc"	,    "fc1"		)
  if "c_proj" in name:
    lowerCamelCase__:      List[Any]			  =name.replace("c_proj"	,    "fc2"		)
  if "text_encoder" in name:
    lowerCamelCase__:      List[Any]			  =name.replace("text_encoder"	,    "text_model"		)
  if "ln_final" in name:
    lowerCamelCase__:      Any			  =name.replace("ln_final"	,    "final_layer_norm"		)
  # projection layers
  if "img_projector.linear_hidden." in name:
    lowerCamelCase__:      Tuple			  =name.replace("img_projector.linear_hidden."	,    "visual_projection."		)
  if "img_projector.linear_out." in name:
    lowerCamelCase__:      Tuple			  =name.replace("img_projector.linear_out."	,    "visual_projection.3."		)
  if "text_projector.linear_hidden" in name:
    lowerCamelCase__:      List[str]			  =name.replace("text_projector.linear_hidden"	,    "text_projection"		)
  if "text_projector.linear_out" in name:
    lowerCamelCase__:      Union[str, Any]			  =name.replace("text_projector.linear_out"	,    "text_projection.3"		)

  return name





def      lowerCAmelCase_ (       __a	,    __a		)     ->		List[Any]:







  """simple docstring"""





  for key in orig_state_dict.copy().keys():
    lowerCamelCase__:      str			  =orig_state_dict.pop(__a		)

    if "qkv" in key:
      # weights and biases of the key, value and query projections of vision encoder's attention layers require special treatment:
      # we need to split them up into separate matrices/vectors
      lowerCamelCase__:      Dict			  =key.split("."		)
      lowerCamelCase__:      Any			  =int(key_split[2]		), int(key_split[4]		)
      lowerCamelCase__:      str			  =config.vision_config.hidden_size
      if "weight" in key:
        lowerCamelCase__:      Dict			  =val[:dim, :]
        lowerCamelCase__:      Any			  =val[dim : dim * 2, :]
        lowerCamelCase__:      str			  =val[-dim:, :]
      else:
        lowerCamelCase__:      List[str]			  =val[:dim]
        lowerCamelCase__:      List[str]			  =val[dim : dim * 2]
        lowerCamelCase__:      str			  =val[-dim:]
    elif "in_proj" in key:
      # weights and biases of the key, value and query projections of text encoder's attention layers require special treatment:
      # we need to split them up into separate matrices/vectors
      lowerCamelCase__:      str			  =key.split("."		)
      lowerCamelCase__:      Tuple			  =int(key_split[3]		)
      lowerCamelCase__:      Optional[int]			  =config.text_config.hidden_size
      if "weight" in key:
        lowerCamelCase__:      List[str]			  =val[:dim, :]
        lowerCamelCase__:      List[Any]			  =val[
            dim : dim * 2, :
        ]
        lowerCamelCase__:      Union[str, Any]			  =val[-dim:, :]
      else:
        lowerCamelCase__:      Tuple			  =val[:dim]
        lowerCamelCase__:      Dict			  =val[dim : dim * 2]
        lowerCamelCase__:      Tuple			  =val[-dim:]
    else:
      lowerCamelCase__:      str			  =rename_key(__a		)
      # squeeze if necessary
      if (
          "text_projection.0" in new_name
          or "text_projection.3" in new_name
          or "visual_projection.0" in new_name
          or "visual_projection.3" in new_name
      ):
        lowerCamelCase__:      int			  =val.squeeze_()
      else:
        lowerCamelCase__:      Optional[Any]			  =val

  return orig_state_dict





def      lowerCAmelCase_ (       )     ->		Any:







  """simple docstring"""





  lowerCamelCase__:      Union[str, Any]			  ="http://images.cocodataset.org/val2017/000000039769.jpg"
  lowerCamelCase__:      str			  =Image.open(requests.get(__a	,    stream=__a		).raw		)
  return im





@torch.no_grad()
def      lowerCAmelCase_ (       __a	,    __a	,    __a="groupvit-gcc-yfcc"	,    __a=False		)     ->		Tuple:







  """simple docstring"""





  lowerCamelCase__:      Optional[int]			  =GroupViTConfig()
  lowerCamelCase__:      Optional[int]			  =GroupViTModel(__a		).eval()

  lowerCamelCase__:      Optional[Any]			  =torch.load(__a	,    map_location="cpu"		)["model"]
  lowerCamelCase__:      Union[str, Any]			  =convert_state_dict(__a	,    __a		)
  lowerCamelCase__:      Union[str, Any]			  =model.load_state_dict(__a	,    strict=__a		)
  assert missing_keys == ["text_model.embeddings.position_ids"]
  assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(__a		) == 0)

  # verify result
  lowerCamelCase__:      Union[str, Any]			  =CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32"		)
  lowerCamelCase__:      int			  =prepare_img()
  lowerCamelCase__:      int			  =processor(text=["a photo of a cat", "a photo of a dog"]	,    images=__a	,    padding=__a	,    return_tensors="pt"		)

  with torch.no_grad():
    lowerCamelCase__:      List[Any]			  =model(**__a		)

  if model_name == "groupvit-gcc-yfcc":
    lowerCamelCase__:      Optional[Any]			  =torch.tensor([[1_3.3_5_2_3, 6.3_6_2_9]]		)
  elif model_name == "groupvit-gcc-redcaps":
    lowerCamelCase__:      Dict			  =torch.tensor([[1_6.1_8_7_3, 8.6_2_3_0]]		)
  else:
    raise ValueError(F"""Model name {model_name} not supported."""		)
  assert torch.allclose(outputs.logits_per_image	,    __a	,    atol=1e-3		)

  processor.save_pretrained(__a		)
  model.save_pretrained(__a		)
  print("Successfully saved processor and model to"	,    __a		)

  if push_to_hub:
    print("Pushing to the hub..."		)
    processor.push_to_hub(__a	,    organization="nielsr"		)
    model.push_to_hub(__a	,    organization="nielsr"		)


if __name__ == "__main__":
     __A									=				argparse.ArgumentParser()
     parser.add_argument(
         "--pytorch_dump_folder_path", default=None, type=str, help="Path to dump the processor and PyTorch model."
     )
     parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to GroupViT checkpoint")
     parser.add_argument(
         "--model_name",
         default="groupvit-gccy-fcc",
         type=str,
         help="Name of the model. Expecting either \'groupvit-gcc-yfcc\' or \'groupvit-gcc-redcaps\'",
     )
     parser.add_argument(
         "--push_to_hub",
         action="store_true",
         help="Whether or not to push the converted model and processor to the 🤗 hub using the provided `model_name`.",
     )
     __A									=				parser.parse_args()

     convert_groupvit_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)





"""simple docstring"""







# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile

from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version


lowerCAmelCase__      				=   get_logger(__name__)


class       SCREAMING_SNAKE_CASE__				:
       """simple docstring"""


       a  :				Optional[Any]   ="dummy_data"
       a  :				int   ="datasets"
       a  :				Tuple   =False

       def __init__(						self		,     snake_case__		,     snake_case__		,     snake_case__		,     snake_case__ = None		,     snake_case__ = False		,     snake_case__ = True		,     snake_case__ = None		,     ):






         """simple docstring"""





         lowerCAmelCase     :     Tuple								=							0
         lowerCAmelCase     :     int								=							dataset_name
         lowerCAmelCase     :     List[Any]								=							cache_dir
         lowerCAmelCase     :     List[str]								=							use_local_dummy_data
         lowerCAmelCase     :     List[str]								=							config
         # download_callbacks take a single url as input
         lowerCAmelCase     :     List[Callable]								=							download_callbacks or []
         # if False, it doesn't load existing files and it returns the paths of the dummy files relative
         # to the dummy_data zip file root
         lowerCAmelCase     :     Tuple								=							load_existing_dummy_data

         # TODO(PVP, QL) might need to make this more general
         lowerCAmelCase     :     Union[str, Any]								=							str(snake_case__						)
         # to be downloaded
         lowerCAmelCase     :     List[Any]								=							None
         lowerCAmelCase     :     List[Any]								=							None

       @property
       def        lowercase__							(						self						):






         """simple docstring"""





         if self._dummy_file is None:
           lowerCAmelCase     :     Any								=							self.download_dummy_data()
         return self._dummy_file

       @property
       def        lowercase__							(						self						):






         """simple docstring"""





         if self.config is not None:
           # structure is dummy / config_name / version_name
           return os.path.join("dummy"		,     self.config.name		,     self.version_name						)
         # structure is dummy / version_name
         return os.path.join("dummy"		,     self.version_name						)

       @property
       def        lowercase__							(						self						):






         """simple docstring"""





         return os.path.join(self.dummy_data_folder		,     "dummy_data.zip"						)

       def        lowercase__							(						self						):






         """simple docstring"""





         lowerCAmelCase     :     Optional[Any]								=							(
             self.local_path_to_dummy_data if self.use_local_dummy_data is True else self.github_path_to_dummy_data
         )

         lowerCAmelCase     :     str								=							cached_path(
             snake_case__		,     cache_dir=self.cache_dir		,     extract_compressed_file=snake_case__		,     force_extract=snake_case__						)

         return os.path.join(snake_case__		,     self.dummy_file_name						)

       @property
       def        lowercase__							(						self						):






         """simple docstring"""





         return os.path.join(self.datasets_scripts_dir		,     self.dataset_name		,     self.dummy_zip_file						)

       @property
       def        lowercase__							(						self						):






         """simple docstring"""





         if self._bucket_url is None:
           lowerCAmelCase     :     Union[str, Any]								=							hf_github_url(self.dataset_name		,     self.dummy_zip_file.replace(os.sep		,     "/"						)						)
         return self._bucket_url

       @property
       def        lowercase__							(						self						):






         """simple docstring"""





         if os.path.isdir(self.dummy_file						):
           return self.dummy_file
         # else cut off path to file -> example `xsum`.
         return "/".join(self.dummy_file.replace(os.sep		,     "/"						).split("/"						)[:-1]						)

       def        lowercase__							(						self		,     snake_case__		,     *snake_case__						):






         """simple docstring"""





         if self.load_existing_dummy_data:
           # dummy data is downloaded and tested
           lowerCAmelCase     :     int								=							self.dummy_file
         else:
           # dummy data cannot be downloaded and only the path to dummy file is returned
           lowerCAmelCase     :     List[Any]								=							self.dummy_file_name

         # special case when data_url is a dict
         if isinstance(snake_case__		,     snake_case__						):
           return self.create_dummy_data_dict(snake_case__		,     snake_case__						)
         elif isinstance(snake_case__		,     (list, tuple)						):
           return self.create_dummy_data_list(snake_case__		,     snake_case__						)
         else:
           return self.create_dummy_data_single(snake_case__		,     snake_case__						)

       def        lowercase__							(						self		,     snake_case__		,     *snake_case__						):






         """simple docstring"""





         return self.download_and_extract(snake_case__						)

       def        lowercase__							(						self		,     snake_case__		,     snake_case__						):






         """simple docstring"""





         return self.download_and_extract(snake_case__						)

       def        lowercase__							(						self		,     snake_case__		,     *snake_case__		,     **snake_case__						):






         """simple docstring"""





         return path

       def        lowercase__							(						self						):






         """simple docstring"""





         return {}

       def        lowercase__							(						self		,     snake_case__		,     snake_case__						):






         """simple docstring"""





         lowerCAmelCase     :     List[Any]								=							{}
         for key, single_urls in data_url.items():
           for download_callback in self.download_callbacks:
             if isinstance(snake_case__		,     snake_case__						):
               for single_url in single_urls:
                 download_callback(snake_case__						)
             else:
               lowerCAmelCase     :     List[str]								=							single_urls
               download_callback(snake_case__						)
            # we force the name of each key to be the last file / folder name of the url path
            # if the url has arguments, we need to encode them with urllib.parse.quote_plus
           if isinstance(snake_case__		,     snake_case__						):
             lowerCAmelCase     :     Tuple								=							[os.path.join(snake_case__		,     urllib.parse.quote_plus(Path(snake_case__						).name						)						) for x in single_urls]
           else:
             lowerCAmelCase     :     int								=							single_urls
             lowerCAmelCase     :     Any								=							os.path.join(snake_case__		,     urllib.parse.quote_plus(Path(snake_case__						).name						)						)
           lowerCAmelCase     :     Union[str, Any]								=							value

         # make sure that values are unique
         if all(isinstance(snake_case__		,     snake_case__						) for i in dummy_data_dict.values()						) and len(set(dummy_data_dict.values()						)						) < len(
             dummy_data_dict.values()						):
           # append key to value to make its name unique
           lowerCAmelCase     :     Union[str, Any]								=							{key: value + key for key, value in dummy_data_dict.items()}

         return dummy_data_dict

       def        lowercase__							(						self		,     snake_case__		,     snake_case__						):






         """simple docstring"""





         lowerCAmelCase     :     Dict								=							[]
         # trick: if there are many shards named like `data.txt-000001-of-00300`, only use the first one
         lowerCAmelCase     :     Optional[Any]								=							all(bool(re.findall("[0-9]{3,}-of-[0-9]{3,}"		,     snake_case__						)						) for url in data_url						)
         lowerCAmelCase     :     Any								=							all(
             url.startswith("https://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed"						) for url in data_url						)
         if data_url and (is_tf_records or is_pubmed_records):
           lowerCAmelCase     :     int								=							[data_url[0]] * len(snake_case__						)
         for single_url in data_url:
           for download_callback in self.download_callbacks:
             download_callback(snake_case__						)
           # we force the name of each key to be the last file / folder name of the url path
           # if the url has arguments, we need to encode them with urllib.parse.quote_plus
           lowerCAmelCase     :     Dict								=							os.path.join(snake_case__		,     urllib.parse.quote_plus(single_url.split("/"						)[-1]						)						)
           dummy_data_list.append(snake_case__						)
         return dummy_data_list

       def        lowercase__							(						self		,     snake_case__		,     snake_case__						):






         """simple docstring"""





         for download_callback in self.download_callbacks:
           download_callback(snake_case__						)
         # we force the name of each key to be the last file / folder name of the url path
         # if the url has arguments, we need to encode them with urllib.parse.quote_plus
         lowerCAmelCase     :     Tuple								=							os.path.join(snake_case__		,     urllib.parse.quote_plus(data_url.split("/"						)[-1]						)						)
         if os.path.exists(snake_case__						) or not self.load_existing_dummy_data:
           return value
         else:
           # Backward compatibility, maybe deprecate at one point.
           # For many datasets with single url calls to dl_manager.download_and_extract,
           # the dummy_data.zip file is actually the zipped downloaded file
           # while now we expected the dummy_data.zip file to be a directory containing
           # the downloaded file.
           return path_to_dummy_data

       def        lowercase__							(						self						):






         """simple docstring"""





         pass

       def        lowercase__							(						self						):






         """simple docstring"""





         pass

       def        lowercase__							(						self		,     snake_case__						):






         """simple docstring"""





         def _iter_archive_members(snake_case__						):
           # this preserves the order of the members inside the ZIP archive
           lowerCAmelCase     :     str								=							Path(self.dummy_file						).parent
           lowerCAmelCase     :     Optional[Any]								=							path.relative_to(snake_case__						)
           with ZipFile(self.local_path_to_dummy_data						) as zip_file:
             lowerCAmelCase     :     List[Any]								=							zip_file.namelist()
           for member in members:
             if member.startswith(relative_path.as_posix()						):
               yield dummy_parent_path.joinpath(snake_case__						)

         lowerCAmelCase     :     List[Any]								=							Path(snake_case__						)
         lowerCAmelCase     :     str								=							_iter_archive_members(snake_case__						) if self.use_local_dummy_data else path.rglob("*"						)
         for file_path in file_paths:
           if file_path.is_file() and not file_path.name.startswith((".", "__")						):
             yield file_path.relative_to(snake_case__						).as_posix(), file_path.open("rb"						)







       def        lowercase__							(						self		,     snake_case__						):






         """simple docstring"""





         if not isinstance(snake_case__		,     snake_case__						):
           lowerCAmelCase     :     List[Any]								=							[paths]
         for path in paths:
           if os.path.isfile(snake_case__						):
             if os.path.basename(snake_case__						).startswith((".", "__")						):
               return
             yield path
           else:
             for dirpath, dirnames, filenames in os.walk(snake_case__						):
               if os.path.basename(snake_case__						).startswith((".", "__")						):
                 continue
               dirnames.sort()
               for filename in sorted(snake_case__						):
                 if filename.startswith((".", "__")						):
                   continue
                 yield os.path.join(snake_case__		,     snake_case__						)





