

"""simple docstring"""





import time
from contextlib import contextmanager
from pathlib import Path

import pytest
import requests
from huggingface_hub.hf_api import HfApi, HfFolder


_a       =							'__DUMMY_TRANSFORMERS_USER__'
_a       =							'Dummy User'
_a       =							'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'

_a       =							'https://hub-ci.huggingface.co'
_a       =							CI_HUB_ENDPOINT + '/datasets/{repo_id}/resolve/{revision}/{path}'
_a       =							CI_HUB_ENDPOINT + '/{repo_id}/resolve/{revision}/{filename}'
_a       =							Path('~/.huggingface/hub_ci_token').expanduser()




@pytest.fixture
def   _A  (  UpperCamelCase_     :       List[Any])	->						Tuple:
   '''simple docstring'''


   monkeypatch.setattr(
       "huggingface_hub.file_download.HUGGINGFACE_CO_URL_TEMPLATE",			UpperCamelCase_)




@pytest.fixture
def   _A  (  UpperCamelCase_     :       int)	->						List[Any]:
   '''simple docstring'''


   monkeypatch.setattr("datasets.config.HF_ENDPOINT",			UpperCamelCase_)
   monkeypatch.setattr("datasets.config.HUB_DATASETS_URL",			UpperCamelCase_)




@pytest.fixture
def   _A  (  UpperCamelCase_     :       str)	->						Dict:
   '''simple docstring'''


   monkeypatch.setattr("huggingface_hub.hf_api.HfFolder.path_token",			UpperCamelCase_)




@pytest.fixture
def   _A  (  UpperCamelCase_     :       Optional[Any],			UpperCamelCase_     :       List[Any])	->						List[str]:
   '''simple docstring'''


   HfFolder.save_token(UpperCamelCase_)
   yield
   HfFolder.delete_token()




@pytest.fixture(scope="session")
def   _A  (  )	->						List[Any]:
   '''simple docstring'''


   return HfApi(endpoint=UpperCamelCase_)




@pytest.fixture(scope="session")
def   _A  (  UpperCamelCase_     :       HfApi)	->						List[Any]:
   '''simple docstring'''


   __lowercase								=						HfFolder.get_token()
   HfFolder.save_token(UpperCamelCase_)
   yield CI_HUB_USER_TOKEN
   if previous_token is not None:
      HfFolder.save_token(UpperCamelCase_)




@pytest.fixture
def   _A  (  UpperCamelCase_     :       Dict)	->						int:
   '''simple docstring'''


   def _cleanup_repo(UpperCamelCase_     :       Optional[int]):
      hf_api.delete_repo(UpperCamelCase_,			token=UpperCamelCase_,			repo_type="dataset")

   return _cleanup_repo




@pytest.fixture
def   _A  (  UpperCamelCase_     :       str)	->						Any:
   '''simple docstring'''


   @contextmanager
   def _temporary_repo(UpperCamelCase_     :       Any):
      try:
         yield repo_id
      finally:
         cleanup_repo(UpperCamelCase_)

   return _temporary_repo




@pytest.fixture(scope="session")
def   _A  (  UpperCamelCase_     :       HfApi,			UpperCamelCase_     :       str,			UpperCamelCase_     :       Optional[int])	->						List[Any]:
   '''simple docstring'''


   __lowercase								=						F"""repo_txt_data-{int(time.time() * 10E3)}"""
   __lowercase								=						F"""{CI_HUB_USER}/{repo_name}"""
   hf_api.create_repo(UpperCamelCase_,			token=UpperCamelCase_,			repo_type="dataset",			private=UpperCamelCase_)
   hf_api.upload_file(
       token=UpperCamelCase_,			path_or_fileobj=str(UpperCamelCase_),			path_in_repo="data/text_data.txt",			repo_id=UpperCamelCase_,			repo_type="dataset",			)
   yield repo_id
   try:
      hf_api.delete_repo(UpperCamelCase_,			token=UpperCamelCase_,			repo_type="dataset")
   except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
      pass




@pytest.fixture()
def   _A  (  UpperCamelCase_     :       Tuple,			UpperCamelCase_     :       Any,			UpperCamelCase_     :       Dict)	->						Optional[int]:
   '''simple docstring'''


   return hf_private_dataset_repo_txt_data_




@pytest.fixture(scope="session")
def   _A  (  UpperCamelCase_     :       HfApi,			UpperCamelCase_     :       int,			UpperCamelCase_     :       Optional[int])	->						int:
   '''simple docstring'''


   __lowercase								=						F"""repo_zipped_txt_data-{int(time.time() * 10E3)}"""
   __lowercase								=						F"""{CI_HUB_USER}/{repo_name}"""
   hf_api.create_repo(UpperCamelCase_,			token=UpperCamelCase_,			repo_type="dataset",			private=UpperCamelCase_)
   hf_api.upload_file(
       token=UpperCamelCase_,			path_or_fileobj=str(UpperCamelCase_),			path_in_repo="data.zip",			repo_id=UpperCamelCase_,			repo_type="dataset",			)
   yield repo_id
   try:
      hf_api.delete_repo(UpperCamelCase_,			token=UpperCamelCase_,			repo_type="dataset")
   except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
      pass




@pytest.fixture()
def   _A  (  UpperCamelCase_     :       List[str],			UpperCamelCase_     :       Dict,			UpperCamelCase_     :       Any)	->						int:
   '''simple docstring'''


   return hf_private_dataset_repo_zipped_txt_data_




@pytest.fixture(scope="session")
def   _A  (  UpperCamelCase_     :       HfApi,			UpperCamelCase_     :       List[str],			UpperCamelCase_     :       List[str])	->						List[Any]:
   '''simple docstring'''


   __lowercase								=						F"""repo_zipped_img_data-{int(time.time() * 10E3)}"""
   __lowercase								=						F"""{CI_HUB_USER}/{repo_name}"""
   hf_api.create_repo(UpperCamelCase_,			token=UpperCamelCase_,			repo_type="dataset",			private=UpperCamelCase_)
   hf_api.upload_file(
       token=UpperCamelCase_,			path_or_fileobj=str(UpperCamelCase_),			path_in_repo="data.zip",			repo_id=UpperCamelCase_,			repo_type="dataset",			)
   yield repo_id
   try:
      hf_api.delete_repo(UpperCamelCase_,			token=UpperCamelCase_,			repo_type="dataset")
   except (requests.exceptions.HTTPError, ValueError):  # catch http error and token invalid error
      pass




@pytest.fixture()
def   _A  (  UpperCamelCase_     :       Union[str, Any],			UpperCamelCase_     :       List[str],			UpperCamelCase_     :       List[str])	->						str:
   '''simple docstring'''


   return hf_private_dataset_repo_zipped_img_data_









import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
_snake_case		=  logging.get_logger(__name__)






def 				A				(					_lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase     :							List[Any]    =    "huggingface/label-files"
							_lowerCAmelCase     :							int    =    "imagenet-1k-id2label.json"
							_lowerCAmelCase     :							Tuple    =    json.load(open(hf_hub_download(_lowerCamelCase				, _lowerCamelCase				, repo_type="dataset"  )				, "r"  )  )
							_lowerCAmelCase     :							Tuple    =    {int(_lowerCamelCase  ): v for k, v in idalabel.items()}
							_lowerCAmelCase     :							Union[str, Any]    =    {v: k for k, v in idalabel.items()}

							_lowerCAmelCase     :							Tuple    =    "std_conv" if "bit" in model_name else False

							# note that when using BiT as backbone for ViT-hybrid checkpoints,
							# one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
							# config.conv_layer = "std_conv_same"
							_lowerCAmelCase     :							Optional[int]    =    BitConfig(
							    conv_layer=_lowerCamelCase				, num_labels=1_000				, idalabel=_lowerCamelCase				, labelaid=_lowerCamelCase				, )

							return config






def 				A				(					_lowerCamelCase  ):





							'''simple docstring'''




							if "stem.conv" in name:
														_lowerCAmelCase     :							List[str]    =    name.replace("stem.conv"				, "bit.embedder.convolution"  )
							if "blocks" in name:
														_lowerCAmelCase     :							Any    =    name.replace("blocks"				, "layers"  )
							if "head.fc" in name:
														_lowerCAmelCase     :							Optional[Any]    =    name.replace("head.fc"				, "classifier.1"  )
							if name.startswith("norm"  ):
														_lowerCAmelCase     :							Any    =    "bit." + name
							if "bit" not in name and "classifier" not in name:
														_lowerCAmelCase     :							Dict    =    "bit.encoder." + name

							return name






def 				A				(					):





							'''simple docstring'''




							_lowerCAmelCase     :							Tuple    =    "http://images.cocodataset.org/val2017/000000039769.jpg"
							_lowerCAmelCase     :							Optional[int]    =    Image.open(requests.get(_lowerCamelCase				, stream=_lowerCamelCase  ).raw  )
							return im






@torch.no_grad()
def 				A				(					_lowerCamelCase				, _lowerCamelCase				, _lowerCamelCase=False  ):





							'''simple docstring'''




							_lowerCAmelCase     :							Dict    =    get_config(_lowerCamelCase  )

							# load original model from timm
							_lowerCAmelCase     :							int    =    create_model(_lowerCamelCase				, pretrained=_lowerCamelCase  )
							timm_model.eval()

							# load state_dict of original model
							_lowerCAmelCase     :							Any    =    timm_model.state_dict()
							for key in state_dict.copy().keys():
														_lowerCAmelCase     :							Dict    =    state_dict.pop(_lowerCamelCase  )
														_lowerCAmelCase     :							Tuple    =    val.squeeze() if "head" in key else val

							# load HuggingFace model
							_lowerCAmelCase     :							Optional[Any]    =    BitForImageClassification(_lowerCamelCase  )
							model.eval()
							model.load_state_dict(_lowerCamelCase  )

							# create image processor
							_lowerCAmelCase     :							Dict    =    create_transform(**resolve_data_config({}				, model=_lowerCamelCase  )  )
							_lowerCAmelCase     :							Optional[int]    =    transform.transforms

							_lowerCAmelCase     :							Tuple    =    {
							    "bilinear": PILImageResampling.BILINEAR,
							    "bicubic": PILImageResampling.BICUBIC,
							    "nearest": PILImageResampling.NEAREST,
							}

							_lowerCAmelCase     :							Tuple    =    BitImageProcessor(
							    do_resize=_lowerCamelCase				, size={"shortest_edge": timm_transforms[0].size}				, resample=pillow_resamplings[timm_transforms[0].interpolation.value]				, do_center_crop=_lowerCamelCase				, crop_size={"height": timm_transforms[1].size[0], "width": timm_transforms[1].size[1]}				, do_normalize=_lowerCamelCase				, image_mean=timm_transforms[-1].mean.tolist()				, image_std=timm_transforms[-1].std.tolist()				, )

							_lowerCAmelCase     :							Optional[int]    =    prepare_img()
							_lowerCAmelCase     :							Any    =    transform(_lowerCamelCase  ).unsqueeze(0  )
							_lowerCAmelCase     :							Optional[int]    =    processor(_lowerCamelCase				, return_tensors="pt"  ).pixel_values

							# verify pixel values
							assert torch.allclose(_lowerCamelCase				, _lowerCamelCase  )

							# verify logits
							with torch.no_grad():
														_lowerCAmelCase     :							Tuple    =    model(_lowerCamelCase  )
														_lowerCAmelCase     :							str    =    outputs.logits

							print("Logits:"				, logits[0, :3]  )
							print("Predicted class:"				, model.config.idalabel[logits.argmax(-1  ).item()]  )
							_lowerCAmelCase     :							Union[str, Any]    =    timm_model(_lowerCamelCase  )
							assert timm_logits.shape == outputs.logits.shape
							assert torch.allclose(_lowerCamelCase				, outputs.logits				, atol=1e-3  )
							print("Looks ok!"  )

							if pytorch_dump_folder_path is not None:
														Path(_lowerCamelCase  ).mkdir(exist_ok=_lowerCamelCase  )
														print(F"Saving model {model_name} and processor to {pytorch_dump_folder_path}"  )
														model.save_pretrained(_lowerCamelCase  )
														processor.save_pretrained(_lowerCamelCase  )

							if push_to_hub:
														print(F"Pushing model {model_name} and processor to the hub"  )
														model.push_to_hub(F"ybelkada/{model_name}"  )
														processor.push_to_hub(F"ybelkada/{model_name}"  )


if __name__ == "__main__":
						_snake_case		=  argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--model_name",
						    default="resnetv2_50x1_bitm",
						    type=str,
						    help="Name of the BiT 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."
						)
						parser.add_argument(
						    "--push_to_hub",
						    action="store_true",
						    help="Whether to push the model to the hub.",
						)

						_snake_case		=  parser.parse_args()
						convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
