





'''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 (
    MobileViTConfig,
    MobileViTForImageClassification,
    MobileViTForSemanticSegmentation,
    MobileViTImageProcessor,
)
from transformers.utils import logging


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

def 						__magic_name__       (							__UpperCAmelCase      )						->					Dict:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       MobileViTConfig()

   # size of the architecture
   if "mobilevit_s" in mobilevit_name:
      __SCREAMING_SNAKE_CASE      							=       [144, 192, 240]
      __SCREAMING_SNAKE_CASE      							=       [16, 32, 64, 96, 128, 160, 640]
   elif "mobilevit_xs" in mobilevit_name:
      __SCREAMING_SNAKE_CASE      							=       [96, 120, 144]
      __SCREAMING_SNAKE_CASE      							=       [16, 32, 48, 64, 80, 96, 384]
   elif "mobilevit_xxs" in mobilevit_name:
      __SCREAMING_SNAKE_CASE      							=       [64, 80, 96]
      __SCREAMING_SNAKE_CASE      							=       [16, 16, 24, 48, 64, 80, 320]
      __SCREAMING_SNAKE_CASE      							=       0.0_5
      __SCREAMING_SNAKE_CASE      							=       2.0

   if mobilevit_name.startswith("""deeplabv3_"""      ):
      __SCREAMING_SNAKE_CASE      							=       512
      __SCREAMING_SNAKE_CASE      							=       16
      __SCREAMING_SNAKE_CASE      							=       21
      __SCREAMING_SNAKE_CASE      							=       """pascal-voc-id2label.json"""
   else:
      __SCREAMING_SNAKE_CASE      							=       1000
      __SCREAMING_SNAKE_CASE      							=       """imagenet-1k-id2label.json"""

   __SCREAMING_SNAKE_CASE      							=       """huggingface/label-files"""
   __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()}

   return config

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase=False      )						->					Tuple:

   '''simple docstring'''







   for i in range(1							,	6      ):
      if f"""layer_{i}.""" in name:
         __SCREAMING_SNAKE_CASE      							=       name.replace(f"""layer_{i}."""							,	f"""encoder.layer.{i - 1}."""      )

   if "conv_1." in name:
      __SCREAMING_SNAKE_CASE      							=       name.replace("""conv_1."""							,	"""conv_stem."""      )
   if ".block." in name:
      __SCREAMING_SNAKE_CASE      							=       name.replace(""".block."""							,	"""."""      )

   if "exp_1x1" in name:
      __SCREAMING_SNAKE_CASE      							=       name.replace("""exp_1x1"""							,	"""expand_1x1"""      )
   if "red_1x1" in name:
      __SCREAMING_SNAKE_CASE      							=       name.replace("""red_1x1"""							,	"""reduce_1x1"""      )
   if ".local_rep.conv_3x3." in name:
      __SCREAMING_SNAKE_CASE      							=       name.replace(""".local_rep.conv_3x3."""							,	""".conv_kxk."""      )
   if ".local_rep.conv_1x1." in name:
      __SCREAMING_SNAKE_CASE      							=       name.replace(""".local_rep.conv_1x1."""							,	""".conv_1x1."""      )
   if ".norm." in name:
      __SCREAMING_SNAKE_CASE      							=       name.replace(""".norm."""							,	""".normalization."""      )
   if ".conv." in name:
      __SCREAMING_SNAKE_CASE      							=       name.replace(""".conv."""							,	""".convolution."""      )
   if ".conv_proj." in name:
      __SCREAMING_SNAKE_CASE      							=       name.replace(""".conv_proj."""							,	""".conv_projection."""      )

   for i in range(0							,	2      ):
      for j in range(0							,	4      ):
         if f""".{i}.{j}.""" in name:
            __SCREAMING_SNAKE_CASE      							=       name.replace(f""".{i}.{j}."""							,	f""".{i}.layer.{j}."""      )

   for i in range(2							,	6      ):
      for j in range(0							,	4      ):
         if f""".{i}.{j}.""" in name:
            __SCREAMING_SNAKE_CASE      							=       name.replace(f""".{i}.{j}."""							,	f""".{i}."""      )
            if "expand_1x1" in name:
               __SCREAMING_SNAKE_CASE      							=       name.replace("""expand_1x1"""							,	"""downsampling_layer.expand_1x1"""      )
            if "conv_3x3" in name:
               __SCREAMING_SNAKE_CASE      							=       name.replace("""conv_3x3"""							,	"""downsampling_layer.conv_3x3"""      )
            if "reduce_1x1" in name:
               __SCREAMING_SNAKE_CASE      							=       name.replace("""reduce_1x1"""							,	"""downsampling_layer.reduce_1x1"""      )

   for i in range(2							,	5      ):
      if f""".global_rep.{i}.weight""" in name:
         __SCREAMING_SNAKE_CASE      							=       name.replace(f""".global_rep.{i}.weight"""							,	""".layernorm.weight"""      )
      if f""".global_rep.{i}.bias""" in name:
         __SCREAMING_SNAKE_CASE      							=       name.replace(f""".global_rep.{i}.bias"""							,	""".layernorm.bias"""      )

   if ".global_rep." in name:
      __SCREAMING_SNAKE_CASE      							=       name.replace(""".global_rep."""							,	""".transformer."""      )
   if ".pre_norm_mha.0." in name:
      __SCREAMING_SNAKE_CASE      							=       name.replace(""".pre_norm_mha.0."""							,	""".layernorm_before."""      )
   if ".pre_norm_mha.1.out_proj." in name:
      __SCREAMING_SNAKE_CASE      							=       name.replace(""".pre_norm_mha.1.out_proj."""							,	""".attention.output.dense."""      )
   if ".pre_norm_ffn.0." in name:
      __SCREAMING_SNAKE_CASE      							=       name.replace(""".pre_norm_ffn.0."""							,	""".layernorm_after."""      )
   if ".pre_norm_ffn.1." in name:
      __SCREAMING_SNAKE_CASE      							=       name.replace(""".pre_norm_ffn.1."""							,	""".intermediate.dense."""      )
   if ".pre_norm_ffn.4." in name:
      __SCREAMING_SNAKE_CASE      							=       name.replace(""".pre_norm_ffn.4."""							,	""".output.dense."""      )
   if ".transformer." in name:
      __SCREAMING_SNAKE_CASE      							=       name.replace(""".transformer."""							,	""".transformer.layer."""      )

   if ".aspp_layer." in name:
      __SCREAMING_SNAKE_CASE      							=       name.replace(""".aspp_layer."""							,	"""."""      )
   if ".aspp_pool." in name:
      __SCREAMING_SNAKE_CASE      							=       name.replace(""".aspp_pool."""							,	"""."""      )
   if "seg_head." in name:
      __SCREAMING_SNAKE_CASE      							=       name.replace("""seg_head."""							,	"""segmentation_head."""      )
   if "segmentation_head.classifier.classifier." in name:
      __SCREAMING_SNAKE_CASE      							=       name.replace("""segmentation_head.classifier.classifier."""							,	"""segmentation_head.classifier."""      )

   if "classifier.fc." in name:
      __SCREAMING_SNAKE_CASE      							=       name.replace("""classifier.fc."""							,	"""classifier."""      )
   elif (not base_model) and ("segmentation_head." not in name):
      __SCREAMING_SNAKE_CASE      							=       """mobilevit.""" + name

   return name

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase=False      )						->					Tuple:

   '''simple docstring'''







   if base_model:
      __SCREAMING_SNAKE_CASE      							=       """"""
   else:
      __SCREAMING_SNAKE_CASE      							=       """mobilevit."""

   for key in orig_state_dict.copy().keys():
      __SCREAMING_SNAKE_CASE      							=       orig_state_dict.pop(__UpperCAmelCase      )

      if key[:8] == "encoder.":
         __SCREAMING_SNAKE_CASE      							=       key[8:]

      if "qkv" in key:
         __SCREAMING_SNAKE_CASE      							=       key.split("""."""      )
         __SCREAMING_SNAKE_CASE      							=       int(key_split[0][6:]      ) - 1
         __SCREAMING_SNAKE_CASE      							=       int(key_split[3]      )
         __SCREAMING_SNAKE_CASE      							=       model.get_submodule(f"""{model_prefix}encoder.layer.{layer_num}"""      )
         __SCREAMING_SNAKE_CASE      							=       layer.transformer.layer[transformer_num].attention.attention.all_head_size
         __SCREAMING_SNAKE_CASE      							=       (
             f"""{model_prefix}encoder.layer.{layer_num}.transformer.layer.{transformer_num}.attention.attention."""
         )
         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 						__magic_name__       (							)						->					Dict:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       """http://images.cocodataset.org/val2017/000000039769.jpg"""
   __SCREAMING_SNAKE_CASE      							=       Image.open(requests.get(__UpperCAmelCase							,	stream=__UpperCAmelCase      ).raw      )
   return im

@torch.no_grad()
def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase=False      )						->					List[Any]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       get_mobilevit_config(__UpperCAmelCase      )

   # load original state_dict
   __SCREAMING_SNAKE_CASE      							=       torch.load(__UpperCAmelCase							,	map_location="""cpu"""      )

   # load 🤗 model
   if mobilevit_name.startswith("""deeplabv3_"""      ):
      __SCREAMING_SNAKE_CASE      							=       MobileViTForSemanticSegmentation(__UpperCAmelCase      ).eval()
   else:
      __SCREAMING_SNAKE_CASE      							=       MobileViTForImageClassification(__UpperCAmelCase      ).eval()

   __SCREAMING_SNAKE_CASE      							=       convert_state_dict(__UpperCAmelCase							,	__UpperCAmelCase      )
   model.load_state_dict(__UpperCAmelCase      )

   # Check outputs on an image, prepared by MobileViTImageProcessor
   __SCREAMING_SNAKE_CASE      							=       MobileViTImageProcessor(crop_size=config.image_size							,	size=config.image_size + 32      )
   __SCREAMING_SNAKE_CASE      							=       image_processor(images=prepare_img()							,	return_tensors="""pt"""      )
   __SCREAMING_SNAKE_CASE      							=       model(**__UpperCAmelCase      )
   __SCREAMING_SNAKE_CASE      							=       outputs.logits

   if mobilevit_name.startswith("""deeplabv3_"""      ):
      assert logits.shape == (1, 21, 32, 32)

      if mobilevit_name == "deeplabv3_mobilevit_s":
         __SCREAMING_SNAKE_CASE      							=       torch.tensor(
             [
                 [[6.2_0_6_5, 6.1_2_9_2, 6.2_0_7_0], [6.1_0_7_9, 6.1_2_5_4, 6.1_7_4_7], [6.0_0_4_2, 6.1_0_7_1, 6.1_0_3_4]],
                 [[-6.9_2_5_3, -6.8_6_5_3, -7.0_3_9_8], [-7.3_2_1_8, -7.3_9_8_3, -7.3_6_7_0], [-7.1_9_6_1, -7.2_4_8_2, -7.1_5_6_9]],
                 [[-4.4_7_2_3, -4.4_3_4_8, -4.3_7_6_9], [-5.3_6_2_9, -5.4_6_3_2, -5.4_5_9_8], [-5.1_5_8_7, -5.3_4_0_2, -5.5_0_5_9]],
             ]      )
      elif mobilevit_name == "deeplabv3_mobilevit_xs":
         __SCREAMING_SNAKE_CASE      							=       torch.tensor(
             [
                 [[5.4_4_4_9, 5.5_7_3_3, 5.6_3_1_4], [5.1_8_1_5, 5.3_9_3_0, 5.5_9_6_3], [5.1_6_5_6, 5.4_3_3_3, 5.4_8_5_3]],
                 [[-9.4_4_2_3, -9.7_7_6_6, -9.6_7_1_4], [-9.1_5_8_1, -9.5_7_2_0, -9.5_5_1_9], [-9.1_0_0_6, -9.6_4_5_8, -9.5_7_0_3]],
                 [[-7.7_7_2_1, -7.3_7_1_6, -7.1_5_8_3], [-8.4_5_9_9, -8.0_6_2_4, -7.7_9_4_4], [-8.4_1_7_2, -7.8_3_6_6, -7.5_0_2_5]],
             ]      )
      elif mobilevit_name == "deeplabv3_mobilevit_xxs":
         __SCREAMING_SNAKE_CASE      							=       torch.tensor(
             [
                 [[6.9_8_1_1, 6.9_7_4_3, 7.3_1_2_3], [7.1_7_7_7, 7.1_9_3_1, 7.3_9_3_8], [7.5_6_3_3, 7.8_0_5_0, 7.8_9_0_1]],
                 [[-1_0.5_5_3_6, -1_0.2_3_3_2, -1_0.2_9_2_4], [-1_0.2_3_3_6, -9.8_6_2_4, -9.5_9_6_4], [-1_0.8_8_4_0, -1_0.8_1_5_8, -1_0.6_6_5_9]],
                 [[-3.4_9_3_8, -3.0_6_3_1, -2.8_6_2_0], [-3.4_2_0_5, -2.8_1_3_5, -2.6_8_7_5], [-3.4_1_7_9, -2.7_9_4_5, -2.8_7_5_0]],
             ]      )
      else:
         raise ValueError(f"""Unknown mobilevit_name: {mobilevit_name}"""      )

      assert torch.allclose(logits[0, :3, :3, :3]							,	__UpperCAmelCase							,	atol=1e-4      )
   else:
      assert logits.shape == (1, 1000)

      if mobilevit_name == "mobilevit_s":
         __SCREAMING_SNAKE_CASE      							=       torch.tensor([-0.9_8_6_6, 0.2_3_9_2, -1.1_2_4_1]      )
      elif mobilevit_name == "mobilevit_xs":
         __SCREAMING_SNAKE_CASE      							=       torch.tensor([-2.4_7_6_1, -0.9_3_9_9, -1.9_5_8_7]      )
      elif mobilevit_name == "mobilevit_xxs":
         __SCREAMING_SNAKE_CASE      							=       torch.tensor([-1.9_3_6_4, -1.2_3_2_7, -0.4_6_5_3]      )
      else:
         raise ValueError(f"""Unknown mobilevit_name: {mobilevit_name}"""      )

      assert torch.allclose(logits[0, :3]							,	__UpperCAmelCase							,	atol=1e-4      )

   Path(__UpperCAmelCase      ).mkdir(exist_ok=__UpperCAmelCase      )
   print(f"""Saving model {mobilevit_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:
      __SCREAMING_SNAKE_CASE      							=       {
          """mobilevit_s""": """mobilevit-small""",
          """mobilevit_xs""": """mobilevit-x-small""",
          """mobilevit_xxs""": """mobilevit-xx-small""",
          """deeplabv3_mobilevit_s""": """deeplabv3-mobilevit-small""",
          """deeplabv3_mobilevit_xs""": """deeplabv3-mobilevit-x-small""",
          """deeplabv3_mobilevit_xxs""": """deeplabv3-mobilevit-xx-small""",
      }

      print("""Pushing to the hub..."""      )
      __SCREAMING_SNAKE_CASE      							=       model_mapping[mobilevit_name]
      image_processor.push_to_hub(__UpperCAmelCase							,	organization="""apple"""      )
      model.push_to_hub(__UpperCAmelCase							,	organization="""apple"""      )


if __name__ == "__main__":
    a        =						argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        "--mobilevit_name",
        default="mobilevit_s",
        type=str,
        help=(
            "Name of the MobileViT model you'd like to convert. Should be one of 'mobilevit_s', 'mobilevit_xs',"
            " 'mobilevit_xxs', 'deeplabv3_mobilevit_s', 'deeplabv3_mobilevit_xs', 'deeplabv3_mobilevit_xxs'."
        ),
    )
    parser.add_argument(
        "--checkpoint_path", required=True, type=str, help="Path to the original state dict (.pt file)."
    )
    parser.add_argument(
        "--pytorch_dump_folder_path", required=True, 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        =						parser.parse_args()
    convert_movilevit_checkpoint(
        args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
    )












'''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 						__magic_name__       (							__UpperCAmelCase      )						->					int:

   '''simple docstring'''







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

@pytest.fixture
def 						__magic_name__       (							__UpperCAmelCase      )						->					Optional[Any]:

   '''simple docstring'''







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

@pytest.fixture
def 						__magic_name__       (							__UpperCAmelCase      )						->					Optional[Any]:

   '''simple docstring'''







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

@pytest.fixture
def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase      )						->					Dict:

   '''simple docstring'''







   HfFolder.save_token(__UpperCAmelCase      )
   yield
   HfFolder.delete_token()

@pytest.fixture(scope="""session"""      )
def 						__magic_name__       (							)						->					Optional[Any]:

   '''simple docstring'''







   return HfApi(endpoint=__UpperCAmelCase      )

@pytest.fixture(scope="""session"""      )
def 						__magic_name__       (							__UpperCAmelCase      )						->					Dict:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       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 						__magic_name__       (							__UpperCAmelCase      )						->					Dict:

   '''simple docstring'''







   def _cleanup_repo(__UpperCAmelCase      ):
      hf_api.delete_repo(__UpperCAmelCase							,	token=__UpperCAmelCase							,	repo_type="""dataset"""      )

   return _cleanup_repo

@pytest.fixture
def 						__magic_name__       (							__UpperCAmelCase      )						->					int:

   '''simple docstring'''







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

   return _temporary_repo

@pytest.fixture(scope="""session"""      )
def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase      )						->					Dict:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       f"""repo_txt_data-{int(time.time() * 1_0e3      )}"""
   __SCREAMING_SNAKE_CASE      							=       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 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase      )						->					Optional[Any]:

   '''simple docstring'''







   return hf_private_dataset_repo_txt_data_

@pytest.fixture(scope="""session"""      )
def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase      )						->					Dict:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       f"""repo_zipped_txt_data-{int(time.time() * 1_0e3      )}"""
   __SCREAMING_SNAKE_CASE      							=       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 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase      )						->					Tuple:

   '''simple docstring'''







   return hf_private_dataset_repo_zipped_txt_data_

@pytest.fixture(scope="""session"""      )
def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase      )						->					Union[str, Any]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       f"""repo_zipped_img_data-{int(time.time() * 1_0e3      )}"""
   __SCREAMING_SNAKE_CASE      							=       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 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase      )						->					Dict:

   '''simple docstring'''







   return hf_private_dataset_repo_zipped_img_data_





