


"""simple docstring"""







import argparse
import collections
import json
from pathlib import Path

import requests
import torch
import yaml
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileViTImageProcessor,
    MobileViTVaConfig,
    MobileViTVaForImageClassification,
    MobileViTVaForSemanticSegmentation,
)
from transformers.utils import logging


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


def 					lowercase      (    A_	)->				List[Any]:
  '''simple docstring'''







  print("Loading config file..."	)

  def flatten_yaml_as_dict(A_  ,       A_=""  ,       A_="."	):
    a						:		Union[str, Any]											=      []
    for k, v in d.items():
      a						:		Any											=      parent_key + sep + k if parent_key else k
      if isinstance(A_  ,       collections.abc.MutableMapping	):
        items.extend(flatten_yaml_as_dict(A_  ,       A_  ,       sep=A_	).items()	)
      else:
        items.append((new_key, v)	)
    return dict(A_	)

  a						:		Union[str, Any]											=      argparse.Namespace()
  with open(A_  ,       "r"	) as yaml_file:
    try:
      a						:		int											=      yaml.load(A_  ,       Loader=yaml.FullLoader	)

      a						:		int											=      flatten_yaml_as_dict(A_	)
      for k, v in flat_cfg.items():
        setattr(A_  ,       A_  ,       A_	)
    except yaml.YAMLError as exc:
      logger.error("Error while loading config file: {}. Error message: {}".format(A_  ,       str(A_	)	)	)
  return config


def 					lowercase      (    A_  ,       A_	)->				str:
  '''simple docstring'''







  a						:		str											=      MobileViTVaConfig()

  a						:		Dict											=      False

  # dataset
  if task_name.startswith("imagenet1k_"	):
    a						:		Optional[int]											=      1_000
    if int(task_name.strip().split("_"	)[-1]	) == 384:
      a						:		Optional[int]											=      384
    else:
      a						:		Union[str, Any]											=      256
    a						:		str											=      "imagenet-1k-id2label.json"
  elif task_name.startswith("imagenet21k_to_1k_"	):
    a						:		Any											=      21_000
    if int(task_name.strip().split("_"	)[-1]	) == 384:
      a						:		Optional[int]											=      384
    else:
      a						:		Union[str, Any]											=      256
    a						:		Optional[int]											=      "imagenet-22k-id2label.json"
  elif task_name.startswith("ade20k_"	):
    a						:		List[str]											=      151
    a						:		Union[str, Any]											=      512
    a						:		Optional[int]											=      "ade20k-id2label.json"
    a						:		Optional[Any]											=      True
  elif task_name.startswith("voc_"	):
    a						:		List[str]											=      21
    a						:		Union[str, Any]											=      512
    a						:		Optional[Any]											=      "pascal-voc-id2label.json"
    a						:		Tuple											=      True

  # orig_config
  a						:		Any											=      load_orig_config_file(A_	)
  assert getattr(A_  ,       "model.classification.name"  ,       -1	) == "mobilevit_v2", "Invalid model"
  a						:		Dict											=      getattr(A_  ,       "model.classification.mitv2.width_multiplier"  ,       1.0	)
  assert (
      getattr(A_  ,       "model.classification.mitv2.attn_norm_layer"  ,       -1	) == "layer_norm_2d"
  ), "Norm layers other than layer_norm_2d is not supported"
  a						:		List[Any]											=      getattr(A_  ,       "model.classification.activation.name"  ,       "swish"	)
  # config.image_size == getattr(orig_config,  'sampler.bs.crop_size_width', 256)

  if is_segmentation_model:
    a						:		str											=      getattr(A_  ,       "model.segmentation.output_stride"  ,       16	)
    if "_deeplabv3" in task_name:
      a						:		Optional[Any]											=      getattr(A_  ,       "model.segmentation.deeplabv3.aspp_rates"  ,       [12, 24, 36]	)
      a						:		List[Any]											=      getattr(A_  ,       "model.segmentation.deeplabv3.aspp_out_channels"  ,       512	)
      a						:		List[Any]											=      getattr(A_  ,       "model.segmentation.deeplabv3.aspp_dropout"  ,       0.1	)

    # id2label
  a						:		Union[str, Any]											=      "huggingface/label-files"
  a						:		Any											=      json.load(open(hf_hub_download(A_  ,       A_  ,       repo_type="dataset"	)  ,       "r"	)	)
  a						:		List[str]											=      {int(A_	): v for k, v in idalabel.items()}
  a						:		List[Any]											=      idalabel
  a						:		Dict											=      {v: k for k, v in idalabel.items()}

  return config


def 					lowercase      (    A_  ,       A_  ,       A_	)->				Optional[int]:
  '''simple docstring'''







  a						:		Any											=      dct.pop(A_	)
  a						:		Dict											=      val


def 					lowercase      (    A_  ,       A_=False	)->				List[Any]:
  '''simple docstring'''







  if base_model:
    a						:		Tuple											=      ""
  else:
    a						:		str											=      "mobilevitv2."

  a						:		Optional[int]											=      []
  for k in state_dict.keys():
    if k[:8] == "encoder.":
      a						:		str											=      k[8:]
    else:
      a						:		List[str]											=      k

    if ".block." in k:
      a						:		Optional[Any]											=      k_new.replace(".block."  ,       "."	)
    if ".conv." in k:
      a						:		Optional[Any]											=      k_new.replace(".conv."  ,       ".convolution."	)
    if ".norm." in k:
      a						:		Dict											=      k_new.replace(".norm."  ,       ".normalization."	)

    if "conv_1." in k:
      a						:		int											=      k_new.replace("conv_1."  ,       F'''{model_prefix}conv_stem.'''	)
    for i in [1, 2]:
      if F'''layer_{i}.''' in k:
        a						:		List[str]											=      k_new.replace(F'''layer_{i}.'''  ,       F'''{model_prefix}encoder.layer.{i-1}.layer.'''	)
    if ".exp_1x1." in k:
      a						:		Optional[int]											=      k_new.replace(".exp_1x1."  ,       ".expand_1x1."	)
    if ".red_1x1." in k:
      a						:		Union[str, Any]											=      k_new.replace(".red_1x1."  ,       ".reduce_1x1."	)

    for i in [3, 4, 5]:
      if F'''layer_{i}.0.''' in k:
        a						:		List[Any]											=      k_new.replace(F'''layer_{i}.0.'''  ,       F'''{model_prefix}encoder.layer.{i-1}.downsampling_layer.'''	)
      if F'''layer_{i}.1.local_rep.0.''' in k:
        a						:		List[Any]											=      k_new.replace(F'''layer_{i}.1.local_rep.0.'''  ,       F'''{model_prefix}encoder.layer.{i-1}.conv_kxk.'''	)
      if F'''layer_{i}.1.local_rep.1.''' in k:
        a						:		Tuple											=      k_new.replace(F'''layer_{i}.1.local_rep.1.'''  ,       F'''{model_prefix}encoder.layer.{i-1}.conv_1x1.'''	)

    for i in [3, 4, 5]:
      if i == 3:
        a						:		Union[str, Any]											=      [0, 1]
      elif i == 4:
        a						:		Any											=      [0, 1, 2, 3]
      elif i == 5:
        a						:		Tuple											=      [0, 1, 2]

      for j in j_in:
        if F'''layer_{i}.1.global_rep.{j}.''' in k:
          a						:		str											=      k_new.replace(
              F'''layer_{i}.1.global_rep.{j}.'''  ,       F'''{model_prefix}encoder.layer.{i-1}.transformer.layer.{j}.'''	)
      if F'''layer_{i}.1.global_rep.{j+1}.''' in k:
        a						:		Any											=      k_new.replace(
            F'''layer_{i}.1.global_rep.{j+1}.'''  ,       F'''{model_prefix}encoder.layer.{i-1}.layernorm.'''	)

      if F'''layer_{i}.1.conv_proj.''' in k:
        a						:		Optional[Any]											=      k_new.replace(F'''layer_{i}.1.conv_proj.'''  ,       F'''{model_prefix}encoder.layer.{i-1}.conv_projection.'''	)

    if "pre_norm_attn.0." in k:
      a						:		Optional[int]											=      k_new.replace("pre_norm_attn.0."  ,       "layernorm_before."	)
    if "pre_norm_attn.1." in k:
      a						:		int											=      k_new.replace("pre_norm_attn.1."  ,       "attention."	)
    if "pre_norm_ffn.0." in k:
      a						:		Optional[int]											=      k_new.replace("pre_norm_ffn.0."  ,       "layernorm_after."	)
    if "pre_norm_ffn.1." in k:
      a						:		Dict											=      k_new.replace("pre_norm_ffn.1."  ,       "ffn.conv1."	)
    if "pre_norm_ffn.3." in k:
      a						:		Tuple											=      k_new.replace("pre_norm_ffn.3."  ,       "ffn.conv2."	)

    if "classifier.1." in k:
      a						:		Any											=      k_new.replace("classifier.1."  ,       "classifier."	)

    if "seg_head." in k:
      a						:		int											=      k_new.replace("seg_head."  ,       "segmentation_head."	)
    if ".aspp_layer." in k:
      a						:		Optional[Any]											=      k_new.replace(".aspp_layer."  ,       "."	)
    if ".aspp_pool." in k:
      a						:		List[str]											=      k_new.replace(".aspp_pool."  ,       "."	)

    rename_keys.append((k, k_new)	)
  return rename_keys


def 					lowercase      (    A_	)->				Dict:
  '''simple docstring'''







  a						:		Optional[Any]											=      []
  for k in state_dict.keys():
    if k.startswith("seg_head.aux_head."	):
      keys_to_ignore.append(A_	)
  for k in keys_to_ignore:
    state_dict.pop(A_  ,       A_	)


def 					lowercase      (    )->				str:
  '''simple docstring'''







  a						:		Dict											=      "http://images.cocodataset.org/val2017/000000039769.jpg"
  # url = "https://cdn.britannica.com/86/141086-050-9D7C75EE/Gulfstream-G450-business-jet-passengers.jpg"
  a						:		Dict											=      Image.open(requests.get(A_  ,       stream=A_	).raw	)
  return im


@torch.no_grad()
def 					lowercase      (    A_  ,       A_  ,       A_  ,       A_	)->				Dict:
  '''simple docstring'''







  a						:		Optional[int]											=      get_mobilevitva_config(A_  ,       A_	)

  # load original state_dict
  a						:		Any											=      torch.load(A_  ,       map_location="cpu"	)

  # load huggingface model
  if task_name.startswith("ade20k_"	) or task_name.startswith("voc_"	):
    a						:		Optional[Any]											=      MobileViTVaForSemanticSegmentation(A_	).eval()
    a						:		int											=      False
  else:
    a						:		Optional[int]											=      MobileViTVaForImageClassification(A_	).eval()
    a						:		Optional[Any]											=      False

  # remove and rename some keys of load the original model
  a						:		List[str]											=      checkpoint
  remove_unused_keys(A_	)
  a						:		Optional[int]											=      create_rename_keys(A_  ,       base_model=A_	)
  for rename_key_src, rename_key_dest in rename_keys:
    rename_key(A_  ,       A_  ,       A_	)

  # load modified state_dict
  model.load_state_dict(A_	)

  # Check outputs on an image, prepared by MobileViTImageProcessor
  a						:		Tuple											=      MobileViTImageProcessor(crop_size=config.image_size  ,       size=config.image_size + 32	)
  a						:		str											=      image_processor(images=prepare_img()  ,       return_tensors="pt"	)
  a						:		List[str]											=      model(**A_	)

  # verify classification model
  if task_name.startswith("imagenet"	):
    a						:		List[Any]											=      outputs.logits
    a						:		Dict											=      logits.argmax(-1	).item()
    print("Predicted class:"  ,       model.config.idalabel[predicted_class_idx]	)
    if task_name.startswith("imagenet1k_256"	) and config.width_multiplier == 1.0:
      # expected_logits for base variant
      a						:		Any											=      torch.tensor([-1.6336e00, -7.3204e-02, -5.1883e-01]	)
      assert torch.allclose(logits[0, :3]  ,       A_  ,       atol=1e-4	)

  Path(A_	).mkdir(exist_ok=A_	)
  print(F'''Saving model {task_name} to {pytorch_dump_folder_path}'''	)
  model.save_pretrained(A_	)
  print(F'''Saving image processor to {pytorch_dump_folder_path}'''	)
  image_processor.save_pretrained(A_	)


if __name__ == "__main__":
 __lowercase    =				argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     """--task""",
     default="""imagenet1k_256""",
     type=str,
     help=(
         """Name of the task for which the MobileViTV2 model you'd like to convert is trained on . """
         """
                Classification (ImageNet-1k)
                    - MobileViTV2 (256x256) : imagenet1k_256
                    - MobileViTV2 (Trained on 256x256 and Finetuned on 384x384) : imagenet1k_384
                    - MobileViTV2 (Trained on ImageNet-21k and Finetuned on ImageNet-1k 256x256) :
                      imagenet21k_to_1k_256
                    - MobileViTV2 (Trained on ImageNet-21k, Finetuned on ImageNet-1k 256x256, and Finetuned on
                      ImageNet-1k 384x384) : imagenet21k_to_1k_384
                Segmentation
                    - ADE20K Dataset : ade20k_deeplabv3
                    - Pascal VOC 2012 Dataset: voc_deeplabv3
            """
     ),
     choices=[
         """imagenet1k_256""",
         """imagenet1k_384""",
         """imagenet21k_to_1k_256""",
         """imagenet21k_to_1k_384""",
         """ade20k_deeplabv3""",
         """voc_deeplabv3""",
     ],
 )

 parser.add_argument(
     """--orig_checkpoint_path""", required=True, type=str, help="""Path to the original state dict (.pt file)."""
 )
 parser.add_argument("""--orig_config_path""", required=True, type=str, help="""Path to the original config file.""")
 parser.add_argument(
     """--pytorch_dump_folder_path""", required=True, type=str, help="""Path to the output PyTorch model directory."""
 )

 __lowercase    =				parser.parse_args()
 convert_mobilevitva_checkpoint(
     args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
 )










"""simple docstring"""







import copy
import fnmatch
import json
import os
import pickle as pkl
import shutil
import sys
import tarfile
import tempfile
from collections import OrderedDict
from contextlib import contextmanager
from functools import partial
from hashlib import shaaaa
from io import BytesIO
from pathlib import Path
from urllib.parse import urlparse
from zipfile import ZipFile, is_zipfile

import cva
import numpy as np
import requests
import wget
from filelock import FileLock
from PIL import Image
from tqdm.auto import tqdm
from yaml import Loader, dump, load


try:
 import torch

 __lowercase    =				True
except ImportError:
 __lowercase    =				False


try:
 from torch.hub import _get_torch_home

 __lowercase    =				_get_torch_home()
except ImportError:
 __lowercase    =				os.path.expanduser(
     os.getenv("""TORCH_HOME""", os.path.join(os.getenv("""XDG_CACHE_HOME""", """~/.cache"""), """torch"""))
 )

__lowercase    =				os.path.join(torch_cache_home, """transformers""")

__lowercase    =				"""https://cdn.huggingface.co"""
__lowercase    =				"""https://s3.amazonaws.com/models.huggingface.co/bert"""
__lowercase    =				"""/""".join(str(Path(__file__).resolve()).split("""/""")[:-1])
__lowercase    =				os.path.join(PATH, """config.yaml""")
__lowercase    =				os.path.join(PATH, """attributes.txt""")
__lowercase    =				os.path.join(PATH, """objects.txt""")
__lowercase    =				os.getenv("""PYTORCH_PRETRAINED_BERT_CACHE""", default_cache_path)
__lowercase    =				os.getenv("""PYTORCH_TRANSFORMERS_CACHE""", PYTORCH_PRETRAINED_BERT_CACHE)
__lowercase    =				os.getenv("""TRANSFORMERS_CACHE""", PYTORCH_TRANSFORMERS_CACHE)
__lowercase    =				"""pytorch_model.bin"""
__lowercase    =				"""config.yaml"""


def 					lowercase      (    A_=OBJECTS  ,       A_=ATTRIBUTES	)->				Union[str, Any]:
  '''simple docstring'''







  a						:		Optional[Any]											=      []
  with open(A_	) as f:
    for object in f.readlines():
      vg_classes.append(object.split(","	)[0].lower().strip()	)

  a						:		Union[str, Any]											=      []
  with open(A_	) as f:
    for object in f.readlines():
      vg_attrs.append(object.split(","	)[0].lower().strip()	)
  return vg_classes, vg_attrs


def 					lowercase      (    A_	)->				Optional[Any]:
  '''simple docstring'''







  a						:		Dict											=      OrderedDict()
  with open(A_  ,       "rb"	) as f:
    a						:		Optional[Any]											=      pkl.load(A_	)["model"]
  for k in copy.deepcopy(list(ckp.keys()	)	):
    a						:		Dict											=      ckp.pop(A_	)
    if isinstance(A_  ,       np.ndarray	):
      a						:		Optional[Any]											=      torch.tensor(A_	)
    else:
      assert isinstance(A_  ,       torch.tensor	), type(A_	)
    a						:		int											=      v
  return r






class 						_A  :







    """simple docstring"""




    UpperCAmelCase	:							int     =	{}






    def __init__( self    :   Any    ,			__UpperCAmelCase    :   dict    ,			__UpperCAmelCase    :   str = "root"    ,			__UpperCAmelCase    :   Optional[int]=0):
      a						:		List[str]											=      name
      a						:		Tuple											=      level
      a						:		int											=      {}
      for k, v in dictionary.items():
        if v is None:
          raise ValueError()
        a						:		List[Any]											=      copy.deepcopy(__UpperCAmelCase)
        a						:		int											=      copy.deepcopy(__UpperCAmelCase)
        if isinstance(__UpperCAmelCase    ,			__UpperCAmelCase):
          a						:		Union[str, Any]											=      Config(__UpperCAmelCase    ,			name=__UpperCAmelCase    ,			level=level + 1)
        a						:		Dict											=      v
        setattr(self    ,			__UpperCAmelCase    ,			__UpperCAmelCase)

      a						:		Tuple											=      d






    def __repr__( self    :   List[str]):
      return str(list((self._pointer.keys())))






    def __setattr__( self    :   Dict    ,			__UpperCAmelCase    :   str    ,			__UpperCAmelCase    :   Tuple):
      a						:		Optional[Any]											=      val
      a						:		Tuple											=      val
      a						:		Dict											=      key.split(".")
      a						:		Union[str, Any]											=      len(__UpperCAmelCase) - 1
      a						:		Optional[int]											=      self._pointer
      if len(__UpperCAmelCase) > 1:
        for i, l in enumerate(__UpperCAmelCase):
          if hasattr(self    ,			__UpperCAmelCase) and isinstance(getattr(self    ,			__UpperCAmelCase)    ,			__UpperCAmelCase):
            setattr(getattr(self    ,			__UpperCAmelCase)    ,			".".join(levels[i:])    ,			__UpperCAmelCase)
          if l == last_level:
            a						:		int											=      val
          else:
            a						:		str											=      pointer[l]






    def      __snake_case							( self    :   str):
      return self._pointer






    def      __snake_case							( self    :   int    ,			__UpperCAmelCase    :   Tuple    ,			__UpperCAmelCase    :   List[Any]):
      with open(f'''{file_name}'''    ,			"w") as stream:
        dump(__UpperCAmelCase    ,			__UpperCAmelCase)






    def      __snake_case							( self    :   int    ,			__UpperCAmelCase    :   Dict    ,			__UpperCAmelCase    :   int):
      with open(f'''{file_name}'''    ,			"w") as stream:
        json.dump(__UpperCAmelCase    ,			__UpperCAmelCase)






    @staticmethod
    def      __snake_case							( __UpperCAmelCase    :   Dict):
      with open(__UpperCAmelCase) as stream:
        a						:		List[str]											=      load(__UpperCAmelCase    ,			Loader=__UpperCAmelCase)
      return data






    def __str__( self    :   Tuple):
      a						:		str											=      "    "
      if self._name != "root":
        a						:		List[str]											=      f'''{t * (self._level-1)}{self._name}:\n'''
      else:
        a						:		Optional[Any]											=      ""
      a						:		List[Any]											=      self._level
      for i, (k, v) in enumerate(self._pointer.items()):
        if isinstance(__UpperCAmelCase    ,			__UpperCAmelCase):
          r += f'''{t * (self._level)}{v}\n'''
          self._level += 1
        else:
          r += f'''{t * (self._level)}{k}: {v} ({type(__UpperCAmelCase).__name__})\n'''
        a						:		Tuple											=      level
      return r[:-1]






    @classmethod
    def      __snake_case							( cls    :   str    ,			__UpperCAmelCase    :   str    ,			**__UpperCAmelCase    :   List[Any]):
      a							,		a						:		Tuple											=      cls.get_config_dict(__UpperCAmelCase    ,			**__UpperCAmelCase)
      return cls(__UpperCAmelCase)






    @classmethod
    def      __snake_case							( cls    :   Union[str, Any]    ,			__UpperCAmelCase    :   str    ,			**__UpperCAmelCase    :   List[str]):
      a						:		int											=      kwargs.pop("cache_dir"    ,			__UpperCAmelCase)
      a						:		List[Any]											=      kwargs.pop("force_download"    ,			__UpperCAmelCase)
      a						:		Optional[int]											=      kwargs.pop("resume_download"    ,			__UpperCAmelCase)
      a						:		Tuple											=      kwargs.pop("proxies"    ,			__UpperCAmelCase)
      a						:		int											=      kwargs.pop("local_files_only"    ,			__UpperCAmelCase)

      if os.path.isdir(__UpperCAmelCase):
        a						:		Union[str, Any]											=      os.path.join(__UpperCAmelCase    ,			__UpperCAmelCase)
      elif os.path.isfile(__UpperCAmelCase) or is_remote_url(__UpperCAmelCase):
        a						:		List[Any]											=      pretrained_model_name_or_path
      else:
        a						:		int											=      hf_bucket_url(__UpperCAmelCase    ,			filename=__UpperCAmelCase    ,			use_cdn=__UpperCAmelCase)

      try:
        # Load from URL or cache if already cached
        a						:		Optional[Any]											=      cached_path(
            __UpperCAmelCase    ,			cache_dir=__UpperCAmelCase    ,			force_download=__UpperCAmelCase    ,			proxies=__UpperCAmelCase    ,			resume_download=__UpperCAmelCase    ,			local_files_only=__UpperCAmelCase    ,			)
        # Load config dict
        if resolved_config_file is None:
          raise EnvironmentError

        a						:		Union[str, Any]											=      Config.load_yaml(__UpperCAmelCase)

      except EnvironmentError:
        a						:		str											=      "Can't load config for"
        raise EnvironmentError(__UpperCAmelCase)

      if resolved_config_file == config_file:
        print("loading configuration file from path")
      else:
        print("loading configuration file cache")

      return Config.load_yaml(__UpperCAmelCase), kwargs






def 					lowercase      (    A_	)->				str:
  '''simple docstring'''







  a						:		Tuple											=      torch.load("dump.pt"  ,       map_location=in_tensor.device	)
  a						:		Any											=      in_tensor.numpy()
  a						:		Optional[int]											=      out_tensor.numpy()[0]
  print(na.shape  ,       na[0, 0, :5]	)
  print(na.shape  ,       na[0, 0, :5]	)
  assert np.allclose(A_  ,       A_  ,       rtol=0.0_1  ,       atol=0.1	), (
      F'''{sum([1 for x in np.isclose(A_  ,       A_  ,       rtol=0.0_1  ,       atol=0.1	).flatten() if x is False]	)/len(na.flatten()	)*100:.4f} %'''
      " element-wise mismatch"
  )
  raise Exception("tensors are all good"	)

  # Hugging face functions below


def 					lowercase      (    A_	)->				Optional[Any]:
  '''simple docstring'''







  a						:		Optional[Any]											=      urlparse(A_	)
  return parsed.scheme in ("http", "https")


def 					lowercase      (    A_  ,       A_  ,       A_=True	)->				str:
  '''simple docstring'''







  a						:		List[Any]											=      CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
  a						:		str											=      "/" not in model_id
  if legacy_format:
    return F'''{endpoint}/{model_id}-{filename}'''
  else:
    return F'''{endpoint}/{model_id}/{filename}'''


def 					lowercase      (    A_  ,       A_  ,       A_=None  ,       A_=0  ,       A_=None  ,       )->				List[str]:
  '''simple docstring'''







  a						:		Optional[int]											=      "python/{}".format(sys.version.split()[0]	)
  if _torch_available:
    ua += "; torch/{}".format(torch.__version__	)
  if isinstance(A_  ,       A_	):
    ua += "; " + "; ".join("{}/{}".format(A_  ,       A_	) for k, v in user_agent.items()	)
  elif isinstance(A_  ,       A_	):
    ua += "; " + user_agent
  a						:		str											=      {"user-agent": ua}
  if resume_size > 0:
    a						:		List[Any]											=      "bytes=%d-" % (resume_size,)
  a						:		str											=      requests.get(A_  ,       stream=A_  ,       proxies=A_  ,       headers=A_	)
  if response.status_code == 416:  # Range not satisfiable
    return
  a						:		Optional[int]											=      response.headers.get("Content-Length"	)
  a						:		List[Any]											=      resume_size + int(A_	) if content_length is not None else None
  a						:		List[Any]											=      tqdm(
      unit="B"  ,       unit_scale=A_  ,       total=A_  ,       initial=A_  ,       desc="Downloading"  ,       )
  for chunk in response.iter_content(chunk_size=1_024	):
    if chunk:  # filter out keep-alive new chunks
      progress.update(len(A_	)	)
      temp_file.write(A_	)
  progress.close()


def 					lowercase      (    A_  ,       A_=None  ,       A_=False  ,       A_=None  ,       A_=10  ,       A_=False  ,       A_=None  ,       A_=False  ,       )->				str:
  '''simple docstring'''







  if cache_dir is None:
    a						:		List[Any]											=      TRANSFORMERS_CACHE
  if isinstance(A_  ,       A_	):
    a						:		Tuple											=      str(A_	)

  os.makedirs(A_  ,       exist_ok=A_	)

  a						:		Optional[Any]											=      None
  if not local_files_only:
    try:
      a						:		Dict											=      requests.head(A_  ,       allow_redirects=A_  ,       proxies=A_  ,       timeout=A_	)
      if response.status_code == 200:
        a						:		int											=      response.headers.get("ETag"	)
    except (EnvironmentError, requests.exceptions.Timeout):
      # etag is already None
      pass

  a						:		List[str]											=      url_to_filename(A_  ,       A_	)

  # get cache path to put the file
  a						:		List[str]											=      os.path.join(A_  ,       A_	)

  # etag is None = we don't have a connection, or url doesn't exist, or is otherwise inaccessible.
  # try to get the last downloaded one
  if etag is None:
    if os.path.exists(A_	):
      return cache_path
    else:
      a						:		Any											=      [
          file
          for file in fnmatch.filter(os.listdir(A_	)  ,       filename + ".*"	)
          if not file.endswith(".json"	) and not file.endswith(".lock"	)
      ]
      if len(A_	) > 0:
        return os.path.join(A_  ,       matching_files[-1]	)
      else:
        # If files cannot be found and local_files_only=True,
        # the models might've been found if local_files_only=False
        # Notify the user about that
        if local_files_only:
          raise ValueError(
              "Cannot find the requested files in the cached path and outgoing traffic has been"
              " disabled. To enable model look-ups and downloads online, set 'local_files_only'"
              " to False."	)
        return None

    # From now on, etag is not None.
  if os.path.exists(A_	) and not force_download:
    return cache_path

  # Prevent parallel downloads of the same file with a lock.
  a						:		Dict											=      cache_path + ".lock"
  with FileLock(A_	):
    # If the download just completed while the lock was activated.
    if os.path.exists(A_	) and not force_download:
      # Even if returning early like here, the lock will be released.
      return cache_path

    if resume_download:
      a						:		Optional[Any]											=      cache_path + ".incomplete"

      @contextmanager
      def _resumable_file_manager():
        with open(A_  ,       "a+b"	) as f:
          yield f

      a						:		Tuple											=      _resumable_file_manager
      if os.path.exists(A_	):
        a						:		Optional[Any]											=      os.stat(A_	).st_size
      else:
        a						:		Optional[int]											=      0
    else:
      a						:		Union[str, Any]											=      partial(tempfile.NamedTemporaryFile  ,       dir=A_  ,       delete=A_	)
      a						:		Dict											=      0

    # Download to temporary file, then copy to cache dir once finished.
    # Otherwise you get corrupt cache entries if the download gets interrupted.
    with temp_file_manager() as temp_file:
      print(
          "%s not found in cache or force_download set to True, downloading to %s"  ,       A_  ,       temp_file.name  ,       )

      http_get(
          A_  ,       A_  ,       proxies=A_  ,       resume_size=A_  ,       user_agent=A_  ,       )

    os.replace(temp_file.name  ,       A_	)

    a						:		List[str]											=      {"url": url, "etag": etag}
    a						:		Tuple											=      cache_path + ".json"
    with open(A_  ,       "w"	) as meta_file:
      json.dump(A_  ,       A_	)

  return cache_path


def 					lowercase      (    A_  ,       A_=None	)->				Any:
  '''simple docstring'''







  a						:		Dict											=      url.encode("utf-8"	)
  a						:		Optional[Any]											=      shaaaa(A_	)
  a						:		Any											=      url_hash.hexdigest()

  if etag:
    a						:		Union[str, Any]											=      etag.encode("utf-8"	)
    a						:		Tuple											=      shaaaa(A_	)
    filename += "." + etag_hash.hexdigest()

  if url.endswith(".h5"	):
    filename += ".h5"

  return filename


def 					lowercase      (    A_  ,       A_=None  ,       A_=False  ,       A_=None  ,       A_=False  ,       A_=None  ,       A_=False  ,       A_=False  ,       A_=False  ,       )->				Tuple:
  '''simple docstring'''







  if cache_dir is None:
    a						:		Union[str, Any]											=      TRANSFORMERS_CACHE
  if isinstance(A_  ,       A_	):
    a						:		List[Any]											=      str(A_	)
  if isinstance(A_  ,       A_	):
    a						:		int											=      str(A_	)

  if is_remote_url(A_	):
    # URL, so get it from the cache (downloading if necessary)
    a						:		Optional[Any]											=      get_from_cache(
        A_  ,       cache_dir=A_  ,       force_download=A_  ,       proxies=A_  ,       resume_download=A_  ,       user_agent=A_  ,       local_files_only=A_  ,       )
  elif os.path.exists(A_	):
    # File, and it exists.
    a						:		Union[str, Any]											=      url_or_filename
  elif urlparse(A_	).scheme == "":
    # File, but it doesn't exist.
    raise EnvironmentError("file {} not found".format(A_	)	)
  else:
    # Something unknown
    raise ValueError("unable to parse {} as a URL or as a local path".format(A_	)	)

  if extract_compressed_file:
    if not is_zipfile(A_	) and not tarfile.is_tarfile(A_	):
      return output_path

    # Path where we extract compressed archives
    # We avoid '.' in dir name and add "-extracted" at the end: "./model.zip" => "./model-zip-extracted/"
    a							,		a						:		Dict											=      os.path.split(A_	)
    a						:		List[str]											=      output_file.replace("."  ,       "-"	) + "-extracted"
    a						:		Optional[Any]											=      os.path.join(A_  ,       A_	)

    if os.path.isdir(A_	) and os.listdir(A_	) and not force_extract:
      return output_path_extracted

    # Prevent parallel extractions
    a						:		Tuple											=      output_path + ".lock"
    with FileLock(A_	):
      shutil.rmtree(A_  ,       ignore_errors=A_	)
      os.makedirs(A_	)
      if is_zipfile(A_	):
        with ZipFile(A_  ,       "r"	) as zip_file:
          zip_file.extractall(A_	)
          zip_file.close()
      elif tarfile.is_tarfile(A_	):
        a						:		List[str]											=      tarfile.open(A_	)
        tar_file.extractall(A_	)
        tar_file.close()
      else:
        raise EnvironmentError("Archive format of {} could not be identified".format(A_	)	)

    return output_path_extracted

  return output_path


def 					lowercase      (    A_  ,       A_=","	)->				Union[str, Any]:
  '''simple docstring'''







  assert isinstance(A_  ,       A_	)
  if os.path.isfile(A_	):
    with open(A_	) as f:
      a						:		str											=      eval(f.read()	)
  else:
    a						:		List[Any]											=      requests.get(A_	)
    try:
      a						:		Any											=      requests.json()
    except Exception:
      a						:		Any											=      req.content.decode()
      assert data is not None, "could not connect"
      try:
        a						:		Optional[Any]											=      eval(A_	)
      except Exception:
        a						:		Any											=      data.split("\n"	)
    req.close()
  return data


def 					lowercase      (    A_	)->				str:
  '''simple docstring'''







  a						:		Optional[int]											=      requests.get(A_	)
  a						:		List[str]											=      np.array(Image.open(BytesIO(response.content	)	)	)
  return img


def 					lowercase      (    A_	)->				Any:
  '''simple docstring'''







  a						:		List[Any]											=      url.split("/"	)[-1]
  if fn not in os.listdir(os.getcwd()	):
    wget.download(A_	)
  with open(A_  ,       "rb"	) as stream:
    a						:		Any											=      pkl.load(A_	)
  a						:		List[str]											=      weights.pop("model"	)
  a						:		Dict											=      {}
  for k, v in model.items():
    a						:		List[str]											=      torch.from_numpy(A_	)
    if "running_var" in k:
      a						:		Dict											=      torch.tensor([0]	)
      a						:		Any											=      k.replace("running_var"  ,       "num_batches_tracked"	)
      a						:		List[Any]											=      zero
  return new


def 					lowercase      (    )->				Optional[int]:
  '''simple docstring'''







  print(F'''{os.path.abspath(os.path.join(A_  ,       os.pardir	)	)}/demo.ipynb'''	)


def 					lowercase      (    A_  ,       A_="RGB"	)->				Any:
  '''simple docstring'''







  assert isinstance(A_  ,       A_	)
  if os.path.isfile(A_	):
    a						:		Dict											=      cva.imread(A_	)
  else:
    a						:		Union[str, Any]											=      get_image_from_url(A_	)
    assert img is not None, F'''could not connect to: {im}'''
  a						:		int											=      cva.cvtColor(A_  ,       cva.COLOR_BGR2RGB	)
  if input_format == "RGB":
    a						:		List[str]											=      img[:, :, ::-1]
  return img







def 					lowercase      (    A_  ,       A_=1	)->				int:
  '''simple docstring'''







  return (images[i : i + batch] for i in range(0  ,       len(A_	)  ,       A_	))






