





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

   A__:    Union[str, Any]  	=	True
except ImportError:
   A__:    int  	=	False


try:
   from torch.hub import _get_torch_home

   A__:    Tuple  	=	_get_torch_home()
except ImportError:
   A__:    Optional[int]  	=	os.path.expanduser(
       os.getenv('''TORCH_HOME''', os.path.join(os.getenv('''XDG_CACHE_HOME''', '''~/.cache'''), '''torch'''))
   )

A__:    Optional[int]  	=	os.path.join(torch_cache_home, '''transformers''')

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



def 				lowerCAmelCase_ (							A_=OBJECTS    ,A_=ATTRIBUTES):
    UpperCamelCase__:	Dict					=     []
    with open(_lowerCAmelCase) as f:
        for object in f.readlines():
            vg_classes.append(object.split(",")[0].lower().strip())

    UpperCamelCase__:	List[Any]					=     []
    with open(_lowerCAmelCase) as f:
        for object in f.readlines():
            vg_attrs.append(object.split(",")[0].lower().strip())
    return vg_classes, vg_attrs



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




class        _a    :

      """simple docstring"""


      UpperCamelCase__  		=       {}






      def __init__(		self:			int	,    __lowerCamelCase:			dict	,    __lowerCamelCase:			str = "root"	,    __lowerCamelCase:			int=0					):
          '''simple docstring'''


          UpperCamelCase__:	Dict					=     name
          UpperCamelCase__:	Union[str, Any]					=     level
          UpperCamelCase__:	Tuple					=     {}
          for k, v in dictionary.items():
              if v is None:
                  raise ValueError()
              UpperCamelCase__:	int					=     copy.deepcopy(_a					)
              UpperCamelCase__:	List[str]					=     copy.deepcopy(_a					)
              if isinstance(_a	,    _a					):
                  UpperCamelCase__:	str					=     Config(_a	,    name=_a	,    level=level + 1					)
              UpperCamelCase__:	Union[str, Any]					=     v
              setattr(self	,    _a	,    _a					)

          UpperCamelCase__:	Optional[int]					=     d






      def __repr__(		self:			int					):
          '''simple docstring'''


          return str(list((self._pointer.keys())					)					)






      def __setattr__(		self:			int	,    __lowerCamelCase:			Tuple	,    __lowerCamelCase:			Optional[int]					):
          '''simple docstring'''


          UpperCamelCase__:	Union[str, Any]					=     val
          UpperCamelCase__:	Optional[Any]					=     val
          UpperCamelCase__:	List[str]					=     key.split("."					)
          UpperCamelCase__:	str					=     len(_a					) - 1
          UpperCamelCase__:	str					=     self._pointer
          if len(_a					) > 1:
              for i, l in enumerate(_a					):
                  if hasattr(self	,    _a					) and isinstance(getattr(self	,    _a					)	,    _a					):
                      setattr(getattr(self	,    _a					)	,    ".".join(levels[i:]					)	,    _a					)
                  if l == last_level:
                      UpperCamelCase__:	Union[str, Any]					=     val
                  else:
                      UpperCamelCase__:	List[Any]					=     pointer[l]






      def 			UpperCAmelCase_       (		self:			Dict					):
          '''simple docstring'''


          return self._pointer






      def 			UpperCAmelCase_       (		self:			Any	,    __lowerCamelCase:			List[str]	,    __lowerCamelCase:			List[str]					):
          '''simple docstring'''


          with open(F"{file_name}"	,    "w"					) as stream:
              dump(_a	,    _a					)






      def 			UpperCAmelCase_       (		self:			Any	,    __lowerCamelCase:			Union[str, Any]	,    __lowerCamelCase:			List[Any]					):
          '''simple docstring'''


          with open(F"{file_name}"	,    "w"					) as stream:
              json.dump(_a	,    _a					)






      @staticmethod
      def 			UpperCAmelCase_       (		__lowerCamelCase:			Any					):
          '''simple docstring'''


          with open(_a					) as stream:
              UpperCamelCase__:	Dict					=     load(_a	,    Loader=_a					)
          return data






      def __str__(		self:			Any					):
          '''simple docstring'''


          UpperCamelCase__:	Dict					=     '    '
          if self._name != "root":
              UpperCamelCase__:	Dict					=     F"{t * (self._level-1)}{self._name}:\n"
          else:
              UpperCamelCase__:	str					=     ''
          UpperCamelCase__:	List[str]					=     self._level
          for i, (k, v) in enumerate(self._pointer.items()					):
              if isinstance(_a	,    _a					):
                  r += F"{t * (self._level)}{v}\n"
                  self._level += 1
              else:
                  r += F"{t * (self._level)}{k}: {v} ({type(_a					).__name__})\n"
              UpperCamelCase__:	Any					=     level
          return r[:-1]






      @classmethod
      def 			UpperCAmelCase_       (		cls:			int	,    __lowerCamelCase:			str	,    **__lowerCamelCase:			List[str]					):
          '''simple docstring'''


          UpperCamelCase__:	str					=     cls.get_config_dict(_a	,    **_a					)
          return cls(_a					)






      @classmethod
      def 			UpperCAmelCase_       (		cls:			Dict	,    __lowerCamelCase:			str	,    **__lowerCamelCase:			List[str]					):
          '''simple docstring'''


          UpperCamelCase__:	str					=     kwargs.pop("cache_dir"	,    _a					)
          UpperCamelCase__:	List[Any]					=     kwargs.pop("force_download"	,    _a					)
          UpperCamelCase__:	List[Any]					=     kwargs.pop("resume_download"	,    _a					)
          UpperCamelCase__:	List[Any]					=     kwargs.pop("proxies"	,    _a					)
          UpperCamelCase__:	Optional[Any]					=     kwargs.pop("local_files_only"	,    _a					)

          if os.path.isdir(_a					):
              UpperCamelCase__:	Tuple					=     os.path.join(_a	,    _a					)
          elif os.path.isfile(_a					) or is_remote_url(_a					):
              UpperCamelCase__:	List[str]					=     pretrained_model_name_or_path
          else:
              UpperCamelCase__:	List[Any]					=     hf_bucket_url(_a	,    filename=_a	,    use_cdn=_a					)

          try:
              # Load from URL or cache if already cached
              UpperCamelCase__:	str					=     cached_path(
                  _a	,    cache_dir=_a	,    force_download=_a	,    proxies=_a	,    resume_download=_a	,    local_files_only=_a	,    )
              # Load config dict
              if resolved_config_file is None:
                  raise EnvironmentError

              UpperCamelCase__:	Optional[Any]					=     Config.load_yaml(_a					)

          except EnvironmentError:
              UpperCamelCase__:	Optional[int]					=     'Can\'t load config for'
              raise EnvironmentError(_a					)

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

          return Config.load_yaml(_a					), kwargs



def 				lowerCAmelCase_ (							A_):
    UpperCamelCase__:	Dict					=     torch.load("dump.pt"    ,map_location=in_tensor.device)
    UpperCamelCase__:	Optional[int]					=     in_tensor.numpy()
    UpperCamelCase__:	int					=     out_tensor.numpy()[0]
    print(na.shape    ,na[0, 0, :5])
    print(na.shape    ,na[0, 0, :5])
    assert np.allclose(_lowerCAmelCase    ,_lowerCAmelCase    ,rtol=0.01    ,atol=0.1), (
        F"{sum([1 for x in np.isclose(_lowerCAmelCase    ,_lowerCAmelCase    ,rtol=0.01    ,atol=0.1).flatten() if x is False])/len(na.flatten())*1_00:.4f} %"
        " element-wise mismatch"
    )
    raise Exception("tensors are all good")

    # Hugging face functions below



def 				lowerCAmelCase_ (							A_):
    UpperCamelCase__:	List[str]					=     urlparse(_lowerCAmelCase)
    return parsed.scheme in ("http", "https")



def 				lowerCAmelCase_ (							A_    ,A_    ,A_=True):
    UpperCamelCase__:	Any					=     CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
    UpperCamelCase__:	List[str]					=     '/' not in model_id
    if legacy_format:
        return F"{endpoint}/{model_id}-{filename}"
    else:
        return F"{endpoint}/{model_id}/{filename}"



def 				lowerCAmelCase_ (							A_    ,A_    ,A_=None    ,A_=0    ,A_=None    ,):
    UpperCamelCase__:	Tuple					=     'python/{}'.format(sys.version.split()[0])
    if _torch_available:
        ua += "; torch/{}".format(torch.__version__)
    if isinstance(_lowerCAmelCase    ,_lowerCAmelCase):
        ua += "; " + "; ".join("{}/{}".format(_lowerCAmelCase    ,_lowerCAmelCase) for k, v in user_agent.items())
    elif isinstance(_lowerCAmelCase    ,_lowerCAmelCase):
        ua += "; " + user_agent
    UpperCamelCase__:	List[Any]					=     {'user-agent': ua}
    if resume_size > 0:
        UpperCamelCase__:	List[str]					=     'bytes=%d-' % (resume_size,)
    UpperCamelCase__:	Optional[Any]					=     requests.get(_lowerCAmelCase    ,stream=_lowerCAmelCase    ,proxies=_lowerCAmelCase    ,headers=_lowerCAmelCase)
    if response.status_code == 4_16:  # Range not satisfiable
        return
    UpperCamelCase__:	List[Any]					=     response.headers.get("Content-Length")
    UpperCamelCase__:	Tuple					=     resume_size + int(_lowerCAmelCase) if content_length is not None else None
    UpperCamelCase__:	int					=     tqdm(
        unit="B"    ,unit_scale=_lowerCAmelCase    ,total=_lowerCAmelCase    ,initial=_lowerCAmelCase    ,desc="Downloading"    ,)
    for chunk in response.iter_content(chunk_size=10_24):
        if chunk:  # filter out keep-alive new chunks
            progress.update(len(_lowerCAmelCase))
            temp_file.write(_lowerCAmelCase)
    progress.close()



def 				lowerCAmelCase_ (							A_    ,A_=None    ,A_=False    ,A_=None    ,A_=10    ,A_=False    ,A_=None    ,A_=False    ,):
    if cache_dir is None:
        UpperCamelCase__:	Any					=     TRANSFORMERS_CACHE
    if isinstance(_lowerCAmelCase    ,_lowerCAmelCase):
        UpperCamelCase__:	Tuple					=     str(_lowerCAmelCase)

    os.makedirs(_lowerCAmelCase    ,exist_ok=_lowerCAmelCase)

    UpperCamelCase__:	int					=     None
    if not local_files_only:
        try:
            UpperCamelCase__:	Optional[int]					=     requests.head(_lowerCAmelCase    ,allow_redirects=_lowerCAmelCase    ,proxies=_lowerCAmelCase    ,timeout=_lowerCAmelCase)
            if response.status_code == 2_00:
                UpperCamelCase__:	List[Any]					=     response.headers.get("ETag")
        except (EnvironmentError, requests.exceptions.Timeout):
            # etag is already None
            pass

    UpperCamelCase__:	Tuple					=     url_to_filename(_lowerCAmelCase    ,_lowerCAmelCase)

    # get cache path to put the file
    UpperCamelCase__:	Tuple					=     os.path.join(_lowerCAmelCase    ,_lowerCAmelCase)

    # 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(_lowerCAmelCase):
            return cache_path
        else:
            UpperCamelCase__:	int					=     [
                file
                for file in fnmatch.filter(os.listdir(_lowerCAmelCase)    ,filename + ".*")
                if not file.endswith(".json") and not file.endswith(".lock")
            ]
            if len(_lowerCAmelCase) > 0:
                return os.path.join(_lowerCAmelCase    ,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(_lowerCAmelCase) and not force_download:
        return cache_path

    # Prevent parallel downloads of the same file with a lock.
    UpperCamelCase__:	Union[str, Any]					=     cache_path + '.lock'
    with FileLock(_lowerCAmelCase):
        # If the download just completed while the lock was activated.
        if os.path.exists(_lowerCAmelCase) and not force_download:
            # Even if returning early like here, the lock will be released.
            return cache_path

        if resume_download:
            UpperCamelCase__:	Optional[int]					=     cache_path + '.incomplete'

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

            UpperCamelCase__:	Optional[int]					=     _resumable_file_manager
            if os.path.exists(_lowerCAmelCase):
                UpperCamelCase__:	int					=     os.stat(_lowerCAmelCase).st_size
            else:
                UpperCamelCase__:	Dict					=     0
        else:
            UpperCamelCase__:	str					=     partial(tempfile.NamedTemporaryFile    ,dir=_lowerCAmelCase    ,delete=_lowerCAmelCase)
            UpperCamelCase__:	List[Any]					=     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"    ,_lowerCAmelCase    ,temp_file.name    ,)

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

        os.replace(temp_file.name    ,_lowerCAmelCase)

        UpperCamelCase__:	List[Any]					=     {'url': url, 'etag': etag}
        UpperCamelCase__:	str					=     cache_path + '.json'
        with open(_lowerCAmelCase    ,"w") as meta_file:
            json.dump(_lowerCAmelCase    ,_lowerCAmelCase)

    return cache_path



def 				lowerCAmelCase_ (							A_    ,A_=None):
    UpperCamelCase__:	Any					=     url.encode("utf-8")
    UpperCamelCase__:	Tuple					=     shaaaa(_lowerCAmelCase)
    UpperCamelCase__:	Dict					=     url_hash.hexdigest()

    if etag:
        UpperCamelCase__:	List[str]					=     etag.encode("utf-8")
        UpperCamelCase__:	Any					=     shaaaa(_lowerCAmelCase)
        filename += "." + etag_hash.hexdigest()

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

    return filename



def 				lowerCAmelCase_ (							A_    ,A_=None    ,A_=False    ,A_=None    ,A_=False    ,A_=None    ,A_=False    ,A_=False    ,A_=False    ,):
    if cache_dir is None:
        UpperCamelCase__:	Optional[Any]					=     TRANSFORMERS_CACHE
    if isinstance(_lowerCAmelCase    ,_lowerCAmelCase):
        UpperCamelCase__:	List[str]					=     str(_lowerCAmelCase)
    if isinstance(_lowerCAmelCase    ,_lowerCAmelCase):
        UpperCamelCase__:	List[str]					=     str(_lowerCAmelCase)

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

    if extract_compressed_file:
        if not is_zipfile(_lowerCAmelCase) and not tarfile.is_tarfile(_lowerCAmelCase):
            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/"
        UpperCamelCase__:	Tuple					=     os.path.split(_lowerCAmelCase)
        UpperCamelCase__:	str					=     output_file.replace("."    ,"-") + '-extracted'
        UpperCamelCase__:	Optional[int]					=     os.path.join(_lowerCAmelCase    ,_lowerCAmelCase)

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

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

        return output_path_extracted

    return output_path



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



def 				lowerCAmelCase_ (							A_):
    UpperCamelCase__:	str					=     requests.get(_lowerCAmelCase)
    UpperCamelCase__:	Dict					=     np.array(Image.open(BytesIO(response.content)))
    return img



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



def 				lowerCAmelCase_ (							):
    print(F"{os.path.abspath(os.path.join(_lowerCAmelCase    ,os.pardir))}/demo.ipynb")



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




def 				lowerCAmelCase_ (							A_    ,A_=1):
    return (images[i : i + batch] for i in range(0    ,len(_lowerCAmelCase)    ,_lowerCAmelCase))






'''simple docstring'''





import functools

def 			a_	(				_lowerCAmelCase			,_lowerCAmelCase			) -> int:

  # Validation
  if not isinstance(_lowerCAmelCase			,_lowerCAmelCase			) or not all(isinstance(_lowerCAmelCase			,_lowerCAmelCase			) for day in days			):
    raise ValueError('The parameter days should be a list of integers'			)

  if len(_lowerCAmelCase			) != 3 or not all(isinstance(_lowerCAmelCase			,_lowerCAmelCase			) for cost in costs			):
    raise ValueError('The parameter costs should be a list of three integers'			)

  if len(_lowerCAmelCase			) == 0:
    return 0

  if min(_lowerCAmelCase			) <= 0:
    raise ValueError('All days elements should be greater than 0'			)

  if max(_lowerCAmelCase			) >= 366:
    raise ValueError('All days elements should be less than 366'			)

  __lowerCamelCase			:			int       			=					set(_lowerCAmelCase			)

  @functools.cache
  def dynamic_programming(_lowerCAmelCase			) -> int:
    if index > 365:
      return 0

    if index not in days_set:
      return dynamic_programming(index + 1			)

    return min(
        costs[0] + dynamic_programming(index + 1			)			,costs[1] + dynamic_programming(index + 7			)			,costs[2] + dynamic_programming(index + 30			)			,)

  return dynamic_programming(1			)


if __name__ == "__main__":
   import doctest

   doctest.testmod()






