



"""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

 __snake_case						:					str	       =      True
except ImportError:
 __snake_case						:					int	       =      False


try:
 from torch.hub import _get_torch_home

 __snake_case						:					Tuple	       =      _get_torch_home()
except ImportError:
 __snake_case						:					Dict	       =      os.path.expanduser(
     os.getenv('TORCH_HOME', os.path.join(os.getenv('XDG_CACHE_HOME', '~/.cache'), 'torch'))
 )

__snake_case						:					List[Any]	       =      os.path.join(torch_cache_home, 'transformers')

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



def 	a_						(      __a=OBJECTS		,				__a=ATTRIBUTES   ):
     A__      						= []
     with open(__a   ) as f:
          for object in f.readlines():
               vg_classes.append(object.split(''','''   )[0].lower().strip()   )

     A__      						= []
     with open(__a   ) as f:
          for object in f.readlines():
               vg_attrs.append(object.split(''','''   )[0].lower().strip()   )
     return vg_classes, vg_attrs



def 	a_						(      __a   ):
     A__      						= OrderedDict()
     with open(__a		,				'''rb'''   ) as f:
          A__      						= pkl.load(__a   )['''model''']
     for k in copy.deepcopy(list(ckp.keys()   )   ):
          A__      						= ckp.pop(__a   )
          if isinstance(__a		,				np.ndarray   ):
               A__      						= torch.tensor(__a   )
          else:
               assert isinstance(__a		,				torch.tensor   ), type(__a   )
          A__      						= v
     return r

class    UpperCamelCase	:





     """simple docstring"""






     _lowerCamelCase       :			Optional[int]					 ={}





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

          A__      						= d





     def __repr__(       self			:				Dict			):
          return str(list((self._pointer.keys())			)			)





     def __setattr__(       self			:				int						,   _lowerCamelCase			:				Any						,   _lowerCamelCase			:				Union[str, Any]			):
          A__      						= val
          A__      						= val
          A__      						= key.split('''.'''			)
          A__      						= len(__lowerCamelCase			) - 1
          A__      						= self._pointer
          if len(__lowerCamelCase			) > 1:
               for i, l in enumerate(__lowerCamelCase			):
                    if hasattr(self						,   __lowerCamelCase			) and isinstance(getattr(self						,   __lowerCamelCase			)						,   __lowerCamelCase			):
                         setattr(getattr(self						,   __lowerCamelCase			)						,   '''.'''.join(levels[i:]			)						,   __lowerCamelCase			)
                    if l == last_level:
                         A__      						= val
                    else:
                         A__      						= pointer[l]





     def     A__	(       self			:				List[str]			):
          return self._pointer





     def     A__	(       self			:				Optional[Any]						,   _lowerCamelCase			:				str						,   _lowerCamelCase			:				Optional[Any]			):




          with open(F'''{file_name}'''						,   '''w'''			) as stream:
               dump(__lowerCamelCase						,   __lowerCamelCase			)





     def     A__	(       self			:				Tuple						,   _lowerCamelCase			:				List[str]						,   _lowerCamelCase			:				Optional[int]			):




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





     @staticmethod
     def     A__	(       _lowerCamelCase			:				Optional[int]			):




          with open(__lowerCamelCase			) as stream:
               A__      						= load(__lowerCamelCase						,   Loader=__lowerCamelCase			)
          return data





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





     @classmethod
     def     A__	(       cls			:				Optional[int]						,   _lowerCamelCase			:				str						,   **_lowerCamelCase			:				List[Any]			):
          A__						,  A__      						= cls.get_config_dict(__lowerCamelCase						,   **__lowerCamelCase			)
          return cls(__lowerCamelCase			)





     @classmethod
     def     A__	(       cls			:				Tuple						,   _lowerCamelCase			:				str						,   **_lowerCamelCase			:				Tuple			):
          A__      						= kwargs.pop('''cache_dir'''						,   __lowerCamelCase			)
          A__      						= kwargs.pop('''force_download'''						,   __lowerCamelCase			)
          A__      						= kwargs.pop('''resume_download'''						,   __lowerCamelCase			)
          A__      						= kwargs.pop('''proxies'''						,   __lowerCamelCase			)
          A__      						= kwargs.pop('''local_files_only'''						,   __lowerCamelCase			)

          if os.path.isdir(__lowerCamelCase			):
               A__      						= os.path.join(__lowerCamelCase						,   __lowerCamelCase			)
          elif os.path.isfile(__lowerCamelCase			) or is_remote_url(__lowerCamelCase			):
               A__      						= pretrained_model_name_or_path
          else:
               A__      						= hf_bucket_url(__lowerCamelCase						,   filename=__lowerCamelCase						,   use_cdn=__lowerCamelCase			)

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

               A__      						= Config.load_yaml(__lowerCamelCase			)

          except EnvironmentError:
               A__      						= '''Can\'t load config for'''
               raise EnvironmentError(__lowerCamelCase			)

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

          return Config.load_yaml(__lowerCamelCase			), kwargs



def 	a_						(      __a   ):
     A__      						= torch.load('''dump.pt'''		,				map_location=in_tensor.device   )
     A__      						= in_tensor.numpy()
     A__      						= out_tensor.numpy()[0]
     print(na.shape		,				na[0, 0, :5]   )
     print(na.shape		,				na[0, 0, :5]   )
     assert np.allclose(__a		,				__a		,				rtol=0.01		,				atol=0.1   ), (
         f'''{sum([1 for x in np.isclose(__a		,				__a		,				rtol=0.01		,				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 	a_						(      __a   ):
     A__      						= urlparse(__a   )
     return parsed.scheme in ("http", "https")



def 	a_						(      __a		,				__a		,				__a=True   ):
     A__      						= CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
     A__      						= '''/''' not in model_id
     if legacy_format:
          return f'''{endpoint}/{model_id}-{filename}'''
     else:
          return f'''{endpoint}/{model_id}/{filename}'''



def 	a_						(      __a		,				__a		,				__a=None		,				__a=0		,				__a=None		,				):
     A__      						= '''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__      						= {'''user-agent''': ua}
     if resume_size > 0:
          A__      						= '''bytes=%d-''' % (resume_size,)
     A__      						= requests.get(__a		,				stream=__a		,				proxies=__a		,				headers=__a   )
     if response.status_code == 416:  # Range not satisfiable
          return
     A__      						= response.headers.get('''Content-Length'''   )
     A__      						= resume_size + int(__a   ) if content_length is not None else None
     A__      						= tqdm(
         unit='''B'''		,				unit_scale=__a		,				total=__a		,				initial=__a		,				desc='''Downloading'''		,				)
     for chunk in response.iter_content(chunk_size=1024   ):
          if chunk:  # filter out keep-alive new chunks
               progress.update(len(__a   )   )
               temp_file.write(__a   )
     progress.close()



def 	a_						(      __a		,				__a=None		,				__a=False		,				__a=None		,				__a=10		,				__a=False		,				__a=None		,				__a=False		,				):
     if cache_dir is None:
          A__      						= TRANSFORMERS_CACHE
     if isinstance(__a		,				__a   ):
          A__      						= str(__a   )

     os.makedirs(__a		,				exist_ok=__a   )

     A__      						= None
     if not local_files_only:
          try:
               A__      						= requests.head(__a		,				allow_redirects=__a		,				proxies=__a		,				timeout=__a   )
               if response.status_code == 200:
                    A__      						= response.headers.get('''ETag'''   )
          except (EnvironmentError, requests.exceptions.Timeout):
               # etag is already None
               pass

     A__      						= url_to_filename(__a		,				__a   )

     # get cache path to put the file
     A__      						= 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__      						= [
                   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__      						= 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__      						= cache_path + '''.incomplete'''

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

               A__      						= _resumable_file_manager
               if os.path.exists(__a   ):
                    A__      						= os.stat(__a   ).st_size
               else:
                    A__      						= 0
          else:
               A__      						= partial(tempfile.NamedTemporaryFile		,				dir=__a		,				delete=__a   )
               A__      						= 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__      						= {'''url''': url, '''etag''': etag}
          A__      						= cache_path + '''.json'''
          with open(__a		,				'''w'''   ) as meta_file:
               json.dump(__a		,				__a   )

     return cache_path



def 	a_						(      __a		,				__a=None   ):
     A__      						= url.encode('''utf-8'''   )
     A__      						= shaaaa(__a   )
     A__      						= url_hash.hexdigest()

     if etag:
          A__      						= etag.encode('''utf-8'''   )
          A__      						= shaaaa(__a   )
          filename += "." + etag_hash.hexdigest()

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

     return filename



def 	a_						(      __a		,				__a=None		,				__a=False		,				__a=None		,				__a=False		,				__a=None		,				__a=False		,				__a=False		,				__a=False		,				):
     if cache_dir is None:
          A__      						= TRANSFORMERS_CACHE
     if isinstance(__a		,				__a   ):
          A__      						= str(__a   )
     if isinstance(__a		,				__a   ):
          A__      						= str(__a   )

     if is_remote_url(__a   ):
          # URL, so get it from the cache (downloading if necessary)
          A__      						= 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__      						= 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__      						= os.path.split(__a   )
          A__      						= output_file.replace('''.'''		,				'''-'''   ) + '''-extracted'''
          A__      						= 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__      						= 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__      						= 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 	a_						(      __a		,				__a=","   ):
     assert isinstance(__a		,				__a   )
     if os.path.isfile(__a   ):
          with open(__a   ) as f:
               A__      						= eval(f.read()   )
     else:
          A__      						= requests.get(__a   )
          try:
               A__      						= requests.json()
          except Exception:
               A__      						= req.content.decode()
               assert data is not None, "could not connect"
               try:
                    A__      						= eval(__a   )
               except Exception:
                    A__      						= data.split('''\n'''   )
          req.close()
     return data



def 	a_						(      __a   ):
     A__      						= requests.get(__a   )
     A__      						= np.array(Image.open(BytesIO(response.content   )   )   )
     return img



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



def 	a_						(      ):
     print(f'''{os.path.abspath(os.path.join(__a		,				os.pardir   )   )}/demo.ipynb'''   )



def 	a_						(      __a		,				__a="RGB"   ):
     assert isinstance(__a		,				__a   )
     if os.path.isfile(__a   ):
          A__      						= cva.imread(__a   )
     else:
          A__      						= get_image_from_url(__a   )
          assert img is not None, f'''could not connect to: {im}'''
     A__      						= cva.cvtColor(__a		,				cva.COLOR_BGR2RGB   )
     if input_format == "RGB":
          A__      						= img[:, :, ::-1]
     return img



def 	a_						(      __a		,				__a=1   ):
     return (images[i : i + batch] for i in range(0		,				len(__a   )		,				__a   ))










SCREAMING_SNAKE_CASE_   :      dict[str, float]			=   {
    "joule": 1.0,
    "kilojoule": 1000,
    "megajoule": 100_0000,
    "gigajoule": 10_0000_0000,
    "wattsecond": 1.0,
    "watthour": 3600,
    "kilowatthour": 360_0000,
    "newtonmeter": 1.0,
    "calorie_nutr": 4186.8,
    "kilocalorie_nutr": 418_6800.00,
    "electronvolt": 1.6_0_2_1_7_6_6_3_4e-1_9,
    "britishthermalunit_it": 1055.0_5585,
    "footpound": 1.35_5818,
}



def  SCREAMING_SNAKE_CASE				(      snake_case						,   snake_case						,   snake_case							)		->    float:
		if to_type not in ENERGY_CONVERSION or from_type not in ENERGY_CONVERSION:
				__lowercase											=     (
				    F"Incorrect 'from_type' or 'to_type' value: {from_type!r}, {to_type!r}\n"
				    F"Valid values are: {', '.join(snake_case							)}"
				)
				raise ValueError(snake_case							)
		return value * ENERGY_CONVERSION[from_type] / ENERGY_CONVERSION[to_type]


if __name__ == "__main__":
		import doctest

		doctest.testmod()




