


'''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

     lowerCAmelCase__  =     True
except ImportError:
     lowerCAmelCase__  =     False


try:
     from torch.hub import _get_torch_home

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

lowerCAmelCase__  =     os.path.join(torch_cache_home, 'transformers')

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



def 				lowerCAmelCase__	(       UpperCAmelCase=OBJECTS    ,       UpperCAmelCase=ATTRIBUTES     ):






       """simple docstring"""





       snake_case__					:	str          =					[]
       with open(UpperCAmelCase_     ) as f:
              for object in f.readlines():
                     vg_classes.append(object.split(""","""     )[0].lower().strip()     )

       snake_case__					:	Tuple          =					[]
       with open(UpperCAmelCase_     ) as f:
              for object in f.readlines():
                     vg_attrs.append(object.split(""","""     )[0].lower().strip()     )
       return vg_classes, vg_attrs



def 				lowerCAmelCase__	(       UpperCAmelCase     ):






       """simple docstring"""





       snake_case__					:	Tuple          =					OrderedDict()
       with open(UpperCAmelCase_    ,       """rb"""     ) as f:
              snake_case__					:	Union[str, Any]          =					pkl.load(UpperCAmelCase_     )["""model"""]
       for k in copy.deepcopy(list(ckp.keys()     )     ):
              snake_case__					:	Tuple          =					ckp.pop(UpperCAmelCase_     )
              if isinstance(UpperCAmelCase_    ,       np.ndarray     ):
                     snake_case__					:	Tuple          =					torch.tensor(UpperCAmelCase_     )
              else:
                     assert isinstance(UpperCAmelCase_    ,       torch.tensor     ), type(UpperCAmelCase_     )
              snake_case__					:	List[Any]          =					v
       return r







class 					_A						:



   '''simple docstring'''






   _lowercase    						= {}




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

          snake_case__					:	Any          =					d




   def __repr__(	self	:					List[str]						)->	Optional[int]:
          return str(list((self._pointer.keys())						)						)




   def __setattr__(	self	:					Optional[Any]					, lowerCamelCase	:					Tuple					, lowerCamelCase	:					Any						)->	Optional[Any]:
          snake_case__					:	str          =					val
          snake_case__					:	List[Any]          =					val
          snake_case__					:	int          =					key.split("""."""						)
          snake_case__					:	Optional[Any]          =					len(__lowerCAmelCase						) - 1
          snake_case__					:	int          =					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:
                               snake_case__					:	str          =					val
                        else:
                               snake_case__					:	List[str]          =					pointer[l]




   def   __lowerCAmelCase						(	self	:					Optional[Any]						)->	str:
          return self._pointer




   def   __lowerCAmelCase						(	self	:					Any					, lowerCamelCase	:					int					, lowerCamelCase	:					Optional[Any]						)->	Any:


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




   def   __lowerCAmelCase						(	self	:					int					, lowerCamelCase	:					Tuple					, lowerCamelCase	:					Tuple						)->	List[Any]:


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




   @staticmethod
   def   __lowerCAmelCase						(	lowerCamelCase	:					str						)->	List[Any]:


          with open(__lowerCAmelCase						) as stream:
                 snake_case__					:	Optional[Any]          =					load(__lowerCAmelCase					, Loader=__lowerCAmelCase						)
          return data




   def __str__(	self	:					Any						)->	Tuple:
          snake_case__					:	Optional[Any]          =					"""    """
          if self._name != "root":
                 snake_case__					:	str          =					F"""{t * (self._level-1)}{self._name}:\n"""
          else:
                 snake_case__					:	Optional[int]          =					""""""
          snake_case__					:	Tuple          =					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"""
                 snake_case__					:	Union[str, Any]          =					level
          return r[:-1]




   @classmethod
   def   __lowerCAmelCase						(	cls	:					Optional[Any]					, lowerCamelCase	:					Union[str, Any]					, **lowerCamelCase	:					List[str]						)->	Dict:
          snake_case__					:	str          =					cls.get_config_dict(__lowerCAmelCase					, **__lowerCAmelCase						)
          return cls(__lowerCAmelCase						)




   @classmethod
   def   __lowerCAmelCase						(	cls	:					Dict					, lowerCamelCase	:					Tuple					, **lowerCamelCase	:					Dict						)->	int:
          snake_case__					:	Optional[int]          =					kwargs.pop("""cache_dir"""					, __lowerCAmelCase						)
          snake_case__					:	List[Any]          =					kwargs.pop("""force_download"""					, __lowerCAmelCase						)
          snake_case__					:	int          =					kwargs.pop("""resume_download"""					, __lowerCAmelCase						)
          snake_case__					:	Optional[int]          =					kwargs.pop("""proxies"""					, __lowerCAmelCase						)
          snake_case__					:	List[Any]          =					kwargs.pop("""local_files_only"""					, __lowerCAmelCase						)

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

          try:
                 # Load from URL or cache if already cached
                 snake_case__					:	str          =					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

                 snake_case__					:	Union[str, Any]          =					Config.load_yaml(__lowerCAmelCase						)

          except EnvironmentError:
                 snake_case__					:	Union[str, Any]          =					"""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 				lowerCAmelCase__	(       UpperCAmelCase     ):






       """simple docstring"""





       snake_case__					:	Optional[int]          =					torch.load("""dump.pt"""    ,       map_location=in_tensor.device     )
       snake_case__					:	Dict          =					in_tensor.numpy()
       snake_case__					:	List[Any]          =					out_tensor.numpy()[0]
       print(na.shape    ,       na[0, 0, :5]     )
       print(na.shape    ,       na[0, 0, :5]     )
       assert np.allclose(UpperCAmelCase_    ,       UpperCAmelCase_    ,       rtol=0.0_1    ,       atol=0.1     ), (
           f"""{sum([1 for x in np.isclose(UpperCAmelCase_    ,       UpperCAmelCase_    ,       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 				lowerCAmelCase__	(       UpperCAmelCase     ):






       """simple docstring"""





       snake_case__					:	int          =					urlparse(UpperCAmelCase_     )
       return parsed.scheme in ("http", "https")



def 				lowerCAmelCase__	(       UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase=True     ):






       """simple docstring"""





       snake_case__					:	List[str]          =					CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
       snake_case__					:	Union[str, Any]          =					"""/""" not in model_id
       if legacy_format:
              return f"""{endpoint}/{model_id}-{filename}"""
       else:
              return f"""{endpoint}/{model_id}/{filename}"""



def 				lowerCAmelCase__	(       UpperCAmelCase    ,       UpperCAmelCase    ,       UpperCAmelCase=None    ,       UpperCAmelCase=0    ,       UpperCAmelCase=None    ,       ):






       """simple docstring"""





       snake_case__					:	Any          =					"""python/{}""".format(sys.version.split()[0]     )
       if _torch_available:
              ua += "; torch/{}".format(torch.__version__     )
       if isinstance(UpperCAmelCase_    ,       UpperCAmelCase_     ):
              ua += "; " + "; ".join("""{}/{}""".format(UpperCAmelCase_    ,       UpperCAmelCase_     ) for k, v in user_agent.items()     )
       elif isinstance(UpperCAmelCase_    ,       UpperCAmelCase_     ):
              ua += "; " + user_agent
       snake_case__					:	Optional[int]          =					{"""user-agent""": ua}
       if resume_size > 0:
              snake_case__					:	Optional[Any]          =					"""bytes=%d-""" % (resume_size,)
       snake_case__					:	List[Any]          =					requests.get(UpperCAmelCase_    ,       stream=UpperCAmelCase_    ,       proxies=UpperCAmelCase_    ,       headers=UpperCAmelCase_     )
       if response.status_code == 416:  # Range not satisfiable
              return
       snake_case__					:	List[str]          =					response.headers.get("""Content-Length"""     )
       snake_case__					:	Any          =					resume_size + int(UpperCAmelCase_     ) if content_length is not None else None
       snake_case__					:	Dict          =					tqdm(
           unit="""B"""    ,       unit_scale=UpperCAmelCase_    ,       total=UpperCAmelCase_    ,       initial=UpperCAmelCase_    ,       desc="""Downloading"""    ,       )
       for chunk in response.iter_content(chunk_size=1024     ):
              if chunk:  # filter out keep-alive new chunks
                     progress.update(len(UpperCAmelCase_     )     )
                     temp_file.write(UpperCAmelCase_     )
       progress.close()



def 				lowerCAmelCase__	(       UpperCAmelCase    ,       UpperCAmelCase=None    ,       UpperCAmelCase=False    ,       UpperCAmelCase=None    ,       UpperCAmelCase=10    ,       UpperCAmelCase=False    ,       UpperCAmelCase=None    ,       UpperCAmelCase=False    ,       ):






       """simple docstring"""





       if cache_dir is None:
              snake_case__					:	int          =					TRANSFORMERS_CACHE
       if isinstance(UpperCAmelCase_    ,       UpperCAmelCase_     ):
              snake_case__					:	List[Any]          =					str(UpperCAmelCase_     )

       os.makedirs(UpperCAmelCase_    ,       exist_ok=UpperCAmelCase_     )

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

       snake_case__					:	Optional[int]          =					url_to_filename(UpperCAmelCase_    ,       UpperCAmelCase_     )

       # get cache path to put the file
       snake_case__					:	Dict          =					os.path.join(UpperCAmelCase_    ,       UpperCAmelCase_     )

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

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

              if resume_download:
                     snake_case__					:	List[str]          =					cache_path + """.incomplete"""

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

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

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

              os.replace(temp_file.name    ,       UpperCAmelCase_     )

              snake_case__					:	Tuple          =					{"""url""": url, """etag""": etag}
              snake_case__					:	Tuple          =					cache_path + """.json"""
              with open(UpperCAmelCase_    ,       """w"""     ) as meta_file:
                     json.dump(UpperCAmelCase_    ,       UpperCAmelCase_     )

       return cache_path



def 				lowerCAmelCase__	(       UpperCAmelCase    ,       UpperCAmelCase=None     ):






       """simple docstring"""





       snake_case__					:	Dict          =					url.encode("""utf-8"""     )
       snake_case__					:	List[str]          =					shaaaa(UpperCAmelCase_     )
       snake_case__					:	Optional[int]          =					url_hash.hexdigest()

       if etag:
              snake_case__					:	List[str]          =					etag.encode("""utf-8"""     )
              snake_case__					:	Tuple          =					shaaaa(UpperCAmelCase_     )
              filename += "." + etag_hash.hexdigest()

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

       return filename



def 				lowerCAmelCase__	(       UpperCAmelCase    ,       UpperCAmelCase=None    ,       UpperCAmelCase=False    ,       UpperCAmelCase=None    ,       UpperCAmelCase=False    ,       UpperCAmelCase=None    ,       UpperCAmelCase=False    ,       UpperCAmelCase=False    ,       UpperCAmelCase=False    ,       ):






       """simple docstring"""





       if cache_dir is None:
              snake_case__					:	Union[str, Any]          =					TRANSFORMERS_CACHE
       if isinstance(UpperCAmelCase_    ,       UpperCAmelCase_     ):
              snake_case__					:	Any          =					str(UpperCAmelCase_     )
       if isinstance(UpperCAmelCase_    ,       UpperCAmelCase_     ):
              snake_case__					:	int          =					str(UpperCAmelCase_     )

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

       if extract_compressed_file:
              if not is_zipfile(UpperCAmelCase_     ) and not tarfile.is_tarfile(UpperCAmelCase_     ):
                     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/"
              snake_case__					:	Union[str, Any]          =					os.path.split(UpperCAmelCase_     )
              snake_case__					:	str          =					output_file.replace("""."""    ,       """-"""     ) + """-extracted"""
              snake_case__					:	Optional[Any]          =					os.path.join(UpperCAmelCase_    ,       UpperCAmelCase_     )

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

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

              return output_path_extracted

       return output_path



def 				lowerCAmelCase__	(       UpperCAmelCase    ,       UpperCAmelCase=","     ):






       """simple docstring"""





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



def 				lowerCAmelCase__	(       UpperCAmelCase     ):






       """simple docstring"""





       snake_case__					:	Union[str, Any]          =					requests.get(UpperCAmelCase_     )
       snake_case__					:	Optional[Any]          =					np.array(Image.open(BytesIO(response.content     )     )     )
       return img



def 				lowerCAmelCase__	(       UpperCAmelCase     ):






       """simple docstring"""





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



def 				lowerCAmelCase__	(       ):






       """simple docstring"""





       print(f"""{os.path.abspath(os.path.join(UpperCAmelCase_    ,       os.pardir     )     )}/demo.ipynb"""     )



def 				lowerCAmelCase__	(       UpperCAmelCase    ,       UpperCAmelCase="RGB"     ):






       """simple docstring"""





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






def 				lowerCAmelCase__	(       UpperCAmelCase    ,       UpperCAmelCase=1     ):






       """simple docstring"""





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










'''simple docstring'''





import functools
from contextlib import contextmanager

import bert_score
from packaging import version

import datasets





@contextmanager
def UpperCAmelCase    (       ):
    def filter_log(UpperCAmelCase_						):
        return False if "This IS expected if you are initializing" in record.msg else True

    _a				:							Optional[Any]							       =      datasets.utils.logging.get_logger("""transformers.modeling_utils"""						)
    logger.addFilter(UpperCAmelCase_						)
    try:
        yield
    finally:
        logger.removeFilter(UpperCAmelCase_						)


a		:	Tuple       =      '''\
@inproceedings{bert-score,
  title={BERTScore: Evaluating Text Generation with BERT},
  author={Tianyi Zhang* and Varsha Kishore* and Felix Wu* and Kilian Q. Weinberger and Yoav Artzi},
  booktitle={International Conference on Learning Representations},
  year={2020},
  url={https://openreview.net/forum?id=SkeHuCVFDr}
}
'''

a		:	List[str]       =      '''\
BERTScore leverages the pre-trained contextual embeddings from BERT and matches words in candidate and reference
sentences by cosine similarity.
It has been shown to correlate with human judgment on sentence-level and system-level evaluation.
Moreover, BERTScore computes precision, recall, and F1 measure, which can be useful for evaluating different language
generation tasks.

See the project\'s README at https://github.com/Tiiiger/bert_score#readme for more information.
'''

a		:	Dict       =      '''
BERTScore Metrics with the hashcode from a source against one or more references.

Args:
    predictions (list of str): Prediction/candidate sentences.
    references (list of str or list of list of str): Reference sentences.
    lang (str): Language of the sentences; required (e.g. \'en\').
    model_type (str): Bert specification, default using the suggested
        model for the target language; has to specify at least one of
        `model_type` or `lang`.
    num_layers (int): The layer of representation to use,
        default using the number of layers tuned on WMT16 correlation data.
    verbose (bool): Turn on intermediate status update.
    idf (bool or dict): Use idf weighting; can also be a precomputed idf_dict.
    device (str): On which the contextual embedding model will be allocated on.
        If this argument is None, the model lives on cuda:0 if cuda is available.
    nthreads (int): Number of threads.
    batch_size (int): Bert score processing batch size,
        at least one of `model_type` or `lang`. `lang` needs to be
        specified when `rescale_with_baseline` is True.
    rescale_with_baseline (bool): Rescale bertscore with pre-computed baseline.
    baseline_path (str): Customized baseline file.
    use_fast_tokenizer (bool): `use_fast` parameter passed to HF tokenizer. New in version 0.3.10.

Returns:
    precision: Precision.
    recall: Recall.
    f1: F1 score.
    hashcode: Hashcode of the library.

Examples:

    >>> predictions = ["hello there", "general kenobi"]
    >>> references = ["hello there", "general kenobi"]
    >>> bertscore = datasets.load_metric("bertscore")
    >>> results = bertscore.compute(predictions=predictions, references=references, lang="en")
    >>> print([round(v, 2) for v in results["f1"]])
    [1.0, 1.0]
'''





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION      ,		_KWARGS_DESCRIPTION       )
class      UpperCamelCase       (	datasets.Metric       ):



  def     a_							(			self ):







      '''simple docstring'''


      return datasets.MetricInfo(
          description=_DESCRIPTION		,     citation=_CITATION		,     homepage="""https://github.com/Tiiiger/bert_score"""		,     inputs_description=_KWARGS_DESCRIPTION		,     features=datasets.Features(
              {
                  """predictions""": datasets.Value("""string"""		,     id="""sequence""" ),
                  """references""": datasets.Sequence(datasets.Value("""string"""		,     id="""sequence""" )		,     id="""references""" ),
              } )		,     codebase_urls=["""https://github.com/Tiiiger/bert_score"""]		,     reference_urls=[
              """https://github.com/Tiiiger/bert_score""",
              """https://arxiv.org/abs/1904.09675""",
          ]		,     )



  def     a_							(			self		,     __lowerCAmelCase		,     __lowerCAmelCase		,     __lowerCAmelCase=None		,     __lowerCAmelCase=None		,     __lowerCAmelCase=None		,     __lowerCAmelCase=False		,     __lowerCAmelCase=False		,     __lowerCAmelCase=None		,     __lowerCAmelCase=64		,     __lowerCAmelCase=4		,     __lowerCAmelCase=False		,     __lowerCAmelCase=False		,     __lowerCAmelCase=None		,     __lowerCAmelCase=False		,     ):







      '''simple docstring'''


      _a				:							Any							       =      bert_score.utils.get_hash
      _a				:							int							       =      bert_score.BERTScorer

      if version.parse(bert_score.__version__ ) >= version.parse("""0.3.10""" ):
          _a				:							Union[str, Any]							       =      functools.partial(__lowerCAmelCase		,     use_fast_tokenizer=__lowerCAmelCase )
          _a				:							Dict							       =      functools.partial(__lowerCAmelCase		,     use_fast_tokenizer=__lowerCAmelCase )
      elif use_fast_tokenizer:
          raise ImportWarning(
              """To use a fast tokenizer, the module `bert-score>=0.3.10` is required, and the current version of `bert-score` doesn't match this condition.\n"""
              """You can install it with `pip install \"bert-score>=0.3.10\"`.""" )

      if model_type is None:
          assert lang is not None, "either lang or model_type should be specified"
          _a				:							List[str]							       =      bert_score.utils.langamodel[lang.lower()]

      if num_layers is None:
          _a				:							Dict							       =      bert_score.utils.modelalayers[model_type]

      _a				:							Union[str, Any]							       =      get_hash(
          model=__lowerCAmelCase		,     num_layers=__lowerCAmelCase		,     idf=__lowerCAmelCase		,     rescale_with_baseline=__lowerCAmelCase		,     use_custom_baseline=baseline_path is not None		,     )

      with filter_logging_context():
          if not hasattr(self		,     """cached_bertscorer""" ) or self.cached_bertscorer.hash != hashcode:
              _a				:							Optional[Any]							       =      scorer(
                  model_type=__lowerCAmelCase		,     num_layers=__lowerCAmelCase		,     batch_size=__lowerCAmelCase		,     nthreads=__lowerCAmelCase		,     all_layers=__lowerCAmelCase		,     idf=__lowerCAmelCase		,     device=__lowerCAmelCase		,     lang=__lowerCAmelCase		,     rescale_with_baseline=__lowerCAmelCase		,     baseline_path=__lowerCAmelCase		,     )

      ((_a)       , (_a)       , (_a))				:							Optional[Any]							       =      self.cached_bertscorer.score(
          cands=__lowerCAmelCase		,     refs=__lowerCAmelCase		,     verbose=__lowerCAmelCase		,     batch_size=__lowerCAmelCase		,     )
      _a				:							Tuple							       =      {
          """precision""": P.tolist(),
          """recall""": R.tolist(),
          """f1""": F.tolist(),
          """hashcode""": hashcode,
      }
      return output_dict



  def     a_							(			self		,     __lowerCAmelCase=None		,     __lowerCAmelCase=None		,     **__lowerCAmelCase ):







      '''simple docstring'''


      if references is not None:
          _a				:							Dict							       =      [[ref] if isinstance(__lowerCAmelCase		,     __lowerCAmelCase ) else ref for ref in references]
      super().add_batch(predictions=__lowerCAmelCase		,     references=__lowerCAmelCase		,     **__lowerCAmelCase )



  def     a_							(			self		,     __lowerCAmelCase=None		,     __lowerCAmelCase=None		,     **__lowerCAmelCase ):







      '''simple docstring'''


      if isinstance(__lowerCAmelCase		,     __lowerCAmelCase ):
          _a				:							Optional[Any]							       =      [reference]
      super().add(prediction=__lowerCAmelCase		,     reference=__lowerCAmelCase		,     **__lowerCAmelCase )


