"""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

   SCREAMING_SNAKE_CASE__:Tuple			=	True
except ImportError:
   SCREAMING_SNAKE_CASE__:Tuple			=	False


try:
   from torch.hub import _get_torch_home

   SCREAMING_SNAKE_CASE__:str			=	_get_torch_home()
except ImportError:
   SCREAMING_SNAKE_CASE__:List[str]			=	os.path.expanduser(
       os.getenv("""TORCH_HOME""", os.path.join(os.getenv("""XDG_CACHE_HOME""", """~/.cache"""), """torch"""))
   )

SCREAMING_SNAKE_CASE__:Optional[Any]			=	os.path.join(torch_cache_home, """transformers""")

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



def 					_lowerCamelCase(					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 					_lowerCamelCase(					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 				snake_case__     :
 _snake_case   :			List[str]           =		{}


 def __init__(				self  ,						lowerCamelCase  ,						lowerCamelCase = "root"  ,						lowerCamelCase=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		):
        return str(list((self._pointer.keys())		)		)


 def __setattr__(				self  ,						lowerCamelCase  ,						lowerCamelCase		):
        __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		):
        return self._pointer


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase		):
        with open(F"{file_name}"  ,						"w"		) as stream:
               dump(lowerCamelCase  ,						lowerCamelCase		)


 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase		):
        with open(F"{file_name}"  ,						"w"		) as stream:
               json.dump(lowerCamelCase  ,						lowerCamelCase		)


 @staticmethod
 def 		a__ (				lowerCamelCase		):
        with open(lowerCamelCase		) as stream:
               __a     				=  load(lowerCamelCase  ,						Loader=lowerCamelCase		)
        return data


 def __str__(				self		):
        __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  ,						lowerCamelCase  ,						**lowerCamelCase		):
        __a				, __a     				=  cls.get_config_dict(lowerCamelCase  ,						**lowerCamelCase		)
        return cls(lowerCamelCase		)






 @classmethod
 def 		a__ (				cls  ,						lowerCamelCase  ,						**lowerCamelCase		):
        __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 					_lowerCamelCase(					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()							)*1_0_0:.4f} %"
           " element-wise mismatch"
       )
       raise Exception("tensors are all good"							)

       # Hugging face functions below



def 					_lowerCamelCase(					a							):
       __a     				=  urlparse(a							)
       return parsed.scheme in ("http", "https")



def 					_lowerCamelCase(					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 					_lowerCamelCase(					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 == 4_1_6:  # 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=1_0_2_4							):
              if chunk:  # filter out keep-alive new chunks
                     progress.update(len(a							)							)
                     temp_file.write(a							)
       progress.close()



def 					_lowerCamelCase(					a ,  a=None ,  a=False ,  a=None ,  a=1_0 ,  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 == 2_0_0:
                            __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 					_lowerCamelCase(					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 					_lowerCamelCase(					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 					_lowerCamelCase(					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 					_lowerCamelCase(					a							):
       __a     				=  requests.get(a							)
       __a     				=  np.array(Image.open(BytesIO(response.content							)							)							)
       return img



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



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

"""simple docstring"""



import json
import os
from typing import Optional, Tuple

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


SCREAMING_SNAKE_CASE__:int			=	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__:List[Any]			=	{"""vocab_file""": """vocab.json"""}

SCREAMING_SNAKE_CASE__:Optional[int]			=	{
    """vocab_file""": {
        """mgp-str""": """https://huggingface.co/alibaba-damo/mgp-str-base/blob/main/vocab.json""",
    }
}

SCREAMING_SNAKE_CASE__:Optional[Any]			=	{"""mgp-str""": 27}






class 				snake_case__     (   snake_case_   ):
 _snake_case   :			Tuple           =		VOCAB_FILES_NAMES
 _snake_case   :			Tuple           =		PRETRAINED_VOCAB_FILES_MAP
 _snake_case   :			Optional[Any]           =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES


 def __init__(				self  ,						lowerCamelCase  ,						lowerCamelCase="[GO]"  ,						lowerCamelCase="[GO]"  ,						lowerCamelCase="[s]"  ,						lowerCamelCase="[GO]"  ,						**lowerCamelCase		):
        super().__init__(
            unk_token=lowerCamelCase  ,						bos_token=lowerCamelCase  ,						eos_token=lowerCamelCase  ,						pad_token=lowerCamelCase  ,						**lowerCamelCase  ,						)

        with open(lowerCamelCase  ,						encoding="utf-8"		) as vocab_handle:
               __a     				=  json.load(lowerCamelCase		)
        __a     				=  {v: k for k, v in self.vocab.items()}


 @property
 def 		a__ (				self		):
        return len(self.vocab		)


 def 		a__ (				self		):
        return dict(self.vocab  ,						**self.added_tokens_encoder		)


 def 		a__ (				self  ,						lowerCamelCase		):
        __a     				=  []
        for s in text:
               char_tokens.extend(lowerCamelCase		)
        return char_tokens


 def 		a__ (				self  ,						lowerCamelCase		):
        return self.vocab.get(lowerCamelCase  ,						self.vocab.get(self.unk_token		)		)


 def 		a__ (				self  ,						lowerCamelCase		):
        return self.decoder.get(lowerCamelCase		)






 def 		a__ (				self  ,						lowerCamelCase  ,						lowerCamelCase = None		):
        if not os.path.isdir(lowerCamelCase		):
               logger.error("Vocabulary path ({}) should be a directory".format(lowerCamelCase		)		)
               return
        __a     				=  os.path.join(
            lowerCamelCase  ,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]		)

        with open(lowerCamelCase  ,						"w"  ,						encoding="utf-8"		) as f:
               f.write(json.dumps(self.vocab  ,						indent=2  ,						sort_keys=lowerCamelCase  ,						ensure_ascii=lowerCamelCase		) + "\n"		)

        return (vocab_file,)
