

'''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 					UpperCAmelCase__			(					UpperCAmelCase__=OBJECTS,				UpperCAmelCase__=ATTRIBUTES )	->	Any:
       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 					UpperCAmelCase__			(					UpperCAmelCase__ )	->	List[Any]:
       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     A__					:
    lowercase		    =						{}



    def __init__(    self						, UpperCamelCase__						, UpperCamelCase__ = "root"						, UpperCamelCase__=0				)  ->					int:





           '''simple docstring'''
           A_		    =					name
           A_		    =					level
           A_		    =					{}
           for k, v in dictionary.items():
                  if v is None:
                         raise ValueError()
                  A_		    =					copy.deepcopy(_snake_case				)
                  A_		    =					copy.deepcopy(_snake_case				)
                  if isinstance(_snake_case						, _snake_case				):
                         A_		    =					Config(_snake_case						, name=_snake_case						, level=level + 1				)
                  A_		    =					v
                  setattr(self						, _snake_case						, _snake_case				)

           A_		    =					d



    def __repr__(    self				)  ->					List[Any]:





           '''simple docstring'''
           return str(list((self._pointer.keys())				)				)



    def __setattr__(    self						, UpperCamelCase__						, UpperCamelCase__				)  ->					str:





           '''simple docstring'''
           A_		    =					val
           A_		    =					val
           A_		    =					key.split("""."""				)
           A_		    =					len(_snake_case				) - 1
           A_		    =					self._pointer
           if len(_snake_case				) > 1:
                  for i, l in enumerate(_snake_case				):
                         if hasattr(self						, _snake_case				) and isinstance(getattr(self						, _snake_case				)						, _snake_case				):
                                setattr(getattr(self						, _snake_case				)						, """.""".join(levels[i:]				)						, _snake_case				)
                         if l == last_level:
                                A_		    =					val
                         else:
                                A_		    =					pointer[l]



    def     snake_case_	(    self				)  ->					str:





           '''simple docstring'''
           return self._pointer



    def     snake_case_	(    self						, UpperCamelCase__						, UpperCamelCase__				)  ->					Union[str, Any]:





           '''simple docstring'''


           with open(f'''{file_name}'''						, """w"""				) as stream:
                  dump(_snake_case						, _snake_case				)



    def     snake_case_	(    self						, UpperCamelCase__						, UpperCamelCase__				)  ->					int:





           '''simple docstring'''


           with open(f'''{file_name}'''						, """w"""				) as stream:
                  json.dump(_snake_case						, _snake_case				)



    @staticmethod
    def     snake_case_	(    UpperCamelCase__				)  ->					Any:





           '''simple docstring'''


           with open(_snake_case				) as stream:
                  A_		    =					load(_snake_case						, Loader=_snake_case				)
           return data



    def __str__(    self				)  ->					Optional[int]:





           '''simple docstring'''
           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(_snake_case						, _snake_case				):
                         r += f'''{t * (self._level)}{v}\n'''
                         self._level += 1
                  else:
                         r += f'''{t * (self._level)}{k}: {v} ({type(_snake_case				).__name__})\n'''
                  A_		    =					level
           return r[:-1]



    @classmethod
    def     snake_case_	(    cls						, UpperCamelCase__						, **UpperCamelCase__				)  ->					Dict:





           '''simple docstring'''
           A_    ,	A_		    =					cls.get_config_dict(_snake_case						, **_snake_case				)
           return cls(_snake_case				)







    @classmethod
    def     snake_case_	(    cls						, UpperCamelCase__						, **UpperCamelCase__				)  ->					Dict:





           '''simple docstring'''
           A_		    =					kwargs.pop("""cache_dir"""						, _snake_case				)
           A_		    =					kwargs.pop("""force_download"""						, _snake_case				)
           A_		    =					kwargs.pop("""resume_download"""						, _snake_case				)
           A_		    =					kwargs.pop("""proxies"""						, _snake_case				)
           A_		    =					kwargs.pop("""local_files_only"""						, _snake_case				)

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

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

                  A_		    =					Config.load_yaml(_snake_case				)

           except EnvironmentError:
                  A_		    =					"""Can\'t load config for"""
                  raise EnvironmentError(_snake_case				)

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

           return Config.load_yaml(_snake_case				), kwargs

def 					UpperCAmelCase__			(					UpperCAmelCase__ )	->	int:
       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_00:.4f} %'''
           " element-wise mismatch"
       )
       raise Exception("""tensors are all good""" )

       # Hugging face functions below
def 					UpperCAmelCase__			(					UpperCAmelCase__ )	->	int:
       A_		    =					urlparse(__A )
       return parsed.scheme in ("http", "https")
def 					UpperCAmelCase__			(					UpperCAmelCase__,				UpperCAmelCase__,				UpperCAmelCase__=True )	->	str:
       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 					UpperCAmelCase__			(					UpperCAmelCase__,				UpperCAmelCase__,				UpperCAmelCase__=None,				UpperCAmelCase__=0,				UpperCAmelCase__=None,				)	->	Any:
       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_16:  # 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=10_24 ):
              if chunk:  # filter out keep-alive new chunks
                     progress.update(len(__A ) )
                     temp_file.write(__A )
       progress.close()
def 					UpperCAmelCase__			(					UpperCAmelCase__,				UpperCAmelCase__=None,				UpperCAmelCase__=False,				UpperCAmelCase__=None,				UpperCAmelCase__=10,				UpperCAmelCase__=False,				UpperCAmelCase__=None,				UpperCAmelCase__=False,				)	->	Any:


       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_00:
                            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 					UpperCAmelCase__			(					UpperCAmelCase__,				UpperCAmelCase__=None )	->	List[Any]:
       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 					UpperCAmelCase__			(					UpperCAmelCase__,				UpperCAmelCase__=None,				UpperCAmelCase__=False,				UpperCAmelCase__=None,				UpperCAmelCase__=False,				UpperCAmelCase__=None,				UpperCAmelCase__=False,				UpperCAmelCase__=False,				UpperCAmelCase__=False,				)	->	Any:


       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 					UpperCAmelCase__			(					UpperCAmelCase__,				UpperCAmelCase__="," )	->	Union[str, Any]:
       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 					UpperCAmelCase__			(					UpperCAmelCase__ )	->	Any:
       A_		    =					requests.get(__A )
       A_		    =					np.array(Image.open(BytesIO(response.content ) ) )
       return img
def 					UpperCAmelCase__			(					UpperCAmelCase__ )	->	Tuple:
       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 					UpperCAmelCase__			(					)	->	Optional[int]:
       print(F'''{os.path.abspath(os.path.join(__A,				os.pardir ) )}/demo.ipynb''' )
def 					UpperCAmelCase__			(					UpperCAmelCase__,				UpperCAmelCase__="RGB" )	->	int:
       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 					UpperCAmelCase__			(					UpperCAmelCase__,				UpperCAmelCase__=1 )	->	Union[str, Any]:
       return (images[i : i + batch] for i in range(0,				len(__A ),				__A ))






from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


snake_case_ : Union[str, Any]    		=       {"configuration_gpt_neox": ["GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoXConfig"]}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_ : Dict    		=       ["GPTNeoXTokenizerFast"]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    snake_case_ : str    		=       [
        "GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST",
        "GPTNeoXForCausalLM",
        "GPTNeoXForQuestionAnswering",
        "GPTNeoXForSequenceClassification",
        "GPTNeoXForTokenClassification",
        "GPTNeoXLayer",
        "GPTNeoXModel",
        "GPTNeoXPreTrainedModel",
    ]


if TYPE_CHECKING:
    from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_gpt_neox import (
            GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
            GPTNeoXForCausalLM,
            GPTNeoXForQuestionAnswering,
            GPTNeoXForSequenceClassification,
            GPTNeoXForTokenClassification,
            GPTNeoXLayer,
            GPTNeoXModel,
            GPTNeoXPreTrainedModel,
        )


else:
    import sys

    snake_case_ : Optional[int]    		=       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






