






'''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

  a_					= True
except ImportError:
  a_					= False


try:
  from torch.hub import _get_torch_home

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

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

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





def  _a(  UpperCamelCase__				:						Union[str, Any]=OBJECTS,	UpperCamelCase__				:						str=ATTRIBUTES							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				str									=[]
      with open(UpperCamelCase__							) as f:
            for object in f.readlines():
                  vg_classes.append(object.split(''','''							)[0].lower().strip()							)

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





def  _a(  UpperCamelCase__				:						List[str]							):




      '''simple docstring'''






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






class      __SCREAMING_SNAKE_CASE      :
 snake_case_  							=							{}




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

       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=d




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




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




 def      __magic_name__	(      self	:							Dict      )	->						Optional[Any]:
       return self._pointer




 def      __magic_name__	(      self	:							int					,      __lowercase	:							Tuple					,      __lowercase	:							str      )	->						Union[str, Any]:
       with open(F"{file_name}"					,      '''w'''      ) as stream:
             dump(__lowercase					,      __lowercase      )




 def      __magic_name__	(      self	:							str					,      __lowercase	:							Dict					,      __lowercase	:							Any      )	->						Tuple:
       with open(F"{file_name}"					,      '''w'''      ) as stream:
             json.dump(__lowercase					,      __lowercase      )




 @staticmethod
 def      __magic_name__	(      __lowercase	:							Any      )	->						Union[str, Any]:
       with open(__lowercase      ) as stream:
             SCREAMING_SNAKE_CASE__     :				str									=load(__lowercase					,      Loader=__lowercase      )
       return data




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




 @classmethod
 def      __magic_name__	(      cls	:							Dict					,      __lowercase	:							str					,      **__lowercase	:							List[Any]      )	->						Any:
       SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__     :				Optional[Any]									=cls.get_config_dict(__lowercase					,      **__lowercase      )
       return cls(__lowercase      )




 @classmethod
 def      __magic_name__	(      cls	:							Union[str, Any]					,      __lowercase	:							str					,      **__lowercase	:							str      )	->						Tuple:
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=kwargs.pop('''cache_dir'''					,      __lowercase      )
       SCREAMING_SNAKE_CASE__     :				Dict									=kwargs.pop('''force_download'''					,      __lowercase      )
       SCREAMING_SNAKE_CASE__     :				Tuple									=kwargs.pop('''resume_download'''					,      __lowercase      )
       SCREAMING_SNAKE_CASE__     :				Tuple									=kwargs.pop('''proxies'''					,      __lowercase      )
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=kwargs.pop('''local_files_only'''					,      __lowercase      )

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

       try:
             # Load from URL or cache if already cached
             SCREAMING_SNAKE_CASE__     :				Optional[int]									=cached_path(
                 __lowercase					,      cache_dir=__lowercase					,      force_download=__lowercase					,      proxies=__lowercase					,      resume_download=__lowercase					,      local_files_only=__lowercase					,      )
             # Load config dict
             if resolved_config_file is None:
                   raise EnvironmentError

             SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=Config.load_yaml(__lowercase      )

       except EnvironmentError:
             SCREAMING_SNAKE_CASE__     :				List[str]									='''Can\'t load config for'''
             raise EnvironmentError(__lowercase      )

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

       return Config.load_yaml(__lowercase      ), kwargs





def  _a(  UpperCamelCase__				:						Optional[Any]							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				str									=torch.load('''dump.pt''',	map_location=in_tensor.device							)
      SCREAMING_SNAKE_CASE__     :				Optional[int]									=in_tensor.numpy()
      SCREAMING_SNAKE_CASE__     :				Tuple									=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()							)*1_0_0:.4f} %"
          " element-wise mismatch"
      )
      raise Exception('''tensors are all good'''							)

      # Hugging face functions below





def  _a(  UpperCamelCase__				:						Dict							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				Any									=urlparse(UpperCamelCase__							)
      return parsed.scheme in ("http", "https")





def  _a(  UpperCamelCase__				:						str,	UpperCamelCase__				:						str,	UpperCamelCase__				:						Dict=True							):




      '''simple docstring'''






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





def  _a(  UpperCamelCase__				:						Optional[int],	UpperCamelCase__				:						Union[str, Any],	UpperCamelCase__				:						List[Any]=None,	UpperCamelCase__				:						Optional[int]=0,	UpperCamelCase__				:						Optional[int]=None,	):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				List[str]									='''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
      SCREAMING_SNAKE_CASE__     :				int									={'''user-agent''': ua}
      if resume_size > 0:
            SCREAMING_SNAKE_CASE__     :				Tuple									='''bytes=%d-''' % (resume_size,)
      SCREAMING_SNAKE_CASE__     :				List[str]									=requests.get(UpperCamelCase__,	stream=UpperCamelCase__,	proxies=UpperCamelCase__,	headers=UpperCamelCase__							)
      if response.status_code == 4_1_6:  # Range not satisfiable
            return
      SCREAMING_SNAKE_CASE__     :				List[str]									=response.headers.get('''Content-Length'''							)
      SCREAMING_SNAKE_CASE__     :				Optional[int]									=resume_size + int(UpperCamelCase__							) if content_length is not None else None
      SCREAMING_SNAKE_CASE__     :				Dict									=tqdm(
          unit='''B''',	unit_scale=UpperCamelCase__,	total=UpperCamelCase__,	initial=UpperCamelCase__,	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(UpperCamelCase__							)							)
                  temp_file.write(UpperCamelCase__							)
      progress.close()





def  _a(  UpperCamelCase__				:						Optional[int],	UpperCamelCase__				:						List[str]=None,	UpperCamelCase__				:						Tuple=False,	UpperCamelCase__				:						Dict=None,	UpperCamelCase__				:						Any=1_0,	UpperCamelCase__				:						Union[str, Any]=False,	UpperCamelCase__				:						str=None,	UpperCamelCase__				:						List[str]=False,	):




      '''simple docstring'''






      if cache_dir is None:
            SCREAMING_SNAKE_CASE__     :				List[Any]									=TRANSFORMERS_CACHE
      if isinstance(UpperCamelCase__,	UpperCamelCase__							):
            SCREAMING_SNAKE_CASE__     :				List[Any]									=str(UpperCamelCase__							)

      os.makedirs(UpperCamelCase__,	exist_ok=UpperCamelCase__							)

      SCREAMING_SNAKE_CASE__     :				Optional[int]									=None
      if not local_files_only:
            try:
                  SCREAMING_SNAKE_CASE__     :				List[str]									=requests.head(UpperCamelCase__,	allow_redirects=UpperCamelCase__,	proxies=UpperCamelCase__,	timeout=UpperCamelCase__							)
                  if response.status_code == 2_0_0:
                        SCREAMING_SNAKE_CASE__     :				Optional[int]									=response.headers.get('''ETag'''							)
            except (EnvironmentError, requests.exceptions.Timeout):
                  # etag is already None
                  pass

      SCREAMING_SNAKE_CASE__     :				Tuple									=url_to_filename(UpperCamelCase__,	UpperCamelCase__							)

      # get cache path to put the file
      SCREAMING_SNAKE_CASE__     :				List[str]									=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:
                  SCREAMING_SNAKE_CASE__     :				Union[str, 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.
      SCREAMING_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:
                  SCREAMING_SNAKE_CASE__     :				str									=cache_path + '''.incomplete'''

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

                  SCREAMING_SNAKE_CASE__     :				Optional[int]									=_resumable_file_manager
                  if os.path.exists(UpperCamelCase__							):
                        SCREAMING_SNAKE_CASE__     :				List[str]									=os.stat(UpperCamelCase__							).st_size
                  else:
                        SCREAMING_SNAKE_CASE__     :				Dict									=0
            else:
                  SCREAMING_SNAKE_CASE__     :				Optional[int]									=partial(tempfile.NamedTemporaryFile,	dir=UpperCamelCase__,	delete=UpperCamelCase__							)
                  SCREAMING_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__							)

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

      return cache_path





def  _a(  UpperCamelCase__				:						Any,	UpperCamelCase__				:						Optional[int]=None							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				str									=url.encode('''utf-8'''							)
      SCREAMING_SNAKE_CASE__     :				int									=shaaaa(UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__     :				List[Any]									=url_hash.hexdigest()

      if etag:
            SCREAMING_SNAKE_CASE__     :				int									=etag.encode('''utf-8'''							)
            SCREAMING_SNAKE_CASE__     :				str									=shaaaa(UpperCamelCase__							)
            filename += "." + etag_hash.hexdigest()

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

      return filename





def  _a(  UpperCamelCase__				:						Optional[int],	UpperCamelCase__				:						List[str]=None,	UpperCamelCase__				:						str=False,	UpperCamelCase__				:						Union[str, Any]=None,	UpperCamelCase__				:						Any=False,	UpperCamelCase__				:						Tuple=None,	UpperCamelCase__				:						Union[str, Any]=False,	UpperCamelCase__				:						int=False,	UpperCamelCase__				:						List[Any]=False,	):




      '''simple docstring'''






      if cache_dir is None:
            SCREAMING_SNAKE_CASE__     :				List[Any]									=TRANSFORMERS_CACHE
      if isinstance(UpperCamelCase__,	UpperCamelCase__							):
            SCREAMING_SNAKE_CASE__     :				str									=str(UpperCamelCase__							)
      if isinstance(UpperCamelCase__,	UpperCamelCase__							):
            SCREAMING_SNAKE_CASE__     :				List[Any]									=str(UpperCamelCase__							)

      if is_remote_url(UpperCamelCase__							):
            # URL, so get it from the cache (downloading if necessary)
            SCREAMING_SNAKE_CASE__     :				Optional[int]									=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.
            SCREAMING_SNAKE_CASE__     :				str									=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/"
            SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__     :				Any									=os.path.split(UpperCamelCase__							)
            SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=output_file.replace('''.''',	'''-'''							) + '''-extracted'''
            SCREAMING_SNAKE_CASE__     :				int									=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
            SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=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__							):
                        SCREAMING_SNAKE_CASE__     :				Tuple									=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  _a(  UpperCamelCase__				:						Optional[Any],	UpperCamelCase__				:						Optional[int]=","							):




      '''simple docstring'''






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





def  _a(  UpperCamelCase__				:						int							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				Optional[int]									=requests.get(UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__     :				Optional[Any]									=np.array(Image.open(BytesIO(response.content							)							)							)
      return img





def  _a(  UpperCamelCase__				:						Any							):




      '''simple docstring'''






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





def  _a(  ):




      '''simple docstring'''






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





def  _a(  UpperCamelCase__				:						List[Any],	UpperCamelCase__				:						Dict="RGB"							):




      '''simple docstring'''






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







def  _a(  UpperCamelCase__				:						Tuple,	UpperCamelCase__				:						int=1							):




      '''simple docstring'''






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







'''simple docstring'''
import os
from argparse import ArgumentParser, Namespace

from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand


if not is_tf_available() and not is_torch_available():
  raise RuntimeError('At least one of PyTorch or TensorFlow 2.0+ should be installed to use CLI training')

# TF training parameters
a_					= False
a_					= False





def  _a(  UpperCamelCase__				:						Namespace							):




      '''simple docstring'''






      return TrainCommand(UpperCamelCase__							)







class      __SCREAMING_SNAKE_CASE      (			lowerCamelCase				):




 @staticmethod
 def      __magic_name__	(      __lowercase	:							ArgumentParser      )	->						Any:
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=parser.add_parser('''train'''					,      help='''CLI tool to train a model on a task.'''      )

       train_parser.add_argument(
           '''--train_data'''					,      type=__lowercase					,      required=__lowercase					,      help='''path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences.'''					,      )
       train_parser.add_argument(
           '''--column_label'''					,      type=__lowercase					,      default=0					,      help='''Column of the dataset csv file with example labels.'''      )
       train_parser.add_argument(
           '''--column_text'''					,      type=__lowercase					,      default=1					,      help='''Column of the dataset csv file with example texts.'''      )
       train_parser.add_argument(
           '''--column_id'''					,      type=__lowercase					,      default=2					,      help='''Column of the dataset csv file with example ids.'''      )
       train_parser.add_argument(
           '''--skip_first_row'''					,      action='''store_true'''					,      help='''Skip the first row of the csv file (headers).'''      )

       train_parser.add_argument('''--validation_data'''					,      type=__lowercase					,      default=''''''					,      help='''path to validation dataset.'''      )
       train_parser.add_argument(
           '''--validation_split'''					,      type=__lowercase					,      default=0.1					,      help='''if validation dataset is not provided, fraction of train dataset to use as validation dataset.'''					,      )

       train_parser.add_argument('''--output'''					,      type=__lowercase					,      default='''./'''					,      help='''path to saved the trained model.'''      )

       train_parser.add_argument(
           '''--task'''					,      type=__lowercase					,      default='''text_classification'''					,      help='''Task to train the model on.'''      )
       train_parser.add_argument(
           '''--model'''					,      type=__lowercase					,      default='''bert-base-uncased'''					,      help='''Model\'s name or path to stored model.'''      )
       train_parser.add_argument('''--train_batch_size'''					,      type=__lowercase					,      default=32					,      help='''Batch size for training.'''      )
       train_parser.add_argument('''--valid_batch_size'''					,      type=__lowercase					,      default=64					,      help='''Batch size for validation.'''      )
       train_parser.add_argument('''--learning_rate'''					,      type=__lowercase					,      default=3e-5					,      help='''Learning rate.'''      )
       train_parser.add_argument('''--adam_epsilon'''					,      type=__lowercase					,      default=1e-08					,      help='''Epsilon for Adam optimizer.'''      )
       train_parser.set_defaults(func=__lowercase      )




 def __init__(      self	:							Tuple					,      __lowercase	:							Namespace      )	->						List[str]:
       SCREAMING_SNAKE_CASE__     :				Tuple									=logging.get_logger('''transformers-cli/training'''      )

       SCREAMING_SNAKE_CASE__     :				int									='''tf''' if is_tf_available() else '''torch'''

       os.makedirs(args.output					,      exist_ok=__lowercase      )
       SCREAMING_SNAKE_CASE__     :				Any									=args.output

       SCREAMING_SNAKE_CASE__     :				str									=args.column_label
       SCREAMING_SNAKE_CASE__     :				List[Any]									=args.column_text
       SCREAMING_SNAKE_CASE__     :				Tuple									=args.column_id

       self.logger.info(F"Loading {args.task} pipeline for {args.model}"      )
       if args.task == "text_classification":
             SCREAMING_SNAKE_CASE__     :				List[str]									=TextClassificationPipeline.from_pretrained(args.model      )
       elif args.task == "token_classification":
             raise NotImplementedError
       elif args.task == "question_answering":
             raise NotImplementedError

       self.logger.info(F"Loading dataset from {args.train_data}"      )
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=Processor.create_from_csv(
           args.train_data					,      column_label=args.column_label					,      column_text=args.column_text					,      column_id=args.column_id					,      skip_first_row=args.skip_first_row					,      )
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=None
       if args.validation_data:
             self.logger.info(F"Loading validation dataset from {args.validation_data}"      )
             SCREAMING_SNAKE_CASE__     :				List[Any]									=Processor.create_from_csv(
                 args.validation_data					,      column_label=args.column_label					,      column_text=args.column_text					,      column_id=args.column_id					,      skip_first_row=args.skip_first_row					,      )

       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=args.validation_split
       SCREAMING_SNAKE_CASE__     :				List[Any]									=args.train_batch_size
       SCREAMING_SNAKE_CASE__     :				Any									=args.valid_batch_size
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=args.learning_rate
       SCREAMING_SNAKE_CASE__     :				int									=args.adam_epsilon




 def      __magic_name__	(      self	:							Any      )	->						str:
       if self.framework == "tf":
             return self.run_tf()
       return self.run_torch()




 def      __magic_name__	(      self	:							Optional[int]      )	->						Tuple:
       raise NotImplementedError




 def      __magic_name__	(      self	:							Dict      )	->						List[Any]:
       self.pipeline.fit(
           self.train_dataset					,      validation_data=self.valid_dataset					,      validation_split=self.validation_split					,      learning_rate=self.learning_rate					,      adam_epsilon=self.adam_epsilon					,      train_batch_size=self.train_batch_size					,      valid_batch_size=self.valid_batch_size					,      )

       # Save trained pipeline
       self.pipeline.save_pretrained(self.output      )