'''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 _UpperCamelCase (							SCREAMING_SNAKE_CASE_=OBJECTS	,							SCREAMING_SNAKE_CASE_=ATTRIBUTES				):
     lowercase_       :							Union[str, Any]  					=       []
     with open(SCREAMING_SNAKE_CASE_				) as f:
          for object in f.readlines():
               vg_classes.append(object.split(','				)[0].lower().strip()				)

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

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


class    UpperCAmelCase__	:






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

        lowercase_       :							Dict  					=       d
   def __repr__(self					)     ->  Optional[int]:
        return str(list((self._pointer.keys())					)					)
   def __setattr__(self							,					_a							,					_a					)     ->  str:
        lowercase_       :							Any  					=       val
        lowercase_       :							Optional[int]  					=       val
        lowercase_       :							Optional[Any]  					=       key.split('.'					)
        lowercase_       :							Optional[Any]  					=       len(_a					) - 1
        lowercase_       :							int  					=       self._pointer
        if len(_a					) > 1:
             for i, l in enumerate(_a					):
                  if hasattr(self							,					_a					) and isinstance(getattr(self							,					_a					)							,					_a					):
                       setattr(getattr(self							,					_a					)							,					'.'.join(levels[i:]					)							,					_a					)
                  if l == last_level:
                       lowercase_       :							Dict  					=       val
                  else:
                       lowercase_       :							Tuple  					=       pointer[l]
   def 		_lowerCamelCase			(self					)     ->  Optional[Any]:
        return self._pointer
   def 		_lowerCamelCase			(self							,					_a							,					_a					)     ->  int:
        with open(f'''{file_name}'''							,					'w'					) as stream:
             dump(_a							,					_a					)
   def 		_lowerCamelCase			(self							,					_a							,					_a					)     ->  Optional[Any]:
        with open(f'''{file_name}'''							,					'w'					) as stream:
             json.dump(_a							,					_a					)
   @staticmethod
   def 		_lowerCamelCase			(_a					)     ->  Union[str, Any]:
        with open(_a					) as stream:
             lowercase_       :							Optional[Any]  					=       load(_a							,					Loader=_a					)
        return data
   def __str__(self					)     ->  Any:
        lowercase_       :							int  					=       '    '
        if self._name != "root":
             lowercase_       :							int  					=       f'''{t * (self._level-1)}{self._name}:\n'''
        else:
             lowercase_       :							Union[str, Any]  					=       ''
        lowercase_       :							List[str]  					=       self._level
        for i, (k, v) in enumerate(self._pointer.items()					):
             if isinstance(_a							,					_a					):
                  r += f'''{t * (self._level)}{v}\n'''
                  self._level += 1
             else:
                  r += f'''{t * (self._level)}{k}: {v} ({type(_a					).__name__})\n'''
             lowercase_       :							Dict  					=       level
        return r[:-1]
   @classmethod
   def 		_lowerCamelCase			(cls							,					_a							,					**_a					)     ->  Union[str, Any]:
        lowercase_					,lowercase_       :							int  					=       cls.get_config_dict(_a							,					**_a					)
        return cls(_a					)





   @classmethod
   def 		_lowerCamelCase			(cls							,					_a							,					**_a					)     ->  List[str]:
        lowercase_       :							Any  					=       kwargs.pop('cache_dir'							,					_a					)
        lowercase_       :							Dict  					=       kwargs.pop('force_download'							,					_a					)
        lowercase_       :							int  					=       kwargs.pop('resume_download'							,					_a					)
        lowercase_       :							int  					=       kwargs.pop('proxies'							,					_a					)
        lowercase_       :							Optional[Any]  					=       kwargs.pop('local_files_only'							,					_a					)

        if os.path.isdir(_a					):
             lowercase_       :							Any  					=       os.path.join(_a							,					_a					)
        elif os.path.isfile(_a					) or is_remote_url(_a					):
             lowercase_       :							Optional[int]  					=       pretrained_model_name_or_path
        else:
             lowercase_       :							Optional[int]  					=       hf_bucket_url(_a							,					filename=_a							,					use_cdn=_a					)

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

             lowercase_       :							str  					=       Config.load_yaml(_a					)

        except EnvironmentError:
             lowercase_       :							str  					=       'Can\'t load config for'
             raise EnvironmentError(_a					)

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

        return Config.load_yaml(_a					), kwargs



def _UpperCamelCase (							SCREAMING_SNAKE_CASE_				):
     lowercase_       :							Union[str, Any]  					=       torch.load('dump.pt'	,							map_location=in_tensor.device				)
     lowercase_       :							Any  					=       in_tensor.numpy()
     lowercase_       :							Optional[Any]  					=       out_tensor.numpy()[0]
     print(na.shape	,							na[0, 0, :5]				)
     print(na.shape	,							na[0, 0, :5]				)
     assert np.allclose(SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	,							rtol=0.01	,							atol=0.1				), (
         f'''{sum([1 for x in np.isclose(SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	,							rtol=0.01	,							atol=0.1				).flatten() if x is False]				)/len(na.flatten()				)*100:.4f} %'''
         " element-wise mismatch"
     )
     raise Exception('tensors are all good'				)

     # Hugging face functions below

def _UpperCamelCase (							SCREAMING_SNAKE_CASE_				):
     lowercase_       :							List[str]  					=       urlparse(SCREAMING_SNAKE_CASE_				)
     return parsed.scheme in ("http", "https")

def _UpperCamelCase (							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_=True				):
     lowercase_       :							Optional[int]  					=       CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
     lowercase_       :							List[str]  					=       '/' not in model_id
     if legacy_format:
          return f'''{endpoint}/{model_id}-{filename}'''
     else:
          return f'''{endpoint}/{model_id}/{filename}'''

def _UpperCamelCase (							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_=None	,							SCREAMING_SNAKE_CASE_=0	,							SCREAMING_SNAKE_CASE_=None	,							):
     lowercase_       :							Any  					=       'python/{}'.format(sys.version.split()[0]				)
     if _torch_available:
          ua += "; torch/{}".format(torch.__version__				)
     if isinstance(SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_				):
          ua += "; " + "; ".join('{}/{}'.format(SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_				) for k, v in user_agent.items()				)
     elif isinstance(SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_				):
          ua += "; " + user_agent
     lowercase_       :							Any  					=       {'user-agent': ua}
     if resume_size > 0:
          lowercase_       :							Union[str, Any]  					=       'bytes=%d-' % (resume_size,)
     lowercase_       :							List[str]  					=       requests.get(SCREAMING_SNAKE_CASE_	,							stream=SCREAMING_SNAKE_CASE_	,							proxies=SCREAMING_SNAKE_CASE_	,							headers=SCREAMING_SNAKE_CASE_				)
     if response.status_code == 416:  # Range not satisfiable
          return
     lowercase_       :							List[str]  					=       response.headers.get('Content-Length'				)
     lowercase_       :							Dict  					=       resume_size + int(SCREAMING_SNAKE_CASE_				) if content_length is not None else None
     lowercase_       :							Any  					=       tqdm(
         unit='B'	,							unit_scale=SCREAMING_SNAKE_CASE_	,							total=SCREAMING_SNAKE_CASE_	,							initial=SCREAMING_SNAKE_CASE_	,							desc='Downloading'	,							)
     for chunk in response.iter_content(chunk_size=1_024				):
          if chunk:  # filter out keep-alive new chunks
               progress.update(len(SCREAMING_SNAKE_CASE_				)				)
               temp_file.write(SCREAMING_SNAKE_CASE_				)
     progress.close()

def _UpperCamelCase (							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_=None	,							SCREAMING_SNAKE_CASE_=False	,							SCREAMING_SNAKE_CASE_=None	,							SCREAMING_SNAKE_CASE_=10	,							SCREAMING_SNAKE_CASE_=False	,							SCREAMING_SNAKE_CASE_=None	,							SCREAMING_SNAKE_CASE_=False	,							):
     if cache_dir is None:
          lowercase_       :							List[Any]  					=       TRANSFORMERS_CACHE
     if isinstance(SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_				):
          lowercase_       :							List[Any]  					=       str(SCREAMING_SNAKE_CASE_				)

     os.makedirs(SCREAMING_SNAKE_CASE_	,							exist_ok=SCREAMING_SNAKE_CASE_				)

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

     lowercase_       :							List[Any]  					=       url_to_filename(SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_				)

     # get cache path to put the file
     lowercase_       :							int  					=       os.path.join(SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_				)

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

     # Prevent parallel downloads of the same file with a lock.
     lowercase_       :							Dict  					=       cache_path + '.lock'
     with FileLock(SCREAMING_SNAKE_CASE_				):
          # If the download just completed while the lock was activated.
          if os.path.exists(SCREAMING_SNAKE_CASE_				) and not force_download:
               # Even if returning early like here, the lock will be released.
               return cache_path

          if resume_download:
               lowercase_       :							Dict  					=       cache_path + '.incomplete'

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

               lowercase_       :							int  					=       _resumable_file_manager
               if os.path.exists(SCREAMING_SNAKE_CASE_				):
                    lowercase_       :							Optional[Any]  					=       os.stat(SCREAMING_SNAKE_CASE_				).st_size
               else:
                    lowercase_       :							List[Any]  					=       0
          else:
               lowercase_       :							List[str]  					=       partial(tempfile.NamedTemporaryFile	,							dir=SCREAMING_SNAKE_CASE_	,							delete=SCREAMING_SNAKE_CASE_				)
               lowercase_       :							Optional[Any]  					=       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'	,							SCREAMING_SNAKE_CASE_	,							temp_file.name	,							)

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

          os.replace(temp_file.name	,							SCREAMING_SNAKE_CASE_				)

          lowercase_       :							int  					=       {'url': url, 'etag': etag}
          lowercase_       :							Optional[int]  					=       cache_path + '.json'
          with open(SCREAMING_SNAKE_CASE_	,							'w'				) as meta_file:
               json.dump(SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_				)

     return cache_path

def _UpperCamelCase (							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_=None				):
     lowercase_       :							Optional[Any]  					=       url.encode('utf-8'				)
     lowercase_       :							str  					=       shaaaa(SCREAMING_SNAKE_CASE_				)
     lowercase_       :							Dict  					=       url_hash.hexdigest()

     if etag:
          lowercase_       :							Tuple  					=       etag.encode('utf-8'				)
          lowercase_       :							Tuple  					=       shaaaa(SCREAMING_SNAKE_CASE_				)
          filename += "." + etag_hash.hexdigest()

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

     return filename

def _UpperCamelCase (							SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_=None	,							SCREAMING_SNAKE_CASE_=False	,							SCREAMING_SNAKE_CASE_=None	,							SCREAMING_SNAKE_CASE_=False	,							SCREAMING_SNAKE_CASE_=None	,							SCREAMING_SNAKE_CASE_=False	,							SCREAMING_SNAKE_CASE_=False	,							SCREAMING_SNAKE_CASE_=False	,							):
     if cache_dir is None:
          lowercase_       :							Union[str, Any]  					=       TRANSFORMERS_CACHE
     if isinstance(SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_				):
          lowercase_       :							Optional[Any]  					=       str(SCREAMING_SNAKE_CASE_				)
     if isinstance(SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_				):
          lowercase_       :							Tuple  					=       str(SCREAMING_SNAKE_CASE_				)

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

     if extract_compressed_file:
          if not is_zipfile(SCREAMING_SNAKE_CASE_				) and not tarfile.is_tarfile(SCREAMING_SNAKE_CASE_				):
               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/"
          lowercase_					,lowercase_       :							Optional[Any]  					=       os.path.split(SCREAMING_SNAKE_CASE_				)
          lowercase_       :							List[Any]  					=       output_file.replace('.'	,							'-'				) + '-extracted'
          lowercase_       :							Optional[Any]  					=       os.path.join(SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_				)

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

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

          return output_path_extracted

     return output_path

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

def _UpperCamelCase (							SCREAMING_SNAKE_CASE_				):
     lowercase_       :							Dict  					=       requests.get(SCREAMING_SNAKE_CASE_				)
     lowercase_       :							Optional[Any]  					=       np.array(Image.open(BytesIO(response.content				)				)				)
     return img

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

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

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






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





'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


_A										= {
    'configuration_rwkv': ['RWKV_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RwkvConfig', 'RwkvOnnxConfig'],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _A										= [
     'RWKV_PRETRAINED_MODEL_ARCHIVE_LIST',
     'RwkvForCausalLM',
     'RwkvModel',
     'RwkvPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_rwkv import RWKV_PRETRAINED_CONFIG_ARCHIVE_MAP, RwkvConfig, RwkvOnnxConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_rwkv import (
      RWKV_PRETRAINED_MODEL_ARCHIVE_LIST,
      RwkvForCausalLM,
      RwkvModel,
      RwkvPreTrainedModel,
  )
else:
 import sys

 _A										= _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




