"""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								=True
except ImportError:
       __SCREAMING_SNAKE_CASE								=False


try:
       from torch.hub import _get_torch_home

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

__SCREAMING_SNAKE_CASE								=os.path.join(torch_cache_home, "transformers")

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



def     lowercase__( __SCREAMING_SNAKE_CASE							:  Any=OBJECTS  ,    __SCREAMING_SNAKE_CASE							:  Any=ATTRIBUTES   ):
     lowercase_			:       List[Any]									=			[]
     with open(__SCREAMING_SNAKE_CASE   ) as f:
          for object in f.readlines():
               vg_classes.append(object.split(','   )[0].lower().strip()   )

     lowercase_			:       Any									=			[]
     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     lowercase__( __SCREAMING_SNAKE_CASE							:  Dict   ):
     lowercase_			:       Dict									=			OrderedDict()
     with open(__SCREAMING_SNAKE_CASE  ,    'rb'   ) as f:
          lowercase_			:       str									=			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_			:       Dict									=			v
     return r







class  UpperCamelCase	:
   lowercase							=						{}
   def __init__(    self  ,__UpperCamelCase  ,__UpperCamelCase = "root"  ,__UpperCamelCase=0    )			->   Optional[int]:


        '''simple docstring'''


        lowercase_			:       List[str]									=			name
        lowercase_			:       Union[str, Any]									=			level
        lowercase_			:       List[Any]									=			{}
        for k, v in dictionary.items():
             if v is None:
                  raise ValueError()
             lowercase_			:       Optional[int]									=			copy.deepcopy(__UpperCamelCase    )
             lowercase_			:       int									=			copy.deepcopy(__UpperCamelCase    )
             if isinstance(__UpperCamelCase  ,__UpperCamelCase    ):
                  lowercase_			:       Union[str, Any]									=			Config(__UpperCamelCase  ,name=__UpperCamelCase  ,level=level + 1    )
             lowercase_			:       Dict									=			v
             setattr(self  ,__UpperCamelCase  ,__UpperCamelCase    )

        lowercase_			:       Dict									=			d
   def __repr__(    self    )			->   List[Any]:


        '''simple docstring'''


        return str(list((self._pointer.keys())    )    )
   def __setattr__(    self  ,__UpperCamelCase  ,__UpperCamelCase    )			->   str:


        '''simple docstring'''


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


        '''simple docstring'''


        return self._pointer
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase    )			->   Any:


        '''simple docstring'''


        with open(f'''{file_name}'''  ,'w'    ) as stream:
             dump(__UpperCamelCase  ,__UpperCamelCase    )
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase    )			->   Optional[Any]:


        '''simple docstring'''


        with open(f'''{file_name}'''  ,'w'    ) as stream:
             json.dump(__UpperCamelCase  ,__UpperCamelCase    )
   @staticmethod
   def     _UpperCAmelCase     (    __UpperCamelCase    )			->   Tuple:


        '''simple docstring'''


        with open(__UpperCamelCase    ) as stream:
             lowercase_			:       Union[str, Any]									=			load(__UpperCamelCase  ,Loader=__UpperCamelCase    )
        return data
   def __str__(    self    )			->   Optional[int]:


        '''simple docstring'''


        lowercase_			:       List[str]									=			'    '
        if self._name != "root":
             lowercase_			:       List[Any]									=			f'''{t * (self._level-1)}{self._name}:\n'''
        else:
             lowercase_			:       List[str]									=			''
        lowercase_			:       Tuple									=			self._level
        for i, (k, v) in enumerate(self._pointer.items()    ):
             if isinstance(__UpperCamelCase  ,__UpperCamelCase    ):
                  r += f'''{t * (self._level)}{v}\n'''
                  self._level += 1
             else:
                  r += f'''{t * (self._level)}{k}: {v} ({type(__UpperCamelCase    ).__name__})\n'''
             lowercase_			:       Any									=			level
        return r[:-1]
   @classmethod
   def     _UpperCAmelCase     (    cls  ,__UpperCamelCase  ,**__UpperCamelCase    )			->   List[Any]:


        '''simple docstring'''


        lowercase_							,						lowercase_			:       str									=			cls.get_config_dict(__UpperCamelCase  ,**__UpperCamelCase    )
        return cls(__UpperCamelCase    )







   @classmethod
   def     _UpperCAmelCase     (    cls  ,__UpperCamelCase  ,**__UpperCamelCase    )			->   Union[str, Any]:


        '''simple docstring'''


        lowercase_			:       Union[str, Any]									=			kwargs.pop('cache_dir'  ,__UpperCamelCase    )
        lowercase_			:       Optional[Any]									=			kwargs.pop('force_download'  ,__UpperCamelCase    )
        lowercase_			:       Tuple									=			kwargs.pop('resume_download'  ,__UpperCamelCase    )
        lowercase_			:       Tuple									=			kwargs.pop('proxies'  ,__UpperCamelCase    )
        lowercase_			:       Dict									=			kwargs.pop('local_files_only'  ,__UpperCamelCase    )

        if os.path.isdir(__UpperCamelCase    ):
             lowercase_			:       List[str]									=			os.path.join(__UpperCamelCase  ,__UpperCamelCase    )
        elif os.path.isfile(__UpperCamelCase    ) or is_remote_url(__UpperCamelCase    ):
             lowercase_			:       int									=			pretrained_model_name_or_path
        else:
             lowercase_			:       Tuple									=			hf_bucket_url(__UpperCamelCase  ,filename=__UpperCamelCase  ,use_cdn=__UpperCamelCase    )

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

             lowercase_			:       str									=			Config.load_yaml(__UpperCamelCase    )

        except EnvironmentError:
             lowercase_			:       Optional[int]									=			'Can\'t load config for'
             raise EnvironmentError(__UpperCamelCase    )

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

        return Config.load_yaml(__UpperCamelCase    ), kwargs






def     lowercase__( __SCREAMING_SNAKE_CASE							:  List[str]   ):
     lowercase_			:       Any									=			torch.load('dump.pt'  ,    map_location=in_tensor.device   )
     lowercase_			:       Any									=			in_tensor.numpy()
     lowercase_			:       Tuple									=			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()   )*1_00:.4f} %'''
         " element-wise mismatch"
     )
     raise Exception('tensors are all good'   )

     # Hugging face functions below



def     lowercase__( __SCREAMING_SNAKE_CASE							:  Dict   ):
     lowercase_			:       Dict									=			urlparse(__SCREAMING_SNAKE_CASE   )
     return parsed.scheme in ("http", "https")



def     lowercase__( __SCREAMING_SNAKE_CASE							:  str  ,    __SCREAMING_SNAKE_CASE							:  str  ,    __SCREAMING_SNAKE_CASE							:  Any=True   ):
     lowercase_			:       int									=			CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
     lowercase_			:       Optional[int]									=			'/' not in model_id
     if legacy_format:
          return F'''{endpoint}/{model_id}-{filename}'''
     else:
          return F'''{endpoint}/{model_id}/{filename}'''



def     lowercase__( __SCREAMING_SNAKE_CASE							:  List[str]  ,    __SCREAMING_SNAKE_CASE							:  Optional[int]  ,    __SCREAMING_SNAKE_CASE							:  List[Any]=None  ,    __SCREAMING_SNAKE_CASE							:  Optional[Any]=0  ,    __SCREAMING_SNAKE_CASE							:  List[Any]=None  ,    ):
     lowercase_			:       int									=			'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_			:       Optional[int]									=			{'user-agent': ua}
     if resume_size > 0:
          lowercase_			:       Dict									=			'bytes=%d-' % (resume_size,)
     lowercase_			:       Optional[int]									=			requests.get(__SCREAMING_SNAKE_CASE  ,    stream=__SCREAMING_SNAKE_CASE  ,    proxies=__SCREAMING_SNAKE_CASE  ,    headers=__SCREAMING_SNAKE_CASE   )
     if response.status_code == 4_16:  # Range not satisfiable
          return
     lowercase_			:       Union[str, Any]									=			response.headers.get('Content-Length'   )
     lowercase_			:       Optional[int]									=			resume_size + int(__SCREAMING_SNAKE_CASE   ) if content_length is not None else None
     lowercase_			:       List[str]									=			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=10_24   ):
          if chunk:  # filter out keep-alive new chunks
               progress.update(len(__SCREAMING_SNAKE_CASE   )   )
               temp_file.write(__SCREAMING_SNAKE_CASE   )
     progress.close()



def     lowercase__( __SCREAMING_SNAKE_CASE							:  str  ,    __SCREAMING_SNAKE_CASE							:  Any=None  ,    __SCREAMING_SNAKE_CASE							:  Optional[Any]=False  ,    __SCREAMING_SNAKE_CASE							:  int=None  ,    __SCREAMING_SNAKE_CASE							:  int=10  ,    __SCREAMING_SNAKE_CASE							:  int=False  ,    __SCREAMING_SNAKE_CASE							:  Any=None  ,    __SCREAMING_SNAKE_CASE							:  Any=False  ,    ):
     if cache_dir is None:
          lowercase_			:       Optional[Any]									=			TRANSFORMERS_CACHE
     if isinstance(__SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE   ):
          lowercase_			:       Dict									=			str(__SCREAMING_SNAKE_CASE   )

     os.makedirs(__SCREAMING_SNAKE_CASE  ,    exist_ok=__SCREAMING_SNAKE_CASE   )

     lowercase_			:       Union[str, Any]									=			None
     if not local_files_only:
          try:
               lowercase_			:       Union[str, Any]									=			requests.head(__SCREAMING_SNAKE_CASE  ,    allow_redirects=__SCREAMING_SNAKE_CASE  ,    proxies=__SCREAMING_SNAKE_CASE  ,    timeout=__SCREAMING_SNAKE_CASE   )
               if response.status_code == 2_00:
                    lowercase_			:       int									=			response.headers.get('ETag'   )
          except (EnvironmentError, requests.exceptions.Timeout):
               # etag is already None
               pass

     lowercase_			:       int									=			url_to_filename(__SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE   )

     # get cache path to put the file
     lowercase_			:       Tuple									=			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_			:       str									=			[
                   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_			:       Tuple									=			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_			:       str									=			_resumable_file_manager
               if os.path.exists(__SCREAMING_SNAKE_CASE   ):
                    lowercase_			:       str									=			os.stat(__SCREAMING_SNAKE_CASE   ).st_size
               else:
                    lowercase_			:       Dict									=			0
          else:
               lowercase_			:       Tuple									=			partial(tempfile.NamedTemporaryFile  ,    dir=__SCREAMING_SNAKE_CASE  ,    delete=__SCREAMING_SNAKE_CASE   )
               lowercase_			:       Union[str, 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_			:       Union[str, Any]									=			{'url': url, 'etag': etag}
          lowercase_			:       str									=			cache_path + '.json'
          with open(__SCREAMING_SNAKE_CASE  ,    'w'   ) as meta_file:
               json.dump(__SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE   )

     return cache_path



def     lowercase__( __SCREAMING_SNAKE_CASE							:  Optional[Any]  ,    __SCREAMING_SNAKE_CASE							:  Tuple=None   ):
     lowercase_			:       Dict									=			url.encode('utf-8'   )
     lowercase_			:       Optional[Any]									=			shaaaa(__SCREAMING_SNAKE_CASE   )
     lowercase_			:       int									=			url_hash.hexdigest()

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

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

     return filename



def     lowercase__( __SCREAMING_SNAKE_CASE							:  List[Any]  ,    __SCREAMING_SNAKE_CASE							:  List[Any]=None  ,    __SCREAMING_SNAKE_CASE							:  List[Any]=False  ,    __SCREAMING_SNAKE_CASE							:  Optional[int]=None  ,    __SCREAMING_SNAKE_CASE							:  Tuple=False  ,    __SCREAMING_SNAKE_CASE							:  List[str]=None  ,    __SCREAMING_SNAKE_CASE							:  Optional[int]=False  ,    __SCREAMING_SNAKE_CASE							:  List[Any]=False  ,    __SCREAMING_SNAKE_CASE							:  Optional[Any]=False  ,    ):
     if cache_dir is None:
          lowercase_			:       int									=			TRANSFORMERS_CACHE
     if isinstance(__SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE   ):
          lowercase_			:       Any									=			str(__SCREAMING_SNAKE_CASE   )
     if isinstance(__SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE   ):
          lowercase_			:       Dict									=			str(__SCREAMING_SNAKE_CASE   )

     if is_remote_url(__SCREAMING_SNAKE_CASE   ):
          # URL, so get it from the cache (downloading if necessary)
          lowercase_			:       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_			:       Optional[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_			:       Tuple									=			os.path.split(__SCREAMING_SNAKE_CASE   )
          lowercase_			:       str									=			output_file.replace('.'  ,    '-'   ) + '-extracted'
          lowercase_			:       Union[str, 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_			:       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_			:       Optional[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     lowercase__( __SCREAMING_SNAKE_CASE							:  List[str]  ,    __SCREAMING_SNAKE_CASE							:  Union[str, Any]=","   ):
     assert isinstance(__SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE   )
     if os.path.isfile(__SCREAMING_SNAKE_CASE   ):
          with open(__SCREAMING_SNAKE_CASE   ) as f:
               lowercase_			:       int									=			eval(f.read()   )
     else:
          lowercase_			:       Dict									=			requests.get(__SCREAMING_SNAKE_CASE   )
          try:
               lowercase_			:       Dict									=			requests.json()
          except Exception:
               lowercase_			:       str									=			req.content.decode()
               assert data is not None, "could not connect"
               try:
                    lowercase_			:       List[Any]									=			eval(__SCREAMING_SNAKE_CASE   )
               except Exception:
                    lowercase_			:       str									=			data.split('\n'   )
          req.close()
     return data



def     lowercase__( __SCREAMING_SNAKE_CASE							:  List[Any]   ):
     lowercase_			:       List[str]									=			requests.get(__SCREAMING_SNAKE_CASE   )
     lowercase_			:       Optional[int]									=			np.array(Image.open(BytesIO(response.content   )   )   )
     return img



def     lowercase__( __SCREAMING_SNAKE_CASE							:  Union[str, Any]   ):
     lowercase_			:       List[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_			:       Dict									=			pkl.load(__SCREAMING_SNAKE_CASE   )
     lowercase_			:       int									=			weights.pop('model'   )
     lowercase_			:       Tuple									=			{}
     for k, v in model.items():
          lowercase_			:       str									=			torch.from_numpy(__SCREAMING_SNAKE_CASE   )
          if "running_var" in k:
               lowercase_			:       Union[str, Any]									=			torch.tensor([0]   )
               lowercase_			:       Optional[int]									=			k.replace('running_var'  ,    'num_batches_tracked'   )
               lowercase_			:       List[Any]									=			zero
     return new



def     lowercase__( ):
     print(F'''{os.path.abspath(os.path.join(__SCREAMING_SNAKE_CASE  ,    os.pardir   )   )}/demo.ipynb'''   )



def     lowercase__( __SCREAMING_SNAKE_CASE							:  Optional[int]  ,    __SCREAMING_SNAKE_CASE							:  Any="RGB"   ):
     assert isinstance(__SCREAMING_SNAKE_CASE  ,    __SCREAMING_SNAKE_CASE   )
     if os.path.isfile(__SCREAMING_SNAKE_CASE   ):
          lowercase_			:       Optional[Any]									=			cva.imread(__SCREAMING_SNAKE_CASE   )
     else:
          lowercase_			:       Tuple									=			get_image_from_url(__SCREAMING_SNAKE_CASE   )
          assert img is not None, F'''could not connect to: {im}'''
     lowercase_			:       Optional[Any]									=			cva.cvtColor(__SCREAMING_SNAKE_CASE  ,    cva.COLOR_BGR2RGB   )
     if input_format == "RGB":
          lowercase_			:       Any									=			img[:, :, ::-1]
     return img







def     lowercase__( __SCREAMING_SNAKE_CASE							:  int  ,    __SCREAMING_SNAKE_CASE							:  Optional[int]=1   ):
     return (images[i : i + batch] for i in range(0  ,    len(__SCREAMING_SNAKE_CASE   )  ,    __SCREAMING_SNAKE_CASE   ))


"""simple docstring"""







import random
import unittest

import torch

from diffusers import IFImgaImgSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin







@skip_mps
class  UpperCamelCase	(    lowercase_		,   lowercase_		,   unittest.TestCase						):
   lowercase							=						IFImgaImgSuperResolutionPipeline
   lowercase							=						TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'width', 'height'}
   lowercase							=						TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'original_image'}						)
   lowercase							=						PipelineTesterMixin.required_optional_params - {'latents'}
   def     _UpperCAmelCase     (    self    )			->   int:


        '''simple docstring'''


        return self._get_superresolution_dummy_components()
   def     _UpperCAmelCase     (    self  ,__UpperCamelCase  ,__UpperCamelCase=0    )			->   List[str]:


        '''simple docstring'''


        if str(__UpperCamelCase    ).startswith('mps'    ):
             lowercase_			:       Any									=			torch.manual_seed(__UpperCamelCase    )
        else:
             lowercase_			:       int									=			torch.Generator(device=__UpperCamelCase    ).manual_seed(__UpperCamelCase    )

        lowercase_			:       Optional[int]									=			floats_tensor((1, 3, 32, 32)  ,rng=random.Random(__UpperCamelCase    )    ).to(__UpperCamelCase    )
        lowercase_			:       List[str]									=			floats_tensor((1, 3, 16, 16)  ,rng=random.Random(__UpperCamelCase    )    ).to(__UpperCamelCase    )

        lowercase_			:       Optional[Any]									=			{
            'prompt': 'A painting of a squirrel eating a burger',
            'image': image,
            'original_image': original_image,
            'generator': generator,
            'num_inference_steps': 2,
            'output_type': 'numpy',
        }

        return inputs
   @unittest.skipIf(
       torch_device != 'cuda' or not is_xformers_available()  ,reason='XFormers attention is only available with CUDA and `xformers` installed'  ,)
   def     _UpperCAmelCase     (    self    )			->   Optional[Any]:


        '''simple docstring'''


        self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3    )
   def     _UpperCAmelCase     (    self    )			->   Dict:


        '''simple docstring'''


        self._test_save_load_optional_components()
   @unittest.skipIf(torch_device != 'cuda'  ,reason='float16 requires CUDA'    )
   def     _UpperCAmelCase     (    self    )			->   Optional[int]:


        '''simple docstring'''


        super().test_save_load_floataa(expected_max_diff=1e-1    )
   def     _UpperCAmelCase     (    self    )			->   Dict:


        '''simple docstring'''


        self._test_attention_slicing_forward_pass(expected_max_diff=1e-2    )
   def     _UpperCAmelCase     (    self    )			->   List[Any]:


        '''simple docstring'''


        self._test_save_load_local()







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


        '''simple docstring'''


        self._test_inference_batch_single_identical(
            expected_max_diff=1e-2  ,)

