



"""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

   _UpperCAmelCase     =			True
except ImportError:
   _UpperCAmelCase     =			False


try:
   from torch.hub import _get_torch_home

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

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

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





def      __magic_name__				(							lowercase=OBJECTS							,			lowercase=ATTRIBUTES ):
   SCREAMING_SNAKE_CASE_: Optional[Any]     			=[]
   with open(lowercase ) as f:
      for object in f.readlines():
         vg_classes.append(object.split(""",""" )[0].lower().strip() )

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





def      __magic_name__				(							lowercase ):
   SCREAMING_SNAKE_CASE_: Any     			=OrderedDict()
   with open(lowercase							,			"""rb""" ) as f:
      SCREAMING_SNAKE_CASE_: Any     			=pkl.load(lowercase )["""model"""]
   for k in copy.deepcopy(list(ckp.keys() ) ):
      SCREAMING_SNAKE_CASE_: Optional[int]     			=ckp.pop(lowercase )
      if isinstance(lowercase							,			np.ndarray ):
         SCREAMING_SNAKE_CASE_: List[Any]     			=torch.tensor(lowercase )
      else:
         assert isinstance(lowercase							,			torch.tensor ), type(lowercase )
      SCREAMING_SNAKE_CASE_: str     			=v
   return r
class 					a	:
  UpperCamelCase	:				Dict     						=					{}



  def __init__(						self :					Tuple       ,		lowerCAmelCase :					dict       ,		lowerCAmelCase :					str = "root"       ,		lowerCAmelCase :					Tuple=0		)     ->					Tuple:


     '''simple docstring'''




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

     SCREAMING_SNAKE_CASE_: int     			=d



  def __repr__(						self :					Any		)     ->					int:


     '''simple docstring'''




     return str(list((self._pointer.keys())		)		)



  def __setattr__(						self :					Any       ,		lowerCAmelCase :					Optional[int]       ,		lowerCAmelCase :					Tuple		)     ->					List[str]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: List[Any]     			=val
     SCREAMING_SNAKE_CASE_: str     			=val
     SCREAMING_SNAKE_CASE_: Optional[int]     			=key.split("""."""		)
     SCREAMING_SNAKE_CASE_: List[str]     			=len(lowerCAmelCase		) - 1
     SCREAMING_SNAKE_CASE_: Tuple     			=self._pointer
     if len(lowerCAmelCase		) > 1:
        for i, l in enumerate(lowerCAmelCase		):
           if hasattr(self       ,		lowerCAmelCase		) and isinstance(getattr(self       ,		lowerCAmelCase		)       ,		lowerCAmelCase		):
              setattr(getattr(self       ,		lowerCAmelCase		)       ,		""".""".join(levels[i:]		)       ,		lowerCAmelCase		)
           if l == last_level:
              SCREAMING_SNAKE_CASE_: Optional[Any]     			=val
           else:
              SCREAMING_SNAKE_CASE_: Tuple     			=pointer[l]



  def 	lowerCamelCase__       (						self :					Tuple		)     ->					Any:


     '''simple docstring'''




     return self._pointer



  def 	lowerCamelCase__       (						self :					Dict       ,		lowerCAmelCase :					Optional[Any]       ,		lowerCAmelCase :					List[Any]		)     ->					List[Any]:


     '''simple docstring'''




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



  def 	lowerCamelCase__       (						self :					List[str]       ,		lowerCAmelCase :					Optional[Any]       ,		lowerCAmelCase :					List[Any]		)     ->					Optional[Any]:


     '''simple docstring'''




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



  @staticmethod
  def 	lowerCamelCase__       (						lowerCAmelCase :					Dict		)     ->					Union[str, Any]:


     '''simple docstring'''




     with open(lowerCAmelCase		) as stream:
        SCREAMING_SNAKE_CASE_: Any     			=load(lowerCAmelCase       ,		Loader=lowerCAmelCase		)
     return data



  def __str__(						self :					Dict		)     ->					List[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Any     			="""    """
     if self._name != "root":
        SCREAMING_SNAKE_CASE_: Dict     			=f'''{t * (self._level-1)}{self._name}:\n'''
     else:
        SCREAMING_SNAKE_CASE_: Dict     			=""""""
     SCREAMING_SNAKE_CASE_: List[Any]     			=self._level
     for i, (k, v) in enumerate(self._pointer.items()		):
        if isinstance(lowerCAmelCase       ,		lowerCAmelCase		):
           r += f'''{t * (self._level)}{v}\n'''
           self._level += 1
        else:
           r += f'''{t * (self._level)}{k}: {v} ({type(lowerCAmelCase		).__name__})\n'''
        SCREAMING_SNAKE_CASE_: Any     			=level
     return r[:-1]



  @classmethod
  def 	lowerCamelCase__       (						cls :					List[Any]       ,		lowerCAmelCase :					str       ,		**lowerCAmelCase :					List[Any]		)     ->					Any:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: Optional[int]     			=cls.get_config_dict(lowerCAmelCase       ,		**lowerCAmelCase		)
     return cls(lowerCAmelCase		)





  @classmethod
  def 	lowerCamelCase__       (						cls :					Tuple       ,		lowerCAmelCase :					str       ,		**lowerCAmelCase :					Optional[int]		)     ->					List[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Optional[Any]     			=kwargs.pop("""cache_dir"""       ,		lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: int     			=kwargs.pop("""force_download"""       ,		lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: Optional[Any]     			=kwargs.pop("""resume_download"""       ,		lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: str     			=kwargs.pop("""proxies"""       ,		lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: int     			=kwargs.pop("""local_files_only"""       ,		lowerCAmelCase		)

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

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

        SCREAMING_SNAKE_CASE_: Tuple     			=Config.load_yaml(lowerCAmelCase		)

     except EnvironmentError:
        SCREAMING_SNAKE_CASE_: Any     			="""Can't load config for"""
        raise EnvironmentError(lowerCAmelCase		)

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

     return Config.load_yaml(lowerCAmelCase		), kwargs







def      __magic_name__				(							lowercase ):
   SCREAMING_SNAKE_CASE_: List[Any]     			=torch.load("""dump.pt"""							,			map_location=in_tensor.device )
   SCREAMING_SNAKE_CASE_: Optional[int]     			=in_tensor.numpy()
   SCREAMING_SNAKE_CASE_: List[Any]     			=out_tensor.numpy()[0]
   print(na.shape							,			na[0, 0, :5] )
   print(na.shape							,			na[0, 0, :5] )
   assert np.allclose(lowercase							,			lowercase							,			rtol=0.01							,			atol=0.1 ), (
       f'''{sum([1 for x in np.isclose(lowercase							,			lowercase							,			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      __magic_name__				(							lowercase ):
   SCREAMING_SNAKE_CASE_: List[str]     			=urlparse(lowercase )
   return parsed.scheme in ("http", "https")





def      __magic_name__				(							lowercase							,			lowercase							,			lowercase=True ):
   SCREAMING_SNAKE_CASE_: List[Any]     			=CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
   SCREAMING_SNAKE_CASE_: Optional[Any]     			="""/""" not in model_id
   if legacy_format:
      return f'''{endpoint}/{model_id}-{filename}'''
   else:
      return f'''{endpoint}/{model_id}/{filename}'''





def      __magic_name__				(							lowercase							,			lowercase							,			lowercase=None							,			lowercase=0							,			lowercase=None							,			):
   SCREAMING_SNAKE_CASE_: Tuple     			="""python/{}""".format(sys.version.split()[0] )
   if _torch_available:
      ua += "; torch/{}".format(torch.__version__ )
   if isinstance(lowercase							,			lowercase ):
      ua += "; " + "; ".join("""{}/{}""".format(lowercase							,			lowercase ) for k, v in user_agent.items() )
   elif isinstance(lowercase							,			lowercase ):
      ua += "; " + user_agent
   SCREAMING_SNAKE_CASE_: Any     			={"""user-agent""": ua}
   if resume_size > 0:
      SCREAMING_SNAKE_CASE_: List[str]     			="""bytes=%d-""" % (resume_size,)
   SCREAMING_SNAKE_CASE_: int     			=requests.get(lowercase							,			stream=lowercase							,			proxies=lowercase							,			headers=lowercase )
   if response.status_code == 416:  # Range not satisfiable
      return
   SCREAMING_SNAKE_CASE_: Optional[Any]     			=response.headers.get("""Content-Length""" )
   SCREAMING_SNAKE_CASE_: int     			=resume_size + int(lowercase ) if content_length is not None else None
   SCREAMING_SNAKE_CASE_: List[Any]     			=tqdm(
       unit="""B"""							,			unit_scale=lowercase							,			total=lowercase							,			initial=lowercase							,			desc="""Downloading"""							,			)
   for chunk in response.iter_content(chunk_size=1024 ):
      if chunk:  # filter out keep-alive new chunks
         progress.update(len(lowercase ) )
         temp_file.write(lowercase )
   progress.close()





def      __magic_name__				(							lowercase							,			lowercase=None							,			lowercase=False							,			lowercase=None							,			lowercase=10							,			lowercase=False							,			lowercase=None							,			lowercase=False							,			):
   if cache_dir is None:
      SCREAMING_SNAKE_CASE_: Tuple     			=TRANSFORMERS_CACHE
   if isinstance(lowercase							,			lowercase ):
      SCREAMING_SNAKE_CASE_: Optional[Any]     			=str(lowercase )

   os.makedirs(lowercase							,			exist_ok=lowercase )

   SCREAMING_SNAKE_CASE_: Union[str, Any]     			=None
   if not local_files_only:
      try:
         SCREAMING_SNAKE_CASE_: int     			=requests.head(lowercase							,			allow_redirects=lowercase							,			proxies=lowercase							,			timeout=lowercase )
         if response.status_code == 200:
            SCREAMING_SNAKE_CASE_: Any     			=response.headers.get("""ETag""" )
      except (EnvironmentError, requests.exceptions.Timeout):
         # etag is already None
         pass

   SCREAMING_SNAKE_CASE_: int     			=url_to_filename(lowercase							,			lowercase )

   # get cache path to put the file
   SCREAMING_SNAKE_CASE_: Union[str, Any]     			=os.path.join(lowercase							,			lowercase )

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

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

      if resume_download:
         SCREAMING_SNAKE_CASE_: Optional[Any]     			=cache_path + """.incomplete"""

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

         SCREAMING_SNAKE_CASE_: Tuple     			=_resumable_file_manager
         if os.path.exists(lowercase ):
            SCREAMING_SNAKE_CASE_: Optional[Any]     			=os.stat(lowercase ).st_size
         else:
            SCREAMING_SNAKE_CASE_: Dict     			=0
      else:
         SCREAMING_SNAKE_CASE_: int     			=partial(tempfile.NamedTemporaryFile							,			dir=lowercase							,			delete=lowercase )
         SCREAMING_SNAKE_CASE_: Dict     			=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"""							,			lowercase							,			temp_file.name							,			)

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

      os.replace(temp_file.name							,			lowercase )

      SCREAMING_SNAKE_CASE_: str     			={"""url""": url, """etag""": etag}
      SCREAMING_SNAKE_CASE_: Dict     			=cache_path + """.json"""
      with open(lowercase							,			"""w""" ) as meta_file:
         json.dump(lowercase							,			lowercase )

   return cache_path





def      __magic_name__				(							lowercase							,			lowercase=None ):
   SCREAMING_SNAKE_CASE_: Any     			=url.encode("""utf-8""" )
   SCREAMING_SNAKE_CASE_: List[Any]     			=shaaaa(lowercase )
   SCREAMING_SNAKE_CASE_: Union[str, Any]     			=url_hash.hexdigest()

   if etag:
      SCREAMING_SNAKE_CASE_: str     			=etag.encode("""utf-8""" )
      SCREAMING_SNAKE_CASE_: Dict     			=shaaaa(lowercase )
      filename += "." + etag_hash.hexdigest()

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

   return filename





def      __magic_name__				(							lowercase							,			lowercase=None							,			lowercase=False							,			lowercase=None							,			lowercase=False							,			lowercase=None							,			lowercase=False							,			lowercase=False							,			lowercase=False							,			):
   if cache_dir is None:
      SCREAMING_SNAKE_CASE_: List[str]     			=TRANSFORMERS_CACHE
   if isinstance(lowercase							,			lowercase ):
      SCREAMING_SNAKE_CASE_: Optional[Any]     			=str(lowercase )
   if isinstance(lowercase							,			lowercase ):
      SCREAMING_SNAKE_CASE_: str     			=str(lowercase )

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

   if extract_compressed_file:
      if not is_zipfile(lowercase ) and not tarfile.is_tarfile(lowercase ):
         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_: str     			=os.path.split(lowercase )
      SCREAMING_SNAKE_CASE_: Optional[int]     			=output_file.replace("""."""							,			"""-""" ) + """-extracted"""
      SCREAMING_SNAKE_CASE_: List[str]     			=os.path.join(lowercase							,			lowercase )

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

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

      return output_path_extracted

   return output_path





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





def      __magic_name__				(							lowercase ):
   SCREAMING_SNAKE_CASE_: str     			=requests.get(lowercase )
   SCREAMING_SNAKE_CASE_: Any     			=np.array(Image.open(BytesIO(response.content ) ) )
   return img





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





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





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





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








"""simple docstring"""
import copy
import inspect
import unittest

from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
    require_torch,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch
   from torch import nn

   from transformers import SwiftFormerForImageClassification, SwiftFormerModel
   from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
   from PIL import Image

   from transformers import ViTImageProcessor
class 					a	:



  def __init__(						self :					Any       ,		lowerCAmelCase :					Any       ,		lowerCAmelCase :					List[str]=13       ,		lowerCAmelCase :					Dict=3       ,		lowerCAmelCase :					Optional[Any]=True       ,		lowerCAmelCase :					Dict=True       ,		lowerCAmelCase :					Dict=0.1       ,		lowerCAmelCase :					str=0.1       ,		lowerCAmelCase :					List[str]=224       ,		lowerCAmelCase :					List[str]=1000       ,		lowerCAmelCase :					Optional[Any]=[3, 3, 6, 4]       ,		lowerCAmelCase :					int=[48, 56, 112, 220]       ,		)     ->					List[str]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Optional[int]     			=parent
     SCREAMING_SNAKE_CASE_: Any     			=batch_size
     SCREAMING_SNAKE_CASE_: Tuple     			=num_channels
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=is_training
     SCREAMING_SNAKE_CASE_: Tuple     			=use_labels
     SCREAMING_SNAKE_CASE_: Optional[int]     			=hidden_dropout_prob
     SCREAMING_SNAKE_CASE_: Tuple     			=attention_probs_dropout_prob
     SCREAMING_SNAKE_CASE_: List[Any]     			=num_labels
     SCREAMING_SNAKE_CASE_: int     			=image_size
     SCREAMING_SNAKE_CASE_: Optional[Any]     			=layer_depths
     SCREAMING_SNAKE_CASE_: List[Any]     			=embed_dims



  def 	lowerCamelCase__       (						self :					List[Any]		)     ->					int:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: List[str]     			=floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

     SCREAMING_SNAKE_CASE_: List[Any]     			=None
     if self.use_labels:
        SCREAMING_SNAKE_CASE_: List[str]     			=ids_tensor([self.batch_size]       ,		self.num_labels		)

     SCREAMING_SNAKE_CASE_: Tuple     			=self.get_config()

     return config, pixel_values, labels



  def 	lowerCamelCase__       (						self :					Optional[Any]		)     ->					Union[str, Any]:


     '''simple docstring'''




     return SwiftFormerConfig(
         depths=self.layer_depths       ,		embed_dims=self.embed_dims       ,		mlp_ratio=4       ,		downsamples=[True, True, True, True]       ,		hidden_act="""gelu"""       ,		num_labels=self.num_labels       ,		down_patch_size=3       ,		down_stride=2       ,		down_pad=1       ,		drop_rate=0.0       ,		drop_path_rate=0.0       ,		use_layer_scale=lowerCAmelCase       ,		layer_scale_init_value=1E-5       ,		)



  def 	lowerCamelCase__       (						self :					Any       ,		lowerCAmelCase :					Union[str, Any]       ,		lowerCAmelCase :					Optional[Any]       ,		lowerCAmelCase :					Optional[int]		)     ->					Dict:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: List[str]     			=SwiftFormerModel(config=lowerCAmelCase		)
     model.to(lowerCAmelCase		)
     model.eval()
     SCREAMING_SNAKE_CASE_: Any     			=model(lowerCAmelCase		)
     self.parent.assertEqual(result.last_hidden_state.shape       ,		(self.batch_size, self.embed_dims[-1], 7, 7)		)



  def 	lowerCamelCase__       (						self :					Union[str, Any]       ,		lowerCAmelCase :					List[Any]       ,		lowerCAmelCase :					Optional[int]       ,		lowerCAmelCase :					Union[str, Any]		)     ->					Any:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: List[Any]     			=self.num_labels
     SCREAMING_SNAKE_CASE_: Dict     			=SwiftFormerForImageClassification(lowerCAmelCase		)
     model.to(lowerCAmelCase		)
     model.eval()
     SCREAMING_SNAKE_CASE_: Dict     			=model(lowerCAmelCase       ,		labels=lowerCAmelCase		)
     self.parent.assertEqual(result.logits.shape       ,		(self.batch_size, self.num_labels)		)

     SCREAMING_SNAKE_CASE_: int     			=SwiftFormerForImageClassification(lowerCAmelCase		)
     model.to(lowerCAmelCase		)
     model.eval()

     SCREAMING_SNAKE_CASE_: Tuple     			=floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)
     SCREAMING_SNAKE_CASE_: Dict     			=model(lowerCAmelCase		)
     self.parent.assertEqual(result.logits.shape       ,		(self.batch_size, self.num_labels)		)





  def 	lowerCamelCase__       (						self :					int		)     ->					Optional[Any]:


     '''simple docstring'''




     ((SCREAMING_SNAKE_CASE_)			,		(SCREAMING_SNAKE_CASE_)			,		(SCREAMING_SNAKE_CASE_)): str     			=self.prepare_config_and_inputs()
     SCREAMING_SNAKE_CASE_: Tuple     			={"""pixel_values""": pixel_values}
     return config, inputs_dict







@require_torch
class 					a	(  UpperCAmelCase__	,      UpperCAmelCase__	,      unittest.TestCase       ):
  UpperCamelCase	:				Optional[int]     						=					(SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
  UpperCamelCase	:				Tuple     						=					(
      {'feature-extraction': SwiftFormerModel, 'image-classification': SwiftFormerForImageClassification}
      if is_torch_available()
      else {}
  )

  UpperCamelCase	:				Any     						=					False
  UpperCamelCase	:				Optional[int]     						=					False
  UpperCamelCase	:				Optional[Any]     						=					False
  UpperCamelCase	:				Dict     						=					False
  UpperCamelCase	:				List[str]     						=					False



  def 	lowerCamelCase__       (						self :					Dict		)     ->					Any:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Dict     			=SwiftFormerModelTester(self		)
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=ConfigTester(
         self       ,		config_class=lowerCAmelCase       ,		has_text_modality=lowerCAmelCase       ,		hidden_size=37       ,		num_attention_heads=12       ,		num_hidden_layers=12       ,		)



  def 	lowerCamelCase__       (						self :					Tuple		)     ->					int:


     '''simple docstring'''




     self.config_tester.run_common_tests()



  @unittest.skip(reason="""SwiftFormer does not use inputs_embeds"""		)
  def 	lowerCamelCase__       (						self :					Tuple		)     ->					List[str]:


     '''simple docstring'''




     pass



  def 	lowerCamelCase__       (						self :					Optional[int]		)     ->					Tuple:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: List[str]     			=self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        SCREAMING_SNAKE_CASE_: Any     			=model_class(lowerCAmelCase		)
        SCREAMING_SNAKE_CASE_: List[Any]     			=model.get_output_embeddings()
        self.assertTrue(x is None or isinstance(lowerCAmelCase       ,		nn.Linear		)		)



  def 	lowerCamelCase__       (						self :					str		)     ->					Dict:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: List[Any]     			=self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        SCREAMING_SNAKE_CASE_: int     			=model_class(lowerCAmelCase		)
        SCREAMING_SNAKE_CASE_: Optional[Any]     			=inspect.signature(model.forward		)
        # signature.parameters is an OrderedDict => so arg_names order is deterministic
        SCREAMING_SNAKE_CASE_: Any     			=[*signature.parameters.keys()]

        SCREAMING_SNAKE_CASE_: Tuple     			=["""pixel_values"""]
        self.assertListEqual(arg_names[:1]       ,		lowerCAmelCase		)



  def 	lowerCamelCase__       (						self :					int		)     ->					List[str]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Dict     			=self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*lowerCAmelCase		)



  def 	lowerCamelCase__       (						self :					Any		)     ->					List[str]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: int     			=self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase		)



  @slow
  def 	lowerCamelCase__       (						self :					Optional[Any]		)     ->					List[str]:


     '''simple docstring'''




     for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        SCREAMING_SNAKE_CASE_: Optional[Any]     			=SwiftFormerModel.from_pretrained(lowerCAmelCase		)
        self.assertIsNotNone(lowerCAmelCase		)



  @unittest.skip(reason="""SwiftFormer does not output attentions"""		)
  def 	lowerCamelCase__       (						self :					Optional[int]		)     ->					str:


     '''simple docstring'''




     pass



  def 	lowerCamelCase__       (						self :					List[Any]		)     ->					List[str]:


     '''simple docstring'''




     def check_hidden_states_output(lowerCAmelCase :					Optional[int]       ,		lowerCAmelCase :					str       ,		lowerCAmelCase :					Optional[int]		):
        SCREAMING_SNAKE_CASE_: Optional[Any]     			=model_class(lowerCAmelCase		)
        model.to(lowerCAmelCase		)
        model.eval()

        with torch.no_grad():
           SCREAMING_SNAKE_CASE_: List[str]     			=model(**self._prepare_for_class(lowerCAmelCase       ,		lowerCAmelCase		)		)

        SCREAMING_SNAKE_CASE_: Dict     			=outputs.hidden_states

        SCREAMING_SNAKE_CASE_: List[Any]     			=8
        self.assertEqual(len(lowerCAmelCase		)       ,		lowerCAmelCase		)  # TODO

        # SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
        # with the width and height being successively divided by 2, after every 2 blocks
        for i in range(len(lowerCAmelCase		)		):
           self.assertEqual(
               hidden_states[i].shape       ,		torch.Size(
                   [
                       self.model_tester.batch_size,
                       self.model_tester.embed_dims[i // 2],
                       (self.model_tester.image_size // 4) // 2 ** (i // 2),
                       (self.model_tester.image_size // 4) // 2 ** (i // 2),
                   ]		)       ,		)

     SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: int     			=self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        SCREAMING_SNAKE_CASE_: Dict     			=True
        check_hidden_states_output(lowerCAmelCase       ,		lowerCAmelCase       ,		lowerCAmelCase		)

        # check that output_hidden_states also work using config
        del inputs_dict["output_hidden_states"]
        SCREAMING_SNAKE_CASE_: Any     			=True

        check_hidden_states_output(lowerCAmelCase       ,		lowerCAmelCase       ,		lowerCAmelCase		)



  def 	lowerCamelCase__       (						self :					Optional[Any]		)     ->					Optional[int]:


     '''simple docstring'''




     def _config_zero_init(lowerCAmelCase :					str		):
        SCREAMING_SNAKE_CASE_: Dict     			=copy.deepcopy(lowerCAmelCase		)
        for key in configs_no_init.__dict__.keys():
           if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
              setattr(lowerCAmelCase       ,		lowerCAmelCase       ,		1E-10		)
           if isinstance(getattr(lowerCAmelCase       ,		lowerCAmelCase       ,		lowerCAmelCase		)       ,		lowerCAmelCase		):
              SCREAMING_SNAKE_CASE_: Tuple     			=_config_zero_init(getattr(lowerCAmelCase       ,		lowerCAmelCase		)		)
              setattr(lowerCAmelCase       ,		lowerCAmelCase       ,		lowerCAmelCase		)
        return configs_no_init

     SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: Optional[Any]     			=self.model_tester.prepare_config_and_inputs_for_common()

     SCREAMING_SNAKE_CASE_: List[Any]     			=_config_zero_init(lowerCAmelCase		)
     for model_class in self.all_model_classes:
        SCREAMING_SNAKE_CASE_: Any     			=model_class(config=lowerCAmelCase		)
        for name, param in model.named_parameters():
           if param.requires_grad:
              self.assertIn(
                  ((param.data.mean() * 1E9) / 1E9).round().item()       ,		[0.0, 1.0]       ,		msg=f'''Parameter {name} of model {model_class} seems not properly initialized'''       ,		)





  @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""		)
  def 	lowerCamelCase__       (						self :					List[str]		)     ->					List[str]:


     '''simple docstring'''




     pass







def      __magic_name__				(							):
   SCREAMING_SNAKE_CASE_: List[Any]     			=Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
   return image
@require_torch
@require_vision
class 					a	(  unittest.TestCase       ):



  @cached_property
  def 	lowerCamelCase__       (						self :					str		)     ->					str:


     '''simple docstring'''




     return ViTImageProcessor.from_pretrained("""MBZUAI/swiftformer-xs"""		) if is_vision_available() else None





  @slow
  def 	lowerCamelCase__       (						self :					Union[str, Any]		)     ->					Any:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: str     			=SwiftFormerForImageClassification.from_pretrained("""MBZUAI/swiftformer-xs"""		).to(lowerCAmelCase		)

     SCREAMING_SNAKE_CASE_: str     			=self.default_image_processor
     SCREAMING_SNAKE_CASE_: int     			=prepare_img()
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=image_processor(images=lowerCAmelCase       ,		return_tensors="""pt"""		).to(lowerCAmelCase		)

     # forward pass
     with torch.no_grad():
        SCREAMING_SNAKE_CASE_: Dict     			=model(**lowerCAmelCase		)

     # verify the logits
     SCREAMING_SNAKE_CASE_: Optional[Any]     			=torch.Size((1, 1000)		)
     self.assertEqual(outputs.logits.shape       ,		lowerCAmelCase		)

     SCREAMING_SNAKE_CASE_: List[str]     			=torch.tensor([[-2.1_703E00, 2.1_107E00, -2.0_811E00]]		).to(lowerCAmelCase		)
     self.assertTrue(torch.allclose(outputs.logits[0, :3]       ,		lowerCAmelCase       ,		atol=1E-4		)		)



