




"""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   _SCREAMING_SNAKE_CASE   (							lowercase_=OBJECTS			, lowercase_=ATTRIBUTES						)						->		Optional[Any]:
 A__       =				[]
 with open(snake_case__						) as f:
  for object in f.readlines():
   vg_classes.append(object.split(","						)[0].lower().strip()						)

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







def   _SCREAMING_SNAKE_CASE   (							lowercase_						)						->		Optional[Any]:
 A__       =				OrderedDict()
 with open(snake_case__			, "rb"						) as f:
  A__       =				pkl.load(snake_case__						)["model"]
 for k in copy.deepcopy(list(ckp.keys()						)						):
  A__       =				ckp.pop(snake_case__						)
  if isinstance(snake_case__			, np.ndarray						):
   A__       =				torch.tensor(snake_case__						)
  else:
   assert isinstance(snake_case__			, torch.tensor						), type(snake_case__						)
  A__       =				v
 return r




class 	UpperCAmelCase_  :
    lowercase__											=      {}




    def __init__(    self						:       Optional[int]	, snake_case_						:       Optional[Any]	, snake_case_						:       Tuple = "root"	, snake_case_						:       Optional[Any]=0      )      ->							Union[str, Any]:




     '''simple docstring'''

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

     A__       =				d




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




     '''simple docstring'''

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




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




     '''simple docstring'''

     A__       =				val
     A__       =				val
     A__       =				key.split("."      )
     A__       =				len(lowerCAmelCase_      ) - 1
     A__       =				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:
        A__       =				val
       else:
        A__       =				pointer[l]




    def 							__magic_name__							(    self						:       Tuple      )      ->							Optional[Any]:




     '''simple docstring'''

     return self._pointer




    def 							__magic_name__							(    self						:       Tuple	, snake_case_						:       Tuple	, snake_case_						:       Any      )      ->							Tuple:




     '''simple docstring'''

     with open(F"""{file_name}"""	, "w"      ) as stream:
      dump(lowerCAmelCase_	, lowerCAmelCase_      )




    def 							__magic_name__							(    self						:       Dict	, snake_case_						:       Dict	, snake_case_						:       Optional[int]      )      ->							Any:




     '''simple docstring'''

     with open(F"""{file_name}"""	, "w"      ) as stream:
      json.dump(lowerCAmelCase_	, lowerCAmelCase_      )




    @staticmethod
    def 							__magic_name__							(    snake_case_						:       List[Any]      )      ->							Tuple:




     '''simple docstring'''

     with open(lowerCAmelCase_      ) as stream:
      A__       =				load(lowerCAmelCase_	, Loader=lowerCAmelCase_      )
     return data




    def __str__(    self						:       Any      )      ->							Dict:




     '''simple docstring'''

     A__       =				"    "
     if self._name != "root":
      A__       =				F"""{t * (self._level-1)}{self._name}:\n"""
     else:
      A__       =				""
     A__       =				self._level
     for i, (k, v) in enumerate(self._pointer.items()      ):
      if isinstance(lowerCAmelCase_	, lowerCAmelCase_      ):
       r += F"""{t * (self._level)}{v}\n"""
       self._level += 1
      else:
       r += F"""{t * (self._level)}{k}: {v} ({type(lowerCAmelCase_      ).__name__})\n"""
      A__       =				level
     return r[:-1]




    @classmethod
    def 							__magic_name__							(    cls						:       List[Any]	, snake_case_						:       int	, **snake_case_						:       Optional[Any]      )      ->							Union[str, Any]:




     '''simple docstring'''

     A__,      A__       =				cls.get_config_dict(lowerCAmelCase_	, **lowerCAmelCase_      )
     return cls(lowerCAmelCase_      )




    @classmethod
    def 							__magic_name__							(    cls						:       Dict	, snake_case_						:       Optional[Any]	, **snake_case_						:       Dict      )      ->							Optional[Any]:




     '''simple docstring'''

     A__       =				kwargs.pop("cache_dir"	, lowerCAmelCase_      )
     A__       =				kwargs.pop("force_download"	, lowerCAmelCase_      )
     A__       =				kwargs.pop("resume_download"	, lowerCAmelCase_      )
     A__       =				kwargs.pop("proxies"	, lowerCAmelCase_      )
     A__       =				kwargs.pop("local_files_only"	, lowerCAmelCase_      )

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

     try:
      # Load from URL or cache if already cached
      A__       =				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

      A__       =				Config.load_yaml(lowerCAmelCase_      )

     except EnvironmentError:
      A__       =				"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   _SCREAMING_SNAKE_CASE   (							lowercase_						)						->		Tuple:
 A__       =				torch.load("dump.pt"			, map_location=in_tensor.device						)
 A__       =				in_tensor.numpy()
 A__       =				out_tensor.numpy()[0]
 print(na.shape			, na[0, 0, :5]						)
 print(na.shape			, na[0, 0, :5]						)
 assert np.allclose(snake_case__			, snake_case__			, rtol=0.0_1			, atol=0.1						), (
     f"""{sum([1 for x in np.isclose(snake_case__			, snake_case__			, rtol=0.0_1			, 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   _SCREAMING_SNAKE_CASE   (							lowercase_						)						->		Union[str, Any]:
 A__       =				urlparse(snake_case__						)
 return parsed.scheme in ("http", "https")







def   _SCREAMING_SNAKE_CASE   (							lowercase_			, lowercase_			, lowercase_=True						)						->		str:
 A__       =				CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
 A__       =				"/" not in model_id
 if legacy_format:
  return f"""{endpoint}/{model_id}-{filename}"""
 else:
  return f"""{endpoint}/{model_id}/{filename}"""







def   _SCREAMING_SNAKE_CASE   (							lowercase_			, lowercase_			, lowercase_=None			, lowercase_=0			, lowercase_=None			, )						->		List[str]:
 A__       =				"python/{}".format(sys.version.split()[0]						)
 if _torch_available:
  ua += "; torch/{}".format(torch.__version__						)
 if isinstance(snake_case__			, snake_case__						):
  ua += "; " + "; ".join("{}/{}".format(snake_case__			, snake_case__						) for k, v in user_agent.items()						)
 elif isinstance(snake_case__			, snake_case__						):
  ua += "; " + user_agent
 A__       =				{"user-agent": ua}
 if resume_size > 0:
  A__       =				"bytes=%d-" % (resume_size,)
 A__       =				requests.get(snake_case__			, stream=snake_case__			, proxies=snake_case__			, headers=snake_case__						)
 if response.status_code == 4_16:  # Range not satisfiable
  return
 A__       =				response.headers.get("Content-Length"						)
 A__       =				resume_size + int(snake_case__						) if content_length is not None else None
 A__       =				tqdm(
     unit="B"			, unit_scale=snake_case__			, total=snake_case__			, initial=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(snake_case__						)						)
   temp_file.write(snake_case__						)
 progress.close()







def   _SCREAMING_SNAKE_CASE   (							lowercase_			, lowercase_=None			, lowercase_=False			, lowercase_=None			, lowercase_=10			, lowercase_=False			, lowercase_=None			, lowercase_=False			, )						->		List[str]:
 if cache_dir is None:
  A__       =				TRANSFORMERS_CACHE
 if isinstance(snake_case__			, snake_case__						):
  A__       =				str(snake_case__						)

 os.makedirs(snake_case__			, exist_ok=snake_case__						)

 A__       =				None
 if not local_files_only:
  try:
   A__       =				requests.head(snake_case__			, allow_redirects=snake_case__			, proxies=snake_case__			, timeout=snake_case__						)
   if response.status_code == 2_00:
    A__       =				response.headers.get("ETag"						)
  except (EnvironmentError, requests.exceptions.Timeout):
   # etag is already None
   pass

 A__       =				url_to_filename(snake_case__			, snake_case__						)

 # get cache path to put the file
 A__       =				os.path.join(snake_case__			, 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(snake_case__						):
   return cache_path
  else:
   A__       =				[
       file
       for file in fnmatch.filter(os.listdir(snake_case__						)			, filename + ".*"						)
       if not file.endswith(".json"						) and not file.endswith(".lock"						)
   ]
   if len(snake_case__						) > 0:
    return os.path.join(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(snake_case__						) and not force_download:
  return cache_path

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

  if resume_download:
   A__       =				cache_path + ".incomplete"

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

   A__       =				_resumable_file_manager
   if os.path.exists(snake_case__						):
    A__       =				os.stat(snake_case__						).st_size
   else:
    A__       =				0
  else:
   A__       =				partial(tempfile.NamedTemporaryFile			, dir=snake_case__			, delete=snake_case__						)
   A__       =				0

  # Download to temporary file, then copy to cache dir once finished.
  # Otherwise you get corrupt cache entries if the download gets interrupted.
  with temp_file_manager() as temp_file:
   print(
       "%s not found in cache or force_download set to True, downloading to %s"			, snake_case__			, temp_file.name			, )

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

  os.replace(temp_file.name			, snake_case__						)

  A__       =				{"url": url, "etag": etag}
  A__       =				cache_path + ".json"
  with open(snake_case__			, "w"						) as meta_file:
   json.dump(snake_case__			, snake_case__						)

 return cache_path







def   _SCREAMING_SNAKE_CASE   (							lowercase_			, lowercase_=None						)						->		Dict:
 A__       =				url.encode("utf-8"						)
 A__       =				shaaaa(snake_case__						)
 A__       =				url_hash.hexdigest()

 if etag:
  A__       =				etag.encode("utf-8"						)
  A__       =				shaaaa(snake_case__						)
  filename += "." + etag_hash.hexdigest()

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

 return filename







def   _SCREAMING_SNAKE_CASE   (							lowercase_			, lowercase_=None			, lowercase_=False			, lowercase_=None			, lowercase_=False			, lowercase_=None			, lowercase_=False			, lowercase_=False			, lowercase_=False			, )						->		Tuple:
 if cache_dir is None:
  A__       =				TRANSFORMERS_CACHE
 if isinstance(snake_case__			, snake_case__						):
  A__       =				str(snake_case__						)
 if isinstance(snake_case__			, snake_case__						):
  A__       =				str(snake_case__						)

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

 if extract_compressed_file:
  if not is_zipfile(snake_case__						) and not tarfile.is_tarfile(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/"
  A__,      A__       =				os.path.split(snake_case__						)
  A__       =				output_file.replace("."			, "-"						) + "-extracted"
  A__       =				os.path.join(snake_case__			, snake_case__						)

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

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

  return output_path_extracted

 return output_path







def   _SCREAMING_SNAKE_CASE   (							lowercase_			, lowercase_=","						)						->		Any:
 assert isinstance(snake_case__			, snake_case__						)
 if os.path.isfile(snake_case__						):
  with open(snake_case__						) as f:
   A__       =				eval(f.read()						)
 else:
  A__       =				requests.get(snake_case__						)
  try:
   A__       =				requests.json()
  except Exception:
   A__       =				req.content.decode()
   assert data is not None, "could not connect"
   try:
    A__       =				eval(snake_case__						)
   except Exception:
    A__       =				data.split("\n"						)
  req.close()
 return data







def   _SCREAMING_SNAKE_CASE   (							lowercase_						)						->		int:
 A__       =				requests.get(snake_case__						)
 A__       =				np.array(Image.open(BytesIO(response.content						)						)						)
 return img







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







def   _SCREAMING_SNAKE_CASE   (							)						->		Optional[int]:
 print(f"""{os.path.abspath(os.path.join(snake_case__			, os.pardir						)						)}/demo.ipynb"""						)







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







def   _SCREAMING_SNAKE_CASE   (							lowercase_			, lowercase_=1						)						->		int:
 return (images[i : i + batch] for i in range(0			, len(snake_case__						)			, snake_case__						))






import inspect
import math
import tempfile
import unittest

import numpy as np

from transformers import ViTMAEConfig
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 ViTMAEForPreTraining, ViTMAEModel
    from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
    from PIL import Image

    from transformers import ViTImageProcessor



class 				a							:







 """simple docstring"""






 def __init__(					self		,				lowerCAmelCase_		,				lowerCAmelCase_=13		,				lowerCAmelCase_=30		,				lowerCAmelCase_=2		,				lowerCAmelCase_=3		,				lowerCAmelCase_=True		,				lowerCAmelCase_=True		,				lowerCAmelCase_=32		,				lowerCAmelCase_=5		,				lowerCAmelCase_=4		,				lowerCAmelCase_=37		,				lowerCAmelCase_="gelu"		,				lowerCAmelCase_=0.1		,				lowerCAmelCase_=0.1		,				lowerCAmelCase_=10		,				lowerCAmelCase_=0.02		,				lowerCAmelCase_=3		,				lowerCAmelCase_=0.6		,				lowerCAmelCase_=None		,				)  ->					int:
        _A        =  parent
        _A        =  batch_size
        _A        =  image_size
        _A        =  patch_size
        _A        =  num_channels
        _A        =  is_training
        _A        =  use_labels
        _A        =  hidden_size
        _A        =  num_hidden_layers
        _A        =  num_attention_heads
        _A        =  intermediate_size
        _A        =  hidden_act
        _A        =  hidden_dropout_prob
        _A        =  attention_probs_dropout_prob
        _A        =  type_sequence_label_size
        _A        =  initializer_range
        _A        =  mask_ratio
        _A        =  scope

        # in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
        # (we add 1 for the [CLS] token)
        _A        =  (image_size // patch_size) ** 2
        _A        =  int(math.ceil((1 - mask_ratio) * (num_patches + 1)							)							)



 def      UpperCAmelCase							(					self							)  ->					Union[str, Any]:
        _A        =  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

        _A        =  None
        if self.use_labels:
               _A        =  ids_tensor([self.batch_size]		,				self.type_sequence_label_size							)

        _A        =  self.get_config()

        return config, pixel_values, labels



 def      UpperCAmelCase							(					self							)  ->					str:
        return ViTMAEConfig(
            image_size=self.image_size		,				patch_size=self.patch_size		,				num_channels=self.num_channels		,				hidden_size=self.hidden_size		,				num_hidden_layers=self.num_hidden_layers		,				num_attention_heads=self.num_attention_heads		,				intermediate_size=self.intermediate_size		,				hidden_act=self.hidden_act		,				hidden_dropout_prob=self.hidden_dropout_prob		,				attention_probs_dropout_prob=self.attention_probs_dropout_prob		,				is_decoder=lowerCAmelCase_		,				initializer_range=self.initializer_range		,				mask_ratio=self.mask_ratio		,				)



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_							)  ->					Any:
        _A        =  ViTMAEModel(config=lowerCAmelCase_							)
        model.to(lowerCAmelCase_							)
        model.eval()
        _A        =  model(lowerCAmelCase_							)
        self.parent.assertEqual(result.last_hidden_state.shape		,				(self.batch_size, self.seq_length, self.hidden_size)							)



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_							)  ->					List[Any]:
        _A        =  ViTMAEForPreTraining(lowerCAmelCase_							)
        model.to(lowerCAmelCase_							)
        model.eval()
        _A        =  model(lowerCAmelCase_							)
        _A        =  (self.image_size // self.patch_size) ** 2
        _A        =  self.patch_size**2 * self.num_channels
        self.parent.assertEqual(result.logits.shape		,				(self.batch_size, num_patches, expected_num_channels)							)

        # test greyscale images
        _A        =  1
        _A        =  ViTMAEForPreTraining(lowerCAmelCase_							)
        model.to(lowerCAmelCase_							)
        model.eval()
        _A        =  floats_tensor([self.batch_size, 1, self.image_size, self.image_size]							)
        _A        =  model(lowerCAmelCase_							)
        _A        =  self.patch_size**2
        self.parent.assertEqual(result.logits.shape		,				(self.batch_size, num_patches, expected_num_channels)							)



 def      UpperCAmelCase							(					self							)  ->					Dict:
        _A        =  self.prepare_config_and_inputs()
        _A   ,  _A   ,  _A        =  config_and_inputs
        _A        =  {"""pixel_values""": pixel_values}
        return config, inputs_dict



@require_torch
class 				a							(			__lowerCAmelCase       ,			__lowerCAmelCase       ,			unittest.TestCase       ):







 """simple docstring"""






 lowerCamelCase	:Union[str, Any]    			=		(ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
 lowerCamelCase	:List[Any]    			=		{'''feature-extraction''': ViTMAEModel} if is_torch_available() else {}

 lowerCamelCase	:List[Any]    			=		False
 lowerCamelCase	:Tuple    			=		False
 lowerCamelCase	:int    			=		False
 lowerCamelCase	:Any    			=		False



 def      UpperCAmelCase							(					self							)  ->					str:
        _A        =  ViTMAEModelTester(self							)
        _A        =  ConfigTester(self		,				config_class=lowerCAmelCase_		,				has_text_modality=lowerCAmelCase_		,				hidden_size=37							)



 def      UpperCAmelCase							(					self							)  ->					str:
        self.config_tester.run_common_tests()



 @unittest.skip(reason="""ViTMAE does not use inputs_embeds"""							)
 def      UpperCAmelCase							(					self							)  ->					Optional[Any]:
        pass



 def      UpperCAmelCase							(					self							)  ->					str:
        _A   ,  _A        =  self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
               _A        =  model_class(lowerCAmelCase_							)
               self.assertIsInstance(model.get_input_embeddings()		,				(nn.Module)							)
               _A        =  model.get_output_embeddings()
               self.assertTrue(x is None or isinstance(lowerCAmelCase_		,				nn.Linear							)							)



 def      UpperCAmelCase							(					self							)  ->					Union[str, Any]:
        _A   ,  _A        =  self.model_tester.prepare_config_and_inputs_for_common()

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

               _A        =  ["""pixel_values"""]
               self.assertListEqual(arg_names[:1]		,				lowerCAmelCase_							)



 def      UpperCAmelCase							(					self							)  ->					Union[str, Any]:
        _A        =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowerCAmelCase_							)



 def      UpperCAmelCase							(					self							)  ->					Dict:
        _A        =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_pretraining(*lowerCAmelCase_							)



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_							)  ->					Any:
        # make masks reproducible
        np.random.seed(2							)

        _A        =  int((pt_model.config.image_size // pt_model.config.patch_size) ** 2							)
        _A        =  np.random.uniform(size=(self.model_tester.batch_size, num_patches)							)
        _A        =  torch.from_numpy(lowerCAmelCase_							)

        # Add `noise` argument.
        # PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
        _A        =  pt_noise

        super().check_pt_tf_models(lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_							)



 def      UpperCAmelCase							(					self							)  ->					Any:
        _A   ,  _A        =  self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
               _A        =  model_class(lowerCAmelCase_							)
               model.to(lowerCAmelCase_							)
               model.eval()
               # make random mask reproducible
               torch.manual_seed(2							)
               with torch.no_grad():
                      _A        =  model(**self._prepare_for_class(lowerCAmelCase_		,				lowerCAmelCase_							)							)

               _A        =  outputs[0].cpu().numpy()
               _A        =  0

               with tempfile.TemporaryDirectory() as tmpdirname:
                      model.save_pretrained(lowerCAmelCase_							)
                      _A        =  model_class.from_pretrained(lowerCAmelCase_							)
                      model.to(lowerCAmelCase_							)
                      # make random mask reproducible
                      torch.manual_seed(2							)
                      with torch.no_grad():
                             _A        =  model(**self._prepare_for_class(lowerCAmelCase_		,				lowerCAmelCase_							)							)

                      # Make sure we don't have nans
                      _A        =  after_outputs[0].cpu().numpy()
                      _A        =  0
                      _A        =  np.amax(np.abs(out_a - out_a							)							)
                      self.assertLessEqual(lowerCAmelCase_		,				1E-5							)



 @unittest.skip(
     reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results."""							)
 def      UpperCAmelCase							(					self							)  ->					Tuple:
        pass



 @unittest.skip(
     reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results."""							)
 def      UpperCAmelCase							(					self							)  ->					str:
        pass



 @unittest.skip(
     reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load
    to get deterministic results."""							)
 def      UpperCAmelCase							(					self							)  ->					str:
        pass



 @unittest.skip(reason="""ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load"""							)
 def      UpperCAmelCase							(					self							)  ->					Dict:
        pass



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



 @slow
 def      UpperCAmelCase							(					self							)  ->					Optional[Any]:
        for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               _A        =  ViTMAEModel.from_pretrained(lowerCAmelCase_							)
               self.assertIsNotNone(lowerCAmelCase_							)






def  snake_case  (       )						->   List[str]:
       _A        =  Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""")
       return image







@require_torch
@require_vision
class 				a							(			unittest.TestCase       ):







 """simple docstring"""






 @cached_property
 def      UpperCAmelCase							(					self							)  ->					int:
        return ViTImageProcessor.from_pretrained("""facebook/vit-mae-base"""							) if is_vision_available() else None



 @slow
 def      UpperCAmelCase							(					self							)  ->					Any:
        # make random mask reproducible across the PT and TF model
        np.random.seed(2							)

        _A        =  ViTMAEForPreTraining.from_pretrained("""facebook/vit-mae-base"""							).to(lowerCAmelCase_							)

        _A        =  self.default_image_processor
        _A        =  prepare_img()
        _A        =  image_processor(images=lowerCAmelCase_		,				return_tensors="""pt"""							).to(lowerCAmelCase_							)

        # prepare a noise vector that will be also used for testing the TF model
        # (this way we can ensure that the PT and TF models operate on the same inputs)
        _A        =  ViTMAEConfig()
        _A        =  int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2							)
        _A        =  np.random.uniform(size=(1, num_patches)							)

        # forward pass
        with torch.no_grad():
               _A        =  model(**lowerCAmelCase_		,				noise=torch.from_numpy(lowerCAmelCase_							).to(device=lowerCAmelCase_							)							)

        # verify the logits
        _A        =  torch.Size((1, 1_96, 7_68)							)
        self.assertEqual(outputs.logits.shape		,				lowerCAmelCase_							)

        _A        =  torch.tensor(
            [[-0.0548, -1.7023, -0.9325], [0.3721, -0.5670, -0.2233], [0.8235, -1.3878, -0.3524]]							)

        self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]		,				expected_slice.to(lowerCAmelCase_							)		,				atol=1E-4							)							)




