'''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  UpperCamelCase__					(					_lowercase    :				Union[str, Any]=OBJECTS   ,    _lowercase    :				str=ATTRIBUTES     )							->	Dict:
  __UpperCAmelCase:					Tuple		= []
  with open(_lowercase     ) as f:
    for object in f.readlines():
      vg_classes.append(object.split(""","""     )[0].lower().strip()     )

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






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




class    a :



  """simple docstring"""
  __lowerCAmelCase				      =					{}







  def __init__(							self   ,    snake_case_   ,    snake_case_ = "root"   ,    snake_case_=0					):

    '''simple docstring'''







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

    __UpperCAmelCase:					Tuple		= d







  def __repr__(							self					):

    '''simple docstring'''







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







  def __setattr__(							self   ,    snake_case_   ,    snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					Optional[int]		= val
    __UpperCAmelCase:					Optional[Any]		= val
    __UpperCAmelCase:					Optional[int]		= key.split("""."""					)
    __UpperCAmelCase:					List[str]		= len(UpperCamelCase__					) - 1
    __UpperCAmelCase:					List[str]		= 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:
          __UpperCAmelCase:					Tuple		= val
        else:
          __UpperCAmelCase:					Any		= pointer[l]







  def    lowercase_  (							self					):

    '''simple docstring'''







    return self._pointer







  def    lowercase_  (							self   ,    snake_case_   ,    snake_case_					):

    '''simple docstring'''







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







  def    lowercase_  (							self   ,    snake_case_   ,    snake_case_					):

    '''simple docstring'''







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







  @staticmethod
  def    lowercase_  (							snake_case_					):

    '''simple docstring'''







    with open(UpperCamelCase__					) as stream:
      __UpperCAmelCase:					Tuple		= load(UpperCamelCase__   ,    Loader=UpperCamelCase__					)
    return data







  def __str__(							self					):

    '''simple docstring'''







    __UpperCAmelCase:					Dict		= '''    '''
    if self._name != "root":
      __UpperCAmelCase:					Optional[int]		= F'''{t * (self._level-1)}{self._name}:\n'''
    else:
      __UpperCAmelCase:					int		= ''''''
    __UpperCAmelCase:					Optional[int]		= 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'''
      __UpperCAmelCase:					Optional[Any]		= level
    return r[:-1]







  @classmethod
  def    lowercase_  (							cls   ,    snake_case_   ,    **snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					List[Any]		= cls.get_config_dict(UpperCamelCase__   ,    **UpperCamelCase__					)
    return cls(UpperCamelCase__					)







  @classmethod
  def    lowercase_  (							cls   ,    snake_case_   ,    **snake_case_					):

    '''simple docstring'''







    __UpperCAmelCase:					Dict		= kwargs.pop("""cache_dir"""   ,    UpperCamelCase__					)
    __UpperCAmelCase:					str		= kwargs.pop("""force_download"""   ,    UpperCamelCase__					)
    __UpperCAmelCase:					Tuple		= kwargs.pop("""resume_download"""   ,    UpperCamelCase__					)
    __UpperCAmelCase:					List[str]		= kwargs.pop("""proxies"""   ,    UpperCamelCase__					)
    __UpperCAmelCase:					Tuple		= kwargs.pop("""local_files_only"""   ,    UpperCamelCase__					)

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

    try:
      # Load from URL or cache if already cached
      __UpperCAmelCase:					Optional[Any]		= 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

      __UpperCAmelCase:					List[Any]		= Config.load_yaml(UpperCamelCase__					)

    except EnvironmentError:
      __UpperCAmelCase:					Dict		= '''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  UpperCamelCase__					(					_lowercase    :				Tuple     )							->	int:
  __UpperCAmelCase:					List[Any]		= torch.load("""dump.pt"""   ,    map_location=in_tensor.device     )
  __UpperCAmelCase:					Tuple		= in_tensor.numpy()
  __UpperCAmelCase:					Optional[int]		= 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()     )*1_0_0:.4f} %'''
      " element-wise mismatch"
  )
  raise Exception("""tensors are all good"""     )

  # Hugging face functions below






def  UpperCamelCase__					(					_lowercase    :				Optional[int]     )							->	List[str]:
  __UpperCAmelCase:					List[str]		= urlparse(_lowercase     )
  return parsed.scheme in ("http", "https")






def  UpperCamelCase__					(					_lowercase    :				List[str]   ,    _lowercase    :				Tuple   ,    _lowercase    :				Dict=True     )							->	Tuple:
  __UpperCAmelCase:					Optional[Any]		= CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
  __UpperCAmelCase:					Optional[int]		= '''/''' not in model_id
  if legacy_format:
    return F'''{endpoint}/{model_id}-{filename}'''
  else:
    return F'''{endpoint}/{model_id}/{filename}'''






def  UpperCamelCase__					(					_lowercase    :				Optional[int]   ,    _lowercase    :				List[str]   ,    _lowercase    :				str=None   ,    _lowercase    :				str=0   ,    _lowercase    :				int=None   ,    )							->	Union[str, Any]:
  __UpperCAmelCase:					List[str]		= '''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
  __UpperCAmelCase:					List[str]		= {'''user-agent''': ua}
  if resume_size > 0:
    __UpperCAmelCase:					Dict		= '''bytes=%d-''' % (resume_size,)
  __UpperCAmelCase:					Tuple		= requests.get(_lowercase   ,    stream=_lowercase   ,    proxies=_lowercase   ,    headers=_lowercase     )
  if response.status_code == 4_1_6:  # Range not satisfiable
    return
  __UpperCAmelCase:					Dict		= response.headers.get("""Content-Length"""     )
  __UpperCAmelCase:					List[Any]		= resume_size + int(_lowercase     ) if content_length is not None else None
  __UpperCAmelCase:					Optional[Any]		= tqdm(
      unit="""B"""   ,    unit_scale=_lowercase   ,    total=_lowercase   ,    initial=_lowercase   ,    desc="""Downloading"""   ,    )
  for chunk in response.iter_content(chunk_size=1_0_2_4     ):
    if chunk:  # filter out keep-alive new chunks
      progress.update(len(_lowercase     )     )
      temp_file.write(_lowercase     )
  progress.close()






def  UpperCamelCase__					(					_lowercase    :				List[Any]   ,    _lowercase    :				Optional[int]=None   ,    _lowercase    :				Union[str, Any]=False   ,    _lowercase    :				Optional[Any]=None   ,    _lowercase    :				str=1_0   ,    _lowercase    :				Union[str, Any]=False   ,    _lowercase    :				List[Any]=None   ,    _lowercase    :				List[Any]=False   ,    )							->	Optional[int]:
  if cache_dir is None:
    __UpperCAmelCase:					List[Any]		= TRANSFORMERS_CACHE
  if isinstance(_lowercase   ,    _lowercase     ):
    __UpperCAmelCase:					Optional[Any]		= str(_lowercase     )

  os.makedirs(_lowercase   ,    exist_ok=_lowercase     )

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

  __UpperCAmelCase:					Optional[int]		= url_to_filename(_lowercase   ,    _lowercase     )

  # get cache path to put the file
  __UpperCAmelCase:					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:
      __UpperCAmelCase:					Any		= [
          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.
  __UpperCAmelCase:					List[str]		= 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:
      __UpperCAmelCase:					int		= cache_path + '''.incomplete'''

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

      __UpperCAmelCase:					List[Any]		= _resumable_file_manager
      if os.path.exists(_lowercase     ):
        __UpperCAmelCase:					List[Any]		= os.stat(_lowercase     ).st_size
      else:
        __UpperCAmelCase:					Dict		= 0
    else:
      __UpperCAmelCase:					Any		= partial(tempfile.NamedTemporaryFile   ,    dir=_lowercase   ,    delete=_lowercase     )
      __UpperCAmelCase:					List[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"""   ,    _lowercase   ,    temp_file.name   ,    )

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

    os.replace(temp_file.name   ,    _lowercase     )

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

  return cache_path






def  UpperCamelCase__					(					_lowercase    :				Optional[int]   ,    _lowercase    :				int=None     )							->	List[str]:
  __UpperCAmelCase:					Optional[int]		= url.encode("""utf-8"""     )
  __UpperCAmelCase:					Optional[Any]		= shaaaa(_lowercase     )
  __UpperCAmelCase:					Optional[Any]		= url_hash.hexdigest()

  if etag:
    __UpperCAmelCase:					Any		= etag.encode("""utf-8"""     )
    __UpperCAmelCase:					Dict		= shaaaa(_lowercase     )
    filename += "." + etag_hash.hexdigest()

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

  return filename






def  UpperCamelCase__					(					_lowercase    :				List[Any]   ,    _lowercase    :				List[str]=None   ,    _lowercase    :				Optional[int]=False   ,    _lowercase    :				int=None   ,    _lowercase    :				int=False   ,    _lowercase    :				Optional[int]=None   ,    _lowercase    :				Dict=False   ,    _lowercase    :				int=False   ,    _lowercase    :				Any=False   ,    )							->	Optional[int]:
  if cache_dir is None:
    __UpperCAmelCase:					List[Any]		= TRANSFORMERS_CACHE
  if isinstance(_lowercase   ,    _lowercase     ):
    __UpperCAmelCase:					int		= str(_lowercase     )
  if isinstance(_lowercase   ,    _lowercase     ):
    __UpperCAmelCase:					int		= str(_lowercase     )

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






def  UpperCamelCase__					(					_lowercase    :				List[Any]     )							->	str:
  __UpperCAmelCase:					Any		= requests.get(_lowercase     )
  __UpperCAmelCase:					Optional[Any]		= np.array(Image.open(BytesIO(response.content     )     )     )
  return img






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






def  UpperCamelCase__					(					)							->	Optional[Any]:
  print(F'''{os.path.abspath(os.path.join(_lowercase   ,    os.pardir     )     )}/demo.ipynb'''     )






def  UpperCamelCase__					(					_lowercase    :				str   ,    _lowercase    :				List[Any]="RGB"     )							->	str:
  assert isinstance(_lowercase   ,    _lowercase     )
  if os.path.isfile(_lowercase     ):
    __UpperCAmelCase:					Any		= cva.imread(_lowercase     )
  else:
    __UpperCAmelCase:					Union[str, Any]		= get_image_from_url(_lowercase     )
    assert img is not None, F'''could not connect to: {im}'''
  __UpperCAmelCase:					int		= cva.cvtColor(_lowercase   ,    cva.COLOR_BGR2RGB     )
  if input_format == "RGB":
    __UpperCAmelCase:					Optional[Any]		= img[:, :, ::-1]
  return img







def  UpperCamelCase__					(					_lowercase    :				List[Any]   ,    _lowercase    :				Union[str, Any]=1     )							->	Dict:
  return (images[i : i + batch] for i in range(0   ,    len(_lowercase     )   ,    _lowercase     ))






'''simple docstring'''






import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import PoolFormerImageProcessor




class    a (       unittest.TestCase				):



  """simple docstring"""







  def __init__(							self   ,    snake_case_   ,    snake_case_=7   ,    snake_case_=3   ,    snake_case_=30   ,    snake_case_=400   ,    snake_case_=True   ,    snake_case_=None   ,    snake_case_=0.9   ,    snake_case_=None   ,    snake_case_=True   ,    snake_case_=[0.5, 0.5, 0.5]   ,    snake_case_=[0.5, 0.5, 0.5]   ,    ):

    '''simple docstring'''







    __UpperCAmelCase:					List[Any]		= size if size is not None else {"""shortest_edge""": 30}
    __UpperCAmelCase:					str		= crop_size if crop_size is not None else {"""height""": 30, """width""": 30}
    __UpperCAmelCase:					Dict		= parent
    __UpperCAmelCase:					Union[str, Any]		= batch_size
    __UpperCAmelCase:					List[Any]		= num_channels
    __UpperCAmelCase:					List[str]		= min_resolution
    __UpperCAmelCase:					List[str]		= max_resolution
    __UpperCAmelCase:					Tuple		= do_resize_and_center_crop
    __UpperCAmelCase:					Union[str, Any]		= size
    __UpperCAmelCase:					str		= crop_pct
    __UpperCAmelCase:					List[Any]		= crop_size
    __UpperCAmelCase:					str		= do_normalize
    __UpperCAmelCase:					Union[str, Any]		= image_mean
    __UpperCAmelCase:					List[str]		= image_std







  def    lowercase_  (							self					):

    '''simple docstring'''







    return {
        "size": self.size,
        "do_resize_and_center_crop": self.do_resize_and_center_crop,
        "crop_pct": self.crop_pct,
        "crop_size": self.crop_size,
        "do_normalize": self.do_normalize,
        "image_mean": self.image_mean,
        "image_std": self.image_std,
    }




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



  """simple docstring"""
  __lowerCAmelCase				      =					PoolFormerImageProcessor if is_vision_available() else None







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					Any		= PoolFormerImageProcessingTester(self					)







  @property
  def    lowercase_  (							self					):

    '''simple docstring'''







    return self.image_processor_tester.prepare_image_processor_dict()







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					Optional[Any]		= self.image_processing_class(**self.image_processor_dict					)
    self.assertTrue(hasattr(snake_case_   ,    """do_resize_and_center_crop"""					)					)
    self.assertTrue(hasattr(snake_case_   ,    """size"""					)					)
    self.assertTrue(hasattr(snake_case_   ,    """crop_pct"""					)					)
    self.assertTrue(hasattr(snake_case_   ,    """do_normalize"""					)					)
    self.assertTrue(hasattr(snake_case_   ,    """image_mean"""					)					)
    self.assertTrue(hasattr(snake_case_   ,    """image_std"""					)					)







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					List[str]		= self.image_processing_class.from_dict(self.image_processor_dict					)
    self.assertEqual(image_processor.size   ,    {"""shortest_edge""": 30}					)
    self.assertEqual(image_processor.crop_size   ,    {"""height""": 30, """width""": 30}					)

    __UpperCAmelCase:					Dict		= self.image_processing_class.from_dict(self.image_processor_dict   ,    size=42   ,    crop_size=84					)
    self.assertEqual(image_processor.size   ,    {"""shortest_edge""": 42}					)
    self.assertEqual(image_processor.crop_size   ,    {"""height""": 84, """width""": 84}					)







  def    lowercase_  (							self					):

    '''simple docstring'''







    pass







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					Any		= self.image_processing_class(**self.image_processor_dict					)
    # create random PIL images
    __UpperCAmelCase:					Optional[Any]		= prepare_image_inputs(self.image_processor_tester   ,    equal_resolution=snake_case_					)
    for image in image_inputs:
      self.assertIsInstance(snake_case_   ,    Image.Image					)

    # Test not batched input
    __UpperCAmelCase:					Optional[int]		= image_processing(image_inputs[0]   ,    return_tensors="""pt"""					).pixel_values

    self.assertEqual(
        encoded_images.shape   ,    (
            1,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size["""height"""],
            self.image_processor_tester.crop_size["""width"""],
        )   ,    )

    # Test batched
    __UpperCAmelCase:					Any		= image_processing(snake_case_   ,    return_tensors="""pt"""					).pixel_values
    self.assertEqual(
        encoded_images.shape   ,    (
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size["""height"""],
            self.image_processor_tester.crop_size["""width"""],
        )   ,    )







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					Optional[Any]		= self.image_processing_class(**self.image_processor_dict					)
    # create random numpy tensors
    __UpperCAmelCase:					Union[str, Any]		= prepare_image_inputs(self.image_processor_tester   ,    equal_resolution=snake_case_   ,    numpify=snake_case_					)
    for image in image_inputs:
      self.assertIsInstance(snake_case_   ,    np.ndarray					)

    # Test not batched input
    __UpperCAmelCase:					Union[str, Any]		= image_processing(image_inputs[0]   ,    return_tensors="""pt"""					).pixel_values
    self.assertEqual(
        encoded_images.shape   ,    (
            1,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size["""height"""],
            self.image_processor_tester.crop_size["""width"""],
        )   ,    )

    # Test batched
    __UpperCAmelCase:					str		= image_processing(snake_case_   ,    return_tensors="""pt"""					).pixel_values
    self.assertEqual(
        encoded_images.shape   ,    (
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size["""height"""],
            self.image_processor_tester.crop_size["""width"""],
        )   ,    )







  def    lowercase_  (							self					):

    '''simple docstring'''







    __UpperCAmelCase:					Any		= self.image_processing_class(**self.image_processor_dict					)
    # create random PyTorch tensors
    __UpperCAmelCase:					Dict		= prepare_image_inputs(self.image_processor_tester   ,    equal_resolution=snake_case_   ,    torchify=snake_case_					)
    for image in image_inputs:
      self.assertIsInstance(snake_case_   ,    torch.Tensor					)

    # Test not batched input
    __UpperCAmelCase:					Optional[int]		= image_processing(image_inputs[0]   ,    return_tensors="""pt"""					).pixel_values
    self.assertEqual(
        encoded_images.shape   ,    (
            1,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size["""height"""],
            self.image_processor_tester.crop_size["""width"""],
        )   ,    )

    # Test batched
    __UpperCAmelCase:					Optional[int]		= image_processing(snake_case_   ,    return_tensors="""pt"""					).pixel_values
    self.assertEqual(
        encoded_images.shape   ,    (
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size["""height"""],
            self.image_processor_tester.crop_size["""width"""],
        )   ,    )