
'''simple docstring'''
import copy
import fnmatch
import json
import os
import pickle as pkl
import shutil
import sys
import tarfile
import tempfile
from collections import OrderedDict
from contextlib import contextmanager
from functools import partial
from hashlib import shaaaa
from io import BytesIO
from pathlib import Path
from urllib.parse import urlparse
from zipfile import ZipFile, is_zipfile

import cva
import numpy as np
import requests
import wget
from filelock import FileLock
from PIL import Image
from tqdm.auto import tqdm
from yaml import Loader, dump, load


try:
  import torch

  a_      =  True
except ImportError:
  a_      =  False


try:
  from torch.hub import _get_torch_home

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

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

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

def   UpperCamelCase_   (		__SCREAMING_SNAKE_CASE=OBJECTS,						__SCREAMING_SNAKE_CASE=ATTRIBUTES				):
    """simple docstring"""






    snake_case_       :			Dict   	=  []
    with open(__UpperCamelCase				) as f:
        for object in f.readlines():
            vg_classes.append(object.split(","				)[0].lower().strip()				)

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

def   UpperCamelCase_   (		__SCREAMING_SNAKE_CASE				):
    """simple docstring"""






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






class       UpperCAmelCase_ :
 UpperCAmelCase_									=  {}





 def __init__(			self       ,				lowercase_       ,				lowercase_ = "root"       ,				lowercase_=0):
     snake_case_       :			List[str]   	=  name
     snake_case_       :			str   	=  level
     snake_case_       :			Tuple   	=  {}
     for k, v in dictionary.items():
         if v is None:
             raise ValueError()
         snake_case_       :			Any   	=  copy.deepcopy(_UpperCamelCase)
         snake_case_       :			Tuple   	=  copy.deepcopy(_UpperCamelCase)
         if isinstance(_UpperCamelCase       ,				_UpperCamelCase):
             snake_case_       :			Dict   	=  Config(_UpperCamelCase       ,				name=_UpperCamelCase       ,				level=level + 1)
         snake_case_       :			List[Any]   	=  v
         setattr(self       ,				_UpperCamelCase       ,				_UpperCamelCase)

     snake_case_       :			Union[str, Any]   	=  d





 def __repr__(			self):
     return str(list((self._pointer.keys())))





 def __setattr__(			self       ,				lowercase_       ,				lowercase_):
     snake_case_       :			Dict   	=  val
     snake_case_       :			Optional[int]   	=  val
     snake_case_       :			Tuple   	=  key.split(".")
     snake_case_       :			List[Any]   	=  len(_UpperCamelCase) - 1
     snake_case_       :			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:
                 snake_case_       :			Any   	=  val
             else:
                 snake_case_       :			List[str]   	=  pointer[l]





 def 				snake_case__						(			self):
     return self._pointer





 def 				snake_case__						(			self       ,				lowercase_       ,				lowercase_):





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





 def 				snake_case__						(			self       ,				lowercase_       ,				lowercase_):





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





 @staticmethod
 def 				snake_case__						(			lowercase_):





     with open(_UpperCamelCase) as stream:
         snake_case_       :			int   	=  load(_UpperCamelCase       ,				Loader=_UpperCamelCase)
     return data





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





 @classmethod
 def 				snake_case__						(			cls       ,				lowercase_       ,				**lowercase_):
     snake_case_	,       snake_case_       :			Optional[Any]   	=  cls.get_config_dict(_UpperCamelCase       ,				**_UpperCamelCase)
     return cls(_UpperCamelCase)





 @classmethod
 def 				snake_case__						(			cls       ,				lowercase_       ,				**lowercase_):
     snake_case_       :			List[str]   	=  kwargs.pop("cache_dir"       ,				_UpperCamelCase)
     snake_case_       :			List[str]   	=  kwargs.pop("force_download"       ,				_UpperCamelCase)
     snake_case_       :			Tuple   	=  kwargs.pop("resume_download"       ,				_UpperCamelCase)
     snake_case_       :			Optional[int]   	=  kwargs.pop("proxies"       ,				_UpperCamelCase)
     snake_case_       :			int   	=  kwargs.pop("local_files_only"       ,				_UpperCamelCase)

     if os.path.isdir(_UpperCamelCase):
         snake_case_       :			Optional[Any]   	=  os.path.join(_UpperCamelCase       ,				_UpperCamelCase)
     elif os.path.isfile(_UpperCamelCase) or is_remote_url(_UpperCamelCase):
         snake_case_       :			Union[str, Any]   	=  pretrained_model_name_or_path
     else:
         snake_case_       :			List[Any]   	=  hf_bucket_url(_UpperCamelCase       ,				filename=_UpperCamelCase       ,				use_cdn=_UpperCamelCase)

     try:
         # Load from URL or cache if already cached
         snake_case_       :			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

         snake_case_       :			List[str]   	=  Config.load_yaml(_UpperCamelCase)

     except EnvironmentError:
         snake_case_       :			Any   	=  "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_   (		__SCREAMING_SNAKE_CASE				):
    """simple docstring"""






    snake_case_       :			Optional[int]   	=  torch.load("dump.pt",						map_location=in_tensor.device				)
    snake_case_       :			List[str]   	=  in_tensor.numpy()
    snake_case_       :			List[str]   	=  out_tensor.numpy()[0]
    print(na.shape,						na[0, 0, :5]				)
    print(na.shape,						na[0, 0, :5]				)
    assert np.allclose(__UpperCamelCase,						__UpperCamelCase,						rtol=0.01,						atol=0.1				), (
        f'{sum([1 for x in np.isclose(__UpperCamelCase,						__UpperCamelCase,						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_   (		__SCREAMING_SNAKE_CASE				):
    """simple docstring"""






    snake_case_       :			Optional[Any]   	=  urlparse(__UpperCamelCase				)
    return parsed.scheme in ("http", "https")

def   UpperCamelCase_   (		__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE=True				):
    """simple docstring"""






    snake_case_       :			str   	=  CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
    snake_case_       :			List[str]   	=  "/" not in model_id
    if legacy_format:
        return f'{endpoint}/{model_id}-{filename}'
    else:
        return f'{endpoint}/{model_id}/{filename}'

def   UpperCamelCase_   (		__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE=None,						__SCREAMING_SNAKE_CASE=0,						__SCREAMING_SNAKE_CASE=None,						):
    """simple docstring"""






    snake_case_       :			Any   	=  "python/{}".format(sys.version.split()[0]				)
    if _torch_available:
        ua += "; torch/{}".format(torch.__version__				)
    if isinstance(__UpperCamelCase,						__UpperCamelCase				):
        ua += "; " + "; ".join("{}/{}".format(__UpperCamelCase,						__UpperCamelCase				) for k, v in user_agent.items()				)
    elif isinstance(__UpperCamelCase,						__UpperCamelCase				):
        ua += "; " + user_agent
    snake_case_       :			Union[str, Any]   	=  {"user-agent": ua}
    if resume_size > 0:
        snake_case_       :			int   	=  "bytes=%d-" % (resume_size,)
    snake_case_       :			Any   	=  requests.get(__UpperCamelCase,						stream=__UpperCamelCase,						proxies=__UpperCamelCase,						headers=__UpperCamelCase				)
    if response.status_code == 4_1_6:  # Range not satisfiable
        return
    snake_case_       :			Any   	=  response.headers.get("Content-Length"				)
    snake_case_       :			List[Any]   	=  resume_size + int(__UpperCamelCase				) if content_length is not None else None
    snake_case_       :			Any   	=  tqdm(
        unit="B",						unit_scale=__UpperCamelCase,						total=__UpperCamelCase,						initial=__UpperCamelCase,						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(__UpperCamelCase				)				)
            temp_file.write(__UpperCamelCase				)
    progress.close()

def   UpperCamelCase_   (		__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE=None,						__SCREAMING_SNAKE_CASE=False,						__SCREAMING_SNAKE_CASE=None,						__SCREAMING_SNAKE_CASE=1_0,						__SCREAMING_SNAKE_CASE=False,						__SCREAMING_SNAKE_CASE=None,						__SCREAMING_SNAKE_CASE=False,						):
    """simple docstring"""






    if cache_dir is None:
        snake_case_       :			Dict   	=  TRANSFORMERS_CACHE
    if isinstance(__UpperCamelCase,						__UpperCamelCase				):
        snake_case_       :			Any   	=  str(__UpperCamelCase				)

    os.makedirs(__UpperCamelCase,						exist_ok=__UpperCamelCase				)

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

    snake_case_       :			str   	=  url_to_filename(__UpperCamelCase,						__UpperCamelCase				)

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

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

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

        if resume_download:
            snake_case_       :			Optional[int]   	=  cache_path + ".incomplete"

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

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

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

        os.replace(temp_file.name,						__UpperCamelCase				)

        snake_case_       :			Any   	=  {"url": url, "etag": etag}
        snake_case_       :			int   	=  cache_path + ".json"
        with open(__UpperCamelCase,						"w"				) as meta_file:
            json.dump(__UpperCamelCase,						__UpperCamelCase				)

    return cache_path

def   UpperCamelCase_   (		__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE=None				):
    """simple docstring"""






    snake_case_       :			Optional[int]   	=  url.encode("utf-8"				)
    snake_case_       :			int   	=  shaaaa(__UpperCamelCase				)
    snake_case_       :			Any   	=  url_hash.hexdigest()

    if etag:
        snake_case_       :			Optional[int]   	=  etag.encode("utf-8"				)
        snake_case_       :			str   	=  shaaaa(__UpperCamelCase				)
        filename += "." + etag_hash.hexdigest()

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

    return filename

def   UpperCamelCase_   (		__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE=None,						__SCREAMING_SNAKE_CASE=False,						__SCREAMING_SNAKE_CASE=None,						__SCREAMING_SNAKE_CASE=False,						__SCREAMING_SNAKE_CASE=None,						__SCREAMING_SNAKE_CASE=False,						__SCREAMING_SNAKE_CASE=False,						__SCREAMING_SNAKE_CASE=False,						):
    """simple docstring"""






    if cache_dir is None:
        snake_case_       :			Optional[int]   	=  TRANSFORMERS_CACHE
    if isinstance(__UpperCamelCase,						__UpperCamelCase				):
        snake_case_       :			str   	=  str(__UpperCamelCase				)
    if isinstance(__UpperCamelCase,						__UpperCamelCase				):
        snake_case_       :			Any   	=  str(__UpperCamelCase				)

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

    if extract_compressed_file:
        if not is_zipfile(__UpperCamelCase				) and not tarfile.is_tarfile(__UpperCamelCase				):
            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/"
        snake_case_	,       snake_case_       :			Tuple   	=  os.path.split(__UpperCamelCase				)
        snake_case_       :			Optional[int]   	=  output_file.replace(".",						"-"				) + "-extracted"
        snake_case_       :			Union[str, Any]   	=  os.path.join(__UpperCamelCase,						__UpperCamelCase				)

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

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

        return output_path_extracted

    return output_path

def   UpperCamelCase_   (		__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE=","				):
    """simple docstring"""






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

def   UpperCamelCase_   (		__SCREAMING_SNAKE_CASE				):
    """simple docstring"""






    snake_case_       :			Dict   	=  requests.get(__UpperCamelCase				)
    snake_case_       :			str   	=  np.array(Image.open(BytesIO(response.content				)				)				)
    return img

def   UpperCamelCase_   (		__SCREAMING_SNAKE_CASE				):
    """simple docstring"""






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

def   UpperCamelCase_   (		):
    """simple docstring"""






    print(f'{os.path.abspath(os.path.join(__UpperCamelCase,						os.pardir				)				)}/demo.ipynb'				)

def   UpperCamelCase_   (		__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE="RGB"				):
    """simple docstring"""






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


def   UpperCamelCase_   (		__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE=1				):
    """simple docstring"""






    return (images[i : i + batch] for i in range(0,						len(__UpperCamelCase				),						__UpperCamelCase				))










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 ConvNextImageProcessor
class       __a					( unittest.TestCase     ):


     """simple docstring"""


     def __init__(	self			: List[str]				,_UpperCamelCase			: int				,_UpperCamelCase			: int=7				,_UpperCamelCase			: int=3				,_UpperCamelCase			: List[str]=1_8				,_UpperCamelCase			: Dict=3_0				,_UpperCamelCase			: Tuple=4_0_0				,_UpperCamelCase			: Optional[int]=True				,_UpperCamelCase			: int=None				,_UpperCamelCase			: str=0.875				,_UpperCamelCase			: Tuple=True				,_UpperCamelCase			: Dict=[0.5, 0.5, 0.5]				,_UpperCamelCase			: str=[0.5, 0.5, 0.5]				,)		->   int:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=size if size is not None else {"""shortest_edge""": 2_0}
         SCREAMING_SNAKE_CASE__								=parent
         SCREAMING_SNAKE_CASE__								=batch_size
         SCREAMING_SNAKE_CASE__								=num_channels
         SCREAMING_SNAKE_CASE__								=image_size
         SCREAMING_SNAKE_CASE__								=min_resolution
         SCREAMING_SNAKE_CASE__								=max_resolution
         SCREAMING_SNAKE_CASE__								=do_resize
         SCREAMING_SNAKE_CASE__								=size
         SCREAMING_SNAKE_CASE__								=crop_pct
         SCREAMING_SNAKE_CASE__								=do_normalize
         SCREAMING_SNAKE_CASE__								=image_mean
         SCREAMING_SNAKE_CASE__								=image_std




     def   __A				(	self			: Dict						)		->   str:


         '''simple docstring'''





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






@require_torch
@require_vision
class       __a					( __lowerCamelCase							,     unittest.TestCase     ):


     """simple docstring"""


     _A				:    Dict									=  ConvNextImageProcessor if is_vision_available() else None




     def   __A				(	self			: List[str]						)		->   Dict:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=ConvNextImageProcessingTester(self						)




     @property
     def   __A				(	self			: str						)		->   Optional[int]:


         '''simple docstring'''





         return self.image_processor_tester.prepare_image_processor_dict()




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


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=self.image_processing_class(**self.image_processor_dict						)
         self.assertTrue(hasattr(_UpperCamelCase				,"""do_resize"""						)						)
         self.assertTrue(hasattr(_UpperCamelCase				,"""size"""						)						)
         self.assertTrue(hasattr(_UpperCamelCase				,"""crop_pct"""						)						)
         self.assertTrue(hasattr(_UpperCamelCase				,"""do_normalize"""						)						)
         self.assertTrue(hasattr(_UpperCamelCase				,"""image_mean"""						)						)
         self.assertTrue(hasattr(_UpperCamelCase				,"""image_std"""						)						)




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


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=self.image_processing_class.from_dict(self.image_processor_dict						)
         self.assertEqual(image_processor.size				,{"""shortest_edge""": 2_0}						)

         SCREAMING_SNAKE_CASE__								=self.image_processing_class.from_dict(self.image_processor_dict				,size=4_2						)
         self.assertEqual(image_processor.size				,{"""shortest_edge""": 4_2}						)




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


         '''simple docstring'''





         pass




     def   __A				(	self			: Optional[int]						)		->   int:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=self.image_processing_class(**self.image_processor_dict						)
         # create random PIL images
         SCREAMING_SNAKE_CASE__								=prepare_image_inputs(self.image_processor_tester				,equal_resolution=_UpperCamelCase						)
         for image in image_inputs:
             self.assertIsInstance(_UpperCamelCase				,Image.Image						)

         # Test not batched input
         SCREAMING_SNAKE_CASE__								=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.size["""shortest_edge"""],
                 self.image_processor_tester.size["""shortest_edge"""],
             )				,)

         # Test batched
         SCREAMING_SNAKE_CASE__								=image_processing(_UpperCamelCase				,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.size["""shortest_edge"""],
                 self.image_processor_tester.size["""shortest_edge"""],
             )				,)




     def   __A				(	self			: str						)		->   str:


         '''simple docstring'''





         SCREAMING_SNAKE_CASE__								=self.image_processing_class(**self.image_processor_dict						)
         # create random numpy tensors
         SCREAMING_SNAKE_CASE__								=prepare_image_inputs(self.image_processor_tester				,equal_resolution=_UpperCamelCase				,numpify=_UpperCamelCase						)
         for image in image_inputs:
             self.assertIsInstance(_UpperCamelCase				,np.ndarray						)

         # Test not batched input
         SCREAMING_SNAKE_CASE__								=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.size["""shortest_edge"""],
                 self.image_processor_tester.size["""shortest_edge"""],
             )				,)

         # Test batched
         SCREAMING_SNAKE_CASE__								=image_processing(_UpperCamelCase				,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.size["""shortest_edge"""],
                 self.image_processor_tester.size["""shortest_edge"""],
             )				,)




     def   __A				(	self			: List[Any]						)		->   Union[str, Any]:


         '''simple docstring'''





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

         # Test not batched input
         SCREAMING_SNAKE_CASE__								=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.size["""shortest_edge"""],
                 self.image_processor_tester.size["""shortest_edge"""],
             )				,)

         # Test batched
         SCREAMING_SNAKE_CASE__								=image_processing(_UpperCamelCase				,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.size["""shortest_edge"""],
                 self.image_processor_tester.size["""shortest_edge"""],
             )				,)






