


'''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_  :				List[Any]						= True
except ImportError:
     a_  :				Tuple						= False


try:
     from torch.hub import _get_torch_home

     a_  :				Optional[int]						= _get_torch_home()
except ImportError:
     a_  :				List[Any]						= os.path.expanduser(
         os.getenv("""TORCH_HOME""", os.path.join(os.getenv("""XDG_CACHE_HOME""", """~/.cache"""), """torch"""))
     )

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

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





def __snake_case					(      UpperCAmelCase_						:						Union[str, Any]=OBJECTS					,				UpperCAmelCase_						:						int=ATTRIBUTES		):
 lowerCamelCase_      =  []
 with open(UpperCAmelCase_		) as f:
  for object in f.readlines():
   vg_classes.append(object.split(","		)[0].lower().strip()		)

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





def __snake_case					(      UpperCAmelCase_						:						Dict		):
 lowerCamelCase_      =  OrderedDict()
 with open(UpperCAmelCase_					,				"rb"		) as f:
  lowerCamelCase_      =  pkl.load(UpperCAmelCase_		)["model"]
 for k in copy.deepcopy(list(ckp.keys()		)		):
  lowerCamelCase_      =  ckp.pop(UpperCAmelCase_		)
  if isinstance(UpperCAmelCase_					,				np.ndarray		):
   lowerCamelCase_      =  torch.tensor(UpperCAmelCase_		)
  else:
   assert isinstance(UpperCAmelCase_					,				torch.tensor		), type(UpperCAmelCase_		)
  lowerCamelCase_      =  v
 return r





class      snake_case    :





  """simple docstring"""




  _lowerCamelCase		    =  {}






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







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

   lowerCamelCase_      =  d






  def __repr__(    self     ):







   """simple docstring"""
   return str(list((self._pointer.keys())     )     )






  def __setattr__(    self			,  UpperCamelCase			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  val
   lowerCamelCase_      =  val
   lowerCamelCase_      =  key.split("."     )
   lowerCamelCase_      =  len(UpperCamelCase     ) - 1
   lowerCamelCase_      =  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:
      lowerCamelCase_      =  val
     else:
      lowerCamelCase_      =  pointer[l]






  def   snake_case		(    self     ):







   """simple docstring"""
   return self._pointer






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase     ):







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






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase     ):







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






  @staticmethod
  def   snake_case		(    UpperCamelCase     ):







   """simple docstring"""
   with open(UpperCamelCase     ) as stream:
    lowerCamelCase_      =  load(UpperCamelCase			,  Loader=UpperCamelCase     )
   return data






  def __str__(    self     ):







   """simple docstring"""
   lowerCamelCase_      =  "    "
   if self._name != "root":
    lowerCamelCase_      =  f'''{t * (self._level-1)}{self._name}:\n'''
   else:
    lowerCamelCase_      =  ""
   lowerCamelCase_      =  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'''
    lowerCamelCase_      =  level
   return r[:-1]






  @classmethod
  def   snake_case		(    cls			,  UpperCamelCase			,  **UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_ ,lowerCamelCase_      =  cls.get_config_dict(UpperCamelCase			,  **UpperCamelCase     )
   return cls(UpperCamelCase     )







  @classmethod
  def   snake_case		(    cls			,  UpperCamelCase			,  **UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  kwargs.pop("cache_dir"			,  UpperCamelCase     )
   lowerCamelCase_      =  kwargs.pop("force_download"			,  UpperCamelCase     )
   lowerCamelCase_      =  kwargs.pop("resume_download"			,  UpperCamelCase     )
   lowerCamelCase_      =  kwargs.pop("proxies"			,  UpperCamelCase     )
   lowerCamelCase_      =  kwargs.pop("local_files_only"			,  UpperCamelCase     )

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

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

    lowerCamelCase_      =  Config.load_yaml(UpperCamelCase     )

   except EnvironmentError:
    lowerCamelCase_      =  "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 __snake_case					(      UpperCAmelCase_						:						Union[str, Any]		):
 lowerCamelCase_      =  torch.load("dump.pt"					,				map_location=in_tensor.device		)
 lowerCamelCase_      =  in_tensor.numpy()
 lowerCamelCase_      =  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()		)*100:.4f} %'''
     " element-wise mismatch"
 )
 raise Exception("tensors are all good"		)

 # Hugging face functions below





def __snake_case					(      UpperCAmelCase_						:						Optional[int]		):
 lowerCamelCase_      =  urlparse(UpperCAmelCase_		)
 return parsed.scheme in ("http", "https")





def __snake_case					(      UpperCAmelCase_						:						str					,				UpperCAmelCase_						:						str					,				UpperCAmelCase_						:						List[Any]=True		):
 lowerCamelCase_      =  CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
 lowerCamelCase_      =  "/" not in model_id
 if legacy_format:
  return F'''{endpoint}/{model_id}-{filename}'''
 else:
  return F'''{endpoint}/{model_id}/{filename}'''





def __snake_case					(      UpperCAmelCase_						:						Optional[int]					,				UpperCAmelCase_						:						List[Any]					,				UpperCAmelCase_						:						Dict=None					,				UpperCAmelCase_						:						int=0					,				UpperCAmelCase_						:						Tuple=None					,				):
 lowerCamelCase_      =  "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
 lowerCamelCase_      =  {"user-agent": ua}
 if resume_size > 0:
  lowerCamelCase_      =  "bytes=%d-" % (resume_size,)
 lowerCamelCase_      =  requests.get(UpperCAmelCase_					,				stream=UpperCAmelCase_					,				proxies=UpperCAmelCase_					,				headers=UpperCAmelCase_		)
 if response.status_code == 416:  # Range not satisfiable
  return
 lowerCamelCase_      =  response.headers.get("Content-Length"		)
 lowerCamelCase_      =  resume_size + int(UpperCAmelCase_		) if content_length is not None else None
 lowerCamelCase_      =  tqdm(
     unit="B"					,				unit_scale=UpperCAmelCase_					,				total=UpperCAmelCase_					,				initial=UpperCAmelCase_					,				desc="Downloading"					,				)
 for chunk in response.iter_content(chunk_size=1024		):
  if chunk:  # filter out keep-alive new chunks
   progress.update(len(UpperCAmelCase_		)		)
   temp_file.write(UpperCAmelCase_		)
 progress.close()





def __snake_case					(      UpperCAmelCase_						:						List[str]					,				UpperCAmelCase_						:						List[Any]=None					,				UpperCAmelCase_						:						Dict=False					,				UpperCAmelCase_						:						Optional[int]=None					,				UpperCAmelCase_						:						Optional[Any]=10					,				UpperCAmelCase_						:						Tuple=False					,				UpperCAmelCase_						:						Union[str, Any]=None					,				UpperCAmelCase_						:						Optional[int]=False					,				):
 if cache_dir is None:
  lowerCamelCase_      =  TRANSFORMERS_CACHE
 if isinstance(UpperCAmelCase_					,				UpperCAmelCase_		):
  lowerCamelCase_      =  str(UpperCAmelCase_		)

 os.makedirs(UpperCAmelCase_					,				exist_ok=UpperCAmelCase_		)

 lowerCamelCase_      =  None
 if not local_files_only:
  try:
   lowerCamelCase_      =  requests.head(UpperCAmelCase_					,				allow_redirects=UpperCAmelCase_					,				proxies=UpperCAmelCase_					,				timeout=UpperCAmelCase_		)
   if response.status_code == 200:
    lowerCamelCase_      =  response.headers.get("ETag"		)
  except (EnvironmentError, requests.exceptions.Timeout):
   # etag is already None
   pass

 lowerCamelCase_      =  url_to_filename(UpperCAmelCase_					,				UpperCAmelCase_		)

 # get cache path to put the file
 lowerCamelCase_      =  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:
   lowerCamelCase_      =  [
       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.
 lowerCamelCase_      =  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:
   lowerCamelCase_      =  cache_path + ".incomplete"

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

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

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

 return cache_path





def __snake_case					(      UpperCAmelCase_						:						Union[str, Any]					,				UpperCAmelCase_						:						List[Any]=None		):
 lowerCamelCase_      =  url.encode("utf-8"		)
 lowerCamelCase_      =  shaaaa(UpperCAmelCase_		)
 lowerCamelCase_      =  url_hash.hexdigest()

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

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

 return filename





def __snake_case					(      UpperCAmelCase_						:						Optional[int]					,				UpperCAmelCase_						:						List[Any]=None					,				UpperCAmelCase_						:						Tuple=False					,				UpperCAmelCase_						:						Tuple=None					,				UpperCAmelCase_						:						Optional[Any]=False					,				UpperCAmelCase_						:						Any=None					,				UpperCAmelCase_						:						List[str]=False					,				UpperCAmelCase_						:						int=False					,				UpperCAmelCase_						:						int=False					,				):
 if cache_dir is None:
  lowerCamelCase_      =  TRANSFORMERS_CACHE
 if isinstance(UpperCAmelCase_					,				UpperCAmelCase_		):
  lowerCamelCase_      =  str(UpperCAmelCase_		)
 if isinstance(UpperCAmelCase_					,				UpperCAmelCase_		):
  lowerCamelCase_      =  str(UpperCAmelCase_		)

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





def __snake_case					(      UpperCAmelCase_						:						Dict		):
 lowerCamelCase_      =  requests.get(UpperCAmelCase_		)
 lowerCamelCase_      =  np.array(Image.open(BytesIO(response.content		)		)		)
 return img





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





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





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







def __snake_case					(      UpperCAmelCase_						:						Optional[Any]					,				UpperCAmelCase_						:						Tuple=1		):
 return (images[i : i + batch] for i in range(0					,				len(UpperCAmelCase_		)					,				UpperCAmelCase_		))





"""simple docstring"""




from typing import Dict, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import flip_channel_order, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_pytesseract_available, is_vision_available, logging, requires_backends


if is_vision_available():
					import PIL

# soft dependency
if is_pytesseract_available():
					import pytesseract

UpperCAmelCase__      :    Union[str, Any]    							= logging.get_logger(__name__)

def lowercase_  (   _snake_case   ,_snake_case   ,_snake_case						):
							return [
							    int(1_000 * (box[0] / width)						),
							    int(1_000 * (box[1] / height)						),
							    int(1_000 * (box[2] / width)						),
							    int(1_000 * (box[3] / height)						),
							]

def lowercase_  (   _snake_case   ,_snake_case   ,_snake_case = None						):
							SCREAMING_SNAKE_CASE__						:       Dict           =   tesseract_config if tesseract_config is not None else """"""

							# apply OCR
							SCREAMING_SNAKE_CASE__						:       List[Any]           =   to_pil_image(_snake_case						)
							SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       Tuple           =   pil_image.size
							SCREAMING_SNAKE_CASE__						:       Tuple           =   pytesseract.image_to_data(_snake_case   ,lang=_snake_case   ,output_type="""dict"""   ,config=_snake_case						)
							SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       Any           =   data["""text"""], data["""left"""], data["""top"""], data["""width"""], data["""height"""]

							# filter empty words and corresponding coordinates
							SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   [idx for idx, word in enumerate(_snake_case						) if not word.strip()]
							SCREAMING_SNAKE_CASE__						:       Dict           =   [word for idx, word in enumerate(_snake_case						) if idx not in irrelevant_indices]
							SCREAMING_SNAKE_CASE__						:       List[str]           =   [coord for idx, coord in enumerate(_snake_case						) if idx not in irrelevant_indices]
							SCREAMING_SNAKE_CASE__						:       Tuple           =   [coord for idx, coord in enumerate(_snake_case						) if idx not in irrelevant_indices]
							SCREAMING_SNAKE_CASE__						:       int           =   [coord for idx, coord in enumerate(_snake_case						) if idx not in irrelevant_indices]
							SCREAMING_SNAKE_CASE__						:       Tuple           =   [coord for idx, coord in enumerate(_snake_case						) if idx not in irrelevant_indices]

							# turn coordinates into (left, top, left+width, top+height) format
							SCREAMING_SNAKE_CASE__						:       List[Any]           =   []
							for x, y, w, h in zip(_snake_case   ,_snake_case   ,_snake_case   ,_snake_case						):
														SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   [x, y, x + w, y + h]
														actual_boxes.append(_snake_case						)

							# finally, normalize the bounding boxes
							SCREAMING_SNAKE_CASE__						:       List[str]           =   []
							for box in actual_boxes:
														normalized_boxes.append(normalize_box(_snake_case   ,_snake_case   ,_snake_case						)						)

							assert len(_snake_case						) == len(_snake_case						), "Not as many words as there are bounding boxes"

							return words, normalized_boxes



class 	lowerCAmelCase_						(a__  ):


				"""simple docstring"""



				__UpperCamelCase      :						Optional[int]					       =   ['''pixel_values''']



				def __init__(self   ,       SCREAMING_SNAKE_CASE__ = True   ,       SCREAMING_SNAKE_CASE__ = None   ,       SCREAMING_SNAKE_CASE__ = PILImageResampling.BILINEAR   ,       SCREAMING_SNAKE_CASE__ = True   ,       SCREAMING_SNAKE_CASE__ = None   ,       SCREAMING_SNAKE_CASE__ = ""   ,       **SCREAMING_SNAKE_CASE__   ,       )					->		None:



											"""simple docstring"""




											super().__init__(**SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   size if size is not None else {"""height""": 2_24, """width""": 2_24}
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   get_size_dict(SCREAMING_SNAKE_CASE__    )

											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   do_resize
											SCREAMING_SNAKE_CASE__						:       Any           =   size
											SCREAMING_SNAKE_CASE__						:       List[Any]           =   resample
											SCREAMING_SNAKE_CASE__						:       Dict           =   apply_ocr
											SCREAMING_SNAKE_CASE__						:       List[str]           =   ocr_lang
											SCREAMING_SNAKE_CASE__						:       Tuple           =   tesseract_config



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__ = PILImageResampling.BILINEAR   ,       SCREAMING_SNAKE_CASE__ = None   ,       **SCREAMING_SNAKE_CASE__   ,       )					->		np.ndarray:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       str           =   get_size_dict(SCREAMING_SNAKE_CASE__    )
											if "height" not in size or "width" not in size:
																		raise ValueError(F'''The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}'''    )
											SCREAMING_SNAKE_CASE__						:       Any           =   (size["""height"""], size["""width"""])
											return resize(SCREAMING_SNAKE_CASE__   ,       size=SCREAMING_SNAKE_CASE__   ,       resample=SCREAMING_SNAKE_CASE__   ,       data_format=SCREAMING_SNAKE_CASE__   ,       **SCREAMING_SNAKE_CASE__    )



				def 	__magic_name__    (self   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__ = None   ,       SCREAMING_SNAKE_CASE__ = None   ,       SCREAMING_SNAKE_CASE__ = None   ,       SCREAMING_SNAKE_CASE__ = None   ,       SCREAMING_SNAKE_CASE__ = None   ,       SCREAMING_SNAKE_CASE__ = None   ,       SCREAMING_SNAKE_CASE__ = None   ,       SCREAMING_SNAKE_CASE__ = ChannelDimension.FIRST   ,       **SCREAMING_SNAKE_CASE__   ,       )					->		PIL.Image.Image:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   do_resize if do_resize is not None else self.do_resize
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   size if size is not None else self.size
											SCREAMING_SNAKE_CASE__						:       Dict           =   get_size_dict(SCREAMING_SNAKE_CASE__    )
											SCREAMING_SNAKE_CASE__						:       str           =   resample if resample is not None else self.resample
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   apply_ocr if apply_ocr is not None else self.apply_ocr
											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   ocr_lang if ocr_lang is not None else self.ocr_lang
											SCREAMING_SNAKE_CASE__						:       Dict           =   tesseract_config if tesseract_config is not None else self.tesseract_config

											SCREAMING_SNAKE_CASE__						:       Optional[int]           =   make_list_of_images(SCREAMING_SNAKE_CASE__    )

											if not valid_images(SCREAMING_SNAKE_CASE__    ):
																		raise ValueError(
																		    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
																		    """torch.Tensor, tf.Tensor or jax.ndarray."""    )

											if do_resize and size is None:
																		raise ValueError("""Size must be specified if do_resize is True."""    )

											# All transformations expect numpy arrays.
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   [to_numpy_array(SCREAMING_SNAKE_CASE__    ) for image in images]

											if apply_ocr:
																		requires_backends(self   ,       """pytesseract"""    )
																		SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   []
																		SCREAMING_SNAKE_CASE__						:       Dict           =   []
																		for image in images:
																									SCREAMING_SNAKE_CASE__      ,      SCREAMING_SNAKE_CASE__						:       str           =   apply_tesseract(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    )
																									words_batch.append(SCREAMING_SNAKE_CASE__    )
																									boxes_batch.append(SCREAMING_SNAKE_CASE__    )

											if do_resize:
																		SCREAMING_SNAKE_CASE__						:       Optional[int]           =   [self.resize(image=SCREAMING_SNAKE_CASE__   ,       size=SCREAMING_SNAKE_CASE__   ,       resample=SCREAMING_SNAKE_CASE__    ) for image in images]

											# flip color channels from RGB to BGR (as Detectron2 requires this)
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   [flip_channel_order(SCREAMING_SNAKE_CASE__    ) for image in images]
											SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   [to_channel_dimension_format(SCREAMING_SNAKE_CASE__   ,       SCREAMING_SNAKE_CASE__    ) for image in images]

											SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   BatchFeature(data={"""pixel_values""": images}   ,       tensor_type=SCREAMING_SNAKE_CASE__    )

											if apply_ocr:
																		SCREAMING_SNAKE_CASE__						:       List[Any]           =   words_batch
																		SCREAMING_SNAKE_CASE__						:       List[str]           =   boxes_batch
											return data




