
import csv
import json
import logging
import os
import re
import subprocess
import tempfile
import unittest
import zipfile
from pathlib import Path
from typing import Optional
from unittest import mock

import torch

# We use TF to parse the logs
from accelerate import Accelerator
from accelerate.test_utils.testing import (
    MockingTestCase,
    TempDirTestCase,
    require_comet_ml,
    require_tensorboard,
    require_wandb,
    skip,
)
from accelerate.tracking import CometMLTracker, GeneralTracker
from accelerate.utils import ProjectConfiguration, is_comet_ml_available, is_tensorboard_available


if is_comet_ml_available():
    from comet_ml import OfflineExperiment

if is_tensorboard_available():
    import struct

    import tensorboard.compat.proto.event_pba as event_pba

__lowerCAmelCase      :					str   	=       logging.getLogger(__name__)




@require_tensorboard
class 	UpperCAmelCase_      (			unittest.TestCase					):


  '''simple docstring'''







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







    """simple docstring"""






    __magic_name__    					=	"""test_project_with_config"""
    with tempfile.TemporaryDirectory() as dirpath:
      __magic_name__    					=	Accelerator(log_with="""tensorboard"""      ,							project_dir=UpperCamelCase__   )
      __magic_name__    					=	{"""num_iterations""": 12, """learning_rate""": 1E-2, """some_boolean""": False, """some_string""": """some_value"""}
      accelerator.init_trackers(UpperCamelCase__      ,							UpperCamelCase__   )
      accelerator.end_training()
      for child in Path(F'''{dirpath}/{project_name}'''   ).glob("""*/**"""   ):
        __magic_name__    					=	list(filter(lambda UpperCamelCase__   : x.is_file()      ,							child.iterdir()   )   )[0]
      self.assertNotEqual(str(UpperCamelCase__   )      ,							""""""   )
  def 							_lowercase							(			self      :			int   )      ->     List[str]:







    """simple docstring"""






    __magic_name__    					=	"""test_project_with_log"""
    with tempfile.TemporaryDirectory() as dirpath:
      __magic_name__    					=	Accelerator(log_with="""tensorboard"""      ,							project_dir=UpperCamelCase__   )
      accelerator.init_trackers(UpperCamelCase__   )
      __magic_name__    					=	{"""total_loss""": 0.1, """iteration""": 1, """my_text""": """some_value"""}
      accelerator.log(UpperCamelCase__      ,							step=0   )
      accelerator.end_training()
      # Logged values are stored in the outermost-tfevents file and can be read in as a TFRecord
      # Names are randomly generated each time
      __magic_name__    					=	list(filter(lambda UpperCamelCase__   : x.is_file()      ,							Path(F'''{dirpath}/{project_name}'''   ).iterdir()   )   )[0]
      self.assertNotEqual(str(UpperCamelCase__   )      ,							""""""   )
  def 							_lowercase							(			self      :			List[str]   )      ->     Any:







    """simple docstring"""






    __magic_name__    					=	"""test_project_with_log"""
    with tempfile.TemporaryDirectory() as dirpath:
      __magic_name__    					=	Accelerator(log_with="""tensorboard"""      ,							project_dir=UpperCamelCase__   )
      accelerator.init_trackers(UpperCamelCase__   )
      __magic_name__    					=	{"""tensor""": torch.tensor(1   )}
      accelerator.log(UpperCamelCase__      ,							step=0   )
      accelerator.end_training()
      # Logged values are stored in the outermost-tfevents file and can be read in as a TFRecord
      # Names are randomly generated each time
      __magic_name__    					=	list(filter(lambda UpperCamelCase__   : x.is_file()      ,							Path(F'''{dirpath}/{project_name}'''   ).iterdir()   )   )[0]
      # Reading implementation based on https://github.com/pytorch/pytorch/issues/45327#issuecomment-703757685
      with open(UpperCamelCase__      ,							"""rb"""   ) as f:
        __magic_name__    					=	f.read()
      __magic_name__    					=	False
      while data:
        __magic_name__    					=	struct.unpack("""Q"""      ,							data[:8]   )

        __magic_name__    					=	data[12 : 12 + int(header[0]   )]  # 8+4
        __magic_name__    					=	data[12 + int(header[0]   ) + 4 :]
        __magic_name__    					=	event_pba.Event()

        event.ParseFromString(UpperCamelCase__   )
        if event.HasField("""summary"""   ):
          for value in event.summary.value:
            if value.simple_value == 1.0 and value.tag == "tensor":
              __magic_name__    					=	True
      self.assertTrue(UpperCamelCase__      ,							"""Converted tensor was not found in the log file!"""   )
  def 							_lowercase							(			self      :			str   )      ->     List[str]:







    """simple docstring"""






    with self.assertRaisesRegex(UpperCamelCase__      ,							"""Logging with `tensorboard` requires a `logging_dir`"""   ):
      __magic_name__    					=	Accelerator(log_with="""tensorboard"""   )
    with tempfile.TemporaryDirectory() as dirpath:
      __magic_name__    					=	Accelerator(log_with="""tensorboard"""      ,							project_dir=UpperCamelCase__   )





  def 							_lowercase							(			self      :			Dict   )      ->     Tuple:







    """simple docstring"""






    __magic_name__    					=	ProjectConfiguration(total_limit=30   )
    with tempfile.TemporaryDirectory() as dirpath:
      __magic_name__    					=	Accelerator(log_with="""tensorboard"""      ,							project_dir=UpperCamelCase__      ,							project_config=UpperCamelCase__   )







@require_wandb
@mock.patch.dict(os.environ  ,	{"""WANDB_MODE""": """offline"""}					)
class 	UpperCAmelCase_      (			_A  ,	_A					):


  '''simple docstring'''







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







    """simple docstring"""






    super().setUp()
    # wandb let's us override where logs are stored to via the WANDB_DIR env var
    self.add_mocks(mock.patch.dict(os.environ      ,							{"""WANDB_DIR""": self.tmpdir}   )   )
  @staticmethod
  def 							_lowercase							(			UpperCamelCase__      :			str      ,							UpperCamelCase__      :			str      ,							UpperCamelCase__      :			bool = True   )      ->     Optional[int]:







    """simple docstring"""






    __magic_name__    					=	RF'''{section} ([\S\s]*?)\n\n'''
    if record:
      __magic_name__    					=	RF'''Record: {pattern}'''
    __magic_name__    					=	re.findall(UpperCamelCase__      ,							UpperCamelCase__   )[0]
    # A config
    if section == "config" or section == "history":
      __magic_name__    					=	re.findall(R"""\"([a-zA-Z0-9_.,]+)"""      ,							UpperCamelCase__   )
      return {key: val for key, val in zip(cleaned_record[0::2]      ,							cleaned_record[1::2]   )}
    # Everything else
    else:
      return dict(re.findall(R"""(\w+): \"([^\s]+)\""""      ,							UpperCamelCase__   )   )





  @skip
  def 							_lowercase							(			self      :			Any   )      ->     str:







    """simple docstring"""






    __magic_name__    					=	"""test_project_with_config"""
    __magic_name__    					=	Accelerator(log_with="""wandb"""   )
    __magic_name__    					=	{"""num_iterations""": 12, """learning_rate""": 1E-2, """some_boolean""": False, """some_string""": """some_value"""}
    __magic_name__    					=	{"""wandb""": {"""tags""": ["""my_tag"""]}}
    accelerator.init_trackers(UpperCamelCase__      ,							UpperCamelCase__      ,							UpperCamelCase__   )
    __magic_name__    					=	{"""total_loss""": 0.1, """iteration""": 1, """my_text""": """some_value"""}
    accelerator.log(UpperCamelCase__      ,							step=0   )
    accelerator.end_training()
    # The latest offline log is stored at wandb/latest-run/*.wandb
    for child in Path(F'''{self.tmpdir}/wandb/latest-run'''   ).glob("""*"""   ):
      if child.is_file() and child.suffix == ".wandb":
        __magic_name__    					=	subprocess.check_output(
            ["""wandb""", """sync""", """--view""", """--verbose""", str(UpperCamelCase__   )]      ,							env=os.environ.copy()   ).decode("""utf8"""      ,							"""ignore"""   )
        break

        # Check HPS through careful parsing and cleaning
    __magic_name__    					=	self.parse_log(UpperCamelCase__      ,							"""config"""   )
    self.assertEqual(logged_items["""num_iterations"""]      ,							"""12"""   )
    self.assertEqual(logged_items["""learning_rate"""]      ,							"""0.01"""   )
    self.assertEqual(logged_items["""some_boolean"""]      ,							"""false"""   )
    self.assertEqual(logged_items["""some_string"""]      ,							"""some_value"""   )
    self.assertEqual(logged_items["""some_string"""]      ,							"""some_value"""   )

    # Run tags
    __magic_name__    					=	self.parse_log(UpperCamelCase__      ,							"""run"""      ,							UpperCamelCase__   )
    self.assertEqual(logged_items["""tags"""]      ,							"""my_tag"""   )

    # Actual logging
    __magic_name__    					=	self.parse_log(UpperCamelCase__      ,							"""history"""   )
    self.assertEqual(logged_items["""total_loss"""]      ,							"""0.1"""   )
    self.assertEqual(logged_items["""iteration"""]      ,							"""1"""   )
    self.assertEqual(logged_items["""my_text"""]      ,							"""some_value"""   )
    self.assertEqual(logged_items["""_step"""]      ,							"""0"""   )







def a__   (	self,					A_,					A_ ):






  '''simple docstring'''





  __magic_name__    					=	run_name
  __magic_name__    					=	OfflineExperiment(project_name=A_,					offline_directory=A_ )
  logger.info(f'''Initialized offline CometML project {self.run_name}''' )
  logger.info("""Make sure to log any initial configurations with `self.store_init_configuration` before training!""" )




@require_comet_ml
@mock.patch.object(_A  ,	"""__init__"""  ,	_A					)
class 	UpperCAmelCase_      (			unittest.TestCase					):


  '''simple docstring'''







  @staticmethod
  def 							_lowercase							(			UpperCamelCase__      :			Union[str, Any]      ,							UpperCamelCase__      :			str      ,							UpperCamelCase__      :			bool = False   )      ->     Optional[Any]:







    """simple docstring"""






    for log in log_list:
      __magic_name__    					=	json.loads(UpperCamelCase__   )["""payload"""]
      if is_param and "param" in j.keys():
        if j["param"]["paramName"] == key:
          return j["param"]["paramValue"]
      if "log_other" in j.keys():
        if j["log_other"]["key"] == key:
          return j["log_other"]["val"]
      if "metric" in j.keys():
        if j["metric"]["metricName"] == key:
          return j["metric"]["metricValue"]
  def 							_lowercase							(			self      :			Any   )      ->     Union[str, Any]:







    """simple docstring"""






    with tempfile.TemporaryDirectory() as d:
      __magic_name__    					=	CometMLTracker("""test_project_with_config"""      ,							UpperCamelCase__   )
      __magic_name__    					=	Accelerator(log_with=UpperCamelCase__   )
      __magic_name__    					=	{"""num_iterations""": 12, """learning_rate""": 1E-2, """some_boolean""": False, """some_string""": """some_value"""}
      accelerator.init_trackers(UpperCamelCase__      ,							UpperCamelCase__   )
      accelerator.end_training()
      __magic_name__    					=	os.listdir(UpperCamelCase__   )[0]  # Comet is nice, it's just a zip file here
      # We parse the raw logs
      __magic_name__    					=	os.path.join(UpperCamelCase__      ,							UpperCamelCase__   )
      __magic_name__    					=	zipfile.ZipFile(UpperCamelCase__      ,							"""r"""   )
      __magic_name__    					=	archive.open("""messages.json"""   ).read().decode("""utf-8"""   )
    __magic_name__    					=	log.split("""\n"""   )[:-1]
    self.assertEqual(self.get_value_from_key(UpperCamelCase__      ,							"""num_iterations"""      ,							UpperCamelCase__   )      ,							12   )
    self.assertEqual(self.get_value_from_key(UpperCamelCase__      ,							"""learning_rate"""      ,							UpperCamelCase__   )      ,							0.01   )
    self.assertEqual(self.get_value_from_key(UpperCamelCase__      ,							"""some_boolean"""      ,							UpperCamelCase__   )      ,							UpperCamelCase__   )
    self.assertEqual(self.get_value_from_key(UpperCamelCase__      ,							"""some_string"""      ,							UpperCamelCase__   )      ,							"""some_value"""   )





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







    """simple docstring"""






    with tempfile.TemporaryDirectory() as d:
      __magic_name__    					=	CometMLTracker("""test_project_with_config"""      ,							UpperCamelCase__   )
      __magic_name__    					=	Accelerator(log_with=UpperCamelCase__   )
      accelerator.init_trackers(UpperCamelCase__   )
      __magic_name__    					=	{"""total_loss""": 0.1, """iteration""": 1, """my_text""": """some_value"""}
      accelerator.log(UpperCamelCase__      ,							step=0   )
      accelerator.end_training()
      __magic_name__    					=	os.listdir(UpperCamelCase__   )[0]  # Comet is nice, it's just a zip file here
      # We parse the raw logs
      __magic_name__    					=	os.path.join(UpperCamelCase__      ,							UpperCamelCase__   )
      __magic_name__    					=	zipfile.ZipFile(UpperCamelCase__      ,							"""r"""   )
      __magic_name__    					=	archive.open("""messages.json"""   ).read().decode("""utf-8"""   )
    __magic_name__    					=	log.split("""\n"""   )[:-1]
    self.assertEqual(self.get_value_from_key(UpperCamelCase__      ,							"""curr_step"""      ,							UpperCamelCase__   )      ,							0   )
    self.assertEqual(self.get_value_from_key(UpperCamelCase__      ,							"""total_loss"""   )      ,							0.1   )
    self.assertEqual(self.get_value_from_key(UpperCamelCase__      ,							"""iteration"""   )      ,							1   )
    self.assertEqual(self.get_value_from_key(UpperCamelCase__      ,							"""my_text"""   )      ,							"""some_value"""   )







class 	UpperCAmelCase_      (			_A					):


  '''simple docstring'''







  a__   	=		[
      """total_loss""",
      """iteration""",
      """my_text""",
      """learning_rate""",
      """num_iterations""",
      """some_boolean""",
      """some_string""",
  ]

  a__   	=		"""my_custom_tracker"""
  a__   	=		False
  def __init__(			self      :			Any      ,							UpperCamelCase__      :			str   )      ->     Optional[int]:







    """simple docstring"""






    __magic_name__    					=	open(F'''{dir}/log.csv'''      ,							"""w+"""   )
    __magic_name__    					=	csv.DictWriter(self.f      ,							fieldnames=self._col_names   )
    self.writer.writeheader()
  @property
  def 							_lowercase							(			self      :			Optional[int]   )      ->     Union[str, Any]:







    """simple docstring"""






    return self.writer
  def 							_lowercase							(			self      :			int      ,							UpperCamelCase__      :			dict   )      ->     Any:







    """simple docstring"""






    logger.info("""Call init"""   )
    self.writer.writerow(UpperCamelCase__   )
  def 							_lowercase							(			self      :			Optional[Any]      ,							UpperCamelCase__      :			dict      ,							UpperCamelCase__      :			Optional[int]   )      ->     str:







    """simple docstring"""






    logger.info("""Call log"""   )
    self.writer.writerow(UpperCamelCase__   )





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







    """simple docstring"""






    self.f.close()







class 	UpperCAmelCase_      (			unittest.TestCase					):


  '''simple docstring'''







  def 							_lowercase							(			self      :			str   )      ->     Any:







    """simple docstring"""






    with tempfile.TemporaryDirectory() as d:
      __magic_name__    					=	MyCustomTracker(UpperCamelCase__   )
      __magic_name__    					=	Accelerator(log_with=UpperCamelCase__   )
      __magic_name__    					=	{"""num_iterations""": 12, """learning_rate""": 1E-2, """some_boolean""": False, """some_string""": """some_value"""}
      accelerator.init_trackers("""Some name"""      ,							UpperCamelCase__   )
      accelerator.end_training()
      with open(F'''{d}/log.csv'''      ,							"""r"""   ) as f:
        __magic_name__    					=	csv.DictReader(UpperCamelCase__   )
        __magic_name__    					=	next(UpperCamelCase__   )
        __magic_name__    					=	{
            """total_loss""": """""",
            """iteration""": """""",
            """my_text""": """""",
            """learning_rate""": """0.01""",
            """num_iterations""": """12""",
            """some_boolean""": """False""",
            """some_string""": """some_value""",
        }
        self.assertDictEqual(UpperCamelCase__      ,							UpperCamelCase__   )





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







    """simple docstring"""






    with tempfile.TemporaryDirectory() as d:
      __magic_name__    					=	MyCustomTracker(UpperCamelCase__   )
      __magic_name__    					=	Accelerator(log_with=UpperCamelCase__   )
      accelerator.init_trackers("""Some name"""   )
      __magic_name__    					=	{"""total_loss""": 0.1, """iteration""": 1, """my_text""": """some_value"""}
      accelerator.log(UpperCamelCase__      ,							step=0   )
      accelerator.end_training()
      with open(F'''{d}/log.csv'''      ,							"""r"""   ) as f:
        __magic_name__    					=	csv.DictReader(UpperCamelCase__   )
        __magic_name__    					=	next(UpperCamelCase__   )
        __magic_name__    					=	{
            """total_loss""": """0.1""",
            """iteration""": """1""",
            """my_text""": """some_value""",
            """learning_rate""": """""",
            """num_iterations""": """""",
            """some_boolean""": """""",
            """some_string""": """""",
        }
        self.assertDictEqual(UpperCamelCase__      ,							UpperCamelCase__   )



import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType




class 	UpperCAmelCase_      (			_A					):


  '''simple docstring'''







  a__   	=		["""image_processor""", """tokenizer"""]
  a__   	=		"""LayoutLMv2ImageProcessor"""
  a__   	=		("""LayoutLMv2Tokenizer""", """LayoutLMv2TokenizerFast""")
  def __init__(			self      :			int      ,							UpperCamelCase__      :			Dict=None      ,							UpperCamelCase__      :			int=None      ,							**UpperCamelCase__      :			str   )      ->     Union[str, Any]:







    """simple docstring"""






    __magic_name__    					=	None
    if "feature_extractor" in kwargs:
      warnings.warn(
          """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
          """ instead."""      ,							UpperCamelCase__      ,							)
      __magic_name__    					=	kwargs.pop("""feature_extractor"""   )

    __magic_name__    					=	image_processor if image_processor is not None else feature_extractor
    if image_processor is None:
      raise ValueError("""You need to specify an `image_processor`."""   )
    if tokenizer is None:
      raise ValueError("""You need to specify a `tokenizer`."""   )

    super().__init__(UpperCamelCase__      ,							UpperCamelCase__   )
  def __call__(			self      :			List[str]      ,							UpperCamelCase__      :			Union[str, Any]      ,							UpperCamelCase__      :			Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None      ,							UpperCamelCase__      :			Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None      ,							UpperCamelCase__      :			Union[List[List[int]], List[List[List[int]]]] = None      ,							UpperCamelCase__      :			Optional[Union[List[int], List[List[int]]]] = None      ,							UpperCamelCase__      :			bool = True      ,							UpperCamelCase__      :			Union[bool, str, PaddingStrategy] = False      ,							UpperCamelCase__      :			Union[bool, str, TruncationStrategy] = False      ,							UpperCamelCase__      :			Optional[int] = None      ,							UpperCamelCase__      :			int = 0      ,							UpperCamelCase__      :			Optional[int] = None      ,							UpperCamelCase__      :			Optional[bool] = None      ,							UpperCamelCase__      :			Optional[bool] = None      ,							UpperCamelCase__      :			bool = False      ,							UpperCamelCase__      :			bool = False      ,							UpperCamelCase__      :			bool = False      ,							UpperCamelCase__      :			bool = False      ,							UpperCamelCase__      :			bool = True      ,							UpperCamelCase__      :			Optional[Union[str, TensorType]] = None      ,							**UpperCamelCase__      :			Union[str, Any]      ,							)      ->     BatchEncoding:







    """simple docstring"""






    if self.image_processor.apply_ocr and (boxes is not None):
      raise ValueError(
          """You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True."""   )

    if self.image_processor.apply_ocr and (word_labels is not None):
      raise ValueError(
          """You cannot provide word labels if you initialized the image processor with apply_ocr set to True."""   )

    if return_overflowing_tokens is True and return_offsets_mapping is False:
      raise ValueError("""You cannot return overflowing tokens without returning the offsets mapping."""   )

    # first, apply the image processor
    __magic_name__    					=	self.image_processor(images=UpperCamelCase__      ,							return_tensors=UpperCamelCase__   )

    # second, apply the tokenizer
    if text is not None and self.image_processor.apply_ocr and text_pair is None:
      if isinstance(UpperCamelCase__      ,							UpperCamelCase__   ):
        __magic_name__    					=	[text]  # add batch dimension (as the image processor always adds a batch dimension)
      __magic_name__    					=	features["""words"""]

    __magic_name__    					=	self.tokenizer(
        text=text if text is not None else features["""words"""]      ,							text_pair=text_pair if text_pair is not None else None      ,							boxes=boxes if boxes is not None else features["""boxes"""]      ,							word_labels=UpperCamelCase__      ,							add_special_tokens=UpperCamelCase__      ,							padding=UpperCamelCase__      ,							truncation=UpperCamelCase__      ,							max_length=UpperCamelCase__      ,							stride=UpperCamelCase__      ,							pad_to_multiple_of=UpperCamelCase__      ,							return_token_type_ids=UpperCamelCase__      ,							return_attention_mask=UpperCamelCase__      ,							return_overflowing_tokens=UpperCamelCase__      ,							return_special_tokens_mask=UpperCamelCase__      ,							return_offsets_mapping=UpperCamelCase__      ,							return_length=UpperCamelCase__      ,							verbose=UpperCamelCase__      ,							return_tensors=UpperCamelCase__      ,							**UpperCamelCase__      ,							)

    # add pixel values
    __magic_name__    					=	features.pop("""pixel_values"""   )
    if return_overflowing_tokens is True:
      __magic_name__    					=	self.get_overflowing_images(UpperCamelCase__      ,							encoded_inputs["""overflow_to_sample_mapping"""]   )
    __magic_name__    					=	images

    return encoded_inputs
  def 							_lowercase							(			self      :			Optional[int]      ,							UpperCamelCase__      :			Tuple      ,							UpperCamelCase__      :			Optional[int]   )      ->     Any:







    """simple docstring"""






    __magic_name__    					=	[]
    for sample_idx in overflow_to_sample_mapping:
      images_with_overflow.append(images[sample_idx]   )

    if len(UpperCamelCase__   ) != len(UpperCamelCase__   ):
      raise ValueError(
          """Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"""
          F''' {len(UpperCamelCase__   )} and {len(UpperCamelCase__   )}'''   )

    return images_with_overflow
  def 							_lowercase							(			self      :			Optional[int]      ,							*UpperCamelCase__      :			str      ,							**UpperCamelCase__      :			int   )      ->     Union[str, Any]:







    """simple docstring"""






    return self.tokenizer.batch_decode(*UpperCamelCase__      ,							**UpperCamelCase__   )
  def 							_lowercase							(			self      :			Dict      ,							*UpperCamelCase__      :			Optional[int]      ,							**UpperCamelCase__      :			str   )      ->     str:







    """simple docstring"""






    return self.tokenizer.decode(*UpperCamelCase__      ,							**UpperCamelCase__   )
  @property
  def 							_lowercase							(			self      :			Optional[Any]   )      ->     Optional[int]:







    """simple docstring"""






    return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]
  @property
  def 							_lowercase							(			self      :			Union[str, Any]   )      ->     Optional[int]:







    """simple docstring"""






    warnings.warn(
        """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""      ,							UpperCamelCase__      ,							)
    return self.image_processor_class





  @property
  def 							_lowercase							(			self      :			Union[str, Any]   )      ->     Dict:







    """simple docstring"""






    warnings.warn(
        """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""      ,							UpperCamelCase__      ,							)
    return self.image_processor

