
import gc
import os
import tempfile
import unittest

import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, StableDiffusionPipeline, UNetaDConditionModel
from diffusers.loaders import AttnProcsLayers, LoraLoaderMixin
from diffusers.models.attention_processor import (
    Attention,
    AttnProcessor,
    AttnProcessor2_0,
    LoRAAttnProcessor,
    LoRAAttnProcessor2_0,
    LoRAXFormersAttnProcessor,
    XFormersAttnProcessor,
)
from diffusers.utils import TEXT_ENCODER_ATTN_MODULE, floats_tensor, torch_device


def 		_lowerCamelCase       (   _a  ):
  """simple docstring"""




  _lowerCamelCase      =						{}
  for name in unet.attn_processors.keys():
    _lowerCamelCase      =						None if name.endswith('''attn1.processor'''  ) else unet.config.cross_attention_dim
    if name.startswith('''mid_block'''  ):
      _lowerCamelCase      =						unet.config.block_out_channels[-1]
    elif name.startswith('''up_blocks'''  ):
      _lowerCamelCase      =						int(name[len('''up_blocks.'''  )]  )
      _lowerCamelCase      =						list(reversed(unet.config.block_out_channels  )  )[block_id]
    elif name.startswith('''down_blocks'''  ):
      _lowerCamelCase      =						int(name[len('''down_blocks.'''  )]  )
      _lowerCamelCase      =						unet.config.block_out_channels[block_id]
    _lowerCamelCase      =						(
        LoRAAttnProcessor2_0 if hasattr(_a			,   '''scaled_dot_product_attention'''  ) else LoRAAttnProcessor
    )
    _lowerCamelCase      =						lora_attn_processor_class(
        hidden_size=_a			,   cross_attention_dim=_a  )
  _lowerCamelCase      =						AttnProcsLayers(_a  )
  return lora_attn_procs, unet_lora_layers


def 		_lowerCamelCase       (   _a  ):
  """simple docstring"""




  _lowerCamelCase      =						{}
  _lowerCamelCase      =						(
      LoRAAttnProcessor2_0 if hasattr(_a			,   '''scaled_dot_product_attention'''  ) else LoRAAttnProcessor
  )
  for name, module in text_encoder.named_modules():
    if name.endswith(_a  ):
      _lowerCamelCase      =						lora_attn_processor_class(
          hidden_size=module.out_proj.out_features			,   cross_attention_dim=_a  )
  return text_lora_attn_procs


def 		_lowerCamelCase       (   _a  ):
  """simple docstring"""




  _lowerCamelCase      =						create_text_encoder_lora_attn_procs(_a  )
  _lowerCamelCase      =						AttnProcsLayers(_a  )
  return text_encoder_lora_layers


def 		_lowerCamelCase       (   _a			,   _a=False  ):
  """simple docstring"""




  for _, attn_proc in text_lora_attn_procs.items():
    # set up.weights
    for layer_name, layer_module in attn_proc.named_modules():
      if layer_name.endswith('''_lora'''  ):
        _lowerCamelCase      =						(
            torch.randn_like(layer_module.up.weight  )
            if randn_weight
            else torch.zeros_like(layer_module.up.weight  )
        )
        _lowerCamelCase      =						torch.nn.Parameter(_a  )






class 		__magic_name__				( unittest.TestCase					):


  """simple docstring"""





  def     _UpperCAmelCase  (					self		):
    torch.manual_seed(0		)
    _lowerCamelCase      =						UNetaDConditionModel(
        block_out_channels=(32, 64)	,		layers_per_block=2	,		sample_size=32	,		in_channels=4	,		out_channels=4	,		down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')	,		up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')	,		cross_attention_dim=32	,		)
    _lowerCamelCase      =						DDIMScheduler(
        beta_start=0.00085	,		beta_end=0.012	,		beta_schedule='''scaled_linear'''	,		clip_sample=__UpperCamelCase	,		set_alpha_to_one=__UpperCamelCase	,		steps_offset=1	,		)
    torch.manual_seed(0		)
    _lowerCamelCase      =						AutoencoderKL(
        block_out_channels=[32, 64]	,		in_channels=3	,		out_channels=3	,		down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''']	,		up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''']	,		latent_channels=4	,		)
    _lowerCamelCase      =						CLIPTextConfig(
        bos_token_id=0	,		eos_token_id=2	,		hidden_size=32	,		intermediate_size=37	,		layer_norm_eps=1E-05	,		num_attention_heads=4	,		num_hidden_layers=5	,		pad_token_id=1	,		vocab_size=10_00	,		)
    _lowerCamelCase      =						CLIPTextModel(__UpperCamelCase		)
    _lowerCamelCase      =						CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''		)

    _lowerCamelCase      =						create_unet_lora_layers(__UpperCamelCase		)
    _lowerCamelCase      =						create_text_encoder_lora_layers(__UpperCamelCase		)

    _lowerCamelCase      =						{
        "unet": unet,
        "scheduler": scheduler,
        "vae": vae,
        "text_encoder": text_encoder,
        "tokenizer": tokenizer,
        "safety_checker": None,
        "feature_extractor": None,
    }
    _lowerCamelCase      =						{
        "unet_lora_layers": unet_lora_layers,
        "text_encoder_lora_layers": text_encoder_lora_layers,
        "unet_lora_attn_procs": unet_lora_attn_procs,
    }
    return pipeline_components, lora_components


  def     _UpperCAmelCase  (					self		):
    _lowerCamelCase      =						1
    _lowerCamelCase      =						10
    _lowerCamelCase      =						4
    _lowerCamelCase      =						(32, 32)

    _lowerCamelCase      =						torch.manual_seed(0		)
    _lowerCamelCase      =						floats_tensor((batch_size, num_channels) + sizes		)
    _lowerCamelCase      =						torch.randint(1	,		__UpperCamelCase	,		size=(batch_size, sequence_length)	,		generator=__UpperCamelCase		)

    _lowerCamelCase      =						{
        "prompt": "A painting of a squirrel eating a burger",
        "generator": generator,
        "num_inference_steps": 2,
        "guidance_scale": 6.0,
        "output_type": "numpy",
    }

    return noise, input_ids, pipeline_inputs

    # copied from: https://colab.research.google.com/gist/sayakpaul/df2ef6e1ae6d8c10a49d859883b10860/scratchpad.ipynb


  def     _UpperCAmelCase  (					self		):
    _lowerCamelCase      =						77

    _lowerCamelCase      =						torch.randint(2	,		56	,		size=(1, max_seq_length)	,		generator=torch.manual_seed(0		)		)

    _lowerCamelCase      =						{}
    _lowerCamelCase      =						inputs
    return prepared_inputs


  def     _UpperCAmelCase  (					self	,		a__		):
    _lowerCamelCase      =						self.get_dummy_components()
    LoraLoaderMixin.save_lora_weights(
        save_directory=__UpperCamelCase	,		unet_lora_layers=lora_components['''unet_lora_layers''']	,		text_encoder_lora_layers=lora_components['''text_encoder_lora_layers''']	,		)
    self.assertTrue(os.path.isfile(os.path.join(__UpperCamelCase	,		'''pytorch_lora_weights.bin'''		)		)		)


  def     _UpperCAmelCase  (					self		):
    _lowerCamelCase      =						self.get_dummy_components()
    _lowerCamelCase      =						StableDiffusionPipeline(**__UpperCamelCase		)
    _lowerCamelCase      =						sd_pipe.to(__UpperCamelCase		)
    sd_pipe.set_progress_bar_config(disable=__UpperCamelCase		)

    _lowerCamelCase      =						self.get_dummy_inputs()

    _lowerCamelCase      =						sd_pipe(**__UpperCamelCase		).images
    _lowerCamelCase      =						original_images[0, -3:, -3:, -1]

    with tempfile.TemporaryDirectory() as tmpdirname:
      LoraLoaderMixin.save_lora_weights(
          save_directory=__UpperCamelCase	,		unet_lora_layers=lora_components['''unet_lora_layers''']	,		text_encoder_lora_layers=lora_components['''text_encoder_lora_layers''']	,		)
      self.assertTrue(os.path.isfile(os.path.join(__UpperCamelCase	,		'''pytorch_lora_weights.bin'''		)		)		)
      sd_pipe.load_lora_weights(__UpperCamelCase		)

    _lowerCamelCase      =						sd_pipe(**__UpperCamelCase		).images
    _lowerCamelCase      =						lora_images[0, -3:, -3:, -1]

    # Outputs shouldn't match.
    self.assertFalse(torch.allclose(torch.from_numpy(__UpperCamelCase		)	,		torch.from_numpy(__UpperCamelCase		)		)		)


  def     _UpperCAmelCase  (					self		):
    _lowerCamelCase      =						self.get_dummy_components()
    _lowerCamelCase      =						StableDiffusionPipeline(**__UpperCamelCase		)
    _lowerCamelCase      =						sd_pipe.to(__UpperCamelCase		)
    sd_pipe.set_progress_bar_config(disable=__UpperCamelCase		)

    _lowerCamelCase      =						self.get_dummy_inputs()

    _lowerCamelCase      =						sd_pipe(**__UpperCamelCase		).images
    _lowerCamelCase      =						original_images[0, -3:, -3:, -1]

    with tempfile.TemporaryDirectory() as tmpdirname:
      LoraLoaderMixin.save_lora_weights(
          save_directory=__UpperCamelCase	,		unet_lora_layers=lora_components['''unet_lora_layers''']	,		text_encoder_lora_layers=lora_components['''text_encoder_lora_layers''']	,		safe_serialization=__UpperCamelCase	,		)
      self.assertTrue(os.path.isfile(os.path.join(__UpperCamelCase	,		'''pytorch_lora_weights.safetensors'''		)		)		)
      sd_pipe.load_lora_weights(__UpperCamelCase		)

    _lowerCamelCase      =						sd_pipe(**__UpperCamelCase		).images
    _lowerCamelCase      =						lora_images[0, -3:, -3:, -1]

    # Outputs shouldn't match.
    self.assertFalse(torch.allclose(torch.from_numpy(__UpperCamelCase		)	,		torch.from_numpy(__UpperCamelCase		)		)		)


  def     _UpperCAmelCase  (					self		):
    _lowerCamelCase      =						self.get_dummy_components()
    _lowerCamelCase      =						lora_components["unet_lora_attn_procs"]
    _lowerCamelCase      =						StableDiffusionPipeline(**__UpperCamelCase		)
    _lowerCamelCase      =						sd_pipe.to(__UpperCamelCase		)
    sd_pipe.set_progress_bar_config(disable=__UpperCamelCase		)

    _lowerCamelCase      =						self.get_dummy_inputs()

    _lowerCamelCase      =						sd_pipe(**__UpperCamelCase		).images
    _lowerCamelCase      =						original_images[0, -3:, -3:, -1]

    with tempfile.TemporaryDirectory() as tmpdirname:
      _lowerCamelCase      =						sd_pipe.unet
      unet.set_attn_processor(__UpperCamelCase		)
      unet.save_attn_procs(__UpperCamelCase		)
      self.assertTrue(os.path.isfile(os.path.join(__UpperCamelCase	,		'''pytorch_lora_weights.bin'''		)		)		)
      sd_pipe.load_lora_weights(__UpperCamelCase		)

    _lowerCamelCase      =						sd_pipe(**__UpperCamelCase		).images
    _lowerCamelCase      =						lora_images[0, -3:, -3:, -1]

    # Outputs shouldn't match.
    self.assertFalse(torch.allclose(torch.from_numpy(__UpperCamelCase		)	,		torch.from_numpy(__UpperCamelCase		)		)		)


  def     _UpperCAmelCase  (					self		):
    _lowerCamelCase      =						self.get_dummy_components()
    _lowerCamelCase      =						StableDiffusionPipeline(**__UpperCamelCase		)

    _lowerCamelCase      =						self.get_dummy_tokens()

    # inference without lora
    _lowerCamelCase      =						pipe.text_encoder(**__UpperCamelCase		)[0]
    assert outputs_without_lora.shape == (1, 77, 32)

    # create lora_attn_procs with zeroed out up.weights
    _lowerCamelCase      =						create_text_encoder_lora_attn_procs(pipe.text_encoder		)
    set_lora_up_weights(__UpperCamelCase	,		randn_weight=__UpperCamelCase		)

    # monkey patch
    pipe._modify_text_encoder(__UpperCamelCase		)

    # verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
    del text_attn_procs
    gc.collect()

    # inference with lora
    _lowerCamelCase      =						pipe.text_encoder(**__UpperCamelCase		)[0]
    assert outputs_with_lora.shape == (1, 77, 32)

    assert torch.allclose(
        __UpperCamelCase	,		__UpperCamelCase		), "lora_up_weight are all zero, so the lora outputs should be the same to without lora outputs"

    # create lora_attn_procs with randn up.weights
    _lowerCamelCase      =						create_text_encoder_lora_attn_procs(pipe.text_encoder		)
    set_lora_up_weights(__UpperCamelCase	,		randn_weight=__UpperCamelCase		)

    # monkey patch
    pipe._modify_text_encoder(__UpperCamelCase		)

    # verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
    del text_attn_procs
    gc.collect()

    # inference with lora
    _lowerCamelCase      =						pipe.text_encoder(**__UpperCamelCase		)[0]
    assert outputs_with_lora.shape == (1, 77, 32)

    assert not torch.allclose(
        __UpperCamelCase	,		__UpperCamelCase		), "lora_up_weight are not zero, so the lora outputs should be different to without lora outputs"


  def     _UpperCAmelCase  (					self		):
    _lowerCamelCase      =						self.get_dummy_components()
    _lowerCamelCase      =						StableDiffusionPipeline(**__UpperCamelCase		)

    _lowerCamelCase      =						self.get_dummy_tokens()

    # inference without lora
    _lowerCamelCase      =						pipe.text_encoder(**__UpperCamelCase		)[0]
    assert outputs_without_lora.shape == (1, 77, 32)

    # create lora_attn_procs with randn up.weights
    _lowerCamelCase      =						create_text_encoder_lora_attn_procs(pipe.text_encoder		)
    set_lora_up_weights(__UpperCamelCase	,		randn_weight=__UpperCamelCase		)

    # monkey patch
    pipe._modify_text_encoder(__UpperCamelCase		)

    # verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
    del text_attn_procs
    gc.collect()

    # inference with lora
    _lowerCamelCase      =						pipe.text_encoder(**__UpperCamelCase		)[0]
    assert outputs_with_lora.shape == (1, 77, 32)

    assert not torch.allclose(
        __UpperCamelCase	,		__UpperCamelCase		), "lora outputs should be different to without lora outputs"

    # remove monkey patch
    pipe._remove_text_encoder_monkey_patch()

    # inference with removed lora
    _lowerCamelCase      =						pipe.text_encoder(**__UpperCamelCase		)[0]
    assert outputs_without_lora_removed.shape == (1, 77, 32)

    assert torch.allclose(
        __UpperCamelCase	,		__UpperCamelCase		), "remove lora monkey patch should restore the original outputs"


  def     _UpperCAmelCase  (					self		):
    _lowerCamelCase      =						self.get_dummy_components()
    _lowerCamelCase      =						StableDiffusionPipeline(**__UpperCamelCase		)
    _lowerCamelCase      =						sd_pipe.to(__UpperCamelCase		)
    sd_pipe.set_progress_bar_config(disable=__UpperCamelCase		)

    _lowerCamelCase      =						self.get_dummy_inputs()

    with tempfile.TemporaryDirectory() as tmpdirname:
      LoraLoaderMixin.save_lora_weights(
          save_directory=__UpperCamelCase	,		unet_lora_layers=lora_components['''unet_lora_layers''']	,		text_encoder_lora_layers=lora_components['''text_encoder_lora_layers''']	,		)
      self.assertTrue(os.path.isfile(os.path.join(__UpperCamelCase	,		'''pytorch_lora_weights.bin'''		)		)		)
      sd_pipe.load_lora_weights(__UpperCamelCase		)

    _lowerCamelCase      =						sd_pipe(**__UpperCamelCase		).images
    _lowerCamelCase      =						lora_images[0, -3:, -3:, -1]

    _lowerCamelCase      =						sd_pipe(**__UpperCamelCase	,		cross_attention_kwargs={'''scale''': 0.5}		).images
    _lowerCamelCase      =						lora_images_with_scale[0, -3:, -3:, -1]

    # Outputs shouldn't match.
    self.assertFalse(
        torch.allclose(torch.from_numpy(__UpperCamelCase		)	,		torch.from_numpy(__UpperCamelCase		)		)		)


  def     _UpperCAmelCase  (					self		):
    with tempfile.TemporaryDirectory() as tmpdirname:
      self.create_lora_weight_file(__UpperCamelCase		)

      _lowerCamelCase      =						self.get_dummy_components()
      _lowerCamelCase      =						StableDiffusionPipeline(**__UpperCamelCase		)
      _lowerCamelCase      =						sd_pipe.to(__UpperCamelCase		)
      sd_pipe.set_progress_bar_config(disable=__UpperCamelCase		)

      # check if vanilla attention processors are used
      for _, module in sd_pipe.unet.named_modules():
        if isinstance(__UpperCamelCase	,		__UpperCamelCase		):
          self.assertIsInstance(module.processor	,		(AttnProcessor, AttnProcessor2_0)		)

            # load LoRA weight file
      sd_pipe.load_lora_weights(__UpperCamelCase		)

      # check if lora attention processors are used
      for _, module in sd_pipe.unet.named_modules():
        if isinstance(__UpperCamelCase	,		__UpperCamelCase		):
          _lowerCamelCase      =						(
              LoRAAttnProcessor2_0 if hasattr(__UpperCamelCase	,		'''scaled_dot_product_attention'''		) else LoRAAttnProcessor
          )
          self.assertIsInstance(module.processor	,		__UpperCamelCase		)


  @unittest.skipIf(torch_device != '''cuda'''	,		'''This test is supposed to run on GPU'''		)
  def     _UpperCAmelCase  (					self		):
    with tempfile.TemporaryDirectory() as tmpdirname:
      self.create_lora_weight_file(__UpperCamelCase		)

      _lowerCamelCase      =						self.get_dummy_components()
      _lowerCamelCase      =						StableDiffusionPipeline(**__UpperCamelCase		)
      _lowerCamelCase      =						sd_pipe.to(__UpperCamelCase		)
      sd_pipe.set_progress_bar_config(disable=__UpperCamelCase		)

      # enable XFormers
      sd_pipe.enable_xformers_memory_efficient_attention()

      # check if xFormers attention processors are used
      for _, module in sd_pipe.unet.named_modules():
        if isinstance(__UpperCamelCase	,		__UpperCamelCase		):
          self.assertIsInstance(module.processor	,		__UpperCamelCase		)

            # load LoRA weight file
      sd_pipe.load_lora_weights(__UpperCamelCase		)

      # check if lora attention processors are used
      for _, module in sd_pipe.unet.named_modules():
        if isinstance(__UpperCamelCase	,		__UpperCamelCase		):
          self.assertIsInstance(module.processor	,		__UpperCamelCase		)


  @unittest.skipIf(torch_device != '''cuda'''	,		'''This test is supposed to run on GPU'''		)
  def     _UpperCAmelCase  (					self		):
    _lowerCamelCase      =						self.get_dummy_components()
    _lowerCamelCase      =						StableDiffusionPipeline(**__UpperCamelCase		)
    _lowerCamelCase      =						sd_pipe.to(__UpperCamelCase		)
    sd_pipe.set_progress_bar_config(disable=__UpperCamelCase		)

    _lowerCamelCase      =						self.get_dummy_inputs()

    # enable XFormers
    sd_pipe.enable_xformers_memory_efficient_attention()

    _lowerCamelCase      =						sd_pipe(**__UpperCamelCase		).images
    _lowerCamelCase      =						original_images[0, -3:, -3:, -1]

    with tempfile.TemporaryDirectory() as tmpdirname:
      LoraLoaderMixin.save_lora_weights(
          save_directory=__UpperCamelCase	,		unet_lora_layers=lora_components['''unet_lora_layers''']	,		text_encoder_lora_layers=lora_components['''text_encoder_lora_layers''']	,		)
      self.assertTrue(os.path.isfile(os.path.join(__UpperCamelCase	,		'''pytorch_lora_weights.bin'''		)		)		)
      sd_pipe.load_lora_weights(__UpperCamelCase		)

    _lowerCamelCase      =						sd_pipe(**__UpperCamelCase		).images
    _lowerCamelCase      =						lora_images[0, -3:, -3:, -1]

    # Outputs shouldn't match.
    self.assertFalse(torch.allclose(torch.from_numpy(__UpperCamelCase		)	,		torch.from_numpy(__UpperCamelCase		)		)		)






"""simple docstring"""




import enum
import inspect
import warnings
from functools import wraps
from typing import Callable, Optional

from .logging import get_logger


A__   :						Optional[Any] 							=     set()
A__   :						List[str] 							=     get_logger(__name__)



def 				a__			(					lowerCAmelCase						:							Optional[str] = None		):






     '''simple docstring'''

     def decorator(lowerCAmelCase						:							Callable		):
          global _emitted_deprecation_warnings

          if inspect.isclass(lowerCAmelCase		):
               UpperCAmelCase__				:			Any							=    deprecated_class_or_function.__init__
               UpperCAmelCase__				:			Optional[int]							=    deprecated_class_or_function.__name__
          else:
               UpperCAmelCase__				:			str							=    deprecated_class_or_function
               UpperCAmelCase__				:			List[Any]							=    deprecated_function.__name__
               # Support deprecating __init__ class method: class name instead
               UpperCAmelCase__				:			List[str]							=    name if name != "__init__" else deprecated_function.__qualname__.split("."		)[-2]

          UpperCAmelCase__				:			Optional[Any]							=    (
              F"{name} is deprecated and will be removed in the next major version of datasets." + F" {help_message}"
              if help_message
              else ""
          )

          @wraps(lowerCAmelCase		)
          def wrapper(*lowerCAmelCase						:							int							,     **lowerCAmelCase						:							str		):
               UpperCAmelCase__				:			List[str]							=    hash(lowerCAmelCase		)
               if func_hash not in _emitted_deprecation_warnings:
                    warnings.warn(lowerCAmelCase							,     category=lowerCAmelCase							,     stacklevel=2		)
                    _emitted_deprecation_warnings.add(lowerCAmelCase		)
               return deprecated_function(*lowerCAmelCase							,     **lowerCAmelCase		)

          UpperCAmelCase__				:			List[str]							=    "deprecated"

          if inspect.isclass(lowerCAmelCase		):
               UpperCAmelCase__				:			List[str]							=    wrapper
               return deprecated_class_or_function
          else:
               return wrapper

     return decorator







class 	_lowercase    (							enum.EnumMeta					):


     '''simple docstring'''





     def __getattribute__(   cls			,      __UpperCamelCase	)->       Optional[Any]:
          UpperCAmelCase__				:			Optional[int]							=    super().__getattribute__(__UpperCamelCase	)
          if isinstance(__UpperCamelCase			,      enum.Enum	) and obj._on_access:
               obj._on_access()
          return obj



     def __getitem__(   cls			,      __UpperCamelCase	)->       Optional[int]:
          UpperCAmelCase__				:			Union[str, Any]							=    super().__getitem__(__UpperCamelCase	)
          if member._on_access:
               member._on_access()
          return member



     def __call__(   cls			,      __UpperCamelCase			,      __UpperCamelCase=None			,      *, __UpperCamelCase=None			,      __UpperCamelCase=None			,      __UpperCamelCase=None			,      __UpperCamelCase=1	)->       Dict:
          UpperCAmelCase__				:			Optional[int]							=    super().__call__(__UpperCamelCase			,      __UpperCamelCase			,      module=__UpperCamelCase			,      qualname=__UpperCamelCase			,      type=__UpperCamelCase			,      start=__UpperCamelCase	)
          if isinstance(__UpperCamelCase			,      enum.Enum	) and obj._on_access:
               obj._on_access()
          return obj







class 	_lowercase    (							enum.Enum						, metaclass=lowerCAmelCase_					):


     '''simple docstring'''





     def __new__(   cls			,      __UpperCamelCase	)->       Union[str, Any]:
          UpperCAmelCase__				:			int							=    object.__new__(cls	)
          UpperCAmelCase__				:			int							=    value
          UpperCAmelCase__				:			Optional[int]							=    member.deprecate
          return member



     @property
     def 			lowerCAmelCase__							(   self	)->       Union[str, Any]:
          return ""



     def 			lowerCAmelCase__							(   self	)->       int:
          UpperCAmelCase__				:			Tuple							=    F" {self.help_message}" if self.help_message else ""
          warnings.warn(
              F"'{self.__objclass__.__name__}' is deprecated and will be removed in the next major version of datasets."
              + help_message			,      __UpperCamelCase			,      stacklevel=3			,      )



