



'''simple docstring'''






import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMInverseScheduler,
    DDIMScheduler,
    DPMSolverMultistepInverseScheduler,
    DPMSolverMultistepScheduler,
    StableDiffusionDiffEditPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_image, slow
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()

class 							a__(      lowerCamelCase__	,				lowerCamelCase__	,				unittest.TestCase			):
    lowercase__				=					StableDiffusionDiffEditPipeline
    lowercase__				=					TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""height""", """width""", """image"""} | {"""image_latents"""}
    lowercase__				=					TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {"""image"""} | {"""image_latents"""}
    lowercase__				=					frozenset(
        []			)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
    lowercase__				=					frozenset([]			)







    def     lowercase_ (   self				:						Any			):
      torch.manual_seed(0			)
      a		:      Dict       =  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		,    attention_head_dim=(2, 4)		,    use_linear_projection=__snake_case		,    )
      a		:      Optional[Any]       =  DDIMScheduler(
          beta_start=0.00085		,    beta_end=0.012		,    beta_schedule='scaled_linear'		,    clip_sample=__snake_case		,    set_alpha_to_one=__snake_case		,    )
      a		:      Any       =  DDIMInverseScheduler(
          beta_start=0.00085		,    beta_end=0.012		,    beta_schedule='scaled_linear'		,    clip_sample=__snake_case		,    set_alpha_to_zero=__snake_case		,    )
      torch.manual_seed(0			)
      a		:      Any       =  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		,    sample_size=1_28		,    )
      torch.manual_seed(0			)
      a		:      Union[str, Any]       =  CLIPTextConfig(
          bos_token_id=0		,    eos_token_id=2		,    hidden_size=32		,    intermediate_size=37		,    layer_norm_eps=1e-0_5		,    num_attention_heads=4		,    num_hidden_layers=5		,    pad_token_id=1		,    vocab_size=10_00		,    hidden_act='gelu'		,    projection_dim=5_12		,    )
      a		:      Optional[Any]       =  CLIPTextModel(__snake_case			)
      a		:      str       =  CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'			)

      a		:      List[str]       =  {
          'unet': unet,
          'scheduler': scheduler,
          'inverse_scheduler': inverse_scheduler,
          'vae': vae,
          'text_encoder': text_encoder,
          'tokenizer': tokenizer,
          'safety_checker': None,
          'feature_extractor': None,
      }

      return components







    def     lowercase_ (   self				:						Any		,    __snake_case				:						Union[str, Any]		,    __snake_case				:						Union[str, Any]=0			):
      a		:      List[Any]       =  floats_tensor((1, 16, 16)		,    rng=random.Random(__snake_case			)			).to(__snake_case			)
      a		:      Tuple       =  floats_tensor((1, 2, 4, 16, 16)		,    rng=random.Random(__snake_case			)			).to(__snake_case			)
      if str(__snake_case			).startswith('mps'			):
        a		:      Tuple       =  torch.manual_seed(__snake_case			)
      else:
        a		:      Dict       =  torch.Generator(device=__snake_case			).manual_seed(__snake_case			)
      a		:      Optional[Any]       =  {
          'prompt': 'a dog and a newt',
          'mask_image': mask,
          'image_latents': latents,
          'generator': generator,
          'num_inference_steps': 2,
          'inpaint_strength': 1.0,
          'guidance_scale': 6.0,
          'output_type': 'numpy',
      }

      return inputs







    def     lowercase_ (   self				:						Optional[Any]		,    __snake_case				:						List[Any]		,    __snake_case				:						List[str]=0			):
      a		:      Dict       =  floats_tensor((1, 3, 32, 32)		,    rng=random.Random(__snake_case			)			).to(__snake_case			)
      a		:      int       =  image.cpu().permute(0		,    2		,    3		,    1			)[0]
      a		:      int       =  Image.fromarray(np.uinta(__snake_case			)			).convert('RGB'			)
      if str(__snake_case			).startswith('mps'			):
        a		:      Dict       =  torch.manual_seed(__snake_case			)
      else:
        a		:      Dict       =  torch.Generator(device=__snake_case			).manual_seed(__snake_case			)
      a		:      List[str]       =  {
          'image': image,
          'source_prompt': 'a cat and a frog',
          'target_prompt': 'a dog and a newt',
          'generator': generator,
          'num_inference_steps': 2,
          'num_maps_per_mask': 2,
          'mask_encode_strength': 1.0,
          'guidance_scale': 6.0,
          'output_type': 'numpy',
      }

      return inputs







    def     lowercase_ (   self				:						Union[str, Any]		,    __snake_case				:						List[Any]		,    __snake_case				:						Tuple=0			):
      a		:      List[Any]       =  floats_tensor((1, 3, 32, 32)		,    rng=random.Random(__snake_case			)			).to(__snake_case			)
      a		:      Any       =  image.cpu().permute(0		,    2		,    3		,    1			)[0]
      a		:      Tuple       =  Image.fromarray(np.uinta(__snake_case			)			).convert('RGB'			)
      if str(__snake_case			).startswith('mps'			):
        a		:      Union[str, Any]       =  torch.manual_seed(__snake_case			)
      else:
        a		:      Optional[Any]       =  torch.Generator(device=__snake_case			).manual_seed(__snake_case			)
      a		:      Union[str, Any]       =  {
          'image': image,
          'prompt': 'a cat and a frog',
          'generator': generator,
          'num_inference_steps': 2,
          'inpaint_strength': 1.0,
          'guidance_scale': 6.0,
          'decode_latents': True,
          'output_type': 'numpy',
      }
      return inputs







    def     lowercase_ (   self				:						Union[str, Any]			):
      if not hasattr(self.pipeline_class		,    '_optional_components'			):
        return

      a		:      Dict       =  self.get_dummy_components()
      a		:      Dict       =  self.pipeline_class(**__snake_case			)
      pipe.to(__snake_case			)
      pipe.set_progress_bar_config(disable=__snake_case			)

      # set all optional components to None and update pipeline config accordingly
      for optional_component in pipe._optional_components:
        setattr(__snake_case		,    __snake_case		,    __snake_case			)
      pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components}			)

      a		:      Any       =  self.get_dummy_inputs(__snake_case			)
      a		:      Any       =  pipe(**__snake_case			)[0]

      with tempfile.TemporaryDirectory() as tmpdir:
        pipe.save_pretrained(__snake_case			)
        a		:      Dict       =  self.pipeline_class.from_pretrained(__snake_case			)
        pipe_loaded.to(__snake_case			)
        pipe_loaded.set_progress_bar_config(disable=__snake_case			)

      for optional_component in pipe._optional_components:
        self.assertTrue(
            getattr(__snake_case		,    __snake_case			) is None		,    F"""`{optional_component}` did not stay set to None after loading."""		,    )

      a		:      int       =  self.get_dummy_inputs(__snake_case			)
      a		:      List[str]       =  pipe_loaded(**__snake_case			)[0]

      a		:      List[str]       =  np.abs(output - output_loaded			).max()
      self.assertLess(__snake_case		,    1e-4			)







    def     lowercase_ (   self				:						List[str]			):
      a		:      List[Any]       =  'cpu'

      a		:      Union[str, Any]       =  self.get_dummy_components()
      a		:      List[str]       =  self.pipeline_class(**__snake_case			)
      pipe.to(__snake_case			)
      pipe.set_progress_bar_config(disable=__snake_case			)

      a		:      Optional[Any]       =  self.get_dummy_mask_inputs(__snake_case			)
      a		:      Dict       =  pipe.generate_mask(**__snake_case			)
      a		:      Union[str, Any]       =  mask[0, -3:, -3:]

      self.assertEqual(mask.shape		,    (1, 16, 16)			)
      a		:      List[str]       =  np.array([0] * 9			)
      a		:      List[Any]       =  np.abs(mask_slice.flatten() - expected_slice			).max()
      self.assertLessEqual(__snake_case		,    1e-3			)
      self.assertEqual(mask[0, -3, -4]		,    0			)







    def     lowercase_ (   self				:						Tuple			):
      a		:      Any       =  'cpu'

      a		:      Any       =  self.get_dummy_components()
      a		:      Optional[int]       =  self.pipeline_class(**__snake_case			)
      pipe.to(__snake_case			)
      pipe.set_progress_bar_config(disable=__snake_case			)

      a		:      List[Any]       =  self.get_dummy_inversion_inputs(__snake_case			)
      a		:      str       =  pipe.invert(**__snake_case			).images
      a		:      Tuple       =  image[0, -1, -3:, -3:]

      self.assertEqual(image.shape		,    (2, 32, 32, 3)			)
      a		:      Optional[int]       =  np.array(
          [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.51050, 0.5015, 0.4407, 0.4799]		,    )
      a		:      str       =  np.abs(image_slice.flatten() - expected_slice			).max()
      self.assertLessEqual(__snake_case		,    1e-3			)







    def     lowercase_ (   self				:						Tuple			):
      super().test_inference_batch_single_identical(expected_max_diff=5e-3			)







    def     lowercase_ (   self				:						List[str]			):
      a		:      Tuple       =  'cpu'

      a		:      Union[str, Any]       =  self.get_dummy_components()

      a		:      Tuple       =  {'beta_start': 0.00085, 'beta_end': 0.012, 'beta_schedule': 'scaled_linear'}
      a		:      int       =  DPMSolverMultistepScheduler(**__snake_case			)
      a		:      int       =  DPMSolverMultistepInverseScheduler(**__snake_case			)

      a		:      Optional[Any]       =  self.pipeline_class(**__snake_case			)
      pipe.to(__snake_case			)
      pipe.set_progress_bar_config(disable=__snake_case			)

      a		:      Any       =  self.get_dummy_inversion_inputs(__snake_case			)
      a		:      Union[str, Any]       =  pipe.invert(**__snake_case			).images
      a		:      List[Any]       =  image[0, -1, -3:, -3:]

      self.assertEqual(image.shape		,    (2, 32, 32, 3)			)
      a		:      List[Any]       =  np.array(
          [0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.51050, 0.5015, 0.4407, 0.4799]		,    )
      a		:      Tuple       =  np.abs(image_slice.flatten() - expected_slice			).max()
      self.assertLessEqual(__snake_case		,    1e-3			)

@require_torch_gpu
@slow
class 							a__(      unittest.TestCase			):







    def     lowercase_ (   self				:						Union[str, Any]			):
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()







    @classmethod
    def     lowercase_ (   cls				:						str			):
      a		:      Optional[int]       =  load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png'			)

      a		:      int       =  raw_image.convert('RGB'			).resize((7_68, 7_68)			)

      a		:      Tuple       =  raw_image







    def     lowercase_ (   self				:						Optional[int]			):
      a		:      Dict       =  torch.manual_seed(0			)

      a		:      int       =  StableDiffusionDiffEditPipeline.from_pretrained(
          'stabilityai/stable-diffusion-2-1'		,    safety_checker=__snake_case		,    torch_dtype=torch.floataa			)
      a		:      Optional[Any]       =  DDIMScheduler.from_config(pipe.scheduler.config			)
      a		:      Tuple       =  DDIMInverseScheduler.from_config(pipe.scheduler.config			)
      pipe.enable_model_cpu_offload()
      pipe.set_progress_bar_config(disable=__snake_case			)

      a		:      List[str]       =  'a bowl of fruit'
      a		:      Any       =  'a bowl of pears'

      a		:      int       =  pipe.generate_mask(
          image=self.raw_image		,    source_prompt=__snake_case		,    target_prompt=__snake_case		,    generator=__snake_case		,    )

      a		:      List[str]       =  pipe.invert(
          prompt=__snake_case		,    image=self.raw_image		,    inpaint_strength=0.7		,    generator=__snake_case			).latents

      a		:      Optional[Any]       =  pipe(
          prompt=__snake_case		,    mask_image=__snake_case		,    image_latents=__snake_case		,    generator=__snake_case		,    negative_prompt=__snake_case		,    inpaint_strength=0.7		,    output_type='numpy'		,    ).images[0]

      a		:      Optional[Any]       =  (
          np.array(
              load_image(
                  'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
                  '/diffedit/pears.png'			).resize((7_68, 7_68)			)			)
          / 2_55
      )
      assert np.abs((expected_image - image).max()			) < 5e-1







    def     lowercase_ (   self				:						str			):
      a		:      Dict       =  torch.manual_seed(0			)

      a		:      Tuple       =  StableDiffusionDiffEditPipeline.from_pretrained(
          'stabilityai/stable-diffusion-2-1'		,    safety_checker=__snake_case		,    torch_dtype=torch.floataa			)
      a		:      List[Any]       =  DPMSolverMultistepScheduler.from_config(pipe.scheduler.config			)
      a		:      Optional[int]       =  DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config			)
      pipe.enable_model_cpu_offload()
      pipe.set_progress_bar_config(disable=__snake_case			)

      a		:      str       =  'a bowl of fruit'
      a		:      Union[str, Any]       =  'a bowl of pears'

      a		:      List[Any]       =  pipe.generate_mask(
          image=self.raw_image		,    source_prompt=__snake_case		,    target_prompt=__snake_case		,    generator=__snake_case		,    )

      a		:      List[str]       =  pipe.invert(
          prompt=__snake_case		,    image=self.raw_image		,    inpaint_strength=0.7		,    generator=__snake_case		,    num_inference_steps=25		,    ).latents

      a		:      Union[str, Any]       =  pipe(
          prompt=__snake_case		,    mask_image=__snake_case		,    image_latents=__snake_case		,    generator=__snake_case		,    negative_prompt=__snake_case		,    inpaint_strength=0.7		,    num_inference_steps=25		,    output_type='numpy'		,    ).images[0]

      a		:      Tuple       =  (
          np.array(
              load_image(
                  'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
                  '/diffedit/pears.png'			).resize((7_68, 7_68)			)			)
          / 2_55
      )
      assert np.abs((expected_image - image).max()			) < 5e-1




'''simple docstring'''






from typing import Dict, List, Optional, Union

import numpy as np

from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy


lowerCAmelCase:      int		 =		logging.get_logger(__name__)

class 							a__(      lowerCamelCase__			):







    def __init__(   self				:						Union[str, Any]		,    __snake_case				:						int		,    __snake_case				:						int		,    __snake_case				:						float		,    **__snake_case				:						Optional[int]			):
      a		:      Optional[Any]       =  feature_size
      a		:      Tuple       =  sampling_rate
      a		:      str       =  padding_value

      a		:      Any       =  kwargs.pop('padding_side'		,    'right'			)
      a		:      Tuple       =  kwargs.pop('return_attention_mask'		,    __snake_case			)

      super().__init__(**__snake_case			)







    def     lowercase_ (   self				:						Tuple		,    __snake_case				:						Union[
            BatchFeature,
            List[BatchFeature],
            Dict[str, BatchFeature],
            Dict[str, List[BatchFeature]],
            List[Dict[str, BatchFeature]],
        ]		,    __snake_case				:						Union[bool, str, PaddingStrategy] = True		,    __snake_case				:						Optional[int] = None		,    __snake_case				:						bool = False		,    __snake_case				:						Optional[int] = None		,    __snake_case				:						Optional[bool] = None		,    __snake_case				:						Optional[Union[str, TensorType]] = None		,    ):
      # If we have a list of dicts, let's convert it in a dict of lists
      # We do this to allow using this method as a collate_fn function in PyTorch Dataloader
      if isinstance(__snake_case		,    (list, tuple)			) and isinstance(processed_features[0]		,    (dict, BatchFeature)			):
        a		:      Union[str, Any]       =  {
            key: [example[key] for example in processed_features] for key in processed_features[0].keys()
        }

      # The model's main input name, usually `input_values`, has be passed for padding
      if self.model_input_names[0] not in processed_features:
        raise ValueError(
            'You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`'
            F""" to this method that includes {self.model_input_names[0]}, but you provided"""
            F""" {list(processed_features.keys()			)}"""			)

      a		:      Tuple       =  processed_features[self.model_input_names[0]]
      a		:      str       =  (
          return_attention_mask if return_attention_mask is not None else self.return_attention_mask
      )

      if len(__snake_case			) == 0:
        if return_attention_mask:
          a		:      Dict       =  []
        return processed_features

      # If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
      # and rebuild them afterwards if no return_tensors is specified
      # Note that we lose the specific device the tensor may be on for PyTorch

      a		:      Optional[int]       =  required_input[0]
      if isinstance(__snake_case		,    (list, tuple)			):
        # first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
        a		:      Dict       =  0
        while len(required_input[index]			) == 0:
          index += 1
        if index < len(__snake_case			):
          a		:      Tuple       =  required_input[index][0]

      if return_tensors is None:
        if is_tf_tensor(__snake_case			):
          a		:      Any       =  'tf'
        elif is_torch_tensor(__snake_case			):
          a		:      Optional[Any]       =  'pt'
        elif isinstance(__snake_case		,    (int, float, list, tuple, np.ndarray)			):
          a		:      List[Any]       =  'np'
        else:
          raise ValueError(
              F"""type of {first_element} unknown: {type(__snake_case			)}. """
              'Should be one of a python, numpy, pytorch or tensorflow object.'			)

      for key, value in processed_features.items():
        if isinstance(value[0]		,    (int, float)			):
          a		:      List[Any]       =  to_numpy(__snake_case			)
        else:
          a		:      Tuple       =  [to_numpy(__snake_case			) for v in value]

        # Convert padding_strategy in PaddingStrategy
      a		:      Any       =  self._get_padding_strategies(padding=__snake_case		,    max_length=__snake_case			)

      a		:      int       =  processed_features[self.model_input_names[0]]

      a		:      Dict       =  len(__snake_case			)
      if not all(len(__snake_case			) == batch_size for v in processed_features.values()			):
        raise ValueError('Some items in the output dictionary have a different batch size than others.'			)

      a		:      Optional[int]       =  []
      for i in range(__snake_case			):
        a		:      int       =  {k: v[i] for k, v in processed_features.items()}
        # truncation
        a		:      List[str]       =  self._truncate(
            __snake_case		,    max_length=__snake_case		,    pad_to_multiple_of=__snake_case		,    truncation=__snake_case		,    )
        truncated_inputs.append(__snake_case			)

      if padding_strategy == PaddingStrategy.LONGEST:
        # make sure that `max_length` cannot be longer than the longest truncated length
        a		:      List[str]       =  max(len(input_slice[self.model_input_names[0]]			) for input_slice in truncated_inputs			)
        a		:      Tuple       =  PaddingStrategy.MAX_LENGTH

      a		:      int       =  {}
      for i in range(__snake_case			):
        # padding
        a		:      int       =  self._pad(
            truncated_inputs[i]		,    max_length=__snake_case		,    padding_strategy=__snake_case		,    pad_to_multiple_of=__snake_case		,    return_attention_mask=__snake_case		,    )

        for key, value in outputs.items():
          if key not in batch_outputs:
            a		:      int       =  []
          if value.dtype is np.dtype(np.floataa			):
            a		:      List[str]       =  value.astype(np.floataa			)
          batch_outputs[key].append(__snake_case			)

      return BatchFeature(__snake_case		,    tensor_type=__snake_case			)







    def     lowercase_ (   self				:						List[str]		,    __snake_case				:						Union[Dict[str, np.ndarray], BatchFeature]		,    __snake_case				:						Optional[int] = None		,    __snake_case				:						PaddingStrategy = PaddingStrategy.DO_NOT_PAD		,    __snake_case				:						Optional[int] = None		,    __snake_case				:						Optional[bool] = None		,    ):
      a		:      List[str]       =  processed_features[self.model_input_names[0]]

      if padding_strategy == PaddingStrategy.LONGEST:
        a		:      Any       =  len(__snake_case			)

      if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
        a		:      Dict       =  ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

      a		:      List[Any]       =  padding_strategy != PaddingStrategy.DO_NOT_PAD and len(__snake_case			) < max_length

      if return_attention_mask and "attention_mask" not in processed_features:
        a		:      Union[str, Any]       =  np.ones(len(__snake_case			)		,    dtype=np.intaa			)

      if needs_to_be_padded:
        a		:      Tuple       =  max_length - len(__snake_case			)
        if self.padding_side == "right":
          if return_attention_mask:
            a		:      Any       =  np.pad(
                processed_features['attention_mask']		,    (0, difference)			)
          a		:      Optional[Any]       =  ((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
          a		:      Optional[int]       =  np.pad(
              __snake_case		,    __snake_case		,    'constant'		,    constant_values=self.padding_value			)
        elif self.padding_side == "left":
          if return_attention_mask:
            a		:      Optional[int]       =  np.pad(
                processed_features['attention_mask']		,    (difference, 0)			)
          a		:      Optional[Any]       =  ((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
          a		:      List[Any]       =  np.pad(
              __snake_case		,    __snake_case		,    'constant'		,    constant_values=self.padding_value			)
        else:
          raise ValueError('Invalid padding strategy:' + str(self.padding_side			)			)

      return processed_features







    def     lowercase_ (   self				:						List[str]		,    __snake_case				:						Union[Dict[str, np.ndarray], BatchFeature]		,    __snake_case				:						Optional[int] = None		,    __snake_case				:						Optional[int] = None		,    __snake_case				:						Optional[bool] = None		,    ):
      if not truncation:
        return processed_features
      elif truncation and max_length is None:
        raise ValueError('When setting ``truncation=True``, make sure that ``max_length`` is defined.'			)

      a		:      Dict       =  processed_features[self.model_input_names[0]]

      # find `max_length` that fits `pad_to_multiple_of`
      if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
        a		:      Optional[Any]       =  ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

      a		:      str       =  len(__snake_case			) > max_length

      if needs_to_be_truncated:
        a		:      str       =  processed_features[self.model_input_names[0]][:max_length]
        if "attention_mask" in processed_features:
          a		:      List[str]       =  processed_features['attention_mask'][:max_length]

      return processed_features







    def     lowercase_ (   self				:						Dict		,    __snake_case				:						Optional[Any]=False		,    __snake_case				:						Optional[int]=None			):

      # Get padding strategy
      if padding is not False:
        if padding is True:
          a		:      Union[str, Any]       =  PaddingStrategy.LONGEST  # Default to pad to the longest sequence in the batch
        elif not isinstance(__snake_case		,    __snake_case			):
          a		:      List[Any]       =  PaddingStrategy(__snake_case			)
        elif isinstance(__snake_case		,    __snake_case			):
          a		:      List[str]       =  padding
      else:
        a		:      Dict       =  PaddingStrategy.DO_NOT_PAD

      # Set max length if needed
      if max_length is None:
        if padding_strategy == PaddingStrategy.MAX_LENGTH:
          raise ValueError(
              F"""When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined"""			)

        # Test if we have a padding value
      if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
        raise ValueError(
            'Asking to pad but the feature_extractor does not have a padding value. Please select a value to use'
            ' as `padding_value`. For example: `feature_extractor.padding_value = 0.0`.'			)

      return padding_strategy