






"""simple docstring"""



import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetImgaImgPipeline,
    KandinskyVaaPriorEmbaEmbPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()






class 							__SCREAMING_SNAKE_CASE						(				UpperCamelCase       ,		unittest.TestCase		):
							snake_case_     =							KandinskyVaaControlnetImgaImgPipeline
							snake_case_     =							['image_embeds', 'negative_image_embeds', 'image', 'hint']
							snake_case_     =							['image_embeds', 'negative_image_embeds', 'image', 'hint']
							snake_case_     =							[
							    'generator',
							    'height',
							    'width',
							    'strength',
							    'guidance_scale',
							    'num_inference_steps',
							    'return_dict',
							    'guidance_scale',
							    'num_images_per_prompt',
							    'output_type',
							    'return_dict',
							]
							snake_case_     =							False


							@property
							def 						_UpperCamelCase       (			self      :						Optional[int]							):
													'''simple docstring'''





													return 32


							@property
							def 						_UpperCamelCase       (			self      :						Optional[Any]							):
													'''simple docstring'''





													return 32


							@property
							def 						_UpperCamelCase       (			self      :						List[Any]							):
													'''simple docstring'''





													return self.time_input_dim


							@property
							def 						_UpperCamelCase       (			self      :						List[str]							):
													'''simple docstring'''





													return self.time_input_dim * 4


							@property
							def 						_UpperCamelCase       (			self      :						int							):
													'''simple docstring'''





													return 100


							@property
							def 						_UpperCamelCase       (			self      :						str							):
													'''simple docstring'''





													torch.manual_seed(0							)

													A__						:  int		  =  {
													    """in_channels""": 8,
													    # Out channels is double in channels because predicts mean and variance
													    """out_channels""": 8,
													    """addition_embed_type""": """image_hint""",
													    """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
													    """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
													    """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
													    """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
													    """layers_per_block""": 1,
													    """encoder_hid_dim""": self.text_embedder_hidden_size,
													    """encoder_hid_dim_type""": """image_proj""",
													    """cross_attention_dim""": self.cross_attention_dim,
													    """attention_head_dim""": 4,
													    """resnet_time_scale_shift""": """scale_shift""",
													    """class_embed_type""": None,
													}

													A__						:  int		  =  UNetaDConditionModel(**snake_case							)
													return model


							@property
							def 						_UpperCamelCase       (			self      :						Any							):
													'''simple docstring'''





													return {
													    "block_out_channels": [32, 32, 64, 64],
													    "down_block_types": [
													        "DownEncoderBlock2D",
													        "DownEncoderBlock2D",
													        "DownEncoderBlock2D",
													        "AttnDownEncoderBlock2D",
													    ],
													    "in_channels": 3,
													    "latent_channels": 4,
													    "layers_per_block": 1,
													    "norm_num_groups": 8,
													    "norm_type": "spatial",
													    "num_vq_embeddings": 12,
													    "out_channels": 3,
													    "up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
													    "vq_embed_dim": 4,
													}


							@property
							def 						_UpperCamelCase       (			self      :						Optional[Any]							):
													'''simple docstring'''





													torch.manual_seed(0							)
													A__						:  Union[str, Any]		  =  VQModel(**self.dummy_movq_kwargs							)
													return model


							def 						_UpperCamelCase       (			self      :						Optional[int]							):
													'''simple docstring'''





													A__						:  Dict		  =  self.dummy_unet
													A__						:  Dict		  =  self.dummy_movq

													A__						:  List[str]		  =  {
													    """num_train_timesteps""": 1000,
													    """beta_schedule""": """linear""",
													    """beta_start""": 0.00085,
													    """beta_end""": 0.012,
													    """clip_sample""": False,
													    """set_alpha_to_one""": False,
													    """steps_offset""": 0,
													    """prediction_type""": """epsilon""",
													    """thresholding""": False,
													}

													A__						:  Any		  =  DDIMScheduler(**snake_case							)

													A__						:  List[str]		  =  {
													    """unet""": unet,
													    """scheduler""": scheduler,
													    """movq""": movq,
													}

													return components


							def 						_UpperCamelCase       (			self      :						Union[str, Any]			,    snake_case      :						Tuple			,    snake_case      :						List[Any]=0							):
													'''simple docstring'''





													A__						:  int		  =  floats_tensor((1, self.text_embedder_hidden_size)			,    rng=random.Random(snake_case							)							).to(snake_case							)
													A__						:  Any		  =  floats_tensor((1, self.text_embedder_hidden_size)			,    rng=random.Random(seed + 1							)							).to(
													    snake_case							)
													# create init_image
													A__						:  Dict		  =  floats_tensor((1, 3, 64, 64)			,    rng=random.Random(snake_case							)							).to(snake_case							)
													A__						:  Dict		  =  image.cpu().permute(0			,    2			,    3			,    1							)[0]
													A__						:  Optional[int]		  =  Image.fromarray(np.uinta(snake_case							)							).convert("""RGB"""							).resize((256, 256)							)
													# create hint
													A__						:  List[str]		  =  floats_tensor((1, 3, 64, 64)			,    rng=random.Random(snake_case							)							).to(snake_case							)

													if str(snake_case							).startswith("""mps"""							):
																			A__						:  Union[str, Any]		  =  torch.manual_seed(snake_case							)
													else:
																			A__						:  Dict		  =  torch.Generator(device=snake_case							).manual_seed(snake_case							)
													A__						:  int		  =  {
													    """image""": init_image,
													    """image_embeds""": image_embeds,
													    """negative_image_embeds""": negative_image_embeds,
													    """hint""": hint,
													    """generator""": generator,
													    """height""": 64,
													    """width""": 64,
													    """num_inference_steps""": 10,
													    """guidance_scale""": 7.0,
													    """strength""": 0.2,
													    """output_type""": """np""",
													}
													return inputs


							def 						_UpperCamelCase       (			self      :						Any							):
													'''simple docstring'''





													A__						:  List[Any]		  =  """cpu"""

													A__						:  Optional[Any]		  =  self.get_dummy_components()

													A__						:  List[Any]		  =  self.pipeline_class(**snake_case							)
													A__						:  Tuple		  =  pipe.to(snake_case							)

													pipe.set_progress_bar_config(disable=snake_case							)

													A__						:  Optional[int]		  =  pipe(**self.get_dummy_inputs(snake_case							)							)
													A__						:  List[Any]		  =  output.images

													A__						:  Dict		  =  pipe(
													    **self.get_dummy_inputs(snake_case							)			,    return_dict=snake_case			,    )[0]

													A__						:  Dict		  =  image[0, -3:, -3:, -1]
													A__						:  List[str]		  =  image_from_tuple[0, -3:, -3:, -1]

													assert image.shape == (1, 64, 64, 3)

													A__						:  List[str]		  =  np.array(
													    [0.54985034, 0.55509365, 0.52561504, 0.5570494, 0.5593818, 0.5263979, 0.50285643, 0.5069846, 0.51196736]							)
													assert (
													    np.abs(image_slice.flatten() - expected_slice							).max() < 1e-2
													), F' expected_slice {expected_slice}, but got {image_slice.flatten()}'
													assert (
													    np.abs(image_from_tuple_slice.flatten() - expected_slice							).max() < 1e-2
													), F' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'






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


							def 						_UpperCamelCase       (			self      :						Any							):
													'''simple docstring'''





													super().tearDown()
													gc.collect()
													torch.cuda.empty_cache()


							def 						_UpperCamelCase       (			self      :						str							):
													'''simple docstring'''





													A__						:  Dict		  =  load_numpy(
													    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
													    """/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy"""							)

													A__						:  Union[str, Any]		  =  load_image(
													    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""							)
													A__						:  str		  =  init_image.resize((512, 512)							)

													A__						:  List[str]		  =  load_image(
													    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
													    """/kandinskyv22/hint_image_cat.png"""							)
													A__						:  List[Any]		  =  torch.from_numpy(np.array(snake_case							)							).float() / 255.0
													A__						:  List[str]		  =  hint.permute(2			,    0			,    1							).unsqueeze(0							)

													A__						:  int		  =  """A robot, 4k photo"""

													A__						:  int		  =  KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
													    """kandinsky-community/kandinsky-2-2-prior"""			,    torch_dtype=torch.floataa							)
													pipe_prior.to(snake_case							)

													A__						:  Optional[Any]		  =  KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
													    """kandinsky-community/kandinsky-2-2-controlnet-depth"""			,    torch_dtype=torch.floataa							)
													A__						:  Any		  =  pipeline.to(snake_case							)

													pipeline.set_progress_bar_config(disable=snake_case							)

													A__						:  List[str]		  =  torch.Generator(device="""cpu"""							).manual_seed(0							)

													A__						:  Dict		  =  pipe_prior(
													    snake_case			,    image=snake_case			,    strength=0.85			,    generator=snake_case			,    negative_prompt=""""""			,    ).to_tuple()

													A__						:  Any		  =  pipeline(
													    image=snake_case			,    image_embeds=snake_case			,    negative_image_embeds=snake_case			,    hint=snake_case			,    generator=snake_case			,    num_inference_steps=100			,    height=512			,    width=512			,    strength=0.5			,    output_type="""np"""			,    )

													A__						:  List[Any]		  =  output.images[0]

													assert image.shape == (512, 512, 3)

													assert_mean_pixel_difference(snake_case			,    snake_case							)














"""simple docstring"""



import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


A_    =							logging.get_logger(__name__)


A_    =							{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt'''}

A_    =							{
    '''vocab_file''': {
        '''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json''',
        '''allenai/longformer-large-4096''': (
            '''https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json'''
        ),
        '''allenai/longformer-large-4096-finetuned-triviaqa''': (
            '''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json'''
        ),
        '''allenai/longformer-base-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json'''
        ),
        '''allenai/longformer-large-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json'''
        ),
    },
    '''merges_file''': {
        '''allenai/longformer-base-4096''': '''https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt''',
        '''allenai/longformer-large-4096''': (
            '''https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt'''
        ),
        '''allenai/longformer-large-4096-finetuned-triviaqa''': (
            '''https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt'''
        ),
        '''allenai/longformer-base-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt'''
        ),
        '''allenai/longformer-large-4096-extra.pos.embd.only''': (
            '''https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt'''
        ),
    },
}

A_    =							{
    '''allenai/longformer-base-4096''': 4096,
    '''allenai/longformer-large-4096''': 4096,
    '''allenai/longformer-large-4096-finetuned-triviaqa''': 4096,
    '''allenai/longformer-base-4096-extra.pos.embd.only''': 4096,
    '''allenai/longformer-large-4096-extra.pos.embd.only''': 4096,
}




@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def      _lowerCAmelCase		(							)		->List[str]:
						A__						:  Tuple		  =  (
						    list(range(ord("""!"""      ),						ord("""~"""      ) + 1      )      ) + list(range(ord("""¡"""      ),						ord("""¬"""      ) + 1      )      ) + list(range(ord("""®"""      ),						ord("""ÿ"""      ) + 1      )      )
						)
						A__						:  int		  =  bs[:]
						A__						:  Optional[Any]		  =  0
						for b in range(2**8      ):
												if b not in bs:
																		bs.append(UpperCAmelCase__      )
																		cs.append(2**8 + n      )
																		n += 1
						A__						:  Optional[Any]		  =  [chr(UpperCAmelCase__      ) for n in cs]
						return dict(zip(UpperCAmelCase__,						UpperCAmelCase__      )      )




def      _lowerCAmelCase		(							UpperCAmelCase__       :					int      )		->Tuple:
						A__						:  Tuple		  =  set()
						A__						:  Tuple		  =  word[0]
						for char in word[1:]:
												pairs.add((prev_char, char)      )
												A__						:  str		  =  char
						return pairs






class 							__SCREAMING_SNAKE_CASE						(				UpperCamelCase		):
							snake_case_     =							VOCAB_FILES_NAMES
							snake_case_     =							PRETRAINED_VOCAB_FILES_MAP
							snake_case_     =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							snake_case_     =							['input_ids', 'attention_mask']


							def __init__(			self      :						List[str]			,    snake_case      :						List[str]			,    snake_case      :						str			,    snake_case      :						Dict="replace"			,    snake_case      :						Optional[int]="<s>"			,    snake_case      :						Any="</s>"			,    snake_case      :						List[Any]="</s>"			,    snake_case      :						str="<s>"			,    snake_case      :						Tuple="<unk>"			,    snake_case      :						List[str]="<pad>"			,    snake_case      :						Optional[Any]="<mask>"			,    snake_case      :						Tuple=False			,    **snake_case      :						Dict			,    ):
													'''simple docstring'''





													A__						:  Optional[Any]		  =  AddedToken(snake_case			,    lstrip=snake_case			,    rstrip=snake_case							) if isinstance(snake_case			,    snake_case							) else bos_token
													A__						:  Optional[Any]		  =  AddedToken(snake_case			,    lstrip=snake_case			,    rstrip=snake_case							) if isinstance(snake_case			,    snake_case							) else eos_token
													A__						:  int		  =  AddedToken(snake_case			,    lstrip=snake_case			,    rstrip=snake_case							) if isinstance(snake_case			,    snake_case							) else sep_token
													A__						:  str		  =  AddedToken(snake_case			,    lstrip=snake_case			,    rstrip=snake_case							) if isinstance(snake_case			,    snake_case							) else cls_token
													A__						:  Optional[Any]		  =  AddedToken(snake_case			,    lstrip=snake_case			,    rstrip=snake_case							) if isinstance(snake_case			,    snake_case							) else unk_token
													A__						:  Optional[int]		  =  AddedToken(snake_case			,    lstrip=snake_case			,    rstrip=snake_case							) if isinstance(snake_case			,    snake_case							) else pad_token

													# Mask token behave like a normal word, i.e. include the space before it
													A__						:  Union[str, Any]		  =  AddedToken(snake_case			,    lstrip=snake_case			,    rstrip=snake_case							) if isinstance(snake_case			,    snake_case							) else mask_token

													super().__init__(
													    errors=snake_case			,    bos_token=snake_case			,    eos_token=snake_case			,    unk_token=snake_case			,    sep_token=snake_case			,    cls_token=snake_case			,    pad_token=snake_case			,    mask_token=snake_case			,    add_prefix_space=snake_case			,    **snake_case			,    )

													with open(snake_case			,    encoding="""utf-8"""							) as vocab_handle:
																			A__						:  Tuple		  =  json.load(snake_case							)
													A__						:  Dict		  =  {v: k for k, v in self.encoder.items()}
													A__						:  Dict		  =  errors  # how to handle errors in decoding
													A__						:  Dict		  =  bytes_to_unicode()
													A__						:  Optional[int]		  =  {v: k for k, v in self.byte_encoder.items()}
													with open(snake_case			,    encoding="""utf-8"""							) as merges_handle:
																			A__						:  List[str]		  =  merges_handle.read().split("""\n"""							)[1:-1]
													A__						:  Dict		  =  [tuple(merge.split()							) for merge in bpe_merges]
													A__						:  Union[str, Any]		  =  dict(zip(snake_case			,    range(len(snake_case							)							)							)							)
													A__						:  Tuple		  =  {}
													A__						:  Optional[int]		  =  add_prefix_space

													# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
													A__						:  Union[str, Any]		  =  re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"""							)


							@property
							def 						_UpperCamelCase       (			self      :						List[Any]							):
													'''simple docstring'''





													return len(self.encoder							)


							def 						_UpperCamelCase       (			self      :						List[Any]							):
													'''simple docstring'''





													return dict(self.encoder			,    **self.added_tokens_encoder							)


							def 						_UpperCamelCase       (			self      :						str			,    snake_case      :						Tuple							):
													'''simple docstring'''





													if token in self.cache:
																			return self.cache[token]
													A__						:  str		  =  tuple(snake_case							)
													A__						:  Any		  =  get_pairs(snake_case							)

													if not pairs:
																			return token

													while True:
																			A__						:  int		  =  min(snake_case			,    key=lambda snake_case							: self.bpe_ranks.get(snake_case			,    float("""inf"""							)							)							)
																			if bigram not in self.bpe_ranks:
																									break
																			A__  ,  A__						:  int		  =  bigram
																			A__						:  List[str]		  =  []
																			A__						:  Dict		  =  0
																			while i < len(snake_case							):
																									try:
																															A__						:  int		  =  word.index(snake_case			,    snake_case							)
																									except ValueError:
																															new_word.extend(word[i:]							)
																															break
																									else:
																															new_word.extend(word[i:j]							)
																															A__						:  str		  =  j

																									if word[i] == first and i < len(snake_case							) - 1 and word[i + 1] == second:
																															new_word.append(first + second							)
																															i += 2
																									else:
																															new_word.append(word[i]							)
																															i += 1
																			A__						:  Any		  =  tuple(snake_case							)
																			A__						:  Optional[Any]		  =  new_word
																			if len(snake_case							) == 1:
																									break
																			else:
																									A__						:  int		  =  get_pairs(snake_case							)
													A__						:  Dict		  =  """ """.join(snake_case							)
													A__						:  int		  =  word
													return word


							def 						_UpperCamelCase       (			self      :						int			,    snake_case      :						List[str]							):
													'''simple docstring'''





													A__						:  int		  =  []
													for token in re.findall(self.pat			,    snake_case							):
																			A__						:  Union[str, Any]		  =  """""".join(
																			    self.byte_encoder[b] for b in token.encode("""utf-8"""							)							)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
																			bpe_tokens.extend(bpe_token for bpe_token in self.bpe(snake_case							).split(""" """							)							)
													return bpe_tokens


							def 						_UpperCamelCase       (			self      :						Optional[Any]			,    snake_case      :						Dict							):
													'''simple docstring'''





													return self.encoder.get(snake_case			,    self.encoder.get(self.unk_token							)							)


							def 						_UpperCamelCase       (			self      :						Optional[int]			,    snake_case      :						int							):
													'''simple docstring'''





													return self.decoder.get(snake_case							)


							def 						_UpperCamelCase       (			self      :						Union[str, Any]			,    snake_case      :						Union[str, Any]							):
													'''simple docstring'''





													A__						:  Union[str, Any]		  =  """""".join(snake_case							)
													A__						:  Optional[Any]		  =  bytearray([self.byte_decoder[c] for c in text]							).decode("""utf-8"""			,    errors=self.errors							)
													return text


							def 						_UpperCamelCase       (			self      :						List[str]			,    snake_case      :						str			,    snake_case      :						Optional[str] = None							):
													'''simple docstring'''





													if not os.path.isdir(snake_case							):
																			logger.error(F'Vocabulary path ({save_directory}) should be a directory'							)
																			return
													A__						:  Any		  =  os.path.join(
													    snake_case			,    (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]							)
													A__						:  int		  =  os.path.join(
													    snake_case			,    (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]							)

													with open(snake_case			,    """w"""			,    encoding="""utf-8"""							) as f:
																			f.write(json.dumps(self.encoder			,    indent=2			,    sort_keys=snake_case			,    ensure_ascii=snake_case							) + """\n"""							)

													A__						:  Optional[int]		  =  0
													with open(snake_case			,    """w"""			,    encoding="""utf-8"""							) as writer:
																			writer.write("""#version: 0.2\n"""							)
																			for bpe_tokens, token_index in sorted(self.bpe_ranks.items()			,    key=lambda snake_case							: kv[1]							):
																									if index != token_index:
																															logger.warning(
																															    F'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
																															    """ Please check that the tokenizer is not corrupted!"""							)
																															A__						:  Dict		  =  token_index
																									writer.write(""" """.join(snake_case							) + """\n"""							)
																									index += 1

													return vocab_file, merge_file


							def 						_UpperCamelCase       (			self      :						List[Any]			,    snake_case      :						List[int]			,    snake_case      :						Optional[List[int]] = None							):
													'''simple docstring'''





													if token_ids_a is None:
																			return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
													A__						:  Optional[int]		  =  [self.cls_token_id]
													A__						:  int		  =  [self.sep_token_id]
													return cls + token_ids_a + sep + sep + token_ids_a + sep


							def 						_UpperCamelCase       (			self      :						int			,    snake_case      :						List[int]			,    snake_case      :						Optional[List[int]] = None			,    snake_case      :						bool = False							):
													'''simple docstring'''





													if already_has_special_tokens:
																			return super().get_special_tokens_mask(
																			    token_ids_a=snake_case			,    token_ids_a=snake_case			,    already_has_special_tokens=snake_case							)

													if token_ids_a is None:
																			return [1] + ([0] * len(snake_case							)) + [1]
													return [1] + ([0] * len(snake_case							)) + [1, 1] + ([0] * len(snake_case							)) + [1]


							def 						_UpperCamelCase       (			self      :						Union[str, Any]			,    snake_case      :						List[int]			,    snake_case      :						Optional[List[int]] = None							):
													'''simple docstring'''





													A__						:  int		  =  [self.sep_token_id]
													A__						:  int		  =  [self.cls_token_id]

													if token_ids_a is None:
																			return len(cls + token_ids_a + sep							) * [0]
													return len(cls + token_ids_a + sep + sep + token_ids_a + sep							) * [0]


							def 						_UpperCamelCase       (			self      :						str			,    snake_case      :						Optional[Any]			,    snake_case      :						List[str]=False			,    **snake_case      :						Optional[Any]							):
													'''simple docstring'''





													A__						:  Optional[Any]		  =  kwargs.pop("""add_prefix_space"""			,    self.add_prefix_space							)
													if (is_split_into_words or add_prefix_space) and (len(snake_case							) > 0 and not text[0].isspace()):
																			A__						:  str		  =  """ """ + text
													return (text, kwargs)






