






"""simple docstring"""



from typing import List, Optional, Union

import torch

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


A_    =							logging.get_logger(__name__)  # pylint: disable=invalid-name

A_    =							'''
    Examples:
        ```py
        >>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline
        >>> import torch

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained("kandinsky-community/kandinsky-2-2-prior")
        >>> pipe_prior.to("cuda")
        >>> prompt = "red cat, 4k photo"
        >>> out = pipe_prior(prompt)
        >>> image_emb = out.image_embeds
        >>> zero_image_emb = out.negative_image_embeds
        >>> pipe = KandinskyV22Pipeline.from_pretrained("kandinsky-community/kandinsky-2-2-decoder")
        >>> pipe.to("cuda")
        >>> image = pipe(
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=50,
        ... ).images
        >>> image[0].save("cat.png")
        ```
'''




def      _lowerCAmelCase		(							UpperCAmelCase__       :					Optional[int],						UpperCAmelCase__       :					Any,						UpperCAmelCase__       :					int=8      )		->Optional[int]:
						A__						:  Any		  =  height // scale_factor**2
						if height % scale_factor**2 != 0:
												new_height += 1
						A__						:  Dict		  =  width // scale_factor**2
						if width % scale_factor**2 != 0:
												new_width += 1
						return new_height * scale_factor, new_width * scale_factor







class 							__SCREAMING_SNAKE_CASE						(				__UpperCAmelCase		):


							def __init__(			self      :						Dict			,    snake_case      :						UNetaDConditionModel			,    snake_case      :						DDPMScheduler			,    snake_case      :						VQModel			,    ):
													'''simple docstring'''





													super().__init__()

													self.register_modules(
													    unet=__SCREAMING_SNAKE_CASE			,    scheduler=__SCREAMING_SNAKE_CASE			,    movq=__SCREAMING_SNAKE_CASE			,    )
													A__						:  Optional[int]		  =  2 ** (len(self.movq.config.block_out_channels							) - 1)


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





													if latents is None:
																			A__						:  str		  =  randn_tensor(__SCREAMING_SNAKE_CASE			,    generator=__SCREAMING_SNAKE_CASE			,    device=__SCREAMING_SNAKE_CASE			,    dtype=__SCREAMING_SNAKE_CASE							)
													else:
																			if latents.shape != shape:
																									raise ValueError(F'Unexpected latents shape, got {latents.shape}, expected {shape}'							)
																			A__						:  List[str]		  =  latents.to(__SCREAMING_SNAKE_CASE							)

													A__						:  List[Any]		  =  latents * scheduler.init_noise_sigma
													return latents


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





													if is_accelerate_available():
																			from accelerate import cpu_offload
													else:
																			raise ImportError("""Please install accelerate via `pip install accelerate`"""							)

													A__						:  List[Any]		  =  torch.device(F'cuda:{gpu_id}'							)

													A__						:  Any		  =  [
													    self.unet,
													    self.movq,
													]
													for cpu_offloaded_model in models:
																			if cpu_offloaded_model is not None:
																									cpu_offload(__SCREAMING_SNAKE_CASE			,    __SCREAMING_SNAKE_CASE							)


							def 						_UpperCamelCase       (			self      :						int			,    snake_case      :						str=0							):
													'''simple docstring'''





													if is_accelerate_available() and is_accelerate_version(""">="""			,    """0.17.0.dev0"""							):
																			from accelerate import cpu_offload_with_hook
													else:
																			raise ImportError("""`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher."""							)

													A__						:  List[Any]		  =  torch.device(F'cuda:{gpu_id}'							)

													if self.device.type != "cpu":
																			self.to("""cpu"""			,    silence_dtype_warnings=__SCREAMING_SNAKE_CASE							)
																			torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

													A__						:  Dict		  =  None
													for cpu_offloaded_model in [self.unet, self.movq]:
																			A__  ,  A__						:  Optional[int]		  =  cpu_offload_with_hook(__SCREAMING_SNAKE_CASE			,    __SCREAMING_SNAKE_CASE			,    prev_module_hook=__SCREAMING_SNAKE_CASE							)

													# We'll offload the last model manually.
													A__						:  List[Any]		  =  hook


							@property
							# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
							def 						_UpperCamelCase       (			self      :						Optional[Any]							):
													'''simple docstring'''





													if not hasattr(self.unet			,    """_hf_hook"""							):
																			return self.device
													for module in self.unet.modules():
																			if (
																			    hasattr(__SCREAMING_SNAKE_CASE			,    """_hf_hook"""							)
																			    and hasattr(module._hf_hook			,    """execution_device"""							)
																			    and module._hf_hook.execution_device is not None
																			):
																									return torch.device(module._hf_hook.execution_device							)
													return self.device


							@torch.no_grad()
							@replace_example_docstring(__SCREAMING_SNAKE_CASE							)
							def __call__(			self      :						str			,    snake_case      :						Union[torch.FloatTensor, List[torch.FloatTensor]]			,    snake_case      :						Union[torch.FloatTensor, List[torch.FloatTensor]]			,    snake_case      :						int = 512			,    snake_case      :						int = 512			,    snake_case      :						int = 100			,    snake_case      :						float = 4.0			,    snake_case      :						int = 1			,    snake_case      :						Optional[Union[torch.Generator, List[torch.Generator]]] = None			,    snake_case      :						Optional[torch.FloatTensor] = None			,    snake_case      :						Optional[str] = "pil"			,    snake_case      :						bool = True			,    ):
													'''simple docstring'''





													A__						:  List[Any]		  =  self._execution_device

													A__						:  int		  =  guidance_scale > 1.0

													if isinstance(__SCREAMING_SNAKE_CASE			,    __SCREAMING_SNAKE_CASE							):
																			A__						:  Any		  =  torch.cat(__SCREAMING_SNAKE_CASE			,    dim=0							)
													A__						:  Tuple		  =  image_embeds.shape[0] * num_images_per_prompt
													if isinstance(__SCREAMING_SNAKE_CASE			,    __SCREAMING_SNAKE_CASE							):
																			A__						:  Dict		  =  torch.cat(__SCREAMING_SNAKE_CASE			,    dim=0							)

													if do_classifier_free_guidance:
																			A__						:  List[str]		  =  image_embeds.repeat_interleave(__SCREAMING_SNAKE_CASE			,    dim=0							)
																			A__						:  Optional[int]		  =  negative_image_embeds.repeat_interleave(__SCREAMING_SNAKE_CASE			,    dim=0							)

													A__						:  Any		  =  torch.cat([negative_image_embeds, image_embeds]			,    dim=0							).to(dtype=self.unet.dtype			,    device=__SCREAMING_SNAKE_CASE							)

													self.scheduler.set_timesteps(__SCREAMING_SNAKE_CASE			,    device=__SCREAMING_SNAKE_CASE							)
													A__						:  Optional[Any]		  =  self.scheduler.timesteps

													A__						:  List[str]		  =  self.unet.config.in_channels

													A__  ,  A__						:  Dict		  =  downscale_height_and_width(__SCREAMING_SNAKE_CASE			,    __SCREAMING_SNAKE_CASE			,    self.movq_scale_factor							)

													# create initial latent
													A__						:  Dict		  =  self.prepare_latents(
													    (batch_size, num_channels_latents, height, width)			,    image_embeds.dtype			,    __SCREAMING_SNAKE_CASE			,    __SCREAMING_SNAKE_CASE			,    __SCREAMING_SNAKE_CASE			,    self.scheduler			,    )

													for i, t in enumerate(self.progress_bar(__SCREAMING_SNAKE_CASE							)							):
																			# expand the latents if we are doing classifier free guidance
																			A__						:  Dict		  =  torch.cat([latents] * 2							) if do_classifier_free_guidance else latents

																			A__						:  str		  =  {"""image_embeds""": image_embeds}
																			A__						:  Optional[int]		  =  self.unet(
																			    sample=__SCREAMING_SNAKE_CASE			,    timestep=__SCREAMING_SNAKE_CASE			,    encoder_hidden_states=__SCREAMING_SNAKE_CASE			,    added_cond_kwargs=__SCREAMING_SNAKE_CASE			,    return_dict=__SCREAMING_SNAKE_CASE			,    )[0]

																			if do_classifier_free_guidance:
																									A__  ,  A__						:  Optional[int]		  =  noise_pred.split(latents.shape[1]			,    dim=1							)
																									A__  ,  A__						:  Union[str, Any]		  =  noise_pred.chunk(2							)
																									A__  ,  A__						:  Any		  =  variance_pred.chunk(2							)
																									A__						:  int		  =  noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																									A__						:  Union[str, Any]		  =  torch.cat([noise_pred, variance_pred_text]			,    dim=1							)

																			if not (
																			    hasattr(self.scheduler.config			,    """variance_type"""							)
																			    and self.scheduler.config.variance_type in ["learned", "learned_range"]
																			):
																									A__  ,  A__						:  Optional[int]		  =  noise_pred.split(latents.shape[1]			,    dim=1							)

																			# compute the previous noisy sample x_t -> x_t-1
																			A__						:  List[str]		  =  self.scheduler.step(
																			    __SCREAMING_SNAKE_CASE			,    __SCREAMING_SNAKE_CASE			,    __SCREAMING_SNAKE_CASE			,    generator=__SCREAMING_SNAKE_CASE			,    )[0]
													# post-processing
													A__						:  Optional[int]		  =  self.movq.decode(__SCREAMING_SNAKE_CASE			,    force_not_quantize=__SCREAMING_SNAKE_CASE							)["""sample"""]

													if output_type not in ["pt", "np", "pil"]:
																			raise ValueError(F'Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}'							)

													if output_type in ["np", "pil"]:
																			A__						:  Dict		  =  image * 0.5 + 0.5
																			A__						:  List[str]		  =  image.clamp(0			,    1							)
																			A__						:  List[Any]		  =  image.cpu().permute(0			,    2			,    3			,    1							).float().numpy()

													if output_type == "pil":
																			A__						:  Tuple		  =  self.numpy_to_pil(__SCREAMING_SNAKE_CASE							)

													if not return_dict:
																			return (image,)

													return ImagePipelineOutput(images=__SCREAMING_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)






