
'''simple docstring'''





import math
from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from transformers import CLIPTextModelWithProjection, CLIPTokenizer

from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteScheduler
from ...utils import (
    BaseOutput,
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from .renderer import ShapERenderer


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

A							     = "\n    Examples:\n        ```py\n        >>> import torch\n        >>> from diffusers import DiffusionPipeline\n        >>> from diffusers.utils import export_to_gif\n\n        >>> device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n\n        >>> repo = \"openai/shap-e\"\n        >>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)\n        >>> pipe = pipe.to(device)\n\n        >>> guidance_scale = 15.0\n        >>> prompt = \"a shark\"\n\n        >>> images = pipe(\n        ...     prompt,\n        ...     guidance_scale=guidance_scale,\n        ...     num_inference_steps=64,\n        ...     frame_size=256,\n        ... ).images\n\n        >>> gif_path = export_to_gif(images[0], \"shark_3d.gif\")\n        ```\n"



@dataclass
class 			lowerCAmelCase__ (							UpperCamelCase							):



  '''simple docstring'''






  _UpperCamelCase							=			42



class 			lowerCAmelCase__ (							UpperCamelCase							):



  '''simple docstring'''






  def __init__(self	:			Optional[Any]						,			lowerCAmelCase_	:			PriorTransformer						,			lowerCAmelCase_	:			CLIPTextModelWithProjection						,			lowerCAmelCase_	:			CLIPTokenizer						,			lowerCAmelCase_	:			HeunDiscreteScheduler						,			lowerCAmelCase_	:			ShapERenderer						,			) ->							Optional[Any]:
        super().__init__()

        self.register_modules(
            prior=lowerCAmelCase_						,			text_encoder=lowerCAmelCase_						,			tokenizer=lowerCAmelCase_						,			scheduler=lowerCAmelCase_						,			renderer=lowerCAmelCase_						,			)

  def 		_lowerCamelCase						(self	:			Union[str, Any]						,			lowerCAmelCase_	:			Optional[int]						,			lowerCAmelCase_	:			List[str]						,			lowerCAmelCase_	:			List[str]						,			lowerCAmelCase_	:			List[str]						,			lowerCAmelCase_	:			List[str]						,			lowerCAmelCase_	:			Optional[int]     ) ->							Optional[int]:
        if latents is None:
              snake_case_    							=				randn_tensor(lowerCAmelCase_						,			generator=lowerCAmelCase_						,			device=lowerCAmelCase_						,			dtype=lowerCAmelCase_     )
        else:
              if latents.shape != shape:
                    raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {shape}'''     )
              snake_case_    							=				latents.to(lowerCAmelCase_     )

        snake_case_    							=				latents * scheduler.init_noise_sigma
        return latents

  def 		_lowerCamelCase						(self	:			str						,			lowerCAmelCase_	:			str=0     ) ->							List[str]:
        if is_accelerate_available():
              from accelerate import cpu_offload
        else:
              raise ImportError("Please install accelerate via `pip install accelerate`"     )

        snake_case_    							=				torch.device(f'''cuda:{gpu_id}'''     )

        snake_case_    							=				[self.text_encoder, self.prior]
        for cpu_offloaded_model in models:
              if cpu_offloaded_model is not None:
                    cpu_offload(lowerCAmelCase_						,			lowerCAmelCase_     )

  def 		_lowerCamelCase						(self	:			Dict						,			lowerCAmelCase_	:			Union[str, Any]=0     ) ->							str:
        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."     )

        snake_case_    							=				torch.device(f'''cuda:{gpu_id}'''     )

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

        snake_case_    							=				None
        for cpu_offloaded_model in [self.text_encoder, self.prior, self.renderer]:
              snake_case_							,						snake_case_    							=				cpu_offload_with_hook(lowerCAmelCase_						,			lowerCAmelCase_						,			prev_module_hook=lowerCAmelCase_     )

        if self.safety_checker is not None:
              snake_case_							,						snake_case_    							=				cpu_offload_with_hook(self.safety_checker						,			lowerCAmelCase_						,			prev_module_hook=lowerCAmelCase_     )

        # We'll offload the last model manually.
        snake_case_    							=				hook

  @property
  def 		_lowerCamelCase						(self	:			List[str]     ) ->							Union[str, Any]:
        if self.device != torch.device("meta"     ) or not hasattr(self.text_encoder						,			"_hf_hook"     ):
              return self.device
        for module in self.text_encoder.modules():
              if (
                  hasattr(lowerCAmelCase_						,			"_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

  def 		_lowerCamelCase						(self	:			Dict						,			lowerCAmelCase_	:			Optional[Any]						,			lowerCAmelCase_	:			Any						,			lowerCAmelCase_	:			List[Any]						,			lowerCAmelCase_	:			Any						,			) ->							int:
        len(lowerCAmelCase_     ) if isinstance(lowerCAmelCase_						,			lowerCAmelCase_     ) else 1

        # YiYi Notes: set pad_token_id to be 0, not sure why I can't set in the config file
        snake_case_    							=				0
        # get prompt text embeddings
        snake_case_    							=				self.tokenizer(
            lowerCAmelCase_						,			padding="max_length"						,			max_length=self.tokenizer.model_max_length						,			truncation=lowerCAmelCase_						,			return_tensors="pt"						,			)
        snake_case_    							=				text_inputs.input_ids
        snake_case_    							=				self.tokenizer(lowerCAmelCase_						,			padding="longest"						,			return_tensors="pt"     ).input_ids

        if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(lowerCAmelCase_						,			lowerCAmelCase_     ):
              snake_case_    							=				self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]     )
              logger.warning(
                  "The following part of your input was truncated because CLIP can only handle sequences up to"
                  f''' {self.tokenizer.model_max_length} tokens: {removed_text}'''     )

        snake_case_    							=				self.text_encoder(text_input_ids.to(lowerCAmelCase_     )     )
        snake_case_    							=				text_encoder_output.text_embeds

        snake_case_    							=				prompt_embeds.repeat_interleave(lowerCAmelCase_						,			dim=0     )
        # in Shap-E it normalize the prompt_embeds and then later rescale it
        snake_case_    							=				prompt_embeds / torch.linalg.norm(lowerCAmelCase_						,			dim=-1						,			keepdim=lowerCAmelCase_     )

        if do_classifier_free_guidance:
              snake_case_    							=				torch.zeros_like(lowerCAmelCase_     )

              # For classifier free guidance, we need to do two forward passes.
              # Here we concatenate the unconditional and text embeddings into a single batch
              # to avoid doing two forward passes
              snake_case_    							=				torch.cat([negative_prompt_embeds, prompt_embeds]     )

        # Rescale the features to have unit variance
        snake_case_    							=				math.sqrt(prompt_embeds.shape[1]     ) * prompt_embeds

        return prompt_embeds


  @torch.no_grad()
  @replace_example_docstring(lowerCAmelCase_     )
  def __call__(self	:			int						,			lowerCAmelCase_	:			str						,			lowerCAmelCase_	:			int = 1						,			lowerCAmelCase_	:			int = 25						,			lowerCAmelCase_	:			Optional[Union[torch.Generator, List[torch.Generator]]] = None						,			lowerCAmelCase_	:			Optional[torch.FloatTensor] = None						,			lowerCAmelCase_	:			float = 4.0						,			lowerCAmelCase_	:			int = 64						,			lowerCAmelCase_	:			Optional[str] = "pil"						,			lowerCAmelCase_	:			bool = True						,			) ->							Dict:

        if isinstance(lowerCAmelCase_						,			lowerCAmelCase_     ):
              snake_case_    							=				1
        elif isinstance(lowerCAmelCase_						,			lowerCAmelCase_     ):
              snake_case_    							=				len(lowerCAmelCase_     )
        else:
              raise ValueError(f'''`prompt` has to be of type `str` or `list` but is {type(lowerCAmelCase_     )}'''     )

        snake_case_    							=				self._execution_device

        snake_case_    							=				batch_size * num_images_per_prompt

        snake_case_    							=				guidance_scale > 1.0
        snake_case_    							=				self._encode_prompt(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_     )

        # prior

        self.scheduler.set_timesteps(lowerCAmelCase_						,			device=lowerCAmelCase_     )
        snake_case_    							=				self.scheduler.timesteps

        snake_case_    							=				self.prior.config.num_embeddings
        snake_case_    							=				self.prior.config.embedding_dim

        snake_case_    							=				self.prepare_latents(
            (batch_size, num_embeddings * embedding_dim)						,			prompt_embeds.dtype						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			self.scheduler						,			)

        # YiYi notes: for testing only to match ldm, we can directly create a latents with desired shape: batch_size, num_embeddings, embedding_dim
        snake_case_    							=				latents.reshape(latents.shape[0]						,			lowerCAmelCase_						,			lowerCAmelCase_     )

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

              snake_case_    							=				self.prior(
                  lowerCAmelCase_						,			timestep=lowerCAmelCase_						,			proj_embedding=lowerCAmelCase_						,			).predicted_image_embedding

              # remove the variance
              snake_case_							,						snake_case_    							=				noise_pred.split(
                  scaled_model_input.shape[2]						,			dim=2     )  # batch_size, num_embeddings, embedding_dim

              if do_classifier_free_guidance is not None:
                    snake_case_							,						snake_case_    							=				noise_pred.chunk(2     )
                    snake_case_    							=				noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)

              snake_case_    							=				self.scheduler.step(
                  lowerCAmelCase_						,			timestep=lowerCAmelCase_						,			sample=lowerCAmelCase_						,			).prev_sample

        if output_type == "latent":
              return ShapEPipelineOutput(images=lowerCAmelCase_     )

        snake_case_    							=				[]
        for i, latent in enumerate(lowerCAmelCase_     ):
              snake_case_    							=				self.renderer.decode(
                  latent[None, :]						,			lowerCAmelCase_						,			size=lowerCAmelCase_						,			ray_batch_size=40_96						,			n_coarse_samples=64						,			n_fine_samples=1_28						,			)
              images.append(lowerCAmelCase_     )

        snake_case_    							=				torch.stack(lowerCAmelCase_     )

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

        snake_case_    							=				images.cpu().numpy()

        if output_type == "pil":
              snake_case_    							=				[self.numpy_to_pil(lowerCAmelCase_     ) for image in images]

        # Offload last model to CPU
        if hasattr(self						,			"final_offload_hook"     ) and self.final_offload_hook is not None:
              self.final_offload_hook.offload()

        if not return_dict:
              return (images,)

        return ShapEPipelineOutput(images=lowerCAmelCase_     )








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


__lowerCAmelCase 	=      logging.get_logger(__name__)


__lowerCAmelCase 	=      {"vocab_file": "vocab.json", "merges_file": "merges.txt"}

# See all MVP models at https://huggingface.co/models?filter=mvp
__lowerCAmelCase 	=      {
    "vocab_file": {
        "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json",
    },
    "added_tokens.json": {
        "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json",
    },
    "merges_file": {
        "RUCAIBox/mvp": "https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt",
    },
}

__lowerCAmelCase 	=      {
    "RUCAIBox/mvp": 1_024,
}

@lru_cache()
def      a    (   )  ->Any:
		UpperCamelCase__								=						(
		    list(range(ord("""!"""							)			,    ord("""~"""							) + 1							)							) + list(range(ord("""¡"""							)			,    ord("""¬"""							) + 1							)							) + list(range(ord("""®"""							)			,    ord("""ÿ"""							) + 1							)							)
		)
		UpperCamelCase__								=						bs[:]
		UpperCamelCase__								=						0
		for b in range(2**8							):
				if b not in bs:
						bs.append(_lowercase							)
						cs.append(2**8 + n							)
						n += 1
		UpperCamelCase__								=						[chr(_lowercase							) for n in cs]
		return dict(zip(_lowercase			,    _lowercase							)							)

def      a    (   _lowercase :  int							)  ->List[Any]:
		UpperCamelCase__								=						set()
		UpperCamelCase__								=						word[0]
		for char in word[1:]:
				pairs.add((prev_char, char)							)
				UpperCamelCase__								=						char
		return pairs




class 	_UpperCAmelCase						( _lowercase       ):
					__A        = VOCAB_FILES_NAMES
					__A        = PRETRAINED_VOCAB_FILES_MAP
					__A        = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					__A        = ["input_ids", "attention_mask"]




					def __init__(    self     ,   __lowerCAmelCase     ,   __lowerCAmelCase     ,   __lowerCAmelCase="replace"     ,   __lowerCAmelCase="<s>"     ,   __lowerCAmelCase="</s>"     ,   __lowerCAmelCase="</s>"     ,   __lowerCAmelCase="<s>"     ,   __lowerCAmelCase="<unk>"     ,   __lowerCAmelCase="<pad>"     ,   __lowerCAmelCase="<mask>"     ,   __lowerCAmelCase=False     ,   **__lowerCAmelCase     ,   )    ->	str:
							UpperCamelCase__								=						AddedToken(__lowerCAmelCase     ,   lstrip=__lowerCAmelCase     ,   rstrip=__lowerCAmelCase							) if isinstance(__lowerCAmelCase     ,   __lowerCAmelCase							) else bos_token
							UpperCamelCase__								=						AddedToken(__lowerCAmelCase     ,   lstrip=__lowerCAmelCase     ,   rstrip=__lowerCAmelCase							) if isinstance(__lowerCAmelCase     ,   __lowerCAmelCase							) else eos_token
							UpperCamelCase__								=						AddedToken(__lowerCAmelCase     ,   lstrip=__lowerCAmelCase     ,   rstrip=__lowerCAmelCase							) if isinstance(__lowerCAmelCase     ,   __lowerCAmelCase							) else sep_token
							UpperCamelCase__								=						AddedToken(__lowerCAmelCase     ,   lstrip=__lowerCAmelCase     ,   rstrip=__lowerCAmelCase							) if isinstance(__lowerCAmelCase     ,   __lowerCAmelCase							) else cls_token
							UpperCamelCase__								=						AddedToken(__lowerCAmelCase     ,   lstrip=__lowerCAmelCase     ,   rstrip=__lowerCAmelCase							) if isinstance(__lowerCAmelCase     ,   __lowerCAmelCase							) else unk_token
							UpperCamelCase__								=						AddedToken(__lowerCAmelCase     ,   lstrip=__lowerCAmelCase     ,   rstrip=__lowerCAmelCase							) if isinstance(__lowerCAmelCase     ,   __lowerCAmelCase							) else pad_token

							# Mask token behave like a normal word, i.e. include the space before it
							UpperCamelCase__								=						AddedToken(__lowerCAmelCase     ,   lstrip=__lowerCAmelCase     ,   rstrip=__lowerCAmelCase							) if isinstance(__lowerCAmelCase     ,   __lowerCAmelCase							) else mask_token

							super().__init__(
							    errors=__lowerCAmelCase     ,   bos_token=__lowerCAmelCase     ,   eos_token=__lowerCAmelCase     ,   unk_token=__lowerCAmelCase     ,   sep_token=__lowerCAmelCase     ,   cls_token=__lowerCAmelCase     ,   pad_token=__lowerCAmelCase     ,   mask_token=__lowerCAmelCase     ,   add_prefix_space=__lowerCAmelCase     ,   **__lowerCAmelCase     ,   )
							with open(__lowerCAmelCase     ,   encoding="""utf-8"""							) as vocab_handle:
									UpperCamelCase__								=						json.load(__lowerCAmelCase							)
							UpperCamelCase__								=						{v: k for k, v in self.encoder.items()}
							UpperCamelCase__								=						errors  # how to handle errors in decoding
							UpperCamelCase__								=						bytes_to_unicode()
							UpperCamelCase__								=						{v: k for k, v in self.byte_encoder.items()}
							with open(__lowerCAmelCase     ,   encoding="""utf-8"""							) as merges_handle:
									UpperCamelCase__								=						merges_handle.read().split("""\n"""							)[1:-1]
							UpperCamelCase__								=						[tuple(merge.split()							) for merge in bpe_merges]
							UpperCamelCase__								=						dict(zip(__lowerCAmelCase     ,   range(len(__lowerCAmelCase							)							)							)							)
							UpperCamelCase__								=						{}
							UpperCamelCase__								=						add_prefix_space

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




					@property
					def 						snake_case_		(    self							)    ->	Union[str, Any]:
							return len(self.encoder							)




					def 						snake_case_		(    self							)    ->	Any:
							return dict(self.encoder     ,   **self.added_tokens_encoder							)




					def 						snake_case_		(    self     ,   __lowerCAmelCase							)    ->	List[str]:
							if token in self.cache:
									return self.cache[token]
							UpperCamelCase__								=						tuple(__lowerCAmelCase							)
							UpperCamelCase__								=						get_pairs(__lowerCAmelCase							)

							if not pairs:
									return token

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

											if word[i] == first and i < len(__lowerCAmelCase							) - 1 and word[i + 1] == second:
													new_word.append(first + second							)
													i += 2
											else:
													new_word.append(word[i]							)
													i += 1
									UpperCamelCase__								=						tuple(__lowerCAmelCase							)
									UpperCamelCase__								=						new_word
									if len(__lowerCAmelCase							) == 1:
											break
									else:
											UpperCamelCase__								=						get_pairs(__lowerCAmelCase							)
							UpperCamelCase__								=						""" """.join(__lowerCAmelCase							)
							UpperCamelCase__								=						word
							return word




					def 						snake_case_		(    self     ,   __lowerCAmelCase							)    ->	int:
							UpperCamelCase__								=						[]
							for token in re.findall(self.pat     ,   __lowerCAmelCase							):
									UpperCamelCase__								=						"""""".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(__lowerCAmelCase							).split(""" """							)							)
							return bpe_tokens




					def 						snake_case_		(    self     ,   __lowerCAmelCase							)    ->	Dict:
							return self.encoder.get(__lowerCAmelCase     ,   self.encoder.get(self.unk_token							)							)




					def 						snake_case_		(    self     ,   __lowerCAmelCase							)    ->	Optional[Any]:
							return self.decoder.get(__lowerCAmelCase							)




					def 						snake_case_		(    self     ,   __lowerCAmelCase							)    ->	Any:
							UpperCamelCase__								=						"""""".join(__lowerCAmelCase							)
							UpperCamelCase__								=						bytearray([self.byte_decoder[c] for c in text]							).decode("""utf-8"""     ,   errors=self.errors							)
							return text




					def 						snake_case_		(    self     ,   __lowerCAmelCase     ,   __lowerCAmelCase = None							)    ->	Tuple[str]:
							if not os.path.isdir(__lowerCAmelCase							):
									logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""							)
									return
							UpperCamelCase__								=						os.path.join(
							    __lowerCAmelCase     ,   (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]							)
							UpperCamelCase__								=						os.path.join(
							    __lowerCAmelCase     ,   (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]							)

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

							UpperCamelCase__								=						0
							with open(__lowerCAmelCase     ,   """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 __lowerCAmelCase							: 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!"""							)
													UpperCamelCase__								=						token_index
											writer.write(""" """.join(__lowerCAmelCase							) + """\n"""							)
											index += 1

							return vocab_file, merge_file




					def 						snake_case_		(    self     ,   __lowerCAmelCase     ,   __lowerCAmelCase = None							)    ->	List[int]:
							if token_ids_a is None:
									return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
							UpperCamelCase__								=						[self.cls_token_id]
							UpperCamelCase__								=						[self.sep_token_id]
							return cls + token_ids_a + sep + sep + token_ids_a + sep




					def 						snake_case_		(    self     ,   __lowerCAmelCase     ,   __lowerCAmelCase = None     ,   __lowerCAmelCase = False							)    ->	List[int]:
							if already_has_special_tokens:
									return super().get_special_tokens_mask(
									    token_ids_a=__lowerCAmelCase     ,   token_ids_a=__lowerCAmelCase     ,   already_has_special_tokens=__lowerCAmelCase							)

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




					def 						snake_case_		(    self     ,   __lowerCAmelCase     ,   __lowerCAmelCase = None							)    ->	List[int]:
							UpperCamelCase__								=						[self.sep_token_id]
							UpperCamelCase__								=						[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 						snake_case_		(    self     ,   __lowerCAmelCase     ,   __lowerCAmelCase=False     ,   **__lowerCAmelCase							)    ->	Optional[Any]:
							UpperCamelCase__								=						kwargs.pop("""add_prefix_space"""     ,   self.add_prefix_space							)
							if (is_split_into_words or add_prefix_space) and (len(__lowerCAmelCase							) > 0 and not text[0].isspace()):
									UpperCamelCase__								=						""" """ + text
							return (text, kwargs)





