"""simple docstring"""
import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer

from diffusers import DiffusionPipeline
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import logging


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



class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""

				def __init__(				self		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    ):






										"""simple docstring"""

										super().__init__()
										self.register_modules(
										    vae=lowerCAmelCase		,    text_encoder=lowerCAmelCase		,    tokenizer=lowerCAmelCase		,    unet=lowerCAmelCase		,    scheduler=lowerCAmelCase		,    safety_checker=lowerCAmelCase		,    feature_extractor=lowerCAmelCase		,    )

				def 		snake_case (				self		,    lowerCAmelCase = "auto"    ):






										"""simple docstring"""

										if slice_size == "auto":
																# half the attention head size is usually a good trade-off between
																# speed and memory
																snake_case								=			self.unet.config.attention_head_dim // 2
										self.unet.set_attention_slice(lowerCAmelCase    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										self.enable_attention_slicing(lowerCAmelCase    )

				@torch.no_grad()
				def __call__(				self		,    lowerCAmelCase		,    lowerCAmelCase = 5_12		,    lowerCAmelCase = 5_12		,    lowerCAmelCase = 50		,    lowerCAmelCase = 7.5		,    lowerCAmelCase = None		,    lowerCAmelCase = 1		,    lowerCAmelCase = 0.0		,    lowerCAmelCase = None		,    lowerCAmelCase = None		,    lowerCAmelCase = "pil"		,    lowerCAmelCase = True		,    lowerCAmelCase = None		,    lowerCAmelCase = 1		,    lowerCAmelCase = None		,    **lowerCAmelCase		,    ):






										"""simple docstring"""

										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    )}"""    )

										if height % 8 != 0 or width % 8 != 0:
																raise ValueError(F"""`height` and `width` have to be divisible by 8 but are {height} and {width}."""    )

										if (callback_steps is None) or (
										    callback_steps is not None and (not isinstance(lowerCAmelCase		,    lowerCAmelCase    ) or callback_steps <= 0)
										):
																raise ValueError(
																    F"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
																    F""" {type(lowerCAmelCase    )}."""    )

										# get prompt text embeddings
										snake_case								=			self.tokenizer(
										    lowerCAmelCase		,    padding='max_length'		,    max_length=self.tokenizer.model_max_length		,    return_tensors='pt'		,    )
										snake_case								=			text_inputs.input_ids

										if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
																snake_case								=			self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]    )
																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								=			text_input_ids[:, : self.tokenizer.model_max_length]

										if text_embeddings is None:
																snake_case								=			self.text_encoder(text_input_ids.to(self.device    )    )[0]

										# duplicate text embeddings for each generation per prompt, using mps friendly method
										snake_case					,snake_case					,snake_case								=			text_embeddings.shape
										snake_case								=			text_embeddings.repeat(1		,    lowerCAmelCase		,    1    )
										snake_case								=			text_embeddings.view(bs_embed * num_images_per_prompt		,    lowerCAmelCase		,    -1    )

										# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
										# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
										# corresponds to doing no classifier free guidance.
										snake_case								=			guidance_scale > 1.0
										# get unconditional embeddings for classifier free guidance
										if do_classifier_free_guidance:
																snake_case								=			42
																if negative_prompt is None:
																						snake_case								=			['']
																elif type(lowerCAmelCase    ) is not type(lowerCAmelCase    ):
																						raise TypeError(
																						    F"""`negative_prompt` should be the same type to `prompt`, but got {type(lowerCAmelCase    )} !="""
																						    F""" {type(lowerCAmelCase    )}."""    )
																elif isinstance(lowerCAmelCase		,    lowerCAmelCase    ):
																						snake_case								=			[negative_prompt]
																elif batch_size != len(lowerCAmelCase    ):
																						raise ValueError(
																						    F"""`negative_prompt`: {negative_prompt} has batch size {len(lowerCAmelCase    )}, but `prompt`:"""
																						    F""" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"""
																						    ' the batch size of `prompt`.'    )
																else:
																						snake_case								=			negative_prompt

																snake_case								=			text_input_ids.shape[-1]
																snake_case								=			self.tokenizer(
																    lowerCAmelCase		,    padding='max_length'		,    max_length=lowerCAmelCase		,    truncation=lowerCAmelCase		,    return_tensors='pt'		,    )
																snake_case								=			self.text_encoder(uncond_input.input_ids.to(self.device    )    )[0]

																# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
																snake_case								=			uncond_embeddings.shape[1]
																snake_case								=			uncond_embeddings.repeat(lowerCAmelCase		,    lowerCAmelCase		,    1    )
																snake_case								=			uncond_embeddings.view(batch_size * num_images_per_prompt		,    lowerCAmelCase		,    -1    )

																# 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([uncond_embeddings, text_embeddings]    )

										# get the initial random noise unless the user supplied it

										# Unlike in other pipelines, latents need to be generated in the target device
										# for 1-to-1 results reproducibility with the CompVis implementation.
										# However this currently doesn't work in `mps`.
										snake_case								=			(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
										snake_case								=			(batch_size * num_images_per_prompt, self.unet.config.in_channels, 64, 64)
										snake_case								=			text_embeddings.dtype
										if latents is None:
																if self.device.type == "mps":
																						# randn does not exist on mps
																						snake_case								=			torch.randn(
																						    lowerCAmelCase		,    generator=lowerCAmelCase		,    device='cpu'		,    dtype=lowerCAmelCase    ).to(self.device    )
																						snake_case								=			torch.randn(lowerCAmelCase		,    generator=lowerCAmelCase		,    device='cpu'		,    dtype=lowerCAmelCase    ).to(
																						    self.device    )
																else:
																						snake_case								=			torch.randn(
																						    lowerCAmelCase		,    generator=lowerCAmelCase		,    device=self.device		,    dtype=lowerCAmelCase    )
																						snake_case								=			torch.randn(lowerCAmelCase		,    generator=lowerCAmelCase		,    device=self.device		,    dtype=lowerCAmelCase    )
										else:
																if latents_reference.shape != latents_shape:
																						raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}"""    )
																snake_case								=			latents_reference.to(self.device    )
																snake_case								=			latents.to(self.device    )

										# This is the key part of the pipeline where we
										# try to ensure that the generated images w/ the same seed
										# but different sizes actually result in similar images
										snake_case								=			(latents_shape[3] - latents_shape_reference[3]) // 2
										snake_case								=			(latents_shape[2] - latents_shape_reference[2]) // 2
										snake_case								=			latents_shape_reference[3] if dx >= 0 else latents_shape_reference[3] + 2 * dx
										snake_case								=			latents_shape_reference[2] if dy >= 0 else latents_shape_reference[2] + 2 * dy
										snake_case								=			0 if dx < 0 else dx
										snake_case								=			0 if dy < 0 else dy
										snake_case								=			max(-dx		,    0    )
										snake_case								=			max(-dy		,    0    )
										# import pdb
										# pdb.set_trace()
										snake_case								=			latents_reference[:, :, dy : dy + h, dx : dx + w]

										# set timesteps
										self.scheduler.set_timesteps(lowerCAmelCase    )

										# Some schedulers like PNDM have timesteps as arrays
										# It's more optimized to move all timesteps to correct device beforehand
										snake_case								=			self.scheduler.timesteps.to(self.device    )

										# scale the initial noise by the standard deviation required by the scheduler
										snake_case								=			latents * self.scheduler.init_noise_sigma

										# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
										# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
										# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
										# and should be between [0, 1]
										snake_case								=			'eta' in set(inspect.signature(self.scheduler.step    ).parameters.keys()    )
										snake_case								=			{}
										if accepts_eta:
																snake_case								=			eta

										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    )

																# predict the noise residual
																snake_case								=			self.unet(lowerCAmelCase		,    lowerCAmelCase		,    encoder_hidden_states=lowerCAmelCase    ).sample

																# perform guidance
																if do_classifier_free_guidance:
																						snake_case					,snake_case								=			noise_pred.chunk(2    )
																						snake_case								=			noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

																# compute the previous noisy sample x_t -> x_t-1
																snake_case								=			self.scheduler.step(lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase		,    **lowerCAmelCase    ).prev_sample

																# call the callback, if provided
																if callback is not None and i % callback_steps == 0:
																						callback(lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase    )

										snake_case								=			1 / 0.1_82_15 * latents
										snake_case								=			self.vae.decode(lowerCAmelCase    ).sample

										snake_case								=			(image / 2 + 0.5).clamp(0		,    1    )

										# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
										snake_case								=			image.cpu().permute(0		,    2		,    3		,    1    ).float().numpy()

										if self.safety_checker is not None:
																snake_case								=			self.feature_extractor(self.numpy_to_pil(lowerCAmelCase    )		,    return_tensors='pt'    ).to(
																    self.device    )
																snake_case					,snake_case								=			self.safety_checker(
																    images=lowerCAmelCase		,    clip_input=safety_checker_input.pixel_values.to(text_embeddings.dtype    )    )
										else:
																snake_case								=			None

										if output_type == "pil":
																snake_case								=			self.numpy_to_pil(lowerCAmelCase    )

										if not return_dict:
																return (image, has_nsfw_concept)

										return StableDiffusionPipelineOutput(images=lowerCAmelCase		,    nsfw_content_detected=lowerCAmelCase    )



"""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


SCREAMING_SNAKE_CASE__      						=  logging.get_logger(__name__)


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

# See all BART models at https://huggingface.co/models?filter=bart
SCREAMING_SNAKE_CASE__      						=  {
    "vocab_file": {
        "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/vocab.json",
        "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/vocab.json",
        "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json",
        "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json",
        "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json",
        "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json",
    },
    "merges_file": {
        "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/merges.txt",
        "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/merges.txt",
        "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt",
        "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt",
        "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt",
        "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt",
    },
}

SCREAMING_SNAKE_CASE__      						=  {
    "facebook/bart-base": 1_024,
    "facebook/bart-large": 1_024,
    "facebook/bart-large-mnli": 1_024,
    "facebook/bart-large-cnn": 1_024,
    "facebook/bart-large-xsum": 1_024,
    "yjernite/bart_eli5": 1_024,
}





@lru_cache()
def    lowerCAmelCase__    (			)					->				Union[str, Any]:





						"""simple docstring"""






						snake_case								=			(
						    list(range(ord('!'					) ,			ord('~'					) + 1					)					) + list(range(ord('¡'					) ,			ord('¬'					) + 1					)					) + list(range(ord('®'					) ,			ord('ÿ'					) + 1					)					)
						)
						snake_case								=			bs[:]
						snake_case								=			0
						for b in range(2**8					):
												if b not in bs:
																		bs.append(_UpperCamelCase					)
																		cs.append(2**8 + n					)
																		n += 1
						snake_case								=			[chr(_UpperCamelCase					) for n in cs]
						return dict(zip(_UpperCamelCase ,			_UpperCamelCase					)					)





def    lowerCAmelCase__    (			_UpperCamelCase       :				List[Any]					)					->				List[str]:





						"""simple docstring"""






						snake_case								=			set()
						snake_case								=			word[0]
						for char in word[1:]:
												pairs.add((prev_char, char)					)
												snake_case								=			char
						return pairs



class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""
				_lowerCAmelCase     :    Union[str, Any]			  =						VOCAB_FILES_NAMES
				_lowerCAmelCase     :    str			  =						PRETRAINED_VOCAB_FILES_MAP
				_lowerCAmelCase     :    int			  =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				_lowerCAmelCase     :    str			  =						["""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		,    ):






										"""simple docstring"""

										snake_case								=			AddedToken(lowerCAmelCase		,    lstrip=lowerCAmelCase		,    rstrip=lowerCAmelCase    ) if isinstance(lowerCAmelCase		,    lowerCAmelCase    ) else bos_token
										snake_case								=			AddedToken(lowerCAmelCase		,    lstrip=lowerCAmelCase		,    rstrip=lowerCAmelCase    ) if isinstance(lowerCAmelCase		,    lowerCAmelCase    ) else eos_token
										snake_case								=			AddedToken(lowerCAmelCase		,    lstrip=lowerCAmelCase		,    rstrip=lowerCAmelCase    ) if isinstance(lowerCAmelCase		,    lowerCAmelCase    ) else sep_token
										snake_case								=			AddedToken(lowerCAmelCase		,    lstrip=lowerCAmelCase		,    rstrip=lowerCAmelCase    ) if isinstance(lowerCAmelCase		,    lowerCAmelCase    ) else cls_token
										snake_case								=			AddedToken(lowerCAmelCase		,    lstrip=lowerCAmelCase		,    rstrip=lowerCAmelCase    ) if isinstance(lowerCAmelCase		,    lowerCAmelCase    ) else unk_token
										snake_case								=			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
										snake_case								=			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:
																snake_case								=			json.load(lowerCAmelCase    )
										snake_case								=			{v: k for k, v in self.encoder.items()}
										snake_case								=			errors  # how to handle errors in decoding
										snake_case								=			bytes_to_unicode()
										snake_case								=			{v: k for k, v in self.byte_encoder.items()}
										with open(lowerCAmelCase		,    encoding='utf-8'    ) as merges_handle:
																snake_case								=			merges_handle.read().split('\n'    )[1:-1]
										snake_case								=			[tuple(merge.split()    ) for merge in bpe_merges]
										snake_case								=			dict(zip(lowerCAmelCase		,    range(len(lowerCAmelCase    )    )    )    )
										snake_case								=			{}
										snake_case								=			add_prefix_space

										# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
										snake_case								=			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    ):






										"""simple docstring"""

										return len(self.encoder    )

				def 		snake_case (				self    ):






										"""simple docstring"""

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

				def 		snake_case (				self		,    lowerCAmelCase    ):






										"""simple docstring"""

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

										if not pairs:
																return token

										while True:
																snake_case								=			min(lowerCAmelCase		,    key=lambda lowerCAmelCase    : self.bpe_ranks.get(lowerCAmelCase		,    float('inf'    )    )    )
																if bigram not in self.bpe_ranks:
																						break
																snake_case					,snake_case								=			bigram
																snake_case								=			[]
																snake_case								=			0
																while i < len(lowerCAmelCase    ):
																						try:
																												snake_case								=			word.index(lowerCAmelCase		,    lowerCAmelCase    )
																						except ValueError:
																												new_word.extend(word[i:]    )
																												break
																						else:
																												new_word.extend(word[i:j]    )
																												snake_case								=			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
																snake_case								=			tuple(lowerCAmelCase    )
																snake_case								=			new_word
																if len(lowerCAmelCase    ) == 1:
																						break
																else:
																						snake_case								=			get_pairs(lowerCAmelCase    )
										snake_case								=			' '.join(lowerCAmelCase    )
										snake_case								=			word
										return word

				def 		snake_case (				self		,    lowerCAmelCase    ):






										"""simple docstring"""

										snake_case								=			[]
										for token in re.findall(self.pat		,    lowerCAmelCase    ):
																snake_case								=			''.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    ):






										"""simple docstring"""

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

				def 		snake_case (				self		,    lowerCAmelCase    ):






										"""simple docstring"""

										return self.decoder.get(lowerCAmelCase    )

				def 		snake_case (				self		,    lowerCAmelCase    ):






										"""simple docstring"""

										snake_case								=			''.join(lowerCAmelCase    )
										snake_case								=			bytearray([self.byte_decoder[c] for c in text]    ).decode('utf-8'		,    errors=self.errors    )
										return text

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase = None    ):






										"""simple docstring"""

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

										snake_case								=			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!'    )
																												snake_case								=			token_index
																						writer.write(' '.join(lowerCAmelCase    ) + '\n'    )
																						index += 1

										return vocab_file, merge_file

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase = None    ):






										"""simple docstring"""

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

				def 		snake_case (				self		,    lowerCAmelCase		,    lowerCAmelCase = None		,    lowerCAmelCase = False    ):






										"""simple docstring"""

										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    ):






										"""simple docstring"""

										snake_case								=			[self.sep_token_id]
										snake_case								=			[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    ):






										"""simple docstring"""

										snake_case								=			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()):
																snake_case								=			' ' + text
										return (text, kwargs)


