


import inspect
from typing import Optional, Union

import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    DPMSolverMultistepScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.utils import (
    PIL_INTERPOLATION,
    randn_tensor,
)


def 			_lowercase       (				UpperCamelCase__       :					Optional[Any],				UpperCamelCase__       :					str,				UpperCamelCase__       :					Optional[Any]			):
 if isinstance(a_,				torch.Tensor			):
  return image
 elif isinstance(a_,				PIL.Image.Image			):
  __A  :		List[Any]       =			[image]

 if isinstance(image[0],				PIL.Image.Image			):
  __A  :		Optional[Any]       =			[np.array(i.resize((w, h),				resample=PIL_INTERPOLATION['lanczos']			)			)[None, :] for i in image]
  __A  :		str       =			np.concatenate(a_,				axis=0			)
  __A  :		Tuple       =			np.array(a_			).astype(np.floataa			) / 255.0
  __A  :		int       =			image.transpose(0,				3,				1,				2			)
  __A  :		str       =			2.0 * image - 1.0
  __A  :		Dict       =			torch.from_numpy(a_			)
 elif isinstance(image[0],				torch.Tensor			):
  __A  :		Optional[Any]       =			torch.cat(a_,				dim=0			)
 return image


def 			_lowercase       (				UpperCamelCase__       :					int,				UpperCamelCase__       :					Dict,				UpperCamelCase__       :					List[str],				UpperCamelCase__       :					Tuple=0.9995			):
 if not isinstance(a_,				np.ndarray			):
  __A  :		List[str]       =			True
  __A  :		int       =			va.device
  __A  :		Union[str, Any]       =			va.cpu().numpy()
  __A  :		List[str]       =			va.cpu().numpy()

 __A  :		Optional[Any]       =			np.sum(va * va / (np.linalg.norm(a_			) * np.linalg.norm(a_			))			)
 if np.abs(a_			) > DOT_THRESHOLD:
  __A  :		int       =			(1 - t) * va + t * va
 else:
  __A  :		Optional[int]       =			np.arccos(a_			)
  __A  :		List[Any]       =			np.sin(a_			)
  __A  :		int       =			theta_a * t
  __A  :		Union[str, Any]       =			np.sin(a_			)
  __A  :		str       =			np.sin(theta_a - theta_t			) / sin_theta_a
  __A  :		Any       =			sin_theta_t / sin_theta_a
  __A  :		Union[str, Any]       =			sa * va + sa * va

 if inputs_are_torch:
  __A  :		Tuple       =			torch.from_numpy(a_			).to(a_			)

 return va


def 			_lowercase       (				UpperCamelCase__       :					Optional[int],				UpperCamelCase__       :					Dict			):
 __A  :		Dict       =			F.normalize(a_,				dim=-1			)
 __A  :		int       =			F.normalize(a_,				dim=-1			)
 return (x - y).norm(dim=-1			).div(2			).arcsin().pow(2			).mul(2			)


def 			_lowercase       (				UpperCamelCase__       :					Any,				UpperCamelCase__       :					List[Any]			):
 for param in model.parameters():
  __A  :		List[str]       =			value


class 						_lowerCamelCase					(    __SCREAMING_SNAKE_CASE				):




 '''simple docstring'''


 def __init__(      self      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase=None      ,      __lowercase=None      ,      __lowercase=None      ,      ):

  """simple docstring"""







  super().__init__()
  self.register_modules(
      vae=__snake_case      ,      text_encoder=__snake_case      ,      clip_model=__snake_case      ,      tokenizer=__snake_case      ,      unet=__snake_case      ,      scheduler=__snake_case      ,      feature_extractor=__snake_case      ,      coca_model=__snake_case      ,      coca_tokenizer=__snake_case      ,      coca_transform=__snake_case      ,      )
  __A  :		Tuple       =			(
      feature_extractor.size
      if isinstance(feature_extractor.size      ,      __snake_case						)
      else feature_extractor.size['''shortest_edge''']
  )
  __A  :		List[Any]       =			transforms.Normalize(mean=feature_extractor.image_mean      ,      std=feature_extractor.image_std						)
  set_requires_grad(self.text_encoder      ,      __snake_case						)
  set_requires_grad(self.clip_model      ,      __snake_case						)


 def  snake_case__   (      self      ,      __lowercase = "auto"						):

  """simple docstring"""







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


 def  snake_case__   (      self						):

  """simple docstring"""







  self.enable_attention_slicing(__snake_case						)


 def  snake_case__   (      self						):

  """simple docstring"""







  set_requires_grad(self.vae      ,      __snake_case						)


 def  snake_case__   (      self						):

  """simple docstring"""







  set_requires_grad(self.vae      ,      __snake_case						)


 def  snake_case__   (      self						):

  """simple docstring"""







  set_requires_grad(self.unet      ,      __snake_case						)


 def  snake_case__   (      self						):

  """simple docstring"""







  set_requires_grad(self.unet      ,      __snake_case						)


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase      ,      __lowercase						):

  """simple docstring"""







  __A  :		Optional[int]       =			min(int(num_inference_steps * strength						)      ,      __snake_case						)

  __A  :		Optional[int]       =			max(num_inference_steps - init_timestep      ,      0						)
  __A  :		List[str]       =			self.scheduler.timesteps[t_start:]

  return timesteps, num_inference_steps - t_start


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase=None						):

  """simple docstring"""







  if not isinstance(__snake_case      ,      torch.Tensor						):
   raise ValueError(F"""`image` has to be of type `torch.Tensor` but is {type(__snake_case						)}"""						)

  __A  :		Dict       =			image.to(device=__snake_case      ,      dtype=__snake_case						)

  if isinstance(__snake_case      ,      __snake_case						):
   __A  :		Any       =			[
       self.vae.encode(image[i : i + 1]						).latent_dist.sample(generator[i]						) for i in range(__snake_case						)
   ]
   __A  :		Union[str, Any]       =			torch.cat(__snake_case      ,      dim=0						)
  else:
   __A  :		Tuple       =			self.vae.encode(__snake_case						).latent_dist.sample(__snake_case						)

  # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
  __A  :		Dict       =			0.1_8_2_1_5 * init_latents
  __A  :		Union[str, Any]       =			init_latents.repeat_interleave(__snake_case      ,      dim=0						)

  __A  :		Optional[int]       =			randn_tensor(init_latents.shape      ,      generator=__snake_case      ,      device=__snake_case      ,      dtype=__snake_case						)

  # get latents
  __A  :		int       =			self.scheduler.add_noise(__snake_case      ,      __snake_case      ,      __snake_case						)
  __A  :		Optional[Any]       =			init_latents

  return latents


 def  snake_case__   (      self      ,      __lowercase						):

  """simple docstring"""







  __A  :		Any       =			self.coca_transform(__snake_case						).unsqueeze(0						)
  with torch.no_grad(), torch.cuda.amp.autocast():
   __A  :		str       =			self.coca_model.generate(transformed_image.to(device=self.device      ,      dtype=self.coca_model.dtype						)						)
  __A  :		Union[str, Any]       =			self.coca_tokenizer.decode(generated[0].cpu().numpy()						)
  return generated.split('<end_of_text>'						)[0].replace('<start_of_text>'      ,      ''						).rstrip(' .,'						)


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase						):

  """simple docstring"""







  __A  :		List[Any]       =			self.feature_extractor.preprocess(__snake_case						)
  __A  :		List[Any]       =			torch.from_numpy(clip_image_input['pixel_values'][0]						).unsqueeze(0						).to(self.device						).half()
  __A  :		Optional[int]       =			self.clip_model.get_image_features(__snake_case						)
  __A  :		int       =			image_embeddings_clip / image_embeddings_clip.norm(p=2      ,      dim=-1      ,      keepdim=__snake_case						)
  __A  :		Dict       =			image_embeddings_clip.repeat_interleave(__snake_case      ,      dim=0						)
  return image_embeddings_clip


 @torch.enable_grad()
 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      ):

  """simple docstring"""







  __A  :		List[Any]       =			latents.detach().requires_grad_()

  __A  :		Union[str, Any]       =			self.scheduler.scale_model_input(__snake_case      ,      __snake_case						)

  # predict the noise residual
  __A  :		Dict       =			self.unet(__snake_case      ,      __snake_case      ,      encoder_hidden_states=__snake_case						).sample

  if isinstance(self.scheduler      ,      (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)						):
   __A  :		List[str]       =			self.scheduler.alphas_cumprod[timestep]
   __A  :		Any       =			1 - alpha_prod_t
   # compute predicted original sample from predicted noise also called
   # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
   __A  :		Optional[Any]       =			(latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

   __A  :		Any       =			torch.sqrt(__snake_case						)
   __A  :		Dict       =			pred_original_sample * (fac) + latents * (1 - fac)
  elif isinstance(self.scheduler      ,      __snake_case						):
   __A  :		Optional[Any]       =			self.scheduler.sigmas[index]
   __A  :		Tuple       =			latents - sigma * noise_pred
  else:
   raise ValueError(F"""scheduler type {type(self.scheduler						)} not supported"""						)

  # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
  __A  :		int       =			1 / 0.1_8_2_1_5 * sample
  __A  :		int       =			self.vae.decode(__snake_case						).sample
  __A  :		Union[str, Any]       =			(image / 2 + 0.5).clamp(0      ,      1						)

  __A  :		int       =			transforms.Resize(self.feature_extractor_size						)(__snake_case						)
  __A  :		str       =			self.normalize(__snake_case						).to(latents.dtype						)

  __A  :		List[Any]       =			self.clip_model.get_image_features(__snake_case						)
  __A  :		Optional[Any]       =			image_embeddings_clip / image_embeddings_clip.norm(p=2      ,      dim=-1      ,      keepdim=__snake_case						)

  __A  :		str       =			spherical_dist_loss(__snake_case      ,      __snake_case						).mean() * clip_guidance_scale

  __A  :		Tuple       =			-torch.autograd.grad(__snake_case      ,      __snake_case						)[0]

  if isinstance(self.scheduler      ,      __snake_case						):
   __A  :		Union[str, Any]       =			latents.detach() + grads * (sigma**2)
   __A  :		Dict       =			noise_pred_original
  else:
   __A  :		Dict       =			noise_pred_original - torch.sqrt(__snake_case						) * grads
  return noise_pred, latents







 @torch.no_grad()
 def __call__(      self      ,      __lowercase      ,      __lowercase      ,      __lowercase = None      ,      __lowercase = None      ,      __lowercase = 512      ,      __lowercase = 512      ,      __lowercase = 0.6      ,      __lowercase = 50      ,      __lowercase = 7.5      ,      __lowercase = 1      ,      __lowercase = 0.0      ,      __lowercase = 100      ,      __lowercase = None      ,      __lowercase = "pil"      ,      __lowercase = True      ,      __lowercase = 0.8      ,      __lowercase = 0.1      ,      __lowercase = 0.1      ,      ):

  """simple docstring"""







  if isinstance(__snake_case      ,      __snake_case						) and len(__snake_case						) != batch_size:
   raise ValueError(F"""You have passed {batch_size} batch_size, but only {len(__snake_case						)} generators."""						)

  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 isinstance(__snake_case      ,      torch.Generator						) and batch_size > 1:
   __A  :		Optional[int]       =			[generator] + [None] * (batch_size - 1)

  __A  :		Optional[Any]       =			[
      ('''model''', self.coca_model is None),
      ('''tokenizer''', self.coca_tokenizer is None),
      ('''transform''', self.coca_transform is None),
  ]
  __A  :		str       =			[x[0] for x in coca_is_none if x[1]]
  __A  :		List[Any]       =			''', '''.join(__snake_case						)
  # generate prompts with coca model if prompt is None
  if content_prompt is None:
   if len(__snake_case						):
    raise ValueError(
        F"""Content prompt is None and CoCa [{coca_is_none_str}] is None."""
        F"""Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline."""						)
   __A  :		Optional[Any]       =			self.get_image_description(__snake_case						)
  if style_prompt is None:
   if len(__snake_case						):
    raise ValueError(
        F"""Style prompt is None and CoCa [{coca_is_none_str}] is None."""
        F""" Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline."""						)
   __A  :		Optional[Any]       =			self.get_image_description(__snake_case						)

  # get prompt text embeddings for content and style
  __A  :		Optional[Any]       =			self.tokenizer(
      __snake_case      ,      padding='max_length'      ,      max_length=self.tokenizer.model_max_length      ,      truncation=__snake_case      ,      return_tensors='pt'      ,      )
  __A  :		str       =			self.text_encoder(content_text_input.input_ids.to(self.device						)						)[0]

  __A  :		Union[str, Any]       =			self.tokenizer(
      __snake_case      ,      padding='max_length'      ,      max_length=self.tokenizer.model_max_length      ,      truncation=__snake_case      ,      return_tensors='pt'      ,      )
  __A  :		List[Any]       =			self.text_encoder(style_text_input.input_ids.to(self.device						)						)[0]

  __A  :		Tuple       =			slerp(__snake_case      ,      __snake_case      ,      __snake_case						)

  # duplicate text embeddings for each generation per prompt
  __A  :		List[Any]       =			text_embeddings.repeat_interleave(__snake_case      ,      dim=0						)

  # set timesteps
  __A  :		Any       =			'''offset''' in set(inspect.signature(self.scheduler.set_timesteps						).parameters.keys()						)
  __A  :		int       =			{}
  if accepts_offset:
   __A  :		Dict       =			1

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

  __A  :		Dict       =			self.get_timesteps(__snake_case      ,      __snake_case      ,      self.device						)
  __A  :		Optional[Any]       =			timesteps[:1].repeat(__snake_case						)

  # Preprocess image
  __A  :		List[Any]       =			preprocess(__snake_case      ,      __snake_case      ,      __snake_case						)
  __A  :		Union[str, Any]       =			self.prepare_latents(
      __snake_case      ,      __snake_case      ,      __snake_case      ,      text_embeddings.dtype      ,      self.device      ,      __snake_case						)

  __A  :		str       =			preprocess(__snake_case      ,      __snake_case      ,      __snake_case						)
  __A  :		Dict       =			self.prepare_latents(
      __snake_case      ,      __snake_case      ,      __snake_case      ,      text_embeddings.dtype      ,      self.device      ,      __snake_case						)

  __A  :		Tuple       =			slerp(__snake_case      ,      __snake_case      ,      __snake_case						)

  if clip_guidance_scale > 0:
   __A  :		Optional[int]       =			self.get_clip_image_embeddings(__snake_case      ,      __snake_case						)
   __A  :		Dict       =			self.get_clip_image_embeddings(__snake_case      ,      __snake_case						)
   __A  :		List[str]       =			slerp(
       __snake_case      ,      __snake_case      ,      __snake_case						)

  # 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.
  __A  :		Tuple       =			guidance_scale > 1.0
  # get unconditional embeddings for classifier free guidance
  if do_classifier_free_guidance:
   __A  :		List[Any]       =			content_text_input.input_ids.shape[-1]
   __A  :		Any       =			self.tokenizer(['']      ,      padding='max_length'      ,      max_length=__snake_case      ,      return_tensors='pt'						)
   __A  :		Dict       =			self.text_encoder(uncond_input.input_ids.to(self.device						)						)[0]
   # duplicate unconditional embeddings for each generation per prompt
   __A  :		Any       =			uncond_embeddings.repeat_interleave(__snake_case      ,      dim=0						)

   # 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
   __A  :		Optional[Any]       =			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`.
  __A  :		Union[str, Any]       =			(batch_size, self.unet.config.in_channels, height // 8, width // 8)
  __A  :		Union[str, Any]       =			text_embeddings.dtype
  if latents is None:
   if self.device.type == "mps":
    # randn does not work reproducibly on mps
    __A  :		Any       =			torch.randn(__snake_case      ,      generator=__snake_case      ,      device='cpu'      ,      dtype=__snake_case						).to(
        self.device						)
   else:
    __A  :		Optional[int]       =			torch.randn(__snake_case      ,      generator=__snake_case      ,      device=self.device      ,      dtype=__snake_case						)
  else:
   if latents.shape != latents_shape:
    raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {latents_shape}"""						)
   __A  :		List[str]       =			latents.to(self.device						)

  # scale the initial noise by the standard deviation required by the scheduler
  __A  :		List[Any]       =			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]
  __A  :		Optional[int]       =			'''eta''' in set(inspect.signature(self.scheduler.step						).parameters.keys()						)
  __A  :		Tuple       =			{}
  if accepts_eta:
   __A  :		List[Any]       =			eta

  # check if the scheduler accepts generator
  __A  :		Optional[int]       =			'''generator''' in set(inspect.signature(self.scheduler.step						).parameters.keys()						)
  if accepts_generator:
   __A  :		Union[str, Any]       =			generator

  with self.progress_bar(total=__snake_case						):
   for i, t in enumerate(__snake_case						):
    # expand the latents if we are doing classifier free guidance
    __A  :		Union[str, Any]       =			torch.cat([latents] * 2						) if do_classifier_free_guidance else latents
    __A  :		List[Any]       =			self.scheduler.scale_model_input(__snake_case      ,      __snake_case						)

    # predict the noise residual
    __A  :		List[str]       =			self.unet(__snake_case      ,      __snake_case      ,      encoder_hidden_states=__snake_case						).sample

    # perform classifier free guidance
    if do_classifier_free_guidance:
     __A  :		Optional[int]       =			noise_pred.chunk(2						)
     __A  :		Dict       =			noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

    # perform clip guidance
    if clip_guidance_scale > 0:
     __A  :		List[Any]       =			(
         text_embeddings.chunk(2						)[1] if do_classifier_free_guidance else text_embeddings
     )
     __A  :		Optional[Any]       =			self.cond_fn(
         __snake_case      ,      __snake_case      ,      __snake_case      ,      __snake_case      ,      __snake_case      ,      __snake_case      ,      __snake_case      ,      )

    # compute the previous noisy sample x_t -> x_t-1
    __A  :		int       =			self.scheduler.step(__snake_case      ,      __snake_case      ,      __snake_case      ,      **__snake_case						).prev_sample

        # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
  __A  :		List[str]       =			1 / 0.1_8_2_1_5 * latents
  __A  :		Optional[int]       =			self.vae.decode(__snake_case						).sample

  __A  :		Dict       =			(image / 2 + 0.5).clamp(0      ,      1						)
  __A  :		Union[str, Any]       =			image.cpu().permute(0      ,      2      ,      3      ,      1						).numpy()

  if output_type == "pil":
   __A  :		Any       =			self.numpy_to_pil(__snake_case						)

  if not return_dict:
   return (image, None)

  return StableDiffusionPipelineOutput(images=__snake_case      ,      nsfw_content_detected=__snake_case						)










'''simple docstring'''
import json
import os
import unittest

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin

class 						_lowerCamelCase					(    snake_case_    ,		unittest.TestCase				):




 '''simple docstring'''
 __lowercase				:		Dict    							=						XLMTokenizer
 __lowercase				:		Any    							=						False


 def  snake_case__   (      self						):

  """simple docstring"""







  super().setUp()

  # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
  __A  :		Optional[int]       =			[
      'l',
      'o',
      'w',
      'e',
      'r',
      's',
      't',
      'i',
      'd',
      'n',
      'w</w>',
      'r</w>',
      't</w>',
      'lo',
      'low',
      'er</w>',
      'low</w>',
      'lowest</w>',
      'newer</w>',
      'wider</w>',
      '<unk>',
  ]
  __A  :		int       =			dict(zip(__lowercase      ,      range(len(__lowercase						)						)						)						)
  __A  :		str       =			['l o 123', 'lo w 1456', 'e r</w> 1789', '']

  __A  :		Dict       =			os.path.join(self.tmpdirname      ,      VOCAB_FILES_NAMES['vocab_file']						)
  __A  :		List[Any]       =			os.path.join(self.tmpdirname      ,      VOCAB_FILES_NAMES['merges_file']						)
  with open(self.vocab_file      ,      'w'						) as fp:
   fp.write(json.dumps(__lowercase						)						)
  with open(self.merges_file      ,      'w'						) as fp:
   fp.write('\n'.join(__lowercase						)						)


 def  snake_case__   (      self      ,      __lowercase						):

  """simple docstring"""







  __A  :		Any       =			'lower newer'
  __A  :		int       =			'lower newer'
  return input_text, output_text


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Any       =			XLMTokenizer(self.vocab_file      ,      self.merges_file						)

  __A  :		Optional[Any]       =			'lower'
  __A  :		Union[str, Any]       =			['low', 'er</w>']
  __A  :		Any       =			tokenizer.tokenize(__lowercase						)
  self.assertListEqual(__lowercase      ,      __lowercase						)

  __A  :		Optional[int]       =			tokens + ['<unk>']
  __A  :		Any       =			[14, 15, 20]
  self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowercase						)      ,      __lowercase						)







 @slow
 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		List[Any]       =			XLMTokenizer.from_pretrained('xlm-mlm-en-2048'						)

  __A  :		Tuple       =			tokenizer.encode('sequence builders'      ,      add_special_tokens=__lowercase						)
  __A  :		Union[str, Any]       =			tokenizer.encode('multi-sequence build'      ,      add_special_tokens=__lowercase						)

  __A  :		int       =			tokenizer.build_inputs_with_special_tokens(__lowercase						)
  __A  :		Tuple       =			tokenizer.build_inputs_with_special_tokens(__lowercase      ,      __lowercase						)

  assert encoded_sentence == [0] + text + [1]
  assert encoded_pair == [0] + text + [1] + text_a + [1]






