


'''simple docstring'''

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 __snake_case					(      UpperCAmelCase_						:						List[Any]					,				UpperCAmelCase_						:						List[Any]					,				UpperCAmelCase_						:						int		):
 if isinstance(UpperCAmelCase_					,				torch.Tensor		):
  return image
 elif isinstance(UpperCAmelCase_					,				PIL.Image.Image		):
  lowerCamelCase_      =  [image]

 if isinstance(image[0]					,				PIL.Image.Image		):
  lowerCamelCase_      =  [np.array(i.resize((w, h)					,				resample=PIL_INTERPOLATION["lanczos"]		)		)[None, :] for i in image]
  lowerCamelCase_      =  np.concatenate(UpperCAmelCase_					,				axis=0		)
  lowerCamelCase_      =  np.array(UpperCAmelCase_		).astype(np.floataa		) / 255.0
  lowerCamelCase_      =  image.transpose(0					,				3					,				1					,				2		)
  lowerCamelCase_      =  2.0 * image - 1.0
  lowerCamelCase_      =  torch.from_numpy(UpperCAmelCase_		)
 elif isinstance(image[0]					,				torch.Tensor		):
  lowerCamelCase_      =  torch.cat(UpperCAmelCase_					,				dim=0		)
 return image





def __snake_case					(      UpperCAmelCase_						:						Any					,				UpperCAmelCase_						:						Optional[Any]					,				UpperCAmelCase_						:						str					,				UpperCAmelCase_						:						Any=0.9995		):
 if not isinstance(UpperCAmelCase_					,				np.ndarray		):
  lowerCamelCase_      =  True
  lowerCamelCase_      =  va.device
  lowerCamelCase_      =  va.cpu().numpy()
  lowerCamelCase_      =  va.cpu().numpy()

 lowerCamelCase_      =  np.sum(va * va / (np.linalg.norm(UpperCAmelCase_		) * np.linalg.norm(UpperCAmelCase_		))		)
 if np.abs(UpperCAmelCase_		) > DOT_THRESHOLD:
  lowerCamelCase_      =  (1 - t) * va + t * va
 else:
  lowerCamelCase_      =  np.arccos(UpperCAmelCase_		)
  lowerCamelCase_      =  np.sin(UpperCAmelCase_		)
  lowerCamelCase_      =  theta_a * t
  lowerCamelCase_      =  np.sin(UpperCAmelCase_		)
  lowerCamelCase_      =  np.sin(theta_a - theta_t		) / sin_theta_a
  lowerCamelCase_      =  sin_theta_t / sin_theta_a
  lowerCamelCase_      =  sa * va + sa * va

 if inputs_are_torch:
  lowerCamelCase_      =  torch.from_numpy(UpperCAmelCase_		).to(UpperCAmelCase_		)

 return va





def __snake_case					(      UpperCAmelCase_						:						int					,				UpperCAmelCase_						:						List[str]		):
 lowerCamelCase_      =  F.normalize(UpperCAmelCase_					,				dim=-1		)
 lowerCamelCase_      =  F.normalize(UpperCAmelCase_					,				dim=-1		)
 return (x - y).norm(dim=-1		).div(2		).arcsin().pow(2		).mul(2		)





def __snake_case					(      UpperCAmelCase_						:						List[str]					,				UpperCAmelCase_						:						Optional[int]		):
 for param in model.parameters():
  lowerCamelCase_      =  value







class      snake_case    (    lowercase			):





  """simple docstring"""




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







   """simple docstring"""
   super().__init__()
   self.register_modules(
       vae=UpperCamelCase			,  text_encoder=UpperCamelCase			,  clip_model=UpperCamelCase			,  tokenizer=UpperCamelCase			,  unet=UpperCamelCase			,  scheduler=UpperCamelCase			,  feature_extractor=UpperCamelCase			,  coca_model=UpperCamelCase			,  coca_tokenizer=UpperCamelCase			,  coca_transform=UpperCamelCase			,  )
   lowerCamelCase_      =  (
       feature_extractor.size
       if isinstance(feature_extractor.size			,  UpperCamelCase     )
       else feature_extractor.size["shortest_edge"]
   )
   lowerCamelCase_      =  transforms.Normalize(mean=feature_extractor.image_mean			,  std=feature_extractor.image_std     )
   set_requires_grad(self.text_encoder			,  UpperCamelCase     )
   set_requires_grad(self.clip_model			,  UpperCamelCase     )






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







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






  def   snake_case		(    self     ):







   """simple docstring"""
   self.enable_attention_slicing(UpperCamelCase     )






  def   snake_case		(    self     ):







   """simple docstring"""
   set_requires_grad(self.vae			,  UpperCamelCase     )






  def   snake_case		(    self     ):







   """simple docstring"""
   set_requires_grad(self.vae			,  UpperCamelCase     )






  def   snake_case		(    self     ):







   """simple docstring"""
   set_requires_grad(self.unet			,  UpperCamelCase     )






  def   snake_case		(    self     ):







   """simple docstring"""
   set_requires_grad(self.unet			,  UpperCamelCase     )






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase     ):







   """simple docstring"""
   # get the original timestep using init_timestep
   lowerCamelCase_      =  min(int(num_inference_steps * strength     )			,  UpperCamelCase     )

   lowerCamelCase_      =  max(num_inference_steps - init_timestep			,  0     )
   lowerCamelCase_      =  self.scheduler.timesteps[t_start:]

   return timesteps, num_inference_steps - t_start






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







   """simple docstring"""
   if not isinstance(UpperCamelCase			,  torch.Tensor     ):
    raise ValueError(f'''`image` has to be of type `torch.Tensor` but is {type(UpperCamelCase     )}'''     )

   lowerCamelCase_      =  image.to(device=UpperCamelCase			,  dtype=UpperCamelCase     )

   if isinstance(UpperCamelCase			,  UpperCamelCase     ):
    lowerCamelCase_      =  [
        self.vae.encode(image[i : i + 1]     ).latent_dist.sample(generator[i]     ) for i in range(UpperCamelCase     )
    ]
    lowerCamelCase_      =  torch.cat(UpperCamelCase			,  dim=0     )
   else:
    lowerCamelCase_      =  self.vae.encode(UpperCamelCase     ).latent_dist.sample(UpperCamelCase     )

   # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
   lowerCamelCase_      =  0.18_215 * init_latents
   lowerCamelCase_      =  init_latents.repeat_interleave(UpperCamelCase			,  dim=0     )

   lowerCamelCase_      =  randn_tensor(init_latents.shape			,  generator=UpperCamelCase			,  device=UpperCamelCase			,  dtype=UpperCamelCase     )

   # get latents
   lowerCamelCase_      =  self.scheduler.add_noise(UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase     )
   lowerCamelCase_      =  init_latents

   return latents






  def   snake_case		(    self			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  self.coca_transform(UpperCamelCase     ).unsqueeze(0     )
   with torch.no_grad(), torch.cuda.amp.autocast():
    lowerCamelCase_      =  self.coca_model.generate(transformed_image.to(device=self.device			,  dtype=self.coca_model.dtype     )     )
   lowerCamelCase_      =  self.coca_tokenizer.decode(generated[0].cpu().numpy()     )
   return generated.split("<end_of_text>"     )[0].replace("<start_of_text>"			,  ""     ).rstrip(" .,"     )






  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase     ):







   """simple docstring"""
   lowerCamelCase_      =  self.feature_extractor.preprocess(UpperCamelCase     )
   lowerCamelCase_      =  torch.from_numpy(clip_image_input["pixel_values"][0]     ).unsqueeze(0     ).to(self.device     ).half()
   lowerCamelCase_      =  self.clip_model.get_image_features(UpperCamelCase     )
   lowerCamelCase_      =  image_embeddings_clip / image_embeddings_clip.norm(p=2			,  dim=-1			,  keepdim=UpperCamelCase     )
   lowerCamelCase_      =  image_embeddings_clip.repeat_interleave(UpperCamelCase			,  dim=0     )
   return image_embeddings_clip






  @torch.enable_grad()
  def   snake_case		(    self			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  ):







   """simple docstring"""
   lowerCamelCase_      =  latents.detach().requires_grad_()

   lowerCamelCase_      =  self.scheduler.scale_model_input(UpperCamelCase			,  UpperCamelCase     )

   # predict the noise residual
   lowerCamelCase_      =  self.unet(UpperCamelCase			,  UpperCamelCase			,  encoder_hidden_states=UpperCamelCase     ).sample

   if isinstance(self.scheduler			,  (PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)     ):
    lowerCamelCase_      =  self.scheduler.alphas_cumprod[timestep]
    lowerCamelCase_      =  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
    lowerCamelCase_      =  (latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

    lowerCamelCase_      =  torch.sqrt(UpperCamelCase     )
    lowerCamelCase_      =  pred_original_sample * (fac) + latents * (1 - fac)
   elif isinstance(self.scheduler			,  UpperCamelCase     ):
    lowerCamelCase_      =  self.scheduler.sigmas[index]
    lowerCamelCase_      =  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
   lowerCamelCase_      =  1 / 0.18_215 * sample
   lowerCamelCase_      =  self.vae.decode(UpperCamelCase     ).sample
   lowerCamelCase_      =  (image / 2 + 0.5).clamp(0			,  1     )

   lowerCamelCase_      =  transforms.Resize(self.feature_extractor_size     )(UpperCamelCase     )
   lowerCamelCase_      =  self.normalize(UpperCamelCase     ).to(latents.dtype     )

   lowerCamelCase_      =  self.clip_model.get_image_features(UpperCamelCase     )
   lowerCamelCase_      =  image_embeddings_clip / image_embeddings_clip.norm(p=2			,  dim=-1			,  keepdim=UpperCamelCase     )

   lowerCamelCase_      =  spherical_dist_loss(UpperCamelCase			,  UpperCamelCase     ).mean() * clip_guidance_scale

   lowerCamelCase_      =  -torch.autograd.grad(UpperCamelCase			,  UpperCamelCase     )[0]

   if isinstance(self.scheduler			,  UpperCamelCase     ):
    lowerCamelCase_      =  latents.detach() + grads * (sigma**2)
    lowerCamelCase_      =  noise_pred_original
   else:
    lowerCamelCase_      =  noise_pred_original - torch.sqrt(UpperCamelCase     ) * grads
   return noise_pred, latents







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







   """simple docstring"""
   if isinstance(UpperCamelCase			,  UpperCamelCase     ) and len(UpperCamelCase     ) != batch_size:
    raise ValueError(f'''You have passed {batch_size} batch_size, but only {len(UpperCamelCase     )} 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(UpperCamelCase			,  torch.Generator     ) and batch_size > 1:
    lowerCamelCase_      =  [generator] + [None] * (batch_size - 1)

   lowerCamelCase_      =  [
       ("model", self.coca_model is None),
       ("tokenizer", self.coca_tokenizer is None),
       ("transform", self.coca_transform is None),
   ]
   lowerCamelCase_      =  [x[0] for x in coca_is_none if x[1]]
   lowerCamelCase_      =  ", ".join(UpperCamelCase     )
   # generate prompts with coca model if prompt is None
   if content_prompt is None:
    if len(UpperCamelCase     ):
     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.'''     )
    lowerCamelCase_      =  self.get_image_description(UpperCamelCase     )
   if style_prompt is None:
    if len(UpperCamelCase     ):
     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.'''     )
    lowerCamelCase_      =  self.get_image_description(UpperCamelCase     )

   # get prompt text embeddings for content and style
   lowerCamelCase_      =  self.tokenizer(
       UpperCamelCase			,  padding="max_length"			,  max_length=self.tokenizer.model_max_length			,  truncation=UpperCamelCase			,  return_tensors="pt"			,  )
   lowerCamelCase_      =  self.text_encoder(content_text_input.input_ids.to(self.device     )     )[0]

   lowerCamelCase_      =  self.tokenizer(
       UpperCamelCase			,  padding="max_length"			,  max_length=self.tokenizer.model_max_length			,  truncation=UpperCamelCase			,  return_tensors="pt"			,  )
   lowerCamelCase_      =  self.text_encoder(style_text_input.input_ids.to(self.device     )     )[0]

   lowerCamelCase_      =  slerp(UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase     )

   # duplicate text embeddings for each generation per prompt
   lowerCamelCase_      =  text_embeddings.repeat_interleave(UpperCamelCase			,  dim=0     )

   # set timesteps
   lowerCamelCase_      =  "offset" in set(inspect.signature(self.scheduler.set_timesteps     ).parameters.keys()     )
   lowerCamelCase_      =  {}
   if accepts_offset:
    lowerCamelCase_      =  1

   self.scheduler.set_timesteps(UpperCamelCase			,  **UpperCamelCase     )
   # 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     )

   lowerCamelCase_ ,lowerCamelCase_      =  self.get_timesteps(UpperCamelCase			,  UpperCamelCase			,  self.device     )
   lowerCamelCase_      =  timesteps[:1].repeat(UpperCamelCase     )

   # Preprocess image
   lowerCamelCase_      =  preprocess(UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase     )
   lowerCamelCase_      =  self.prepare_latents(
       UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  text_embeddings.dtype			,  self.device			,  UpperCamelCase     )

   lowerCamelCase_      =  preprocess(UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase     )
   lowerCamelCase_      =  self.prepare_latents(
       UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  text_embeddings.dtype			,  self.device			,  UpperCamelCase     )

   lowerCamelCase_      =  slerp(UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase     )

   if clip_guidance_scale > 0:
    lowerCamelCase_      =  self.get_clip_image_embeddings(UpperCamelCase			,  UpperCamelCase     )
    lowerCamelCase_      =  self.get_clip_image_embeddings(UpperCamelCase			,  UpperCamelCase     )
    lowerCamelCase_      =  slerp(
        UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase     )

   # 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.
   lowerCamelCase_      =  guidance_scale > 1.0
   # get unconditional embeddings for classifier free guidance
   if do_classifier_free_guidance:
    lowerCamelCase_      =  content_text_input.input_ids.shape[-1]
    lowerCamelCase_      =  self.tokenizer([""]			,  padding="max_length"			,  max_length=UpperCamelCase			,  return_tensors="pt"     )
    lowerCamelCase_      =  self.text_encoder(uncond_input.input_ids.to(self.device     )     )[0]
    # duplicate unconditional embeddings for each generation per prompt
    lowerCamelCase_      =  uncond_embeddings.repeat_interleave(UpperCamelCase			,  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
    lowerCamelCase_      =  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`.
   lowerCamelCase_      =  (batch_size, self.unet.config.in_channels, height // 8, width // 8)
   lowerCamelCase_      =  text_embeddings.dtype
   if latents is None:
    if self.device.type == "mps":
     # randn does not work reproducibly on mps
     lowerCamelCase_      =  torch.randn(UpperCamelCase			,  generator=UpperCamelCase			,  device="cpu"			,  dtype=UpperCamelCase     ).to(
         self.device     )
    else:
     lowerCamelCase_      =  torch.randn(UpperCamelCase			,  generator=UpperCamelCase			,  device=self.device			,  dtype=UpperCamelCase     )
   else:
    if latents.shape != latents_shape:
     raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}'''     )
    lowerCamelCase_      =  latents.to(self.device     )

   # scale the initial noise by the standard deviation required by the scheduler
   lowerCamelCase_      =  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]
   lowerCamelCase_      =  "eta" in set(inspect.signature(self.scheduler.step     ).parameters.keys()     )
   lowerCamelCase_      =  {}
   if accepts_eta:
    lowerCamelCase_      =  eta

   # check if the scheduler accepts generator
   lowerCamelCase_      =  "generator" in set(inspect.signature(self.scheduler.step     ).parameters.keys()     )
   if accepts_generator:
    lowerCamelCase_      =  generator

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

     # predict the noise residual
     lowerCamelCase_      =  self.unet(UpperCamelCase			,  UpperCamelCase			,  encoder_hidden_states=UpperCamelCase     ).sample

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

     # perform clip guidance
     if clip_guidance_scale > 0:
      lowerCamelCase_      =  (
          text_embeddings.chunk(2     )[1] if do_classifier_free_guidance else text_embeddings
      )
      lowerCamelCase_ ,lowerCamelCase_      =  self.cond_fn(
          UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  UpperCamelCase			,  )

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

        # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
   lowerCamelCase_      =  1 / 0.18_215 * latents
   lowerCamelCase_      =  self.vae.decode(UpperCamelCase     ).sample

   lowerCamelCase_      =  (image / 2 + 0.5).clamp(0			,  1     )
   lowerCamelCase_      =  image.cpu().permute(0			,  2			,  3			,  1     ).numpy()

   if output_type == "pil":
    lowerCamelCase_      =  self.numpy_to_pil(UpperCamelCase     )

   if not return_dict:
    return (image, None)

   return StableDiffusionPipelineOutput(images=UpperCamelCase			,  nsfw_content_detected=UpperCamelCase     )







import unittest

from transformers import BigBirdTokenizer, BigBirdTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


lowerCamelCase											=       '▁'

lowerCamelCase											=       get_tests_dir('fixtures/test_sentencepiece.model')




@require_sentencepiece
@require_tokenizers
class A  (   UpperCamelCase_    ,							unittest.TestCase     ):
      UpperCamelCase__							:  Tuple      =BigBirdTokenizer
      UpperCamelCase__							:  Union[str, Any]      =BigBirdTokenizerFast
      UpperCamelCase__							:  Any      =True
      UpperCamelCase__							:  Optional[Any]      =True






      def        lowerCamelCase       (					self						:     List[Any] )					->						Dict:
             """simple docstring"""




             super().setUp()

             _lowerCamelCase       :		List[Any]		=self.tokenizer_class(lowercase_			,   keep_accents=lowercase_ )
             tokenizer.save_pretrained(self.tmpdirname )






      def        lowerCamelCase       (					self						:     Union[str, Any] )					->						int:
             """simple docstring"""




             _lowerCamelCase       :		List[Any]		='<s>'
             _lowerCamelCase       :		Optional[Any]		=1

             self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowercase_ )			,   lowercase_ )
             self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowercase_ )			,   lowercase_ )






      def        lowerCamelCase       (					self						:     int )					->						Union[str, Any]:
             """simple docstring"""




             _lowerCamelCase       :		Optional[int]		=list(self.get_tokenizer().get_vocab().keys() )

             self.assertEqual(vocab_keys[0]			,   '<unk>' )
             self.assertEqual(vocab_keys[1]			,   '<s>' )
             self.assertEqual(vocab_keys[-1]			,   '[MASK]' )
             self.assertEqual(len(lowercase_ )			,   1004 )






      def        lowerCamelCase       (					self						:     Any )					->						Union[str, Any]:
             """simple docstring"""




             self.assertEqual(self.get_tokenizer().vocab_size			,   1000 )






      def        lowerCamelCase       (					self						:     Any )					->						Dict:
             """simple docstring"""




             if not self.test_rust_tokenizer:
                    return

             _lowerCamelCase       :		Union[str, Any]		=self.get_tokenizer()
             _lowerCamelCase       :		int		=self.get_rust_tokenizer()

             _lowerCamelCase       :		int		='I was born in 92000, and this is falsé.'

             _lowerCamelCase       :		int		=tokenizer.tokenize(lowercase_ )
             _lowerCamelCase       :		List[Any]		=rust_tokenizer.tokenize(lowercase_ )
             self.assertListEqual(lowercase_			,   lowercase_ )

             _lowerCamelCase       :		Any		=tokenizer.encode(lowercase_			,   add_special_tokens=lowercase_ )
             _lowerCamelCase       :		str		=rust_tokenizer.encode(lowercase_			,   add_special_tokens=lowercase_ )
             self.assertListEqual(lowercase_			,   lowercase_ )

             _lowerCamelCase       :		str		=self.get_rust_tokenizer()
             _lowerCamelCase       :		Union[str, Any]		=tokenizer.encode(lowercase_ )
             _lowerCamelCase       :		List[Any]		=rust_tokenizer.encode(lowercase_ )
             self.assertListEqual(lowercase_			,   lowercase_ )






      def        lowerCamelCase       (					self						:     Dict )					->						Union[str, Any]:
             """simple docstring"""




             _lowerCamelCase       :		str		=BigBirdTokenizer(lowercase_			,   keep_accents=lowercase_ )

             _lowerCamelCase       :		int		=tokenizer.tokenize('This is a test' )
             self.assertListEqual(lowercase_			,   ['▁This', '▁is', '▁a', '▁t', 'est'] )

             self.assertListEqual(
                 tokenizer.convert_tokens_to_ids(lowercase_ )			,   [285, 46, 10, 170, 382]			,   )

             _lowerCamelCase       :		Optional[Any]		=tokenizer.tokenize('I was born in 92000, and this is falsé.' )
             self.assertListEqual(
                 lowercase_			,   [
                     SPIECE_UNDERLINE + 'I',
                     SPIECE_UNDERLINE + 'was',
                     SPIECE_UNDERLINE + 'b',
                     'or',
                     'n',
                     SPIECE_UNDERLINE + 'in',
                     SPIECE_UNDERLINE + '',
                     '9',
                     '2',
                     '0',
                     '0',
                     '0',
                     ',',
                     SPIECE_UNDERLINE + 'and',
                     SPIECE_UNDERLINE + 'this',
                     SPIECE_UNDERLINE + 'is',
                     SPIECE_UNDERLINE + 'f',
                     'al',
                     's',
                     'é',
                     '.',
                 ]			,   )
             _lowerCamelCase       :		Any		=tokenizer.convert_tokens_to_ids(lowercase_ )
             self.assertListEqual(
                 lowercase_			,   [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]			,   )

             _lowerCamelCase       :		Optional[int]		=tokenizer.convert_ids_to_tokens(lowercase_ )
             self.assertListEqual(
                 lowercase_			,   [
                     SPIECE_UNDERLINE + 'I',
                     SPIECE_UNDERLINE + 'was',
                     SPIECE_UNDERLINE + 'b',
                     'or',
                     'n',
                     SPIECE_UNDERLINE + 'in',
                     SPIECE_UNDERLINE + '',
                     '<unk>',
                     '2',
                     '0',
                     '0',
                     '0',
                     ',',
                     SPIECE_UNDERLINE + 'and',
                     SPIECE_UNDERLINE + 'this',
                     SPIECE_UNDERLINE + 'is',
                     SPIECE_UNDERLINE + 'f',
                     'al',
                     's',
                     '<unk>',
                     '.',
                 ]			,   )






      @cached_property
      def        lowerCamelCase       (					self						:     Union[str, Any] )					->						str:
             """simple docstring"""




             return BigBirdTokenizer.from_pretrained('google/bigbird-roberta-base' )






      @slow
      def        lowerCamelCase       (					self						:     Any )					->						Dict:
             """simple docstring"""




             _lowerCamelCase       :		List[str]		='Hello World!'
             _lowerCamelCase       :		Tuple		=[65, 1_8536, 2260, 101, 66]

             self.assertListEqual(lowercase_			,   self.big_tokenizer.encode(lowercase_ ) )






      @slow
      def        lowerCamelCase       (					self						:     str )					->						Optional[Any]:
             """simple docstring"""




             _lowerCamelCase       :		int		=(
                 'This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'
                 ' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'
             )
             # fmt: off
             _lowerCamelCase       :		Tuple		=[65, 871, 419, 358, 946, 991, 2521, 452, 358, 1357, 387, 7751, 3536, 112, 985, 456, 126, 865, 938, 5400, 5734, 458, 1368, 467, 786, 2462, 5246, 1159, 633, 865, 4519, 457, 582, 852, 2557, 427, 916, 508, 405, 3_4324, 497, 391, 408, 1_1342, 1244, 385, 100, 938, 985, 456, 574, 362, 1_2597, 3200, 3129, 1172, 66]  # noqa: E231
             # fmt: on
             self.assertListEqual(lowercase_			,   self.big_tokenizer.encode(lowercase_ ) )






      @require_torch
      @slow
      def        lowerCamelCase       (					self						:     Any )					->						Any:
             """simple docstring"""




             import torch

             from transformers import BigBirdConfig, BigBirdModel

             # Build sequence
             _lowerCamelCase       :		Union[str, Any]		=list(self.big_tokenizer.get_vocab().keys() )[:10]
             _lowerCamelCase       :		List[Any]		=' '.join(lowercase_ )
             _lowerCamelCase       :		List[str]		=self.big_tokenizer.encode_plus(lowercase_			,   return_tensors='pt'			,   return_token_type_ids=lowercase_ )
             _lowerCamelCase       :		Optional[int]		=self.big_tokenizer.batch_encode_plus(
                 [sequence + ' ' + sequence]			,   return_tensors='pt'			,   return_token_type_ids=lowercase_ )

             _lowerCamelCase       :		List[str]		=BigBirdConfig(attention_type='original_full' )
             _lowerCamelCase       :		Optional[Any]		=BigBirdModel(lowercase_ )

             assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

             with torch.no_grad():
                    model(**lowercase_ )
                    model(**lowercase_ )






      @slow
      def        lowerCamelCase       (					self						:     Dict )					->						Optional[int]:
             """simple docstring"""




             _lowerCamelCase       :		Dict		=BigBirdTokenizer.from_pretrained('google/bigbird-roberta-base' )
             _lowerCamelCase       :		int		=tokenizer.decode(tokenizer('Paris is the [MASK].' ).input_ids )

             self.assertTrue(decoded_text == '[CLS] Paris is the[MASK].[SEP]' )






      @slow
      def        lowerCamelCase       (					self						:     Optional[int] )					->						List[Any]:
             """simple docstring"""




             _lowerCamelCase       :		Union[str, Any]		={'input_ids': [[65, 3_9286, 458, 3_6335, 2001, 456, 1_3073, 1_3266, 455, 113, 7746, 1741, 1_1157, 391, 1_3073, 1_3266, 455, 113, 3967, 3_5412, 113, 4936, 109, 3870, 2377, 113, 3_0084, 4_5720, 458, 134, 1_7496, 112, 503, 1_1672, 113, 118, 112, 5665, 1_3347, 3_8687, 112, 1496, 3_1389, 112, 3268, 4_7264, 134, 962, 112, 1_6377, 8035, 2_3130, 430, 1_2169, 1_5518, 2_8592, 458, 146, 4_1697, 109, 391, 1_2169, 1_5518, 1_6689, 458, 146, 4_1358, 109, 452, 726, 4034, 111, 763, 3_5412, 5082, 388, 1903, 111, 9051, 391, 2870, 4_8918, 1900, 1123, 550, 998, 112, 9586, 1_5985, 455, 391, 410, 2_2955, 3_7636, 114, 66], [65, 448, 1_7496, 419, 3663, 385, 763, 113, 2_7533, 2870, 3283, 1_3043, 1639, 2_4713, 523, 656, 2_4013, 1_8550, 2521, 517, 2_7014, 2_1244, 420, 1212, 1465, 391, 927, 4833, 388, 578, 1_1786, 114, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [65, 484, 2169, 7687, 2_1932, 1_8146, 726, 363, 1_7032, 3391, 114, 66, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
             # fmt: on

             self.tokenizer_integration_test_util(
                 expected_encoding=lowercase_			,   model_name='google/bigbird-roberta-base'			,   revision='215c99f1600e06f83acce68422f2035b2b5c3510'			,   )


