




"""simple docstring"""







import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput





def     __snake_case   (							_lowercase ,_lowercase=0.999 ,_lowercase="cosine" ,):


    """simple docstring"""







    if alpha_transform_type == "cosine":

        def alpha_bar_fn(_lowercase						):
            return math.cos((t + 0.008) / 1.008 * math.pi / 2						) ** 2

    elif alpha_transform_type == "exp":

        def alpha_bar_fn(_lowercase						):
            return math.exp(t * -12.0						)

    else:
        raise ValueError(f'Unsupported alpha_tranform_type: {alpha_transform_type}'						)

    UpperCamelCase										=  []
    for i in range(_lowercase						):
        UpperCamelCase										=  i / num_diffusion_timesteps
        UpperCamelCase										=  (i + 1) / num_diffusion_timesteps
        betas.append(min(1 - alpha_bar_fn(_lowercase						) / alpha_bar_fn(_lowercase						) ,_lowercase						)						)
    return torch.tensor(_lowercase ,dtype=torch.floataa						)


class 							snake_case_ (				lowerCamelCase_  ,   lowerCamelCase_ ):







   """simple docstring"""




   A_										=					[e.name for e in KarrasDiffusionSchedulers]
   A_										=					2







   @register_to_config
   def __init__(		self					,				lowerCamelCase_ = 1_0_0_0					,				lowerCamelCase_ = 0.0_0085					,				lowerCamelCase_ = 0.012					,				lowerCamelCase_ = "linear"					,				lowerCamelCase_ = None					,				lowerCamelCase_ = "epsilon"					,				lowerCamelCase_ = "linspace"					,				lowerCamelCase_ = 0					,				)							->      Dict:
       if trained_betas is not None:
           UpperCamelCase										=  torch.tensor(lowerCamelCase_					,				dtype=torch.floataa)
       elif beta_schedule == "linear":
           UpperCamelCase										=  torch.linspace(lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_					,				dtype=torch.floataa)
       elif beta_schedule == "scaled_linear":
           # this schedule is very specific to the latent diffusion model.
           UpperCamelCase										=  (
               torch.linspace(beta_start**0.5					,				beta_end**0.5					,				lowerCamelCase_					,				dtype=torch.floataa) ** 2
           )
       elif beta_schedule == "squaredcos_cap_v2":
           # Glide cosine schedule
           UpperCamelCase										=  betas_for_alpha_bar(lowerCamelCase_)
       else:
           raise NotImplementedError(F'{beta_schedule} does is not implemented for {self.__class__}')

       UpperCamelCase										=  1.0 - self.betas
       UpperCamelCase										=  torch.cumprod(self.alphas					,				dim=0)

       #  set all values
       self.set_timesteps(lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_)







   def      UpperCAmelCase__ (		self					,				lowerCamelCase_					,				lowerCamelCase_=None)							->      List[Any]:
       if schedule_timesteps is None:
           UpperCamelCase										=  self.timesteps

       UpperCamelCase										=  (schedule_timesteps == timestep).nonzero()

       # The sigma index that is taken for the **very** first `step`
       # is always the second index (or the last index if there is only 1)
       # This way we can ensure we don't accidentally skip a sigma in
       # case we start in the middle of the denoising schedule (e.g. for image-to-image)
       if len(self._index_counter) == 0:
           UpperCamelCase										=  1 if len(lowerCamelCase_) > 1 else 0
       else:
           UpperCamelCase										=  timestep.cpu().item() if torch.is_tensor(lowerCamelCase_) else timestep
           UpperCamelCase										=  self._index_counter[timestep_int]

       return indices[pos].item()







   @property
   def      UpperCAmelCase__ (		self)							->      int:
       # standard deviation of the initial noise distribution
       if self.config.timestep_spacing in ["linspace", "trailing"]:
           return self.sigmas.max()

       return (self.sigmas.max() ** 2 + 1) ** 0.5







   def      UpperCAmelCase__ (		self					,				lowerCamelCase_					,				lowerCamelCase_					,				)							->      torch.FloatTensor:
       UpperCamelCase										=  self.index_for_timestep(lowerCamelCase_)

       if self.state_in_first_order:
           UpperCamelCase										=  self.sigmas[step_index]
       else:
           UpperCamelCase										=  self.sigmas_interpol[step_index - 1]

       UpperCamelCase										=  sample / ((sigma**2 + 1) ** 0.5)
       return sample







   def      UpperCAmelCase__ (		self					,				lowerCamelCase_					,				lowerCamelCase_ = None					,				lowerCamelCase_ = None					,				)							->      List[str]:
       UpperCamelCase										=  num_inference_steps

       UpperCamelCase										=  num_train_timesteps or self.config.num_train_timesteps

       # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
       if self.config.timestep_spacing == "linspace":
           UpperCamelCase										=  np.linspace(0					,				num_train_timesteps - 1					,				lowerCamelCase_					,				dtype=lowerCamelCase_)[::-1].copy()
       elif self.config.timestep_spacing == "leading":
           UpperCamelCase										=  num_train_timesteps // self.num_inference_steps
           # creates integer timesteps by multiplying by ratio
           # casting to int to avoid issues when num_inference_step is power of 3
           UpperCamelCase										=  (np.arange(0					,				lowerCamelCase_) * step_ratio).round()[::-1].copy().astype(lowerCamelCase_)
           timesteps += self.config.steps_offset
       elif self.config.timestep_spacing == "trailing":
           UpperCamelCase										=  num_train_timesteps / self.num_inference_steps
           # creates integer timesteps by multiplying by ratio
           # casting to int to avoid issues when num_inference_step is power of 3
           UpperCamelCase										=  (np.arange(lowerCamelCase_					,				0					,				-step_ratio)).round().copy().astype(lowerCamelCase_)
           timesteps -= 1
       else:
           raise ValueError(
               F'{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.')

       UpperCamelCase										=  np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
       UpperCamelCase										=  torch.from_numpy(np.log(lowerCamelCase_)).to(lowerCamelCase_)

       UpperCamelCase										=  np.interp(lowerCamelCase_					,				np.arange(0					,				len(lowerCamelCase_))					,				lowerCamelCase_)
       UpperCamelCase										=  np.concatenate([sigmas, [0.0]]).astype(np.floataa)
       UpperCamelCase										=  torch.from_numpy(lowerCamelCase_).to(device=lowerCamelCase_)

       # compute up and down sigmas
       UpperCamelCase										=  sigmas.roll(-1)
       UpperCamelCase										=  0.0
       UpperCamelCase										=  (sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
       UpperCamelCase										=  (sigmas_next**2 - sigmas_up**2) ** 0.5
       UpperCamelCase										=  0.0

       # compute interpolated sigmas
       UpperCamelCase										=  sigmas.log().lerp(sigmas_down.log()					,				0.5).exp()
       UpperCamelCase										=  0.0

       # set sigmas
       UpperCamelCase										=  torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2), sigmas[-1:]])
       UpperCamelCase										=  torch.cat(
           [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2), sigmas_interpol[-1:]])
       UpperCamelCase										=  torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2), sigmas_up[-1:]])
       UpperCamelCase										=  torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2), sigmas_down[-1:]])

       if str(lowerCamelCase_).startswith('''mps'''):
           # mps does not support float64
           UpperCamelCase										=  torch.from_numpy(lowerCamelCase_).to(lowerCamelCase_					,				dtype=torch.floataa)
       else:
           UpperCamelCase										=  torch.from_numpy(lowerCamelCase_).to(lowerCamelCase_)

       UpperCamelCase										=  self.sigma_to_t(lowerCamelCase_).to(lowerCamelCase_					,				dtype=timesteps.dtype)
       UpperCamelCase										=  torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None])					,				dim=-1).flatten()

       UpperCamelCase										=  torch.cat([timesteps[:1], interleaved_timesteps])

       UpperCamelCase										=  None

       # for exp beta schedules, such as the one for `pipeline_shap_e.py`
       # we need an index counter
       UpperCamelCase										=  defaultdict(lowerCamelCase_)







   def      UpperCAmelCase__ (		self					,				lowerCamelCase_)							->      Any:
       # get log sigma
       UpperCamelCase										=  sigma.log()

       # get distribution
       UpperCamelCase										=  log_sigma - self.log_sigmas[:, None]

       # get sigmas range
       UpperCamelCase										=  dists.ge(0).cumsum(dim=0).argmax(dim=0).clamp(max=self.log_sigmas.shape[0] - 2)
       UpperCamelCase										=  low_idx + 1

       UpperCamelCase										=  self.log_sigmas[low_idx]
       UpperCamelCase										=  self.log_sigmas[high_idx]

       # interpolate sigmas
       UpperCamelCase										=  (low - log_sigma) / (low - high)
       UpperCamelCase										=  w.clamp(0					,				1)

       # transform interpolation to time range
       UpperCamelCase										=  (1 - w) * low_idx + w * high_idx
       UpperCamelCase										=  t.view(sigma.shape)
       return t







   @property
   def      UpperCAmelCase__ (		self)							->      Union[str, Any]:
       return self.sample is None







   def      UpperCAmelCase__ (		self					,				lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_ = None					,				lowerCamelCase_ = True					,				)							->      Union[SchedulerOutput, Tuple]:
       UpperCamelCase										=  self.index_for_timestep(lowerCamelCase_)

       # advance index counter by 1
       UpperCamelCase										=  timestep.cpu().item() if torch.is_tensor(lowerCamelCase_) else timestep
       self._index_counter[timestep_int] += 1

       if self.state_in_first_order:
           UpperCamelCase										=  self.sigmas[step_index]
           UpperCamelCase										=  self.sigmas_interpol[step_index]
           UpperCamelCase										=  self.sigmas_up[step_index]
           UpperCamelCase										=  self.sigmas_down[step_index - 1]
       else:
           # 2nd order / KPDM2's method
           UpperCamelCase										=  self.sigmas[step_index - 1]
           UpperCamelCase										=  self.sigmas_interpol[step_index - 1]
           UpperCamelCase										=  self.sigmas_up[step_index - 1]
           UpperCamelCase										=  self.sigmas_down[step_index - 1]

       # currently only gamma=0 is supported. This usually works best anyways.
       # We can support gamma in the future but then need to scale the timestep before
       # passing it to the model which requires a change in API
       UpperCamelCase										=  0
       UpperCamelCase										=  sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

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

       # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
       if self.config.prediction_type == "epsilon":
           UpperCamelCase										=  sigma_hat if self.state_in_first_order else sigma_interpol
           UpperCamelCase										=  sample - sigma_input * model_output
       elif self.config.prediction_type == "v_prediction":
           UpperCamelCase										=  sigma_hat if self.state_in_first_order else sigma_interpol
           UpperCamelCase										=  model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
               sample / (sigma_input**2 + 1)
           )
       elif self.config.prediction_type == "sample":
           raise NotImplementedError('''prediction_type not implemented yet: sample''')
       else:
           raise ValueError(
               F'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`')

       if self.state_in_first_order:
           # 2. Convert to an ODE derivative for 1st order
           UpperCamelCase										=  (sample - pred_original_sample) / sigma_hat
           # 3. delta timestep
           UpperCamelCase										=  sigma_interpol - sigma_hat

           # store for 2nd order step
           UpperCamelCase										=  sample
           UpperCamelCase										=  dt
           UpperCamelCase										=  sample + derivative * dt
       else:
           # DPM-Solver-2
           # 2. Convert to an ODE derivative for 2nd order
           UpperCamelCase										=  (sample - pred_original_sample) / sigma_interpol
           # 3. delta timestep
           UpperCamelCase										=  sigma_down - sigma_hat

           UpperCamelCase										=  self.sample
           UpperCamelCase										=  None

           UpperCamelCase										=  sample + derivative * dt
           UpperCamelCase										=  prev_sample + noise * sigma_up

       if not return_dict:
           return (prev_sample,)

       return SchedulerOutput(prev_sample=lowerCamelCase_)







   def      UpperCAmelCase__ (		self					,				lowerCamelCase_					,				lowerCamelCase_					,				lowerCamelCase_					,				)							->      torch.FloatTensor:
       # Make sure sigmas and timesteps have the same device and dtype as original_samples
       UpperCamelCase										=  self.sigmas.to(device=original_samples.device					,				dtype=original_samples.dtype)
       if original_samples.device.type == "mps" and torch.is_floating_point(lowerCamelCase_):
           # mps does not support float64
           UpperCamelCase										=  self.timesteps.to(original_samples.device					,				dtype=torch.floataa)
           UpperCamelCase										=  timesteps.to(original_samples.device					,				dtype=torch.floataa)
       else:
           UpperCamelCase										=  self.timesteps.to(original_samples.device)
           UpperCamelCase										=  timesteps.to(original_samples.device)

       UpperCamelCase										=  [self.index_for_timestep(lowerCamelCase_					,				lowerCamelCase_) for t in timesteps]

       UpperCamelCase										=  sigmas[step_indices].flatten()
       while len(sigma.shape) < len(original_samples.shape):
           UpperCamelCase										=  sigma.unsqueeze(-1)

       UpperCamelCase										=  original_samples + noise * sigma
       return noisy_samples







   def __len__(		self)							->      Dict:
       return self.config.num_train_timesteps





"""simple docstring"""







import unittest

from transformers import ErnieMTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


SCREAMING_SNAKE_CASE_      =					get_tests_dir('fixtures/spiece.model')

@require_sentencepiece
@require_tokenizers
class 							snake_case_ (				lowerCamelCase_  ,   unittest.TestCase ):







   """simple docstring"""




   A_										=					ErnieMTokenizer
   A_										=					False
   A_										=					True
   A_										=					False
   A_										=					False







   def      UpperCAmelCase__ (		self)							->      List[str]:
       super().setUp()

       # We have a SentencePiece fixture for testing
       UpperCamelCase										=  ErnieMTokenizer(lowerCamelCase_					,				unk_token='''<unk>'''					,				pad_token='''<pad>''')
       tokenizer.save_pretrained(self.tmpdirname)







   def      UpperCAmelCase__ (		self					,				lowerCamelCase_)							->      Optional[Any]:
       UpperCamelCase										=  '''this is a test'''
       UpperCamelCase										=  '''this is a test'''
       return input_text, output_text







   def      UpperCAmelCase__ (		self)							->      Optional[Any]:
       UpperCamelCase										=  '''<pad>'''
       UpperCamelCase										=  0

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







   def      UpperCAmelCase__ (		self)							->      Optional[Any]:
       UpperCamelCase										=  list(self.get_tokenizer().get_vocab().keys())

       self.assertEqual(vocab_keys[0]					,				'''<pad>''')
       self.assertEqual(vocab_keys[1]					,				'''<unk>''')
       self.assertEqual(vocab_keys[-1]					,				'''▁eloquent''')
       self.assertEqual(len(lowerCamelCase_)					,				3_0_0_0_0)







   def      UpperCAmelCase__ (		self)							->      int:
       self.assertEqual(self.get_tokenizer().vocab_size					,				3_0_0_0_0)







   def      UpperCAmelCase__ (		self)							->      List[Any]:
       if not self.test_rust_tokenizer:
           return

       UpperCamelCase										=  self.get_tokenizer()
       UpperCamelCase										=  self.get_rust_tokenizer()

       UpperCamelCase										=  '''I was born in 92000, and this is falsé.'''

       UpperCamelCase										=  tokenizer.tokenize(lowerCamelCase_)
       UpperCamelCase										=  rust_tokenizer.tokenize(lowerCamelCase_)
       self.assertListEqual(lowerCamelCase_					,				lowerCamelCase_)

       UpperCamelCase										=  tokenizer.encode(lowerCamelCase_					,				add_special_tokens=lowerCamelCase_)
       UpperCamelCase										=  rust_tokenizer.encode(lowerCamelCase_					,				add_special_tokens=lowerCamelCase_)
       self.assertListEqual(lowerCamelCase_					,				lowerCamelCase_)

       UpperCamelCase										=  self.get_rust_tokenizer()
       UpperCamelCase										=  tokenizer.encode(lowerCamelCase_)
       UpperCamelCase										=  rust_tokenizer.encode(lowerCamelCase_)
       self.assertListEqual(lowerCamelCase_					,				lowerCamelCase_)







   def      UpperCAmelCase__ (		self)							->      List[Any]:
       UpperCamelCase										=  ErnieMTokenizer(lowerCamelCase_					,				do_lower_case=lowerCamelCase_					,				unk_token='''<unk>'''					,				pad_token='''<pad>''')

       UpperCamelCase										=  tokenizer.tokenize('''This is a test''')
       self.assertListEqual(lowerCamelCase_					,				['''▁this''', '''▁is''', '''▁a''', '''▁test'''])

       self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase_)					,				[4_8, 2_5, 2_1, 1_2_8_9])

       UpperCamelCase										=  tokenizer.tokenize('''I was born in 92000, and this is falsé.''')
       # ErnieMTokenizer(paddlenlp implementation) outputs '9' instead of '_9' so to mimic that '_9' is changed to '9'
       self.assertListEqual(
           lowerCamelCase_					,				['''▁i''', '''▁was''', '''▁born''', '''▁in''', '''9''', '''2000''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''é''', '''.'''])
       UpperCamelCase										=  tokenizer.convert_tokens_to_ids(lowerCamelCase_)
       self.assertListEqual(lowerCamelCase_					,				[3_1, 2_3, 3_8_6, 1_9, 5_1_8, 3_0_5_0, 1_5, 1_7, 4_8, 2_5, 8_2_5_6, 1_8, 1, 9])

       UpperCamelCase										=  tokenizer.convert_ids_to_tokens(lowerCamelCase_)
       self.assertListEqual(
           lowerCamelCase_					,				['''▁i''', '''▁was''', '''▁born''', '''▁in''', '''9''', '''2000''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁fal''', '''s''', '''<unk>''', '''.''']					,				)







   def      UpperCAmelCase__ (		self)							->      Dict:
       UpperCamelCase										=  ErnieMTokenizer(lowerCamelCase_					,				unk_token='''<unk>'''					,				pad_token='''<pad>''')

       UpperCamelCase										=  tokenizer.encode('''sequence builders''')
       UpperCamelCase										=  tokenizer.encode('''multi-sequence build''')

       UpperCamelCase										=  tokenizer.build_inputs_with_special_tokens(lowerCamelCase_)
       UpperCamelCase										=  tokenizer.build_inputs_with_special_tokens(lowerCamelCase_					,				lowerCamelCase_)

       assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
       assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + [
           tokenizer.sep_token_id
       ] + text_a + [tokenizer.sep_token_id]







   @slow
   def      UpperCAmelCase__ (		self)							->      List[str]:
       # fmt: off
       UpperCamelCase										=  {'''input_ids''': [[0, 1_1_0_6_2, 8_2_7_7_2, 7, 1_5, 8_2_7_7_2, 5_3_8, 5_1_5_2_9, 2_3_7, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 2_1_5_1_7_5, 1_3_1_4, 1_3_6, 1_7_1_9_8, 1_2_9_0, 2_0_6, 9, 5_6_3_5_9, 4_2, 1_2_2_0_0_9, 9, 1_6_4_6_6, 1_6, 8_7_3_4_4, 4_5_3_7, 9, 4_7_1_7, 7_8_3_8_1, 6, 1_5_9_9_5_8, 7, 1_5, 2_4_4_8_0, 6_1_8, 4, 5_2_7, 2_2_6_9_3, 9, 3_0_4, 4, 2_7_7_7, 2_4_4_8_0, 9_8_7_4, 4, 4_3_5_2_3, 5_9_4, 4, 8_0_3, 1_8_3_9_2, 3_3_1_8_9, 1_8, 4, 4_3_5_2_3, 2_4_4_4_7, 5, 5, 5, 1_6, 1_0_0, 2_4_9_5_5, 8_3_6_5_8, 9_6_2_6, 1_4_4_0_5_7, 1_5, 8_3_9, 2_2_3_3_5, 1_6, 1_3_6, 2_4_9_5_5, 8_3_6_5_8, 8_3_4_7_9, 1_5, 3_9_1_0_2, 7_2_4, 1_6, 6_7_8, 6_4_5, 6_4_6_0, 1_3_2_8, 4_5_8_9, 4_2, 1_2_2_0_0_9, 1_1_5_7_7_4, 2_3, 3_5_5_9, 1_3_2_8, 4_6_8_7_6, 7, 1_3_6, 5_3_8_9_4, 1_9_4_0, 4_2_2_2_7, 4_1_1_5_9, 1_7_7_2_1, 8_2_3, 4_2_5, 4, 2_7_5_1_2, 9_8_7_2_2, 2_0_6, 1_3_6, 5_5_3_1, 4_9_7_0, 9_1_9, 1_7_3_3_6, 5, 2], [0, 2_0_0_8_0, 6_1_8, 8_3, 8_2_7_7_5, 4_7, 4_7_9, 9, 1_5_1_7, 7_3, 5_3_8_9_4, 3_3_3, 8_0_5_8_1, 1_1_0_1_1_7, 1_8_8_1_1, 5_2_5_6, 1_2_9_5, 5_1, 1_5_2_5_2_6, 2_9_7, 7_9_8_6, 3_9_0, 1_2_4_4_1_6, 5_3_8, 3_5_4_3_1, 2_1_4, 9_8, 1_5_0_4_4, 2_5_7_3_7, 1_3_6, 7_1_0_8, 4_3_7_0_1, 2_3, 7_5_6, 1_3_5_3_5_5, 7, 5, 2, 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, 5_8_1, 6_3_7_7_3, 1_1_9_4_5_5, 6, 1_4_7_7_9_7, 8_8_2_0_3, 7, 6_4_5, 7_0, 2_1, 3_2_8_5, 1_0_2_6_9, 5, 2, 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]]}
       # fmt: on

       self.tokenizer_integration_test_util(
           expected_encoding=lowerCamelCase_					,				model_name='''susnato/ernie-m-base_pytorch'''					,				sequences=[
               '''Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides '''
               '''general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet...) for Natural '''
               '''Language Understanding (NLU) and Natural Language Generation (NLG) with over32+ pretrained '''
               '''models in100+ languages and deep interoperability between Jax, PyTorch and TensorFlow.''',
               '''BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly '''
               '''conditioning on both left and right context in all layers.''',
               '''The quick brown fox jumps over the lazy dog.''',
           ]					,				)