






"""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 .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput


def       UpperCAmelCase__ (snake_case__							:      List[Any]	,     snake_case__							:      str=0.9_99	,     snake_case__							:      Dict="cosine"	,     ):
  """simple docstring"""

  if alpha_transform_type == "cosine":

    def alpha_bar_fn(snake_case__							:      Optional[Any]					):
      return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2					) ** 2

  elif alpha_transform_type == "exp":

    def alpha_bar_fn(snake_case__							:      Optional[int]					):
      return math.exp(t * -12.0					)

  else:
    raise ValueError(F"Unsupported alpha_tranform_type: {alpha_transform_type}"					)

  _snake_case							:	List[Any]			=						[]
  for i in range(snake_case__					):
    _snake_case							:	Tuple			=						i / num_diffusion_timesteps
    _snake_case							:	Optional[int]			=						(i + 1) / num_diffusion_timesteps
    betas.append(min(1 - alpha_bar_fn(snake_case__					) / alpha_bar_fn(snake_case__					)	,     snake_case__					)					)
  return torch.tensor(snake_case__	,     dtype=torch.floataa					)






class       lowercase(  __a ,    __a				):







 '''simple docstring'''





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


 @register_to_config
 def __init__(       self:	Tuple,   a_:	int = 1_000,   a_:	float = 0.00_085,   a_:	float = 0.012,   a_:	str = "linear",   a_:	Optional[Union[np.ndarray, List[float]]] = None,   a_:	str = "epsilon",   a_:	str = "linspace",   a_:	int = 0,   ):




   '''simple docstring'''



   if trained_betas is not None:
     _snake_case							:	Tuple			=						torch.tensor(a_,   dtype=torch.floataa	)
   elif beta_schedule == "linear":
     _snake_case							:	Dict			=						torch.linspace(a_,   a_,   a_,   dtype=torch.floataa	)
   elif beta_schedule == "scaled_linear":
     # this schedule is very specific to the latent diffusion model.
     _snake_case							:	List[str]			=						(
         torch.linspace(beta_start**0.5,   beta_end**0.5,   a_,   dtype=torch.floataa	) ** 2
     )
   elif beta_schedule == "squaredcos_cap_v2":
     # Glide cosine schedule
     _snake_case							:	str			=						betas_for_alpha_bar(a_	)
   else:
     raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}"	)

   _snake_case							:	int			=						1.0 - self.betas
   _snake_case							:	Optional[Any]			=						torch.cumprod(self.alphas,   dim=0	)

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


 def 				UpperCamelCase_     (       self:	Tuple,   a_:	List[str],   a_:	Dict=None	):




   '''simple docstring'''



   if schedule_timesteps is None:
     _snake_case							:	List[Any]			=						self.timesteps

   _snake_case							:	Any			=						(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:
     _snake_case							:	List[Any]			=						1 if len(a_	) > 1 else 0
   else:
     _snake_case							:	int			=						timestep.cpu().item() if torch.is_tensor(a_	) else timestep
     _snake_case							:	Tuple			=						self._index_counter[timestep_int]

   return indices[pos].item()


 @property
 def 				UpperCamelCase_     (       self:	List[str]	):




   '''simple docstring'''



   if self.config.timestep_spacing in ["linspace", "trailing"]:
     return self.sigmas.max()

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


 def 				UpperCamelCase_     (       self:	Optional[Any],   a_:	torch.FloatTensor,   a_:	Union[float, torch.FloatTensor],   ):




   '''simple docstring'''



   _snake_case							:	Union[str, Any]			=						self.index_for_timestep(a_	)

   if self.state_in_first_order:
     _snake_case							:	List[str]			=						self.sigmas[step_index]
   else:
     _snake_case							:	Any			=						self.sigmas_interpol[step_index]

   _snake_case							:	List[str]			=						sample / ((sigma**2 + 1) ** 0.5)
   return sample


 def 				UpperCamelCase_     (       self:	List[str],   a_:	int,   a_:	Union[str, torch.device] = None,   a_:	Optional[int] = None,   ):




   '''simple docstring'''



   _snake_case							:	Union[str, Any]			=						num_inference_steps

   _snake_case							:	Optional[int]			=						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":
     _snake_case							:	Optional[int]			=						np.linspace(0,   num_train_timesteps - 1,   a_,   dtype=a_	)[::-1].copy()
   elif self.config.timestep_spacing == "leading":
     _snake_case							:	int			=						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
     _snake_case							:	int			=						(np.arange(0,   a_	) * step_ratio).round()[::-1].copy().astype(a_	)
     timesteps += self.config.steps_offset
   elif self.config.timestep_spacing == "trailing":
     _snake_case							:	Optional[int]			=						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
     _snake_case							:	Tuple			=						(np.arange(a_,   0,   -step_ratio	)).round().copy().astype(a_	)
     timesteps -= 1
   else:
     raise ValueError(
         f"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."	)

   _snake_case							:	Dict			=						np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5	)
   _snake_case							:	Dict			=						torch.from_numpy(np.log(a_	)	).to(a_	)

   _snake_case							:	int			=						np.interp(a_,   np.arange(0,   len(a_	)	),   a_	)
   _snake_case							:	Tuple			=						np.concatenate([sigmas, [0.0]]	).astype(np.floataa	)
   _snake_case							:	str			=						torch.from_numpy(a_	).to(device=a_	)

   # interpolate sigmas
   _snake_case							:	int			=						sigmas.log().lerp(sigmas.roll(1	).log(),   0.5	).exp()

   _snake_case							:	Optional[int]			=						torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2	), sigmas[-1:]]	)
   _snake_case							:	Optional[Any]			=						torch.cat(
       [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2	), sigmas_interpol[-1:]]	)

   if str(a_	).startswith("""mps"""	):
     # mps does not support float64
     _snake_case							:	Dict			=						torch.from_numpy(a_	).to(a_,   dtype=torch.floataa	)
   else:
     _snake_case							:	Optional[Any]			=						torch.from_numpy(a_	).to(a_	)

   # interpolate timesteps
   _snake_case							:	Tuple			=						self.sigma_to_t(a_	).to(a_,   dtype=timesteps.dtype	)
   _snake_case							:	List[Any]			=						torch.stack((timesteps_interpol[1:-1, None], timesteps[1:, None]),   dim=-1	).flatten()

   _snake_case							:	List[str]			=						torch.cat([timesteps[:1], interleaved_timesteps]	)

   _snake_case							:	Optional[Any]			=						None

   # for exp beta schedules, such as the one for `pipeline_shap_e.py`
   # we need an index counter
   _snake_case							:	List[str]			=						defaultdict(a_	)


 def 				UpperCamelCase_     (       self:	Optional[Any],   a_:	Tuple	):




   '''simple docstring'''



   _snake_case							:	Tuple			=						sigma.log()

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

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

   _snake_case							:	List[str]			=						self.log_sigmas[low_idx]
   _snake_case							:	int			=						self.log_sigmas[high_idx]

   # interpolate sigmas
   _snake_case							:	Any			=						(low - log_sigma) / (low - high)
   _snake_case							:	Dict			=						w.clamp(0,   1	)

   # transform interpolation to time range
   _snake_case							:	Tuple			=						(1 - w) * low_idx + w * high_idx
   _snake_case							:	List[Any]			=						t.view(sigma.shape	)
   return t


 @property
 def 				UpperCamelCase_     (       self:	Any	):




   '''simple docstring'''



   return self.sample is None


 def 				UpperCamelCase_     (       self:	Any,   a_:	Union[torch.FloatTensor, np.ndarray],   a_:	Union[float, torch.FloatTensor],   a_:	Union[torch.FloatTensor, np.ndarray],   a_:	bool = True,   ):




   '''simple docstring'''



   _snake_case							:	Any			=						self.index_for_timestep(a_	)

   # advance index counter by 1
   _snake_case							:	List[str]			=						timestep.cpu().item() if torch.is_tensor(a_	) else timestep
   self._index_counter[timestep_int] += 1

   if self.state_in_first_order:
     _snake_case							:	Union[str, Any]			=						self.sigmas[step_index]
     _snake_case							:	Optional[Any]			=						self.sigmas_interpol[step_index + 1]
     _snake_case							:	Union[str, Any]			=						self.sigmas[step_index + 1]
   else:
     # 2nd order / KDPM2's method
     _snake_case							:	int			=						self.sigmas[step_index - 1]
     _snake_case							:	Any			=						self.sigmas_interpol[step_index]
     _snake_case							:	Tuple			=						self.sigmas[step_index]

   # 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
   _snake_case							:	Optional[int]			=						0
   _snake_case							:	List[str]			=						sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

   # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
   if self.config.prediction_type == "epsilon":
     _snake_case							:	str			=						sigma_hat if self.state_in_first_order else sigma_interpol
     _snake_case							:	Optional[Any]			=						sample - sigma_input * model_output
   elif self.config.prediction_type == "v_prediction":
     _snake_case							:	Dict			=						sigma_hat if self.state_in_first_order else sigma_interpol
     _snake_case							:	Optional[Any]			=						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
     _snake_case							:	int			=						(sample - pred_original_sample) / sigma_hat
     # 3. delta timestep
     _snake_case							:	Tuple			=						sigma_interpol - sigma_hat

     # store for 2nd order step
     _snake_case							:	str			=						sample
   else:
     # DPM-Solver-2
     # 2. Convert to an ODE derivative for 2nd order
     _snake_case							:	Optional[Any]			=						(sample - pred_original_sample) / sigma_interpol

     # 3. delta timestep
     _snake_case							:	Any			=						sigma_next - sigma_hat

     _snake_case							:	Optional[int]			=						self.sample
     _snake_case							:	Optional[Any]			=						None

   _snake_case							:	Union[str, Any]			=						sample + derivative * dt

   if not return_dict:
     return (prev_sample,)

   return SchedulerOutput(prev_sample=a_	)


 def 				UpperCamelCase_     (       self:	Optional[Any],   a_:	torch.FloatTensor,   a_:	torch.FloatTensor,   a_:	torch.FloatTensor,   ):




   '''simple docstring'''



   _snake_case							:	Union[str, Any]			=						self.sigmas.to(device=original_samples.device,   dtype=original_samples.dtype	)
   if original_samples.device.type == "mps" and torch.is_floating_point(a_	):
     # mps does not support float64
     _snake_case							:	Dict			=						self.timesteps.to(original_samples.device,   dtype=torch.floataa	)
     _snake_case							:	List[Any]			=						timesteps.to(original_samples.device,   dtype=torch.floataa	)
   else:
     _snake_case							:	Optional[Any]			=						self.timesteps.to(original_samples.device	)
     _snake_case							:	Any			=						timesteps.to(original_samples.device	)

   _snake_case							:	List[str]			=						[self.index_for_timestep(a_,   a_	) for t in timesteps]

   _snake_case							:	Tuple			=						sigmas[step_indices].flatten()
   while len(sigma.shape	) < len(original_samples.shape	):
     _snake_case							:	List[Any]			=						sigma.unsqueeze(-1	)

   _snake_case							:	Any			=						original_samples + noise * sigma
   return noisy_samples


 def __len__(       self:	Union[str, Any]	):




   '''simple docstring'''



   return self.config.num_train_timesteps








"""simple docstring"""


import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple

from transformers import AddedToken, BatchEncoding, ByTaTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
     A_      			=       '''pt'''
elif is_tf_available():
     A_      			=       '''tf'''
else:
     A_      			=       '''jax'''






class       lowercase(  __a ,    unittest.TestCase				):







 '''simple docstring'''





 lowercase__				      =      ByTaTokenizer
 lowercase__				      =      False


 def 				UpperCamelCase_     (       self:	List[str]	):




   '''simple docstring'''



   super().setUp()
   _snake_case							:	List[str]			=						ByTaTokenizer()
   tokenizer.save_pretrained(self.tmpdirname	)


 @cached_property
 def 				UpperCamelCase_     (       self:	List[str]	):




   '''simple docstring'''



   return ByTaTokenizer.from_pretrained("""google/byt5-small"""	)


 def 				UpperCamelCase_     (       self:	List[Any],   **a_:	int	):




   '''simple docstring'''



   return self.tokenizer_class.from_pretrained(self.tmpdirname,   **a_	)


 def 				UpperCamelCase_     (       self:	Optional[Any],   a_:	Optional[Any],   a_:	List[Any]=False,   a_:	int=20,   a_:	Union[str, Any]=5	):




   '''simple docstring'''



   _snake_case							:	List[Any]			=						[]
   for i in range(len(a_	)	):
     try:
       _snake_case							:	Optional[Any]			=						tokenizer.decode([i],   clean_up_tokenization_spaces=a_	)
     except UnicodeDecodeError:
       pass
     toks.append((i, tok)	)

   _snake_case							:	str			=						list(filter(lambda a_	: re.match(r"""^[ a-zA-Z]+$""",   t[1]	),   a_	)	)
   _snake_case							:	List[Any]			=						list(filter(lambda a_	: [t[0]] == tokenizer.encode(t[1],   add_special_tokens=a_	),   a_	)	)
   if max_length is not None and len(a_	) > max_length:
     _snake_case							:	Tuple			=						toks[:max_length]
   if min_length is not None and len(a_	) < min_length and len(a_	) > 0:
     while len(a_	) < min_length:
       _snake_case							:	List[str]			=						toks + toks
        # toks_str = [t[1] for t in toks]
   _snake_case							:	Tuple			=						[t[0] for t in toks]

   # Ensure consistency
   _snake_case							:	Optional[Any]			=						tokenizer.decode(a_,   clean_up_tokenization_spaces=a_	)
   if " " not in output_txt and len(a_	) > 1:
     _snake_case							:	Dict			=						(
         tokenizer.decode([toks_ids[0]],   clean_up_tokenization_spaces=a_	)
         + """ """
         + tokenizer.decode(toks_ids[1:],   clean_up_tokenization_spaces=a_	)
     )
   if with_prefix_space:
     _snake_case							:	Union[str, Any]			=						""" """ + output_txt
   _snake_case							:	Any			=						tokenizer.encode(a_,   add_special_tokens=a_	)
   return output_txt, output_ids


 def 				UpperCamelCase_     (       self:	int	):




   '''simple docstring'''



   _snake_case							:	Optional[int]			=						self.ta_base_tokenizer
   _snake_case							:	Optional[Any]			=						tokenizer(["""hi</s>""", """I went to the gym</s>""", """</s>"""]	)
   _snake_case							:	int			=						tokenizer(["""hi""", """I went to the gym""", """"""]	)
   self.assertListEqual(batch_with_eos_added["""input_ids"""],   batch_without_eos_added["""input_ids"""]	)


 def 				UpperCamelCase_     (       self:	Optional[int]	):




   '''simple docstring'''



   _snake_case							:	List[str]			=						self.ta_base_tokenizer
   _snake_case							:	Tuple			=						"""Unicode €."""
   _snake_case							:	List[Any]			=						tokenizer(a_	)
   _snake_case							:	Tuple			=						[88, 113, 108, 102, 114, 103, 104, 35, 229, 133, 175, 49, 1]
   self.assertEqual(encoded["""input_ids"""],   a_	)

   # decoding
   _snake_case							:	Tuple			=						tokenizer.decode(a_	)
   self.assertEqual(a_,   """Unicode €.</s>"""	)

   _snake_case							:	Tuple			=						tokenizer("""e è é ê ë"""	)
   _snake_case							:	List[Any]			=						[104, 35, 198, 171, 35, 198, 172, 35, 198, 173, 35, 198, 174, 1]
   self.assertEqual(encoded["""input_ids"""],   a_	)
   # decoding
   _snake_case							:	int			=						tokenizer.decode(a_	)
   self.assertEqual(a_,   """e è é ê ë</s>"""	)

   # encode/decode, but with `encode` instead of `__call__`
   self.assertEqual(tokenizer.decode(tokenizer.encode("""e è é ê ë"""	)	),   """e è é ê ë</s>"""	)


 def 				UpperCamelCase_     (       self:	Optional[Any]	):




   '''simple docstring'''



   _snake_case							:	Dict			=						self.ta_base_tokenizer
   _snake_case							:	List[Any]			=						["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
   # fmt: off
   _snake_case							:	Union[str, Any]			=						[68, 35, 111, 114, 113, 106, 35, 115, 100, 117, 100, 106, 117, 100, 115, 107, 35, 105, 114, 117, 35, 118, 120, 112, 112, 100, 117, 108, 125, 100, 119, 108, 114, 113, 49, 1, 0]
   # fmt: on
   _snake_case							:	int			=						tokenizer(a_,   padding=a_,   return_tensors=a_	)
   self.assertIsInstance(a_,   a_	)

   if FRAMEWORK != "jax":
     _snake_case							:	List[str]			=						list(batch.input_ids.numpy()[0]	)
   else:
     _snake_case							:	Optional[int]			=						list(batch.input_ids.tolist()[0]	)

   self.assertListEqual(a_,   a_	)

   self.assertEqual((2, 37),   batch.input_ids.shape	)
   self.assertEqual((2, 37),   batch.attention_mask.shape	)


 def 				UpperCamelCase_     (       self:	List[Any]	):




   '''simple docstring'''



   _snake_case							:	List[Any]			=						self.ta_base_tokenizer
   _snake_case							:	Optional[int]			=						["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
   _snake_case							:	Tuple			=						tokenizer(a_,   padding=a_,   return_tensors=a_	)
   # check if input_ids are returned and no decoder_input_ids
   self.assertIn("""input_ids""",   a_	)
   self.assertIn("""attention_mask""",   a_	)
   self.assertNotIn("""decoder_input_ids""",   a_	)
   self.assertNotIn("""decoder_attention_mask""",   a_	)


 def 				UpperCamelCase_     (       self:	Tuple	):




   '''simple docstring'''



   _snake_case							:	Union[str, Any]			=						self.ta_base_tokenizer
   _snake_case							:	Dict			=						[
       """Summary of the text.""",
       """Another summary.""",
   ]
   _snake_case							:	Optional[int]			=						tokenizer(
       text_target=a_,   max_length=32,   padding="""max_length""",   truncation=a_,   return_tensors=a_	)
   self.assertEqual(32,   targets["""input_ids"""].shape[1]	)


 def 				UpperCamelCase_     (       self:	Union[str, Any]	):




   '''simple docstring'''



   _snake_case							:	int			=						self.ta_base_tokenizer
   _snake_case							:	Optional[int]			=						["""A long paragraph for summarization. </s>"""]
   _snake_case							:	Dict			=						["""Summary of the text. </s>"""]
   # fmt: off
   _snake_case							:	Optional[int]			=						[68, 35, 111, 114, 113, 106, 35, 115, 100, 117, 100, 106, 117, 100, 115, 107, 35, 105, 114, 117, 35, 118, 120, 112, 112, 100, 117, 108, 125, 100, 119, 108, 114, 113, 49, 35, 1]
   _snake_case							:	Optional[Any]			=						[86, 120, 112, 112, 100, 117, 124, 35, 114, 105, 35, 119, 107, 104, 35, 119, 104, 123, 119, 49, 35, 1]
   # fmt: on

   _snake_case							:	Optional[Any]			=						tokenizer(a_,   text_target=a_	)

   self.assertEqual(a_,   batch["""input_ids"""][0]	)
   self.assertEqual(a_,   batch["""labels"""][0]	)


 def 				UpperCamelCase_     (       self:	Optional[Any]	):




   '''simple docstring'''



   _snake_case							:	List[str]			=						self.get_tokenizers()
   for tokenizer in tokenizers:
     with self.subTest(f"{tokenizer.__class__.__name__}"	):
       self.assertNotEqual(tokenizer.model_max_length,   42	)

        # Now let's start the test
   _snake_case							:	str			=						self.get_tokenizers()
   for tokenizer in tokenizers:
     with self.subTest(f"{tokenizer.__class__.__name__}"	):
       # Isolate this from the other tests because we save additional tokens/etc
       _snake_case							:	List[str]			=						tempfile.mkdtemp()

       _snake_case							:	List[str]			=						""" He is very happy, UNwant\u00E9d,running"""
       _snake_case							:	Any			=						tokenizer.encode(a_,   add_special_tokens=a_	)
       tokenizer.save_pretrained(a_	)

       _snake_case							:	List[Any]			=						tokenizer.__class__.from_pretrained(a_	)
       _snake_case							:	Dict			=						after_tokenizer.encode(a_,   add_special_tokens=a_	)
       self.assertListEqual(a_,   a_	)

       shutil.rmtree(a_	)

   _snake_case							:	Tuple			=						self.get_tokenizers(model_max_length=42	)
   for tokenizer in tokenizers:
     with self.subTest(f"{tokenizer.__class__.__name__}"	):
       # Isolate this from the other tests because we save additional tokens/etc
       _snake_case							:	Union[str, Any]			=						tempfile.mkdtemp()

       _snake_case							:	List[Any]			=						""" He is very happy, UNwant\u00E9d,running"""
       tokenizer.add_tokens(["""bim""", """bambam"""]	)
       _snake_case							:	Optional[Any]			=						tokenizer.additional_special_tokens
       additional_special_tokens.append("""new_additional_special_token"""	)
       tokenizer.add_special_tokens({"""additional_special_tokens""": additional_special_tokens}	)
       _snake_case							:	Any			=						tokenizer.encode(a_,   add_special_tokens=a_	)
       tokenizer.save_pretrained(a_	)

       _snake_case							:	Optional[Any]			=						tokenizer.__class__.from_pretrained(a_	)
       _snake_case							:	str			=						after_tokenizer.encode(a_,   add_special_tokens=a_	)
       self.assertListEqual(a_,   a_	)
       self.assertIn("""new_additional_special_token""",   after_tokenizer.additional_special_tokens	)
       self.assertEqual(after_tokenizer.model_max_length,   42	)

       _snake_case							:	Optional[int]			=						tokenizer.__class__.from_pretrained(a_,   model_max_length=43	)
       self.assertEqual(tokenizer.model_max_length,   43	)

       shutil.rmtree(a_	)


 def 				UpperCamelCase_     (       self:	Optional[int]	):




   '''simple docstring'''



   _snake_case							:	Optional[Any]			=						[]
   if self.test_slow_tokenizer:
     tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())	)

   if self.test_rust_tokenizer:
     tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())	)

   for tokenizer_class, tokenizer_utils in tokenizer_list:
     with tempfile.TemporaryDirectory() as tmp_dir:
       tokenizer_utils.save_pretrained(a_	)

       with open(os.path.join(a_,   """special_tokens_map.json"""	),   encoding="""utf-8"""	) as json_file:
         _snake_case							:	Union[str, Any]			=						json.load(a_	)

       with open(os.path.join(a_,   """tokenizer_config.json"""	),   encoding="""utf-8"""	) as json_file:
         _snake_case							:	List[Any]			=						json.load(a_	)

       _snake_case							:	int			=						[f"<extra_id_{i}>" for i in range(125	)]

       _snake_case							:	Optional[int]			=						added_tokens_extra_ids + [
           """an_additional_special_token"""
       ]
       _snake_case							:	Dict			=						added_tokens_extra_ids + [
           """an_additional_special_token"""
       ]

       with open(os.path.join(a_,   """special_tokens_map.json"""	),   """w""",   encoding="""utf-8"""	) as outfile:
         json.dump(a_,   a_	)
       with open(os.path.join(a_,   """tokenizer_config.json"""	),   """w""",   encoding="""utf-8"""	) as outfile:
         json.dump(a_,   a_	)

       # the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
       # into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
       # "special_tokens_map.json" files
       _snake_case							:	Optional[int]			=						tokenizer_class.from_pretrained(
           a_,   )
       self.assertIn(
           """an_additional_special_token""",   tokenizer_without_change_in_init.additional_special_tokens	)
       # self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
       self.assertEqual(
           ["""an_additional_special_token"""],   tokenizer_without_change_in_init.convert_ids_to_tokens(
               tokenizer_without_change_in_init.convert_tokens_to_ids(["""an_additional_special_token"""]	)	),   )

       # Now we test that we can change the value of additional_special_tokens in the from_pretrained
       _snake_case							:	Union[str, Any]			=						added_tokens_extra_ids + [AddedToken("""a_new_additional_special_token""",   lstrip=a_	)]
       _snake_case							:	List[Any]			=						tokenizer_class.from_pretrained(
           a_,   additional_special_tokens=a_,   )

       self.assertIn("""a_new_additional_special_token""",   tokenizer.additional_special_tokens	)
       self.assertEqual(
           ["""a_new_additional_special_token"""],   tokenizer.convert_ids_to_tokens(
               tokenizer.convert_tokens_to_ids(["""a_new_additional_special_token"""]	)	),   )


 def 				UpperCamelCase_     (       self:	int	):




   '''simple docstring'''



   _snake_case							:	List[Any]			=						[]
   if self.test_slow_tokenizer:
     tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())	)

   if self.test_rust_tokenizer:
     tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())	)

   for tokenizer_class, tokenizer_utils in tokenizer_list:
     with tempfile.TemporaryDirectory() as tmp_dir:
       tokenizer_utils.save_pretrained(a_	)

       _snake_case							:	Optional[Any]			=						tokenizer_class.from_pretrained(a_	)

       self.assertTrue(tokenizer.decode([255]	) == """"""	)


 def 				UpperCamelCase_     (       self:	Optional[Any]	):




   '''simple docstring'''



   pass


 def 				UpperCamelCase_     (       self:	int	):




   '''simple docstring'''



   pass


 def 				UpperCamelCase_     (       self:	int	):




   '''simple docstring'''



   pass


 def 				UpperCamelCase_     (       self:	Any	):




   '''simple docstring'''



   pass


 def 				UpperCamelCase_     (       self:	Union[str, Any]	):




   '''simple docstring'''



   _snake_case							:	Optional[Any]			=						self.get_tokenizers(fast=a_,   do_lower_case=a_	)
   for tokenizer in tokenizers:
     with self.subTest(f"{tokenizer.__class__.__name__}"	):
       _snake_case							:	Dict			=						["""t""", """h""", """i""", """s""", """ """, """i""", """s""", """ """, """a""", """ """, """t""", """e""", """x""", """t""", """</s>"""]
       _snake_case							:	List[Any]			=						tokenizer.convert_tokens_to_string(a_	)

       self.assertIsInstance(a_,   a_	)


 def 				UpperCamelCase_     (       self:	Dict	):




   '''simple docstring'''



   _snake_case							:	str			=						self.get_tokenizers()
   for tokenizer in tokenizers:
     with self.subTest(f"{tokenizer.__class__.__name__}"	):
       _snake_case							:	Optional[int]			=						[
           """bos_token""",
           """eos_token""",
           """unk_token""",
           """sep_token""",
           """pad_token""",
           """cls_token""",
           """mask_token""",
       ]

       _snake_case							:	Any			=						0
       _snake_case							:	Union[str, Any]			=						tokenizer.convert_ids_to_tokens(
           a_,   skip_special_tokens=a_	)

       for attr in attributes_list:
         setattr(a_,   attr + """_id""",   a_	)
         self.assertEqual(getattr(a_,   a_	),   a_	)
         self.assertEqual(getattr(a_,   attr + """_id"""	),   a_	)

         setattr(a_,   attr + """_id""",   a_	)
         self.assertEqual(getattr(a_,   a_	),   a_	)
         self.assertEqual(getattr(a_,   attr + """_id"""	),   a_	)

       setattr(a_,   """additional_special_tokens_ids""",   []	)
       self.assertListEqual(getattr(a_,   """additional_special_tokens"""	),   []	)
       self.assertListEqual(getattr(a_,   """additional_special_tokens_ids"""	),   []	)

       setattr(a_,   """additional_special_tokens_ids""",   [token_id_to_test_setters]	)
       self.assertListEqual(getattr(a_,   """additional_special_tokens"""	),   [token_to_test_setters]	)
       self.assertListEqual(getattr(a_,   """additional_special_tokens_ids"""	),   [token_id_to_test_setters]	)


