

'''simple docstring'''





import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest







class    UpperCamelCase_			(  snake_case__			):


  """simple docstring"""
  SCREAMING_SNAKE_CASE__				: List[str]										=      (VQDiffusionScheduler,)



  def      _UpperCamelCase    (	self    :				Tuple     ,  **A_    :				str	)					-> Optional[Any]:
      SCREAMING_SNAKE_CASE__					     =						{
          "num_vec_classes": 4097,
          "num_train_timesteps": 100,
      }

      config.update(**__snake_case	)
      return config



  def      _UpperCamelCase    (	self    :				List[str]     ,  A_    :				Tuple	)					-> Any:
      SCREAMING_SNAKE_CASE__					     =						4
      SCREAMING_SNAKE_CASE__					     =						8
      SCREAMING_SNAKE_CASE__					     =						8

      SCREAMING_SNAKE_CASE__					     =						torch.randint(0     ,  __snake_case     ,  (batch_size, height * width)	)

      return sample



  @property
  def      _UpperCamelCase    (	self    :				Dict	)					-> Optional[Any]:
      assert False



  def      _UpperCamelCase    (	self    :				str     ,  A_    :				Optional[int]	)					-> int:
      def model(A_    :				str     ,  A_    :				Any     ,  *A_    :				Any	):
          SCREAMING_SNAKE_CASE__     ,		SCREAMING_SNAKE_CASE__					     =						sample.shape
          SCREAMING_SNAKE_CASE__					     =						torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels)	)
          SCREAMING_SNAKE_CASE__					     =						F.log_softmax(logits.double()     ,  dim=1	).float()
          return return_value

      return model



  def      _UpperCamelCase    (	self    :				List[str]	)					-> Union[str, Any]:
      for timesteps in [2, 5, 100, 1000]:
          self.check_over_configs(num_train_timesteps=__snake_case	)



  def      _UpperCamelCase    (	self    :				Union[str, Any]	)					-> Dict:
      for num_vec_classes in [5, 100, 1000, 4000]:
          self.check_over_configs(num_vec_classes=__snake_case	)



  def      _UpperCamelCase    (	self    :				Tuple	)					-> Dict:
      for t in [0, 50, 99]:
          self.check_over_forward(time_step=__snake_case	)







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





import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_splinter import SplinterTokenizer


A__        =			logging.get_logger(__name__)

A__        =			{"vocab_file": "vocab.txt"}

A__        =			{
    "vocab_file": {
        "tau/splinter-base": "https://huggingface.co/tau/splinter-base/resolve/main/vocab.txt",
        "tau/splinter-base-qass": "https://huggingface.co/tau/splinter-base-qass/resolve/main/vocab.txt",
        "tau/splinter-large": "https://huggingface.co/tau/splinter-large/resolve/main/vocab.txt",
        "tau/splinter-large-qass": "https://huggingface.co/tau/splinter-large-qass/resolve/main/vocab.txt",
    }
}

A__        =			{
    "tau/splinter-base": 512,
    "tau/splinter-base-qass": 512,
    "tau/splinter-large": 512,
    "tau/splinter-large-qass": 512,
}

A__        =			{
    "tau/splinter-base": {"do_lower_case": False},
    "tau/splinter-base-qass": {"do_lower_case": False},
    "tau/splinter-large": {"do_lower_case": False},
    "tau/splinter-large-qass": {"do_lower_case": False},
}







class    UpperCamelCase_			(  _snake_case			):


  """simple docstring"""
  SCREAMING_SNAKE_CASE__				: Dict										=      VOCAB_FILES_NAMES
  SCREAMING_SNAKE_CASE__				: Optional[int]										=      PRETRAINED_VOCAB_FILES_MAP
  SCREAMING_SNAKE_CASE__				: List[Any]										=      PRETRAINED_INIT_CONFIGURATION
  SCREAMING_SNAKE_CASE__				: str										=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  SCREAMING_SNAKE_CASE__				: Optional[Any]										=      SplinterTokenizer



  def __init__(	self    :				Tuple     ,  A_    :				Optional[Any]=None     ,  A_    :				Optional[int]=None     ,  A_    :				Optional[Any]=True     ,  A_    :				str="[UNK]"     ,  A_    :				Optional[Any]="[SEP]"     ,  A_    :				str="[PAD]"     ,  A_    :				List[Any]="[CLS]"     ,  A_    :				Union[str, Any]="[MASK]"     ,  A_    :				Any="[QUESTION]"     ,  A_    :				List[Any]=True     ,  A_    :				Union[str, Any]=None     ,  **A_    :				Optional[Any]     ,  )					-> Optional[Any]:
      super().__init__(
          A_     ,  tokenizer_file=A_     ,  do_lower_case=A_     ,  unk_token=A_     ,  sep_token=A_     ,  pad_token=A_     ,  cls_token=A_     ,  mask_token=A_     ,  tokenize_chinese_chars=A_     ,  strip_accents=A_     ,  additional_special_tokens=(question_token,)     ,  **A_     ,  )

      SCREAMING_SNAKE_CASE__					     =						json.loads(self.backend_tokenizer.normalizer.__getstate__()	)
      if (
          pre_tok_state.get("lowercase"     ,  A_	) != do_lower_case
          or pre_tok_state.get("strip_accents"     ,  A_	) != strip_accents
      ):
          SCREAMING_SNAKE_CASE__					     =						getattr(A_     ,  pre_tok_state.pop("type"	)	)
          SCREAMING_SNAKE_CASE__					     =						do_lower_case
          SCREAMING_SNAKE_CASE__					     =						strip_accents
          SCREAMING_SNAKE_CASE__					     =						pre_tok_class(**A_	)

      SCREAMING_SNAKE_CASE__					     =						do_lower_case



  @property
  def      _UpperCamelCase    (	self    :				List[Any]	)					-> Optional[Any]:
      return self.convert_tokens_to_ids(self.question_token	)



  def      _UpperCamelCase    (	self    :				List[str]     ,  A_    :				List[int]     ,  A_    :				Optional[List[int]] = None	)					-> List[int]:
      if token_ids_a is None:
          return [self.cls_token_id] + token_ids_a + [self.sep_token_id]

      SCREAMING_SNAKE_CASE__					     =						[self.cls_token_id]
      SCREAMING_SNAKE_CASE__					     =						[self.sep_token_id]
      SCREAMING_SNAKE_CASE__					     =						[self.question_token_id] + [self.convert_tokens_to_ids("."	)]
      if self.padding_side == "right":
          # Input is question-then-context
          return cls + token_ids_a + question_suffix + sep + token_ids_a + sep
      else:
          # Input is context-then-question
          return cls + token_ids_a + sep + token_ids_a + question_suffix + sep



  def      _UpperCamelCase    (	self    :				Optional[int]     ,  A_    :				List[int]     ,  A_    :				Optional[List[int]] = None	)					-> List[int]:
      SCREAMING_SNAKE_CASE__					     =						[self.sep_token_id]
      SCREAMING_SNAKE_CASE__					     =						[self.cls_token_id]
      SCREAMING_SNAKE_CASE__					     =						[self.question_token_id] + [self.convert_tokens_to_ids("."	)]
      if token_ids_a is None:
          return len(cls + token_ids_a + sep	) * [0]

      if self.padding_side == "right":
          # Input is question-then-context
          return len(cls + token_ids_a + question_suffix + sep	) * [0] + len(token_ids_a + sep	) * [1]
      else:
          # Input is context-then-question
          return len(cls + token_ids_a + sep	) * [0] + len(token_ids_a + question_suffix + sep	) * [1]







  def      _UpperCamelCase    (	self    :				Optional[int]     ,  A_    :				str     ,  A_    :				Optional[str] = None	)					-> Tuple[str]:
      SCREAMING_SNAKE_CASE__					     =						self._tokenizer.model.save(A_     ,  name=A_	)
      return tuple(A_	)


