

import os
import re
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


UpperCamelCase__					: Optional[Any]								=      logging.get_logger(__name__)
UpperCamelCase__					: Union[str, Any]								=      {"vocab_file": "spiece.model"}

UpperCamelCase__					: Union[str, Any]								=      {
    "vocab_file": {
        "google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/spiece.model",
        "google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/spiece.model",
    },
}

UpperCamelCase__					: Union[str, Any]								=      {
    "google/fnet-base": 512,
    "google/fnet-large": 512,
}

UpperCamelCase__					: List[Any]								=      "▁"



class  __snake_case					(      lowerCAmelCase__    ):
  __lowerCAmelCase       :		List[Any]  	=					VOCAB_FILES_NAMES
  __lowerCAmelCase       :		str  	=					PRETRAINED_VOCAB_FILES_MAP
  __lowerCAmelCase       :		List[str]  	=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  __lowerCAmelCase       :		Union[str, Any]  	=					['input_ids', 'token_type_ids']

  def __init__(   self  ,						_A  ,						_A=False  ,						_A=True  ,						_A=True  ,						_A="<unk>"  ,						_A="[SEP]"  ,						_A="<pad>"  ,						_A="[CLS]"  ,						_A="[MASK]"  ,						_A = None  ,						**_A  ,						):
      # Mask token behave like a normal word, i.e. include the space before it and
      # is included in the raw text, there should be a match in a non-normalized sentence.
      SCREAMING_SNAKE_CASE_													=  (
          AddedToken(_A  ,						lstrip=_A  ,						rstrip=_A  ,						normalized=_A)
          if isinstance(_A  ,						_A)
          else mask_token
      )

      SCREAMING_SNAKE_CASE_													=  {} if sp_model_kwargs is None else sp_model_kwargs

      super().__init__(
          do_lower_case=_A  ,						remove_space=_A  ,						keep_accents=_A  ,						unk_token=_A  ,						sep_token=_A  ,						pad_token=_A  ,						cls_token=_A  ,						mask_token=_A  ,						sp_model_kwargs=self.sp_model_kwargs  ,						**_A  ,						)

      SCREAMING_SNAKE_CASE_													=  do_lower_case
      SCREAMING_SNAKE_CASE_													=  remove_space
      SCREAMING_SNAKE_CASE_													=  keep_accents
      SCREAMING_SNAKE_CASE_													=  vocab_file

      SCREAMING_SNAKE_CASE_													=  spm.SentencePieceProcessor(**self.sp_model_kwargs)
      self.sp_model.Load(_A)

  @property
  def   lowerCAmelCase__       (   self):
      return len(self.sp_model)

  def   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_													=  {self.convert_ids_to_tokens(_A): i for i in range(self.vocab_size)}
      vocab.update(self.added_tokens_encoder)
      return vocab

  def __getstate__(   self):
      SCREAMING_SNAKE_CASE_													=  self.__dict__.copy()
      SCREAMING_SNAKE_CASE_													=  None
      return state

  def __setstate__(   self  ,						_A):
      SCREAMING_SNAKE_CASE_													=  d

      # for backward compatibility
      if not hasattr(self  ,						'sp_model_kwargs'):
          SCREAMING_SNAKE_CASE_													=  {}

      SCREAMING_SNAKE_CASE_													=  spm.SentencePieceProcessor(**self.sp_model_kwargs)
      self.sp_model.Load(self.vocab_file)

  def   lowerCAmelCase__       (   self  ,						_A):
      if self.remove_space:
          SCREAMING_SNAKE_CASE_													=  ' '.join(inputs.strip().split())
      else:
          SCREAMING_SNAKE_CASE_													=  inputs
      SCREAMING_SNAKE_CASE_													=  outputs.replace('``'  ,						'"').replace('\'\''  ,						'"')

      if not self.keep_accents:
          SCREAMING_SNAKE_CASE_													=  unicodedata.normalize('NFKD'  ,						_A)
          SCREAMING_SNAKE_CASE_													=  ''.join([c for c in outputs if not unicodedata.combining(_A)])
      if self.do_lower_case:
          SCREAMING_SNAKE_CASE_													=  outputs.lower()

      return outputs

  def   lowerCAmelCase__       (   self  ,						_A):
      SCREAMING_SNAKE_CASE_													=  self.preprocess_text(_A)
      SCREAMING_SNAKE_CASE_													=  self.sp_model.encode(_A  ,						out_type=_A)
      SCREAMING_SNAKE_CASE_													=  []
      for piece in pieces:
          if len(_A) > 1 and piece[-1] == str(',') and piece[-2].isdigit():
              SCREAMING_SNAKE_CASE_													=  self.sp_model.EncodeAsPieces(piece[:-1].replace(_A  ,						''))
              if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
                  if len(cur_pieces[0]) == 1:
                      SCREAMING_SNAKE_CASE_													=  cur_pieces[1:]
                  else:
                      SCREAMING_SNAKE_CASE_													=  cur_pieces[0][1:]
              cur_pieces.append(piece[-1])
              new_pieces.extend(_A)
          else:
              new_pieces.append(_A)

      return new_pieces

  def   lowerCAmelCase__       (   self  ,						_A):
      return self.sp_model.PieceToId(_A)

  def   lowerCAmelCase__       (   self  ,						_A):
      return self.sp_model.IdToPiece(_A)

  def   lowerCAmelCase__       (   self  ,						_A):
      SCREAMING_SNAKE_CASE_													=  []
      SCREAMING_SNAKE_CASE_													=  ''
      SCREAMING_SNAKE_CASE_													=  False
      for token in tokens:
          # make sure that special tokens are not decoded using sentencepiece model
          if token in self.all_special_tokens:
              if not prev_is_special:
                  out_string += " "
              out_string += self.sp_model.decode(_A) + token
              SCREAMING_SNAKE_CASE_													=  True
              SCREAMING_SNAKE_CASE_													=  []
          else:
              current_sub_tokens.append(_A)
              SCREAMING_SNAKE_CASE_													=  False
      out_string += self.sp_model.decode(_A)
      return out_string.strip()

  def   lowerCAmelCase__       (   self  ,						_A  ,						_A = False  ,						_A = None  ,						_A = True  ,						**_A  ,						):
      SCREAMING_SNAKE_CASE_													=  kwargs.pop('use_source_tokenizer'  ,						_A)

      SCREAMING_SNAKE_CASE_													=  self.convert_ids_to_tokens(_A  ,						skip_special_tokens=_A)

      # To avoid mixing byte-level and unicode for byte-level BPT
      # we need to build string separately for added tokens and byte-level tokens
      # cf. https://github.com/huggingface/transformers/issues/1133
      SCREAMING_SNAKE_CASE_													=  []
      SCREAMING_SNAKE_CASE_													=  []
      for token in filtered_tokens:
          if skip_special_tokens and token in self.all_special_ids:
              continue
          if token in self.added_tokens_encoder:
              if current_sub_text:
                  sub_texts.append(self.convert_tokens_to_string(_A))
                  SCREAMING_SNAKE_CASE_													=  []
              sub_texts.append(_A)
          else:
              current_sub_text.append(_A)
      if current_sub_text:
          sub_texts.append(self.convert_tokens_to_string(_A))

      # Mimic the behavior of the Rust tokenizer:
      # No space after <unk>
      if spaces_between_special_tokens:
          SCREAMING_SNAKE_CASE_													=  re.sub(r'(<unk>) '  ,						r'\1'  ,						' '.join(_A))
      else:
          SCREAMING_SNAKE_CASE_													=  ''.join(_A)

      SCREAMING_SNAKE_CASE_													=  (
          clean_up_tokenization_spaces
          if clean_up_tokenization_spaces is not None
          else self.clean_up_tokenization_spaces
      )
      if clean_up_tokenization_spaces:
          SCREAMING_SNAKE_CASE_													=  self.clean_up_tokenization(_A)
          return clean_text
      else:
          return text

  def   lowerCAmelCase__       (   self  ,						_A  ,						_A = None):
      SCREAMING_SNAKE_CASE_													=  [self.sep_token_id]
      SCREAMING_SNAKE_CASE_													=  [self.cls_token_id]
      if token_ids_a is None:
          return cls + token_ids_a + sep
      return cls + token_ids_a + sep + token_ids_a + sep

  def   lowerCAmelCase__       (   self  ,						_A  ,						_A = None  ,						_A = False):

      if already_has_special_tokens:
          return super().get_special_tokens_mask(
              token_ids_a=_A  ,						token_ids_a=_A  ,						already_has_special_tokens=_A)

      if token_ids_a is not None:
          return [1] + ([0] * len(_A)) + [1] + ([0] * len(_A)) + [1]
      return [1] + ([0] * len(_A)) + [1]

  def   lowerCAmelCase__       (   self  ,						_A  ,						_A = None):
      SCREAMING_SNAKE_CASE_													=  [self.sep_token_id]
      SCREAMING_SNAKE_CASE_													=  [self.cls_token_id]

      if token_ids_a is None:
          return len(cls + token_ids_a + sep) * [0]
      return len(cls + token_ids_a + sep) * [0] + len(token_ids_a + sep) * [1]


  def   lowerCAmelCase__       (   self  ,						_A  ,						_A = None):
      if not os.path.isdir(_A):
          logger.error(f"""Vocabulary path ({save_directory}) should be a directory""")
          return
      SCREAMING_SNAKE_CASE_													=  os.path.join(
          _A  ,						(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])

      if os.path.abspath(self.vocab_file) != os.path.abspath(_A) and os.path.isfile(self.vocab_file):
          copyfile(self.vocab_file  ,						_A)
      elif not os.path.isfile(self.vocab_file):
          with open(_A  ,						'wb') as fi:
              SCREAMING_SNAKE_CASE_													=  self.sp_model.serialized_model_proto()
              fi.write(_A)

      return (out_vocab_file,)





import torch

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class  __snake_case					(      lowerCAmelCase__    ):
  __lowerCAmelCase       :		Union[str, Any]  	=					(HeunDiscreteScheduler,)
  __lowerCAmelCase       :		Optional[int]  	=					10

  def   lowerCAmelCase__       (   self  ,						**_A):
      SCREAMING_SNAKE_CASE_													=  {
          'num_train_timesteps': 1100,
          'beta_start': 0.0_0_0_1,
          'beta_end': 0.0_2,
          'beta_schedule': 'linear',
      }

      config.update(**_A)
      return config

  def   lowerCAmelCase__       (   self):
      for timesteps in [10, 50, 100, 1000]:
          self.check_over_configs(num_train_timesteps=_A)

  def   lowerCAmelCase__       (   self):
      for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]  ,						[0.0_0_0_2, 0.0_0_2, 0.0_2]):
          self.check_over_configs(beta_start=_A  ,						beta_end=_A)

  def   lowerCAmelCase__       (   self):
      for schedule in ["linear", "scaled_linear", "exp"]:
          self.check_over_configs(beta_schedule=_A)

  def   lowerCAmelCase__       (   self):
      for clip_sample_range in [1.0, 2.0, 3.0]:
          self.check_over_configs(clip_sample_range=_A  ,						clip_sample=_A)

  def   lowerCAmelCase__       (   self):
      for prediction_type in ["epsilon", "v_prediction", "sample"]:
          self.check_over_configs(prediction_type=_A)

  def   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_													=  self.scheduler_classes[0]
      SCREAMING_SNAKE_CASE_													=  self.get_scheduler_config()
      SCREAMING_SNAKE_CASE_													=  scheduler_class(**_A)

      scheduler.set_timesteps(self.num_inference_steps)

      SCREAMING_SNAKE_CASE_													=  self.dummy_model()
      SCREAMING_SNAKE_CASE_													=  self.dummy_sample_deter * scheduler.init_noise_sigma
      SCREAMING_SNAKE_CASE_													=  sample.to(_A)

      for i, t in enumerate(scheduler.timesteps):
          SCREAMING_SNAKE_CASE_													=  scheduler.scale_model_input(_A  ,						_A)

          SCREAMING_SNAKE_CASE_													=  model(_A  ,						_A)

          SCREAMING_SNAKE_CASE_													=  scheduler.step(_A  ,						_A  ,						_A)
          SCREAMING_SNAKE_CASE_													=  output.prev_sample

      SCREAMING_SNAKE_CASE_													=  torch.sum(torch.abs(_A))
      SCREAMING_SNAKE_CASE_													=  torch.mean(torch.abs(_A))

      if torch_device in ["cpu", "mps"]:
          assert abs(result_sum.item() - 0.1_2_3_3) < 1E-2
          assert abs(result_mean.item() - 0.0_0_0_2) < 1E-3
      else:
          # CUDA
          assert abs(result_sum.item() - 0.1_2_3_3) < 1E-2
          assert abs(result_mean.item() - 0.0_0_0_2) < 1E-3

  def   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_													=  self.scheduler_classes[0]
      SCREAMING_SNAKE_CASE_													=  self.get_scheduler_config(prediction_type='v_prediction')
      SCREAMING_SNAKE_CASE_													=  scheduler_class(**_A)

      scheduler.set_timesteps(self.num_inference_steps)

      SCREAMING_SNAKE_CASE_													=  self.dummy_model()
      SCREAMING_SNAKE_CASE_													=  self.dummy_sample_deter * scheduler.init_noise_sigma
      SCREAMING_SNAKE_CASE_													=  sample.to(_A)

      for i, t in enumerate(scheduler.timesteps):
          SCREAMING_SNAKE_CASE_													=  scheduler.scale_model_input(_A  ,						_A)

          SCREAMING_SNAKE_CASE_													=  model(_A  ,						_A)

          SCREAMING_SNAKE_CASE_													=  scheduler.step(_A  ,						_A  ,						_A)
          SCREAMING_SNAKE_CASE_													=  output.prev_sample

      SCREAMING_SNAKE_CASE_													=  torch.sum(torch.abs(_A))
      SCREAMING_SNAKE_CASE_													=  torch.mean(torch.abs(_A))

      if torch_device in ["cpu", "mps"]:
          assert abs(result_sum.item() - 4.6_934E-07) < 1E-2
          assert abs(result_mean.item() - 6.1_112E-10) < 1E-3
      else:
          # CUDA
          assert abs(result_sum.item() - 4.693_428_650_170_972E-07) < 1E-2
          assert abs(result_mean.item() - 0.0_0_0_2) < 1E-3

  def   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_													=  self.scheduler_classes[0]
      SCREAMING_SNAKE_CASE_													=  self.get_scheduler_config()
      SCREAMING_SNAKE_CASE_													=  scheduler_class(**_A)

      scheduler.set_timesteps(self.num_inference_steps  ,						device=_A)

      SCREAMING_SNAKE_CASE_													=  self.dummy_model()
      SCREAMING_SNAKE_CASE_													=  self.dummy_sample_deter.to(_A) * scheduler.init_noise_sigma

      for t in scheduler.timesteps:
          SCREAMING_SNAKE_CASE_													=  scheduler.scale_model_input(_A  ,						_A)

          SCREAMING_SNAKE_CASE_													=  model(_A  ,						_A)

          SCREAMING_SNAKE_CASE_													=  scheduler.step(_A  ,						_A  ,						_A)
          SCREAMING_SNAKE_CASE_													=  output.prev_sample

      SCREAMING_SNAKE_CASE_													=  torch.sum(torch.abs(_A))
      SCREAMING_SNAKE_CASE_													=  torch.mean(torch.abs(_A))

      if str(_A).startswith('cpu'):
          # The following sum varies between 148 and 156 on mps. Why?
          assert abs(result_sum.item() - 0.1_2_3_3) < 1E-2
          assert abs(result_mean.item() - 0.0_0_0_2) < 1E-3
      elif str(_A).startswith('mps'):
          # Larger tolerance on mps
          assert abs(result_mean.item() - 0.0_0_0_2) < 1E-2
      else:
          # CUDA
          assert abs(result_sum.item() - 0.1_2_3_3) < 1E-2
          assert abs(result_mean.item() - 0.0_0_0_2) < 1E-3


  def   lowerCAmelCase__       (   self):
      SCREAMING_SNAKE_CASE_													=  self.scheduler_classes[0]
      SCREAMING_SNAKE_CASE_													=  self.get_scheduler_config()
      SCREAMING_SNAKE_CASE_													=  scheduler_class(**_A  ,						use_karras_sigmas=_A)

      scheduler.set_timesteps(self.num_inference_steps  ,						device=_A)

      SCREAMING_SNAKE_CASE_													=  self.dummy_model()
      SCREAMING_SNAKE_CASE_													=  self.dummy_sample_deter.to(_A) * scheduler.init_noise_sigma
      SCREAMING_SNAKE_CASE_													=  sample.to(_A)

      for t in scheduler.timesteps:
          SCREAMING_SNAKE_CASE_													=  scheduler.scale_model_input(_A  ,						_A)

          SCREAMING_SNAKE_CASE_													=  model(_A  ,						_A)

          SCREAMING_SNAKE_CASE_													=  scheduler.step(_A  ,						_A  ,						_A)
          SCREAMING_SNAKE_CASE_													=  output.prev_sample

      SCREAMING_SNAKE_CASE_													=  torch.sum(torch.abs(_A))
      SCREAMING_SNAKE_CASE_													=  torch.mean(torch.abs(_A))

      assert abs(result_sum.item() - 0.0_0_0_1_5) < 1E-2
      assert abs(result_mean.item() - 1.9_869_554_535_034_695E-07) < 1E-2


