


'''simple docstring'''







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


a__  : Optional[int]				=	logging.get_logger(__name__)
a__  : Tuple				=	{'vocab_file': 'spiece.model'}

a__  : str				=	{
    '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',
    },
}

a__  : Tuple				=	{
    'google/fnet-base': 5_1_2,
    'google/fnet-large': 5_1_2,
}

a__  : int				=	'▁'
class  lowercase_					(   a__       ):
    __UpperCAmelCase             =     VOCAB_FILES_NAMES
    __UpperCAmelCase             =     PRETRAINED_VOCAB_FILES_MAP
    __UpperCAmelCase             =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __UpperCAmelCase             =     ['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  , ):
          UpperCamelCase__  =    (
              AddedToken(a  , lstrip=a  , rstrip=a  , normalized=a   )
              if isinstance(a  , a   )
              else mask_token
          )

          UpperCamelCase__  =    {} 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  , )

          UpperCamelCase__  =    do_lower_case
          UpperCamelCase__  =    remove_space
          UpperCamelCase__  =    keep_accents
          UpperCamelCase__  =    vocab_file

          UpperCamelCase__  =    spm.SentencePieceProcessor(**self.sp_model_kwargs   )
          self.sp_model.Load(a   )


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


    def        __a							(					self   ):
          UpperCamelCase__  =    {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   ):
          UpperCamelCase__  =    self.__dict__.copy()
          UpperCamelCase__  =    None
          return state


    def __setstate__(					self  , a   ):
          UpperCamelCase__  =    d

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

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


    def        __a							(					self  , a   ):



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

          if not self.keep_accents:
                UpperCamelCase__  =    unicodedata.normalize("NFKD"  , a   )
                UpperCamelCase__  =    "".join([c for c in outputs if not unicodedata.combining(a   )]   )
          if self.do_lower_case:
                UpperCamelCase__  =    outputs.lower()

          return outputs


    def        __a							(					self  , a   ):
          UpperCamelCase__  =    self.preprocess_text(a   )
          UpperCamelCase__  =    self.sp_model.encode(a  , out_type=a   )
          UpperCamelCase__  =    []
          for piece in pieces:
                if len(a   ) > 1 and piece[-1] == str(","   ) and piece[-2].isdigit():
                      UpperCamelCase__  =    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:
                                  UpperCamelCase__  =    cur_pieces[1:]
                            else:
                                  UpperCamelCase__  =    cur_pieces[0][1:]
                      cur_pieces.append(piece[-1]   )
                      new_pieces.extend(a   )
                else:
                      new_pieces.append(a   )

          return new_pieces


    def        __a							(					self  , a   ):
          return self.sp_model.PieceToId(a   )


    def        __a							(					self  , a   ):
          return self.sp_model.IdToPiece(a   )


    def        __a							(					self  , a   ):
          UpperCamelCase__  =    []
          UpperCamelCase__  =    ""
          UpperCamelCase__  =    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
                      UpperCamelCase__  =    True
                      UpperCamelCase__  =    []
                else:
                      current_sub_tokens.append(a   )
                      UpperCamelCase__  =    False
          out_string += self.sp_model.decode(a   )
          return out_string.strip()


    def        __a							(					self  , a  , a = False  , a = None  , a = True  , **a  , ):
          UpperCamelCase__  =    kwargs.pop("use_source_tokenizer"  , a   )

          UpperCamelCase__  =    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
          UpperCamelCase__  =    []
          UpperCamelCase__  =    []
          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   )   )
                            UpperCamelCase__  =    []
                      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:
                UpperCamelCase__  =    re.sub(r"(<unk>) "  , r"\1"  , " ".join(a   )   )
          else:
                UpperCamelCase__  =    "".join(a   )

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


    def        __a							(					self  , a  , a = None   ):
          UpperCamelCase__  =    [self.sep_token_id]
          UpperCamelCase__  =    [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        __a							(					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        __a							(					self  , a  , a = None   ):
          UpperCamelCase__  =    [self.sep_token_id]
          UpperCamelCase__  =    [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        __a							(					self  , a  , a = None   ):



          if not os.path.isdir(a   ):
                logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''   )
                return
          UpperCamelCase__  =    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:
                      UpperCamelCase__  =    self.sp_model.serialized_model_proto()
                      fi.write(a   )

          return (out_vocab_file,)









'''simple docstring'''







import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest
class  lowercase_					(   a__       ):
    __UpperCAmelCase             =     (DPMSolverMultistepScheduler,)
    __UpperCAmelCase             =     (('num_inference_steps', 25),)


    def        __a							(					self  , **a   ):
          UpperCamelCase__  =    {
              "num_train_timesteps": 10_00,
              "beta_start": 0.0001,
              "beta_end": 0.02,
              "beta_schedule": "linear",
              "solver_order": 2,
              "prediction_type": "epsilon",
              "thresholding": False,
              "sample_max_value": 1.0,
              "algorithm_type": "dpmsolver++",
              "solver_type": "midpoint",
              "lower_order_final": False,
              "lambda_min_clipped": -float("inf"   ),
              "variance_type": None,
          }

          config.update(**a   )
          return config


    def        __a							(					self  , a=0  , **a   ):
          UpperCamelCase__  =    dict(self.forward_default_kwargs   )
          UpperCamelCase__  =    kwargs.pop("num_inference_steps"  , a   )
          UpperCamelCase__  =    self.dummy_sample
          UpperCamelCase__  =    0.1 * sample
          UpperCamelCase__  =    [residual + 0.2, residual + 0.15, residual + 0.10]

          for scheduler_class in self.scheduler_classes:
                UpperCamelCase__  =    self.get_scheduler_config(**a   )
                UpperCamelCase__  =    scheduler_class(**a   )
                scheduler.set_timesteps(a   )
                # copy over dummy past residuals
                UpperCamelCase__  =    dummy_past_residuals[: scheduler.config.solver_order]

                with tempfile.TemporaryDirectory() as tmpdirname:
                      scheduler.save_config(a   )
                      UpperCamelCase__  =    scheduler_class.from_pretrained(a   )
                      new_scheduler.set_timesteps(a   )
                      # copy over dummy past residuals
                      UpperCamelCase__  =    dummy_past_residuals[: new_scheduler.config.solver_order]

                UpperCamelCase__ ,     UpperCamelCase__  =    sample, sample
                for t in range(a  , time_step + scheduler.config.solver_order + 1   ):
                      UpperCamelCase__  =    scheduler.step(a  , a  , a  , **a   ).prev_sample
                      UpperCamelCase__  =    new_scheduler.step(a  , a  , a  , **a   ).prev_sample

                      assert torch.sum(torch.abs(output - new_output   )   ) < 1e-5, "Scheduler outputs are not identical"


    def        __a							(					self   ):
          pass


    def        __a							(					self  , a=0  , **a   ):
          UpperCamelCase__  =    dict(self.forward_default_kwargs   )
          UpperCamelCase__  =    kwargs.pop("num_inference_steps"  , a   )
          UpperCamelCase__  =    self.dummy_sample
          UpperCamelCase__  =    0.1 * sample
          UpperCamelCase__  =    [residual + 0.2, residual + 0.15, residual + 0.10]

          for scheduler_class in self.scheduler_classes:
                UpperCamelCase__  =    self.get_scheduler_config()
                UpperCamelCase__  =    scheduler_class(**a   )
                scheduler.set_timesteps(a   )

                # copy over dummy past residuals (must be after setting timesteps)
                UpperCamelCase__  =    dummy_past_residuals[: scheduler.config.solver_order]

                with tempfile.TemporaryDirectory() as tmpdirname:
                      scheduler.save_config(a   )
                      UpperCamelCase__  =    scheduler_class.from_pretrained(a   )
                      # copy over dummy past residuals
                      new_scheduler.set_timesteps(a   )

                      # copy over dummy past residual (must be after setting timesteps)
                      UpperCamelCase__  =    dummy_past_residuals[: new_scheduler.config.solver_order]

                UpperCamelCase__  =    scheduler.step(a  , a  , a  , **a   ).prev_sample
                UpperCamelCase__  =    new_scheduler.step(a  , a  , a  , **a   ).prev_sample

                assert torch.sum(torch.abs(output - new_output   )   ) < 1e-5, "Scheduler outputs are not identical"


    def        __a							(					self  , a=None  , **a   ):
          if scheduler is None:
                UpperCamelCase__  =    self.scheduler_classes[0]
                UpperCamelCase__  =    self.get_scheduler_config(**a   )
                UpperCamelCase__  =    scheduler_class(**a   )

          UpperCamelCase__  =    10
          UpperCamelCase__  =    self.dummy_model()
          UpperCamelCase__  =    self.dummy_sample_deter
          scheduler.set_timesteps(a   )

          for i, t in enumerate(scheduler.timesteps   ):
                UpperCamelCase__  =    model(a  , a   )
                UpperCamelCase__  =    scheduler.step(a  , a  , a   ).prev_sample

          return sample


    def        __a							(					self   ):
          UpperCamelCase__  =    dict(self.forward_default_kwargs   )

          UpperCamelCase__  =    kwargs.pop("num_inference_steps"  , a   )

          for scheduler_class in self.scheduler_classes:
                UpperCamelCase__  =    self.get_scheduler_config()
                UpperCamelCase__  =    scheduler_class(**a   )

                UpperCamelCase__  =    self.dummy_sample
                UpperCamelCase__  =    0.1 * sample

                if num_inference_steps is not None and hasattr(a  , "set_timesteps"   ):
                      scheduler.set_timesteps(a   )
                elif num_inference_steps is not None and not hasattr(a  , "set_timesteps"   ):
                      UpperCamelCase__  =    num_inference_steps

                # copy over dummy past residuals (must be done after set_timesteps)
                UpperCamelCase__  =    [residual + 0.2, residual + 0.15, residual + 0.10]
                UpperCamelCase__  =    dummy_past_residuals[: scheduler.config.solver_order]

                UpperCamelCase__  =    scheduler.timesteps[5]
                UpperCamelCase__  =    scheduler.timesteps[6]

                UpperCamelCase__  =    scheduler.step(a  , a  , a  , **a   ).prev_sample
                UpperCamelCase__  =    scheduler.step(a  , a  , a  , **a   ).prev_sample

                self.assertEqual(output_a.shape  , sample.shape   )
                self.assertEqual(output_a.shape  , output_a.shape   )


    def        __a							(					self   ):
          for timesteps in [25, 50, 1_00, 9_99, 10_00]:
                self.check_over_configs(num_train_timesteps=a   )


    def        __a							(					self   ):
          self.check_over_configs(thresholding=a   )
          for order in [1, 2, 3]:
                for solver_type in ["midpoint", "heun"]:
                      for threshold in [0.5, 1.0, 2.0]:
                            for prediction_type in ["epsilon", "sample"]:
                                  self.check_over_configs(
                                      thresholding=a  , prediction_type=a  , sample_max_value=a  , algorithm_type="dpmsolver++"  , solver_order=a  , solver_type=a  , )


    def        __a							(					self   ):
          for prediction_type in ["epsilon", "v_prediction"]:
                self.check_over_configs(prediction_type=a   )


    def        __a							(					self   ):
          for algorithm_type in ["dpmsolver", "dpmsolver++", "sde-dpmsolver", "sde-dpmsolver++"]:
                for solver_type in ["midpoint", "heun"]:
                      for order in [1, 2, 3]:
                            for prediction_type in ["epsilon", "sample"]:
                                  if algorithm_type in ["sde-dpmsolver", "sde-dpmsolver++"]:
                                        if order == 3:
                                              continue
                                  else:
                                        self.check_over_configs(
                                            solver_order=a  , solver_type=a  , prediction_type=a  , algorithm_type=a  , )
                                  UpperCamelCase__  =    self.full_loop(
                                      solver_order=a  , solver_type=a  , prediction_type=a  , algorithm_type=a  , )
                                  assert not torch.isnan(a   ).any(), "Samples have nan numbers"


    def        __a							(					self   ):
          self.check_over_configs(lower_order_final=a   )
          self.check_over_configs(lower_order_final=a   )


    def        __a							(					self   ):
          self.check_over_configs(lambda_min_clipped=-float("inf"   )   )
          self.check_over_configs(lambda_min_clipped=-5.1   )


    def        __a							(					self   ):
          self.check_over_configs(variance_type=a   )
          self.check_over_configs(variance_type="learned_range"   )


    def        __a							(					self   ):
          for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
                self.check_over_forward(num_inference_steps=a  , time_step=0   )


    def        __a							(					self   ):
          UpperCamelCase__  =    self.full_loop()
          UpperCamelCase__  =    torch.mean(torch.abs(a   )   )

          assert abs(result_mean.item() - 0.3301   ) < 1e-3


    def        __a							(					self   ):
          UpperCamelCase__  =    self.full_loop(thresholding=a  , dynamic_thresholding_ratio=0.87  , sample_max_value=0.5   )
          UpperCamelCase__  =    torch.mean(torch.abs(a   )   )

          assert abs(result_mean.item() - 1.1364   ) < 1e-3


    def        __a							(					self   ):
          UpperCamelCase__  =    self.full_loop(prediction_type="v_prediction"   )
          UpperCamelCase__  =    torch.mean(torch.abs(a   )   )

          assert abs(result_mean.item() - 0.2251   ) < 1e-3


    def        __a							(					self   ):
          UpperCamelCase__  =    self.full_loop(prediction_type="v_prediction"  , use_karras_sigmas=a   )
          UpperCamelCase__  =    torch.mean(torch.abs(a   )   )

          assert abs(result_mean.item() - 0.2096   ) < 1e-3


    def        __a							(					self   ):
          # make sure that iterating over schedulers with same config names gives same results
          # for defaults
          UpperCamelCase__  =    DPMSolverMultistepScheduler(**self.get_scheduler_config()   )
          UpperCamelCase__  =    self.full_loop(scheduler=a   )
          UpperCamelCase__  =    torch.mean(torch.abs(a   )   )

          assert abs(result_mean.item() - 0.3301   ) < 1e-3

          UpperCamelCase__  =    DPMSolverSinglestepScheduler.from_config(scheduler.config   )
          UpperCamelCase__  =    UniPCMultistepScheduler.from_config(scheduler.config   )
          UpperCamelCase__  =    DEISMultistepScheduler.from_config(scheduler.config   )
          UpperCamelCase__  =    DPMSolverMultistepScheduler.from_config(scheduler.config   )

          UpperCamelCase__  =    self.full_loop(scheduler=a   )
          UpperCamelCase__  =    torch.mean(torch.abs(a   )   )

          assert abs(result_mean.item() - 0.3301   ) < 1e-3


    def        __a							(					self   ):
          UpperCamelCase__  =    self.scheduler_classes[0]
          UpperCamelCase__  =    self.get_scheduler_config(thresholding=a  , dynamic_thresholding_ratio=0   )
          UpperCamelCase__  =    scheduler_class(**a   )

          UpperCamelCase__  =    10
          UpperCamelCase__  =    self.dummy_model()
          UpperCamelCase__  =    self.dummy_sample_deter.half()
          scheduler.set_timesteps(a   )

          for i, t in enumerate(scheduler.timesteps   ):
                UpperCamelCase__  =    model(a  , a   )
                UpperCamelCase__  =    scheduler.step(a  , a  , a   ).prev_sample

          assert sample.dtype == torch.floataa


    def        __a							(					self  , **a   ):
          for scheduler_class in self.scheduler_classes:
                UpperCamelCase__  =    self.get_scheduler_config(**a   )
                UpperCamelCase__  =    scheduler_class(**a   )

                scheduler.set_timesteps(scheduler.config.num_train_timesteps   )
                assert len(scheduler.timesteps.unique()   ) == scheduler.num_inference_steps





