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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowercase_ 				=				'▁'

lowercase_ 				=				{'vocab_file': 'spiece.model'}

lowercase_ 				=				{
    'vocab_file': {'google/pegasus-xsum': 'https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'}
}

lowercase_ 				=				{
    'google/pegasus-xsum': 5_1_2,
}


lowercase_ 				=				logging.get_logger(__name__)






class 		_lowerCamelCase		(   UpperCamelCase_    ):
    __a  =      VOCAB_FILES_NAMES

    __a  =      VOCAB_FILES_NAMES
    __a  =      PRETRAINED_VOCAB_FILES_MAP
    __a  =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __a  =      ["input_ids", "attention_mask"]
    def __init__(  self , lowerCAmelCase , lowerCAmelCase="<pad>" , lowerCAmelCase="</s>" , lowerCAmelCase="<unk>" , lowerCAmelCase="<mask_2>" , lowerCAmelCase="<mask_1>" , lowerCAmelCase=None , lowerCAmelCase=103 , lowerCAmelCase = None , **lowerCAmelCase , )       ->					None:
     SCREAMING_SNAKE_CASE__:   Tuple=      offset
     if additional_special_tokens is not None:
      if not isinstance(lowerCAmelCase , lowerCAmelCase   ):
       raise TypeError(
           f'additional_special_tokens should be of type {type(lowerCAmelCase   )}, but is'
           f' {type(lowerCAmelCase   )}'   )

      SCREAMING_SNAKE_CASE__:   Any=      (
          ([mask_token_sent] + additional_special_tokens)
          if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
          else additional_special_tokens
      )
      # fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
      additional_special_tokens_extended += [
          f'<unk_{i}>' for i in range(len(lowerCAmelCase   ) , self.offset - 1   )
      ]

      if len(set(lowerCAmelCase   )   ) != len(lowerCAmelCase   ):
       raise ValueError(
           '''Please make sure that the provided additional_special_tokens do not contain an incorrectly'''
           f' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.'   )
      SCREAMING_SNAKE_CASE__:   Union[str, Any]=      additional_special_tokens_extended
     else:
      SCREAMING_SNAKE_CASE__:   Optional[Any]=      [mask_token_sent] if mask_token_sent is not None else []
      additional_special_tokens += [f'<unk_{i}>' for i in range(2 , self.offset   )]

     SCREAMING_SNAKE_CASE__:   List[Any]=      {} if sp_model_kwargs is None else sp_model_kwargs

     super().__init__(
         eos_token=lowerCAmelCase , unk_token=lowerCAmelCase , mask_token=lowerCAmelCase , pad_token=lowerCAmelCase , mask_token_sent=lowerCAmelCase , offset=lowerCAmelCase , additional_special_tokens=lowerCAmelCase , sp_model_kwargs=self.sp_model_kwargs , **lowerCAmelCase , )
     SCREAMING_SNAKE_CASE__:   Dict=      mask_token_sent
     SCREAMING_SNAKE_CASE__:   Tuple=      vocab_file
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      spm.SentencePieceProcessor(**self.sp_model_kwargs   )
     self.sp_model.Load(lowerCAmelCase   )

     # add special tokens to encoder dict
     SCREAMING_SNAKE_CASE__:   Dict[int, str]=      {
         0: self.pad_token,
         1: self.eos_token,
     }

     if self.mask_token_sent is not None:
      self.encoder.update(
          {
              2: self.mask_token_sent,
              3: self.mask_token,
          }   )

     if self.offset > 0:
      # entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
      # mask_token_sent is already added to list -> so start at 1
      self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1 , self.offset - 1   )}   )

     SCREAMING_SNAKE_CASE__:   Dict[str, int]=      {v: k for k, v in self.encoder.items()}
    @property
    def 						UpperCamelCase_				(  self   )       ->					int:
     return len(self.sp_model   ) + self.offset
    def 						UpperCamelCase_				(  self   )       ->					Dict[str, int]:
     SCREAMING_SNAKE_CASE__:   Any=      {self.convert_ids_to_tokens(lowerCAmelCase   ): i for i in range(self.vocab_size   )}
     vocab.update(self.added_tokens_encoder   )
     return vocab
    def __getstate__(  self   )       ->					str:
     SCREAMING_SNAKE_CASE__:   str=      self.__dict__.copy()
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      None
     return state
    def __setstate__(  self , lowerCAmelCase   )       ->					Tuple:
     SCREAMING_SNAKE_CASE__:   Tuple=      d

     # for backward compatibility
     if not hasattr(self , '''sp_model_kwargs'''   ):
      SCREAMING_SNAKE_CASE__:   List[str]=      {}

     SCREAMING_SNAKE_CASE__:   str=      spm.SentencePieceProcessor(**self.sp_model_kwargs   )
     self.sp_model.Load(self.vocab_file   )
    def 						UpperCamelCase_				(  self , lowerCAmelCase   )       ->					List[str]:
     return self.sp_model.encode(lowerCAmelCase , out_type=lowerCAmelCase   )
    def 						UpperCamelCase_				(  self , lowerCAmelCase   )       ->					int:
     if token in self.decoder:
      return self.decoder[token]
     elif token in self.added_tokens_decoder:
      return self.added_tokens_decoder[token]
     SCREAMING_SNAKE_CASE__:   List[Any]=      self.sp_model.piece_to_id(lowerCAmelCase   )
     return sp_id + self.offset
    def 						UpperCamelCase_				(  self , lowerCAmelCase   )       ->					str:
     if index in self.encoder:
      return self.encoder[index]
     elif index in self.added_tokens_encoder:
      return self.added_tokens_encoder[index]
     else:
      SCREAMING_SNAKE_CASE__:   List[Any]=      self.sp_model.IdToPiece(index - self.offset   )
     return token
    def 						UpperCamelCase_				(  self , lowerCAmelCase   )       ->					Optional[int]:
     SCREAMING_SNAKE_CASE__:   List[Any]=      []
     SCREAMING_SNAKE_CASE__:   Dict=      ''''''
     for token in tokens:
      # make sure that special tokens are not decoded using sentencepiece model
      if token in self.all_special_tokens:
       out_string += self.sp_model.decode(lowerCAmelCase   ) + token
       SCREAMING_SNAKE_CASE__:   Optional[Any]=      []
      else:
       current_sub_tokens.append(lowerCAmelCase   )
     out_string += self.sp_model.decode(lowerCAmelCase   )
     return out_string.strip()
    def 						UpperCamelCase_				(  self , lowerCAmelCase=False   )       ->					List[Any]:
     return 1
    def 						UpperCamelCase_				(  self , lowerCAmelCase   )       ->					List[Any]:
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      set(self.all_special_ids   )  # call it once instead of inside list comp
     all_special_ids.remove(self.unk_token_id   )  # <unk> is only sometimes special

     return [1 if x in all_special_ids else 0 for x in seq]
    def 						UpperCamelCase_				(  self , lowerCAmelCase , lowerCAmelCase = None , lowerCAmelCase = False   )       ->					List[int]:
     if already_has_special_tokens:
      return self._special_token_mask(lowerCAmelCase   )
     elif token_ids_a is None:
      return self._special_token_mask(lowerCAmelCase   ) + [1]
     else:
      return self._special_token_mask(token_ids_a + token_ids_a   ) + [1]
    def 						UpperCamelCase_				(  self , lowerCAmelCase , lowerCAmelCase=None   )       ->					List[int]:
     if token_ids_a is None:
      return token_ids_a + [self.eos_token_id]
     # We don't expect to process pairs, but leave the pair logic for API consistency
     return token_ids_a + token_ids_a + [self.eos_token_id]



    def 						UpperCamelCase_				(  self , lowerCAmelCase , lowerCAmelCase = None   )       ->					Tuple[str]:
     if not os.path.isdir(lowerCAmelCase   ):
      logger.error(f'Vocabulary path ({save_directory}) should be a directory'   )
      return
     SCREAMING_SNAKE_CASE__:   Optional[int]=      os.path.join(
         lowerCAmelCase , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']   )

     if os.path.abspath(self.vocab_file   ) != os.path.abspath(lowerCAmelCase   ) and os.path.isfile(self.vocab_file   ):
      copyfile(self.vocab_file , lowerCAmelCase   )
     elif not os.path.isfile(self.vocab_file   ):
      with open(lowerCAmelCase , '''wb'''   ) as fi:
       SCREAMING_SNAKE_CASE__:   Optional[int]=      self.sp_model.serialized_model_proto()
       fi.write(lowerCAmelCase   )

     return (out_vocab_file,)


lowercase_     :   Optional[int] 				=				'0.18.2'

from .configuration_utils import ConfigMixin
from .utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_inflect_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_librosa_available,
    is_note_seq_available,
    is_onnx_available,
    is_scipy_available,
    is_torch_available,
    is_torchsde_available,
    is_transformers_available,
    is_transformers_version,
    is_unidecode_available,
    logging,
)


try:
      if not is_onnx_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      from .utils.dummy_onnx_objects import *  # noqa F403
else:
      from .pipelines import OnnxRuntimeModel

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      from .utils.dummy_pt_objects import *  # noqa F403
else:
      from .models import (
          AutoencoderKL,
          ControlNetModel,
          ModelMixin,
          PriorTransformer,
          TaFilmDecoder,
          TransformeraDModel,
          UNetaDModel,
          UNetaDConditionModel,
          UNetaDModel,
          UNetaDConditionModel,
          VQModel,
      )
      from .optimization import (
          get_constant_schedule,
          get_constant_schedule_with_warmup,
          get_cosine_schedule_with_warmup,
          get_cosine_with_hard_restarts_schedule_with_warmup,
          get_linear_schedule_with_warmup,
          get_polynomial_decay_schedule_with_warmup,
          get_scheduler,
      )
      from .pipelines import (
          AudioPipelineOutput,
          ConsistencyModelPipeline,
          DanceDiffusionPipeline,
          DDIMPipeline,
          DDPMPipeline,
          DiffusionPipeline,
          DiTPipeline,
          ImagePipelineOutput,
          KarrasVePipeline,
          LDMPipeline,
          LDMSuperResolutionPipeline,
          PNDMPipeline,
          RePaintPipeline,
          ScoreSdeVePipeline,
      )
      from .schedulers import (
          CMStochasticIterativeScheduler,
          DDIMInverseScheduler,
          DDIMParallelScheduler,
          DDIMScheduler,
          DDPMParallelScheduler,
          DDPMScheduler,
          DEISMultistepScheduler,
          DPMSolverMultistepInverseScheduler,
          DPMSolverMultistepScheduler,
          DPMSolverSinglestepScheduler,
          EulerAncestralDiscreteScheduler,
          EulerDiscreteScheduler,
          HeunDiscreteScheduler,
          IPNDMScheduler,
          KarrasVeScheduler,
          KDPMaAncestralDiscreteScheduler,
          KDPMaDiscreteScheduler,
          PNDMScheduler,
          RePaintScheduler,
          SchedulerMixin,
          ScoreSdeVeScheduler,
          UnCLIPScheduler,
          UniPCMultistepScheduler,
          VQDiffusionScheduler,
      )
      from .training_utils import EMAModel

try:
      if not (is_torch_available() and is_scipy_available()):
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      from .utils.dummy_torch_and_scipy_objects import *  # noqa F403
else:
      from .schedulers import LMSDiscreteScheduler

try:
      if not (is_torch_available() and is_torchsde_available()):
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      from .utils.dummy_torch_and_torchsde_objects import *  # noqa F403
else:
      from .schedulers import DPMSolverSDEScheduler

try:
      if not (is_torch_available() and is_transformers_available()):
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      from .utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
      from .pipelines import (
          AltDiffusionImgaImgPipeline,
          AltDiffusionPipeline,
          AudioLDMPipeline,
          CycleDiffusionPipeline,
          IFImgaImgPipeline,
          IFImgaImgSuperResolutionPipeline,
          IFInpaintingPipeline,
          IFInpaintingSuperResolutionPipeline,
          IFPipeline,
          IFSuperResolutionPipeline,
          ImageTextPipelineOutput,
          KandinskyImgaImgPipeline,
          KandinskyInpaintPipeline,
          KandinskyPipeline,
          KandinskyPriorPipeline,
          KandinskyVaaControlnetImgaImgPipeline,
          KandinskyVaaControlnetPipeline,
          KandinskyVaaImgaImgPipeline,
          KandinskyVaaInpaintPipeline,
          KandinskyVaaPipeline,
          KandinskyVaaPriorEmbaEmbPipeline,
          KandinskyVaaPriorPipeline,
          LDMTextToImagePipeline,
          PaintByExamplePipeline,
          SemanticStableDiffusionPipeline,
          ShapEImgaImgPipeline,
          ShapEPipeline,
          StableDiffusionAttendAndExcitePipeline,
          StableDiffusionControlNetImgaImgPipeline,
          StableDiffusionControlNetInpaintPipeline,
          StableDiffusionControlNetPipeline,
          StableDiffusionDepthaImgPipeline,
          StableDiffusionDiffEditPipeline,
          StableDiffusionImageVariationPipeline,
          StableDiffusionImgaImgPipeline,
          StableDiffusionInpaintPipeline,
          StableDiffusionInpaintPipelineLegacy,
          StableDiffusionInstructPixaPixPipeline,
          StableDiffusionLatentUpscalePipeline,
          StableDiffusionLDMaDPipeline,
          StableDiffusionModelEditingPipeline,
          StableDiffusionPanoramaPipeline,
          StableDiffusionParadigmsPipeline,
          StableDiffusionPipeline,
          StableDiffusionPipelineSafe,
          StableDiffusionPixaPixZeroPipeline,
          StableDiffusionSAGPipeline,
          StableDiffusionUpscalePipeline,
          StableUnCLIPImgaImgPipeline,
          StableUnCLIPPipeline,
          TextToVideoSDPipeline,
          TextToVideoZeroPipeline,
          UnCLIPImageVariationPipeline,
          UnCLIPPipeline,
          UniDiffuserModel,
          UniDiffuserPipeline,
          UniDiffuserTextDecoder,
          VersatileDiffusionDualGuidedPipeline,
          VersatileDiffusionImageVariationPipeline,
          VersatileDiffusionPipeline,
          VersatileDiffusionTextToImagePipeline,
          VideoToVideoSDPipeline,
          VQDiffusionPipeline,
      )

try:
      if not (is_torch_available() and is_transformers_available() and is_invisible_watermark_available()):
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      from .utils.dummy_torch_and_transformers_and_invisible_watermark_objects import *  # noqa F403
else:
      from .pipelines import StableDiffusionXLImgaImgPipeline, StableDiffusionXLPipeline

try:
      if not (is_torch_available() and is_transformers_available() and is_k_diffusion_available()):
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      from .utils.dummy_torch_and_transformers_and_k_diffusion_objects import *  # noqa F403
else:
      from .pipelines import StableDiffusionKDiffusionPipeline

try:
      if not (is_torch_available() and is_transformers_available() and is_onnx_available()):
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      from .utils.dummy_torch_and_transformers_and_onnx_objects import *  # noqa F403
else:
      from .pipelines import (
          OnnxStableDiffusionImgaImgPipeline,
          OnnxStableDiffusionInpaintPipeline,
          OnnxStableDiffusionInpaintPipelineLegacy,
          OnnxStableDiffusionPipeline,
          OnnxStableDiffusionUpscalePipeline,
          StableDiffusionOnnxPipeline,
      )

try:
      if not (is_torch_available() and is_librosa_available()):
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      from .utils.dummy_torch_and_librosa_objects import *  # noqa F403
else:
      from .pipelines import AudioDiffusionPipeline, Mel

try:
      if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      from .utils.dummy_transformers_and_torch_and_note_seq_objects import *  # noqa F403
else:
      from .pipelines import SpectrogramDiffusionPipeline

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      from .utils.dummy_flax_objects import *  # noqa F403
else:
      from .models.controlnet_flax import FlaxControlNetModel
      from .models.modeling_flax_utils import FlaxModelMixin
      from .models.unet_ad_condition_flax import FlaxUNetaDConditionModel
      from .models.vae_flax import FlaxAutoencoderKL
      from .pipelines import FlaxDiffusionPipeline
      from .schedulers import (
          FlaxDDIMScheduler,
          FlaxDDPMScheduler,
          FlaxDPMSolverMultistepScheduler,
          FlaxKarrasVeScheduler,
          FlaxLMSDiscreteScheduler,
          FlaxPNDMScheduler,
          FlaxSchedulerMixin,
          FlaxScoreSdeVeScheduler,
      )


try:
      if not (is_flax_available() and is_transformers_available()):
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      from .utils.dummy_flax_and_transformers_objects import *  # noqa F403
else:
      from .pipelines import (
          FlaxStableDiffusionControlNetPipeline,
          FlaxStableDiffusionImgaImgPipeline,
          FlaxStableDiffusionInpaintPipeline,
          FlaxStableDiffusionPipeline,
      )

try:
      if not (is_note_seq_available()):
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      from .utils.dummy_note_seq_objects import *  # noqa F403
else:
      from .pipelines import MidiProcessor

