



'''simple docstring'''





_lowerCAmelCase      						=	'''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









import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bert import BertTokenizer


snake_case__					:							List[Any]													=     logging.get_logger(__name__)

snake_case__					:							Union[str, Any]													=     {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

snake_case__					:							int													=     {
    'vocab_file': {
        'bert-base-uncased': 'https://huggingface.co/bert-base-uncased/resolve/main/vocab.txt',
        'bert-large-uncased': 'https://huggingface.co/bert-large-uncased/resolve/main/vocab.txt',
        'bert-base-cased': 'https://huggingface.co/bert-base-cased/resolve/main/vocab.txt',
        'bert-large-cased': 'https://huggingface.co/bert-large-cased/resolve/main/vocab.txt',
        'bert-base-multilingual-uncased': (
            'https://huggingface.co/bert-base-multilingual-uncased/resolve/main/vocab.txt'
        ),
        'bert-base-multilingual-cased': 'https://huggingface.co/bert-base-multilingual-cased/resolve/main/vocab.txt',
        'bert-base-chinese': 'https://huggingface.co/bert-base-chinese/resolve/main/vocab.txt',
        'bert-base-german-cased': 'https://huggingface.co/bert-base-german-cased/resolve/main/vocab.txt',
        'bert-large-uncased-whole-word-masking': (
            'https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/vocab.txt'
        ),
        'bert-large-cased-whole-word-masking': (
            'https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/vocab.txt'
        ),
        'bert-large-uncased-whole-word-masking-finetuned-squad': (
            'https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt'
        ),
        'bert-large-cased-whole-word-masking-finetuned-squad': (
            'https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/vocab.txt'
        ),
        'bert-base-cased-finetuned-mrpc': (
            'https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/vocab.txt'
        ),
        'bert-base-german-dbmdz-cased': 'https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/vocab.txt',
        'bert-base-german-dbmdz-uncased': (
            'https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/vocab.txt'
        ),
        'TurkuNLP/bert-base-finnish-cased-v1': (
            'https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/vocab.txt'
        ),
        'TurkuNLP/bert-base-finnish-uncased-v1': (
            'https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/vocab.txt'
        ),
        'wietsedv/bert-base-dutch-cased': (
            'https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'bert-base-uncased': 'https://huggingface.co/bert-base-uncased/resolve/main/tokenizer.json',
        'bert-large-uncased': 'https://huggingface.co/bert-large-uncased/resolve/main/tokenizer.json',
        'bert-base-cased': 'https://huggingface.co/bert-base-cased/resolve/main/tokenizer.json',
        'bert-large-cased': 'https://huggingface.co/bert-large-cased/resolve/main/tokenizer.json',
        'bert-base-multilingual-uncased': (
            'https://huggingface.co/bert-base-multilingual-uncased/resolve/main/tokenizer.json'
        ),
        'bert-base-multilingual-cased': (
            'https://huggingface.co/bert-base-multilingual-cased/resolve/main/tokenizer.json'
        ),
        'bert-base-chinese': 'https://huggingface.co/bert-base-chinese/resolve/main/tokenizer.json',
        'bert-base-german-cased': 'https://huggingface.co/bert-base-german-cased/resolve/main/tokenizer.json',
        'bert-large-uncased-whole-word-masking': (
            'https://huggingface.co/bert-large-uncased-whole-word-masking/resolve/main/tokenizer.json'
        ),
        'bert-large-cased-whole-word-masking': (
            'https://huggingface.co/bert-large-cased-whole-word-masking/resolve/main/tokenizer.json'
        ),
        'bert-large-uncased-whole-word-masking-finetuned-squad': (
            'https://huggingface.co/bert-large-uncased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json'
        ),
        'bert-large-cased-whole-word-masking-finetuned-squad': (
            'https://huggingface.co/bert-large-cased-whole-word-masking-finetuned-squad/resolve/main/tokenizer.json'
        ),
        'bert-base-cased-finetuned-mrpc': (
            'https://huggingface.co/bert-base-cased-finetuned-mrpc/resolve/main/tokenizer.json'
        ),
        'bert-base-german-dbmdz-cased': (
            'https://huggingface.co/bert-base-german-dbmdz-cased/resolve/main/tokenizer.json'
        ),
        'bert-base-german-dbmdz-uncased': (
            'https://huggingface.co/bert-base-german-dbmdz-uncased/resolve/main/tokenizer.json'
        ),
        'TurkuNLP/bert-base-finnish-cased-v1': (
            'https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1/resolve/main/tokenizer.json'
        ),
        'TurkuNLP/bert-base-finnish-uncased-v1': (
            'https://huggingface.co/TurkuNLP/bert-base-finnish-uncased-v1/resolve/main/tokenizer.json'
        ),
        'wietsedv/bert-base-dutch-cased': (
            'https://huggingface.co/wietsedv/bert-base-dutch-cased/resolve/main/tokenizer.json'
        ),
    },
}

snake_case__					:							int													=     {
    'bert-base-uncased': 512,
    'bert-large-uncased': 512,
    'bert-base-cased': 512,
    'bert-large-cased': 512,
    'bert-base-multilingual-uncased': 512,
    'bert-base-multilingual-cased': 512,
    'bert-base-chinese': 512,
    'bert-base-german-cased': 512,
    'bert-large-uncased-whole-word-masking': 512,
    'bert-large-cased-whole-word-masking': 512,
    'bert-large-uncased-whole-word-masking-finetuned-squad': 512,
    'bert-large-cased-whole-word-masking-finetuned-squad': 512,
    'bert-base-cased-finetuned-mrpc': 512,
    'bert-base-german-dbmdz-cased': 512,
    'bert-base-german-dbmdz-uncased': 512,
    'TurkuNLP/bert-base-finnish-cased-v1': 512,
    'TurkuNLP/bert-base-finnish-uncased-v1': 512,
    'wietsedv/bert-base-dutch-cased': 512,
}

snake_case__					:							str													=     {
    'bert-base-uncased': {'do_lower_case': True},
    'bert-large-uncased': {'do_lower_case': True},
    'bert-base-cased': {'do_lower_case': False},
    'bert-large-cased': {'do_lower_case': False},
    'bert-base-multilingual-uncased': {'do_lower_case': True},
    'bert-base-multilingual-cased': {'do_lower_case': False},
    'bert-base-chinese': {'do_lower_case': False},
    'bert-base-german-cased': {'do_lower_case': False},
    'bert-large-uncased-whole-word-masking': {'do_lower_case': True},
    'bert-large-cased-whole-word-masking': {'do_lower_case': False},
    'bert-large-uncased-whole-word-masking-finetuned-squad': {'do_lower_case': True},
    'bert-large-cased-whole-word-masking-finetuned-squad': {'do_lower_case': False},
    'bert-base-cased-finetuned-mrpc': {'do_lower_case': False},
    'bert-base-german-dbmdz-cased': {'do_lower_case': False},
    'bert-base-german-dbmdz-uncased': {'do_lower_case': True},
    'TurkuNLP/bert-base-finnish-cased-v1': {'do_lower_case': False},
    'TurkuNLP/bert-base-finnish-uncased-v1': {'do_lower_case': True},
    'wietsedv/bert-base-dutch-cased': {'do_lower_case': False},
}





class A_					(			_lowerCamelCase   ):
   lowerCAmelCase__        = VOCAB_FILES_NAMES
   lowerCAmelCase__        = PRETRAINED_VOCAB_FILES_MAP
   lowerCAmelCase__        = PRETRAINED_INIT_CONFIGURATION
   lowerCAmelCase__        = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowerCAmelCase__        = BertTokenizer




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

         __A          =				json.loads(self.backend_tokenizer.normalizer.__getstate__() )
         if (
             normalizer_state.get('''lowercase'''    ,   _UpperCamelCase ) != do_lower_case
             or normalizer_state.get('''strip_accents'''    ,   _UpperCamelCase ) != strip_accents
             or normalizer_state.get('''handle_chinese_chars'''    ,   _UpperCamelCase ) != tokenize_chinese_chars
         ):
               __A          =				getattr(_UpperCamelCase    ,   normalizer_state.pop('''type''' ) )
               __A          =				do_lower_case
               __A          =				strip_accents
               __A          =				tokenize_chinese_chars
               __A          =				normalizer_class(**_UpperCamelCase )

         __A          =				do_lower_case




   def _lowerCAmelCase      (self	:Any    ,   _UpperCamelCase	:int    ,   _UpperCamelCase	:List[str]=None )->					List[Any]:
         __A          =				[self.cls_token_id] + token_ids_a + [self.sep_token_id]

         if token_ids_a:
               output += token_ids_a + [self.sep_token_id]

         return output




   def _lowerCAmelCase      (self	:List[str]    ,   _UpperCamelCase	:List[int]    ,   _UpperCamelCase	:Optional[List[int]] = None )->					List[int]:
         __A          =				[self.sep_token_id]
         __A          =				[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	:Any    ,   _UpperCamelCase	:str    ,   _UpperCamelCase	:Optional[str] = None )->					Tuple[str]:
         __A          =				self._tokenizer.model.save(_UpperCamelCase    ,   name=_UpperCamelCase )
         return tuple(_UpperCamelCase )





