import argparse
import os

import transformers

from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging


logging.set_verbosity_info()

lowercase_     :   Union[str, Any] 				=				logging.get_logger(__name__)


lowercase_     :   List[Any] 				=				{name: getattr(transformers, name + 'Fast') for name in SLOW_TO_FAST_CONVERTERS}







def 			A__      (	snake_case_ :		Optional[Any]       ,				snake_case_ :		str       ,				snake_case_ :		Tuple       ,				snake_case_ :		List[Any]      ):
 if tokenizer_name is not None and tokenizer_name not in TOKENIZER_CLASSES:
  raise ValueError(F'Unrecognized tokenizer name, should be one of {list(TOKENIZER_CLASSES.keys()      )}.'      )

 if tokenizer_name is None:
  SCREAMING_SNAKE_CASE__:   Tuple=      TOKENIZER_CLASSES
 else:
  SCREAMING_SNAKE_CASE__:   List[str]=      {tokenizer_name: getattr(snake_case_       ,				tokenizer_name + '''Fast'''      )}

 logger.info(F'Loading tokenizer classes: {tokenizer_names}'      )

 for tokenizer_name in tokenizer_names:
  SCREAMING_SNAKE_CASE__:   Optional[Any]=      TOKENIZER_CLASSES[tokenizer_name]

  SCREAMING_SNAKE_CASE__:   Optional[int]=      True
  if checkpoint_name is None:
   SCREAMING_SNAKE_CASE__:   Optional[Any]=      list(tokenizer_class.max_model_input_sizes.keys()      )
  else:
   SCREAMING_SNAKE_CASE__:   List[str]=      [checkpoint_name]

  logger.info(F'For tokenizer {tokenizer_class.__class__.__name__} loading checkpoints: {checkpoint_names}'      )

  for checkpoint in checkpoint_names:
   logger.info(F'Loading {tokenizer_class.__class__.__name__} {checkpoint}'      )

   # Load tokenizer
   SCREAMING_SNAKE_CASE__:   int=      tokenizer_class.from_pretrained(snake_case_       ,				force_download=snake_case_      )

   # Save fast tokenizer
   logger.info(F'Save fast tokenizer to {dump_path} with prefix {checkpoint} add_prefix {add_prefix}'      )

   # For organization names we create sub-directories
   if "/" in checkpoint:
    SCREAMING_SNAKE_CASE__,	SCREAMING_SNAKE_CASE__:   Optional[Any]=      checkpoint.split('''/'''      )
    SCREAMING_SNAKE_CASE__:   Optional[Any]=      os.path.join(snake_case_       ,				snake_case_      )
   elif add_prefix:
    SCREAMING_SNAKE_CASE__:   Tuple=      checkpoint
    SCREAMING_SNAKE_CASE__:   Tuple=      dump_path
   else:
    SCREAMING_SNAKE_CASE__:   Optional[int]=      None
    SCREAMING_SNAKE_CASE__:   int=      dump_path

   logger.info(F'=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}'      )

   if checkpoint in list(tokenizer.pretrained_vocab_files_map.values()      )[0]:
    SCREAMING_SNAKE_CASE__:   Any=      list(tokenizer.pretrained_vocab_files_map.values()      )[0][checkpoint]
    SCREAMING_SNAKE_CASE__:   str=      file_path.split(snake_case_      )[-1][0]
    if next_char == "/":
     SCREAMING_SNAKE_CASE__:   Optional[Any]=      os.path.join(snake_case_       ,				snake_case_      )
     SCREAMING_SNAKE_CASE__:   Union[str, Any]=      None

    logger.info(F'=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}'      )

   SCREAMING_SNAKE_CASE__:   Tuple=      tokenizer.save_pretrained(
       snake_case_       ,				legacy_format=snake_case_       ,				filename_prefix=snake_case_      )
   logger.info(F'=> File names {file_names}'      )

   for file_name in file_names:
    if not file_name.endswith('''tokenizer.json'''      ):
     os.remove(snake_case_      )
     logger.info(F'=> removing {file_name}'      )


if __name__ == "__main__":
      lowercase_     :   Optional[int] 				=				argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '--dump_path', default=None, type=str, required=True, help='Path to output generated fast tokenizer files.'
      )
      parser.add_argument(
          '--tokenizer_name',
          default=None,
          type=str,
          help=(
              f'''Optional tokenizer type selected in the list of {list(TOKENIZER_CLASSES.keys())}. If not given, will '''
              'download and convert all the checkpoints from AWS.'
          ),
      )
      parser.add_argument(
          '--checkpoint_name',
          default=None,
          type=str,
          help='Optional checkpoint name. If not given, will download and convert the canonical checkpoints from AWS.',
      )
      parser.add_argument(
          '--force_download',
          action='store_true',
          help='Re-download checkpoints.',
      )
      lowercase_     :   Tuple 				=				parser.parse_args()

      convert_slow_checkpoint_to_fast(args.tokenizer_name, args.checkpoint_name, args.dump_path, args.force_download)


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

