




import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


if __name__ == "__main__":
   lowerCamelCase__					:  Dict      							=				argparse.ArgumentParser()

   parser.add_argument(
       '--checkpoint_path', default=None, type=str, required=True, help='Path to the checkpoint to convert.'
   )
   # !wget https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml
   parser.add_argument(
       '--original_config_file',
       default=None,
       type=str,
       help='The YAML config file corresponding to the original architecture.',
   )
   parser.add_argument(
       '--num_in_channels',
       default=None,
       type=int,
       help='The number of input channels. If `None` number of input channels will be automatically inferred.',
   )
   parser.add_argument(
       '--scheduler_type',
       default='pndm',
       type=str,
       help='Type of scheduler to use. Should be one of [\'pndm\', \'lms\', \'ddim\', \'euler\', \'euler-ancestral\', \'dpm\']',
   )
   parser.add_argument(
       '--pipeline_type',
       default=None,
       type=str,
       help=(
           'The pipeline type. One of \'FrozenOpenCLIPEmbedder\', \'FrozenCLIPEmbedder\', \'PaintByExample\''
           '. If `None` pipeline will be automatically inferred.'
       ),
   )
   parser.add_argument(
       '--image_size',
       default=None,
       type=int,
       help=(
           'The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2'
           ' Base. Use 768 for Stable Diffusion v2.'
       ),
   )
   parser.add_argument(
       '--prediction_type',
       default=None,
       type=str,
       help=(
           'The prediction type that the model was trained on. Use \'epsilon\' for Stable Diffusion v1.X and Stable'
           ' Diffusion v2 Base. Use \'v_prediction\' for Stable Diffusion v2.'
       ),
   )
   parser.add_argument(
       '--extract_ema',
       action='store_true',
       help=(
           'Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights'
           ' or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield'
           ' higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning.'
       ),
   )
   parser.add_argument(
       '--upcast_attention',
       action='store_true',
       help=(
           'Whether the attention computation should always be upcasted. This is necessary when running stable'
           ' diffusion 2.1.'
       ),
   )
   parser.add_argument(
       '--from_safetensors',
       action='store_true',
       help='If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.',
   )
   parser.add_argument(
       '--to_safetensors',
       action='store_true',
       help='Whether to store pipeline in safetensors format or not.',
   )
   parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
   parser.add_argument('--device', type=str, help='Device to use (e.g. cpu, cuda:0, cuda:1, etc.)')
   parser.add_argument(
       '--stable_unclip',
       type=str,
       default=None,
       required=False,
       help='Set if this is a stable unCLIP model. One of \'txt2img\' or \'img2img\'.',
   )
   parser.add_argument(
       '--stable_unclip_prior',
       type=str,
       default=None,
       required=False,
       help='Set if this is a stable unCLIP txt2img model. Selects which prior to use. If `--stable_unclip` is set to `txt2img`, the karlo prior (https://huggingface.co/kakaobrain/karlo-v1-alpha/tree/main/prior) is selected by default.',
   )
   parser.add_argument(
       '--clip_stats_path',
       type=str,
       help='Path to the clip stats file. Only required if the stable unclip model\'s config specifies `model.params.noise_aug_config.params.clip_stats_path`.',
       required=False,
   )
   parser.add_argument(
       '--controlnet', action='store_true', default=None, help='Set flag if this is a controlnet checkpoint.'
   )
   parser.add_argument('--half', action='store_true', help='Save weights in half precision.')
   parser.add_argument(
       '--vae_path',
       type=str,
       default=None,
       required=False,
       help='Set to a path, hub id to an already converted vae to not convert it again.',
   )
   lowerCamelCase__					:  List[Any]      							=				parser.parse_args()

   lowerCamelCase__					:  Union[str, Any]      							=				download_from_original_stable_diffusion_ckpt(
       checkpoint_path=args.checkpoint_path,
       original_config_file=args.original_config_file,
       image_size=args.image_size,
       prediction_type=args.prediction_type,
       model_type=args.pipeline_type,
       extract_ema=args.extract_ema,
       scheduler_type=args.scheduler_type,
       num_in_channels=args.num_in_channels,
       upcast_attention=args.upcast_attention,
       from_safetensors=args.from_safetensors,
       device=args.device,
       stable_unclip=args.stable_unclip,
       stable_unclip_prior=args.stable_unclip_prior,
       clip_stats_path=args.clip_stats_path,
       controlnet=args.controlnet,
       vae_path=args.vae_path,
   )

   if args.half:
      pipe.to(torch_dtype=torch.floataa)

   if args.controlnet:
      # only save the controlnet model
      pipe.controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
   else:
      pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)





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

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
   from .tokenization_fnet import FNetTokenizer
else:
   lowerCamelCase__					:  Optional[Any]      							=				None

lowerCamelCase__					:  List[str]      							=				logging.get_logger(__name__)
lowerCamelCase__					:  List[str]      							=				{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

lowerCamelCase__					:  List[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',
    },
    'tokenizer_file': {
        'google/fnet-base': 'https://huggingface.co/google/fnet-base/resolve/main/tokenizer.json',
        'google/fnet-large': 'https://huggingface.co/google/fnet-large/resolve/main/tokenizer.json',
    },
}

lowerCamelCase__					:  Optional[Any]      							=				{
    'google/fnet-base': 512,
    'google/fnet-large': 512,
}

lowerCamelCase__					:  List[Any]      							=				'▁'





class 			lowerCamelCase_				( _SCREAMING_SNAKE_CASE   ):


 '''simple docstring'''







 lowercase_							       =       VOCAB_FILES_NAMES
 lowercase_							       =       PRETRAINED_VOCAB_FILES_MAP
 lowercase_							       =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowercase_							       =       ["input_ids", "token_type_ids"]
 lowercase_							       =       FNetTokenizer



 def __init__(				self    :						List[Any]							,							_lowerCAmelCase    :						Dict=None							,							_lowerCAmelCase    :						Dict=None							,							_lowerCAmelCase    :						List[str]=False							,							_lowerCAmelCase    :						Optional[Any]=True							,							_lowerCAmelCase    :						Tuple=True							,							_lowerCAmelCase    :						List[Any]="<unk>"							,							_lowerCAmelCase    :						Optional[Any]="[SEP]"							,							_lowerCAmelCase    :						Optional[Any]="<pad>"							,							_lowerCAmelCase    :						Optional[int]="[CLS]"							,							_lowerCAmelCase    :						Optional[Any]="[MASK]"							,							**_lowerCAmelCase    :						Any							,							):
       # 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(_lowerCAmelCase							,							lstrip=_lowerCAmelCase							,							rstrip=_lowerCAmelCase							,							normalized=_lowerCAmelCase		)
           if isinstance(_lowerCAmelCase							,							_lowerCAmelCase		)
           else mask_token
       )

       super().__init__(
           _lowerCAmelCase							,							tokenizer_file=_lowerCAmelCase							,							do_lower_case=_lowerCAmelCase							,							remove_space=_lowerCAmelCase							,							keep_accents=_lowerCAmelCase							,							unk_token=_lowerCAmelCase							,							sep_token=_lowerCAmelCase							,							pad_token=_lowerCAmelCase							,							cls_token=_lowerCAmelCase							,							mask_token=_lowerCAmelCase							,							**_lowerCAmelCase							,							)

       SCREAMING_SNAKE_CASE_      		=     do_lower_case
       SCREAMING_SNAKE_CASE_      		=     remove_space
       SCREAMING_SNAKE_CASE_      		=     keep_accents
       SCREAMING_SNAKE_CASE_      		=     vocab_file
       SCREAMING_SNAKE_CASE_      		=     False if not self.vocab_file else True



 def  lowerCAmelCase_    (				self    :						Any							,							_lowerCAmelCase    :						List[int]							,							_lowerCAmelCase    :						Optional[List[int]] = 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    :						Optional[int]							,							_lowerCAmelCase    :						List[int]							,							_lowerCAmelCase    :						Optional[List[int]] = 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    :						Optional[Any]							,							_lowerCAmelCase    :						str							,							_lowerCAmelCase    :						Optional[str] = None		):
       if not os.path.isdir(_lowerCAmelCase		):
             logger.error(F"Vocabulary path ({save_directory}) should be a directory"		)
             return
       SCREAMING_SNAKE_CASE_      		=     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		):
             copyfile(self.vocab_file							,							_lowerCAmelCase		)

       return (out_vocab_file,)