





import json
import os
from typing import Dict, List, Optional, Tuple

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


SCREAMING_SNAKE_CASE_		     = logging.get_logger(__name__)


SCREAMING_SNAKE_CASE_		     = {
    'vocab_file': 'vocab.json',
    'tokenizer_config_file': 'tokenizer_config.json',
    'merges_file': 'merges.txt',
}

SCREAMING_SNAKE_CASE_		     = {
    'vocab_file': {
        'facebook/s2t-wav2vec2-large-en-de': (
            'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/vocab.json'
        ),
    },
    'tokenizer_config_file': {
        'facebook/s2t-wav2vec2-large-en-de': (
            'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/tokenizer_config.json'
        ),
    },
    'merges_file': {
        'facebook/s2t-wav2vec2-large-en-de': (
            'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/merges.txt'
        ),
    },
}

SCREAMING_SNAKE_CASE_		     = '</w>'
SCREAMING_SNAKE_CASE_		     = '@@ '



def      __lowercase     (   _SCREAMING_SNAKE_CASE			)				->     Dict:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE	=     set()
      SCREAMING_SNAKE_CASE	=     word[0]
      for char in word[1:]:
            pairs.add((prev_char, char)			)
            SCREAMING_SNAKE_CASE	=     char
      return pairs


# Speech2Text2 has no max input length
SCREAMING_SNAKE_CASE_		     = {'facebook/s2t-wav2vec2-large-en-de': 1_0_2_4}



class 	UpperCamelCase__				(   lowerCamelCase__     ):



     '''simple docstring'''

     __snake_case					:			Dict				=    VOCAB_FILES_NAMES
     __snake_case					:			List[str]				=    PRETRAINED_VOCAB_FILES_MAP
     __snake_case					:			Optional[int]				=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __snake_case					:			int				=    ["input_ids", "attention_mask"]






     def __init__(							self		:					List[str]		,lowerCamelCase__		:					List[str]		,lowerCamelCase__		:					List[str]="<s>"		,lowerCamelCase__		:					Tuple="<pad>"		,lowerCamelCase__		:					Optional[Any]="</s>"		,lowerCamelCase__		:					Optional[Any]="<unk>"		,lowerCamelCase__		:					Union[str, Any]=False		,lowerCamelCase__		:					Union[str, Any]=None		,**lowerCamelCase__		:					Union[str, Any]		,)  ->	str:




           '''simple docstring'''


           super().__init__(
               unk_token=__lowerCamelCase		,bos_token=__lowerCamelCase		,eos_token=__lowerCamelCase		,pad_token=__lowerCamelCase		,do_lower_case=__lowerCamelCase		,**__lowerCamelCase		,)

           SCREAMING_SNAKE_CASE	=     do_lower_case

           with open(__lowerCamelCase		,encoding="""utf-8""" ) as vocab_handle:
                 SCREAMING_SNAKE_CASE	=     json.load(__lowerCamelCase )
           SCREAMING_SNAKE_CASE	=     {v: k for k, v in self.encoder.items()}

           if merges_file is None:
                 logger.info(F"""No merges files provided. {self.__class__.__name__} can only be used for decoding.""" )

                 SCREAMING_SNAKE_CASE	=     None
                 SCREAMING_SNAKE_CASE	=     None
           else:
                 with open(__lowerCamelCase		,encoding="""utf-8""" ) as merges_handle:
                       SCREAMING_SNAKE_CASE	=     merges_handle.read().split("""\n""" )[:-1]

                 SCREAMING_SNAKE_CASE	=     [tuple(merge.split()[:2] ) for merge in merges]
                 SCREAMING_SNAKE_CASE	=     dict(zip(__lowerCamelCase		,range(len(__lowerCamelCase ) ) ) )
                 SCREAMING_SNAKE_CASE	=     {}






     @property
     def        SCREAMING_SNAKE_CASE__						(							self		:					List[str] )  ->	int:




           '''simple docstring'''


           return len(self.decoder )






     def        SCREAMING_SNAKE_CASE__						(							self		:					List[Any] )  ->	Dict:




           '''simple docstring'''


           return dict(self.encoder		,**self.added_tokens_encoder )






     def        SCREAMING_SNAKE_CASE__						(							self		:					str		,lowerCamelCase__		:					List[Any] )  ->	Any:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=     tuple(token[:-1] ) + (token[-1] + BPE_TOKEN_MERGES,)
           if token in self.cache:
                 return self.cache[token]
           SCREAMING_SNAKE_CASE	=     get_pairs(__lowerCamelCase )

           if not pairs:
                 return token

           while True:
                 SCREAMING_SNAKE_CASE	=     min(__lowerCamelCase		,key=lambda lowerCamelCase__ : self.bpe_ranks.get(__lowerCamelCase		,float("""inf""" ) ) )
                 if bigram not in self.bpe_ranks:
                       break
                 SCREAMING_SNAKE_CASE	=     bigram
                 SCREAMING_SNAKE_CASE	=     []
                 SCREAMING_SNAKE_CASE	=     0
                 while i < len(__lowerCamelCase ):
                       try:
                             SCREAMING_SNAKE_CASE	=     word.index(__lowerCamelCase		,__lowerCamelCase )
                       except ValueError:
                             new_word.extend(word[i:] )
                             break
                       else:
                             new_word.extend(word[i:j] )
                             SCREAMING_SNAKE_CASE	=     j

                       if word[i] == first and i < len(__lowerCamelCase ) - 1 and word[i + 1] == second:
                             new_word.append(first + second )
                             i += 2
                       else:
                             new_word.append(word[i] )
                             i += 1
                 SCREAMING_SNAKE_CASE	=     tuple(__lowerCamelCase )
                 SCREAMING_SNAKE_CASE	=     new_word
                 if len(__lowerCamelCase ) == 1:
                       break
                 else:
                       SCREAMING_SNAKE_CASE	=     get_pairs(__lowerCamelCase )
           SCREAMING_SNAKE_CASE	=     ''' '''.join(__lowerCamelCase )
           if word == "\n  " + BPE_TOKEN_MERGES:
                 SCREAMING_SNAKE_CASE	=     '''\n''' + BPE_TOKEN_MERGES

           if word.endswith(__lowerCamelCase ):
                 SCREAMING_SNAKE_CASE	=     word.replace(__lowerCamelCase		,"""""" )

           SCREAMING_SNAKE_CASE	=     word.replace(""" """		,__lowerCamelCase )
           SCREAMING_SNAKE_CASE	=     word
           return word






     def        SCREAMING_SNAKE_CASE__						(							self		:					Dict		,lowerCamelCase__		:					List[str] )  ->	int:




           '''simple docstring'''


           if self.bpe_ranks is None:
                 raise ValueError(
                     """This tokenizer was instantiated without a `merges.txt` file, so"""
                     """ that it can only be used for decoding, not for encoding."""
                     """Make sure to provide `merges.txt` file at instantiation to enable """
                     """encoding.""" )

           if self.do_lower_case:
                 SCREAMING_SNAKE_CASE	=     text.lower()

           SCREAMING_SNAKE_CASE	=     text.split()

           SCREAMING_SNAKE_CASE	=     []
           for token in text:
                 if token:
                       split_tokens.extend(list(self.bpe(__lowerCamelCase ).split(""" """ ) ) )

           return split_tokens






     def        SCREAMING_SNAKE_CASE__						(							self		:					Any		,lowerCamelCase__		:					str )  ->	int:




           '''simple docstring'''


           return self.encoder.get(__lowerCamelCase		,self.encoder.get(self.unk_token ) )






     def        SCREAMING_SNAKE_CASE__						(							self		:					int		,lowerCamelCase__		:					List[str] )  ->	str:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=     self.decoder.get(__lowerCamelCase		,self.unk_token )
           return result






     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[Any]		,lowerCamelCase__		:					Any )  ->	str:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=     ''' '''.join(__lowerCamelCase )

           # make sure @@ tokens are concatenated
           SCREAMING_SNAKE_CASE	=     ''''''.join(string.split(__lowerCamelCase ) )

           return string






     def        SCREAMING_SNAKE_CASE__						(							self		:					Union[str, Any]		,lowerCamelCase__		:					str		,lowerCamelCase__		:					Tuple = None )  ->	Tuple[str]:




           '''simple docstring'''


           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"""] )
           SCREAMING_SNAKE_CASE	=     os.path.join(
               __lowerCamelCase		,(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""] )

           with open(__lowerCamelCase		,"""w"""		,encoding="""utf-8""" ) as f:
                 f.write(json.dumps(self.encoder		,indent=2		,sort_keys=__lowerCamelCase		,ensure_ascii=__lowerCamelCase ) + """\n""" )

           SCREAMING_SNAKE_CASE	=     0
           if self.bpe_ranks is None:
                 return (vocab_file,)

           with open(__lowerCamelCase		,"""w"""		,encoding="""utf-8""" ) as writer:
                 for bpe_tokens, token_index in sorted(self.bpe_ranks.items()		,key=lambda lowerCamelCase__ : kv[1] ):
                       if index != token_index:
                             logger.warning(
                                 F"""Saving vocabulary to {merges_file}: BPE merge indices are not consecutive."""
                                 """ Please check that the tokenizer is not corrupted!""" )
                             SCREAMING_SNAKE_CASE	=     token_index
                       writer.write(""" """.join(__lowerCamelCase ) + """\n""" )
                       index += 1

           return (vocab_file, merges_file)









import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE_		     = 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.""",
      )
      SCREAMING_SNAKE_CASE_		     = parser.parse_args()

      SCREAMING_SNAKE_CASE_		     = 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)


