


import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer


def 				lowerCamelCase__	(      a	:   Any	,  a	:   Dict	,  a	:   Optional[Any]	,  a	:   Union[str, Any]=1_024				)    ->	List[Any]:





   """simple docstring"""
   a__ :Any						  =    [], []

   a__ :Union[str, Any]						  =    list(zip(SCREAMING_SNAKE_CASE__	,  SCREAMING_SNAKE_CASE__				)				)
   a__ :Union[str, Any]						  =    sorted_examples[0]

   def is_too_big(a	:   Union[str, Any]				):
      return tok(SCREAMING_SNAKE_CASE__	,  return_tensors="pt"				).input_ids.shape[1] > max_tokens

   for src, tgt in tqdm(sorted_examples[1:]				):
      a__ :Any						  =    new_src + """ """ + src
      a__ :str						  =    new_tgt + """ """ + tgt
      if is_too_big(SCREAMING_SNAKE_CASE__				) or is_too_big(SCREAMING_SNAKE_CASE__				):  # cant fit, finalize example
         finished_src.append(SCREAMING_SNAKE_CASE__				)
         finished_tgt.append(SCREAMING_SNAKE_CASE__				)
         a__ :int						  =    src, tgt
      else:  # can fit, keep adding
         a__ :Any						  =    cand_src, cand_tgt

    # cleanup
   if new_src:
      assert new_tgt
      finished_src.append(SCREAMING_SNAKE_CASE__				)
      finished_tgt.append(SCREAMING_SNAKE_CASE__				)
   return finished_src, finished_tgt


def 				lowerCamelCase__	(      a	:   Union[str, Any]	,  a	:   Path	,  a	:   Tuple	,  a	:   Tuple				)    ->	int:





   """simple docstring"""
   a__ :Any						  =    Path(SCREAMING_SNAKE_CASE__				)
   save_path.mkdir(exist_ok=SCREAMING_SNAKE_CASE__				)
   for split in ["train"]:
      a__ :List[str]						  =    data_dir / F'''{split}.source''', data_dir / F'''{split}.target'''
      a__ :Tuple						  =    [x.rstrip() for x in Path(SCREAMING_SNAKE_CASE__				).open().readlines()]
      a__ :int						  =    [x.rstrip() for x in Path(SCREAMING_SNAKE_CASE__				).open().readlines()]
      a__ :Any						  =    pack_examples(SCREAMING_SNAKE_CASE__	,  SCREAMING_SNAKE_CASE__	,  SCREAMING_SNAKE_CASE__	,  SCREAMING_SNAKE_CASE__				)
      print(F'''packed {split} split from {len(SCREAMING_SNAKE_CASE__				)} examples -> {len(SCREAMING_SNAKE_CASE__				)}.'''				)
      Path(save_path / F'''{split}.source'''				).open("w"				).write("\n".join(SCREAMING_SNAKE_CASE__				)				)
      Path(save_path / F'''{split}.target'''				).open("w"				).write("\n".join(SCREAMING_SNAKE_CASE__				)				)
   for split in ["val", "test"]:
      a__ :Union[str, Any]						  =    data_dir / F'''{split}.source''', data_dir / F'''{split}.target'''
      shutil.copyfile(SCREAMING_SNAKE_CASE__	,  save_path / F'''{split}.source'''				)
      shutil.copyfile(SCREAMING_SNAKE_CASE__	,  save_path / F'''{split}.target'''				)


def 				lowerCamelCase__	(      )    ->	str:





   """simple docstring"""
   a__ :str						  =    argparse.ArgumentParser()
   parser.add_argument("--tok_name"	,  type=SCREAMING_SNAKE_CASE__	,  help="like facebook/bart-large-cnn,t5-base, etc."				)
   parser.add_argument("--max_seq_len"	,  type=SCREAMING_SNAKE_CASE__	,  default=128				)
   parser.add_argument("--data_dir"	,  type=SCREAMING_SNAKE_CASE__				)
   parser.add_argument("--save_path"	,  type=SCREAMING_SNAKE_CASE__				)
   a__ :Optional[int]						  =    parser.parse_args()
   a__ :Optional[Any]						  =    AutoTokenizer.from_pretrained(args.tok_name				)
   return pack_data_dir(SCREAMING_SNAKE_CASE__	,  Path(args.data_dir				)	,  args.max_seq_len	,  args.save_path				)


if __name__ == "__main__":
  packer_cli()






import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


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

		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)


