



'''simple docstring'''





import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


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

   _UpperCamelCase											=						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)











'''simple docstring'''





import warnings
from typing import List

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available






class 					lowerCamelCase_    (				SCREAMING_SNAKE_CASE_     ):
  """simple docstring"""




  a_												=["""image_processor""", """tokenizer"""]
  a_												="""OwlViTImageProcessor"""
  a_												=("""CLIPTokenizer""", """CLIPTokenizerFast""")




  def __init__( self      :	Any			,	_a      :	str=None			,	_a      :	Optional[Any]=None			,	**_a      :	int   )					->					List[str]:
    __lowerCamelCase       :       List[Any]           = None
    if "feature_extractor" in kwargs:
      warnings.warn(
          'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
          ' instead.'			,	_a			,	)
      __lowerCamelCase       :       Tuple           = kwargs.pop('feature_extractor'   )

    __lowerCamelCase       :       int           = image_processor if image_processor is not None else feature_extractor
    if image_processor is None:
      raise ValueError('You need to specify an `image_processor`.'   )
    if tokenizer is None:
      raise ValueError('You need to specify a `tokenizer`.'   )

    super().__init__(_a			,	_a   )




  def __call__( self      :	Tuple			,	_a      :	Tuple=None			,	_a      :	int=None			,	_a      :	List[Any]=None			,	_a      :	List[Any]="max_length"			,	_a      :	Union[str, Any]="np"			,	**_a      :	Union[str, Any]   )					->					List[str]:

    if text is None and query_images is None and images is None:
      raise ValueError(
          'You have to specify at least one text or query image or image. All three cannot be none.'   )

    if text is not None:
      if isinstance(_a			,	_a   ) or (isinstance(_a			,	_a   ) and not isinstance(text[0]			,	_a   )):
        __lowerCamelCase       :       Any           = [self.tokenizer(_a			,	padding=_a			,	return_tensors=_a			,	**_a   )]

      elif isinstance(_a			,	_a   ) and isinstance(text[0]			,	_a   ):
        __lowerCamelCase       :       List[Any]           = []

        # Maximum number of queries across batch
        __lowerCamelCase       :       str           = max([len(_a   ) for t in text]   )

        # Pad all batch samples to max number of text queries
        for t in text:
          if len(_a   ) != max_num_queries:
            __lowerCamelCase       :       List[Any]           = t + [' '] * (max_num_queries - len(_a   ))

          __lowerCamelCase       :       Dict           = self.tokenizer(_a			,	padding=_a			,	return_tensors=_a			,	**_a   )
          encodings.append(_a   )
      else:
        raise TypeError('Input text should be a string, a list of strings or a nested list of strings'   )

      if return_tensors == "np":
        __lowerCamelCase       :       Optional[Any]           = np.concatenate([encoding['input_ids'] for encoding in encodings]			,	axis=0   )
        __lowerCamelCase       :       str           = np.concatenate([encoding['attention_mask'] for encoding in encodings]			,	axis=0   )

      elif return_tensors == "jax" and is_flax_available():
        import jax.numpy as jnp

        __lowerCamelCase       :       int           = jnp.concatenate([encoding['input_ids'] for encoding in encodings]			,	axis=0   )
        __lowerCamelCase       :       List[str]           = jnp.concatenate([encoding['attention_mask'] for encoding in encodings]			,	axis=0   )

      elif return_tensors == "pt" and is_torch_available():
        import torch

        __lowerCamelCase       :       str           = torch.cat([encoding['input_ids'] for encoding in encodings]			,	dim=0   )
        __lowerCamelCase       :       Optional[Any]           = torch.cat([encoding['attention_mask'] for encoding in encodings]			,	dim=0   )

      elif return_tensors == "tf" and is_tf_available():
        import tensorflow as tf

        __lowerCamelCase       :       List[Any]           = tf.stack([encoding['input_ids'] for encoding in encodings]			,	axis=0   )
        __lowerCamelCase       :       int           = tf.stack([encoding['attention_mask'] for encoding in encodings]			,	axis=0   )

      else:
        raise ValueError('Target return tensor type could not be returned'   )

      __lowerCamelCase       :       Any           = BatchEncoding()
      __lowerCamelCase       :       Dict           = input_ids
      __lowerCamelCase       :       str           = attention_mask

    if query_images is not None:
      __lowerCamelCase       :       Optional[int]           = BatchEncoding()
      __lowerCamelCase       :       List[Any]           = self.image_processor(
          _a			,	return_tensors=_a			,	**_a   ).pixel_values
      __lowerCamelCase       :       str           = query_pixel_values

    if images is not None:
      __lowerCamelCase       :       Union[str, Any]           = self.image_processor(_a			,	return_tensors=_a			,	**_a   )

    if text is not None and images is not None:
      __lowerCamelCase       :       Tuple           = image_features.pixel_values
      return encoding
    elif query_images is not None and images is not None:
      __lowerCamelCase       :       Tuple           = image_features.pixel_values
      return encoding
    elif text is not None or query_images is not None:
      return encoding
    else:
      return BatchEncoding(data=dict(**_a   )			,	tensor_type=_a   )




  def 						_lowercase     ( self      :	Optional[Any]			,	*_a      :	List[str]			,	**_a      :	Dict   )					->					int:
    return self.image_processor.post_process(*_a			,	**_a   )




  def 						_lowercase     ( self      :	str			,	*_a      :	str			,	**_a      :	List[str]   )					->					int:
    return self.image_processor.post_process_object_detection(*_a			,	**_a   )




  def 						_lowercase     ( self      :	int			,	*_a      :	List[Any]			,	**_a      :	Optional[int]   )					->					str:
    return self.image_processor.post_process_image_guided_detection(*_a			,	**_a   )




  def 						_lowercase     ( self      :	Tuple			,	*_a      :	Optional[Any]			,	**_a      :	List[Any]   )					->					Tuple:
    return self.tokenizer.batch_decode(*_a			,	**_a   )




  def 						_lowercase     ( self      :	str			,	*_a      :	Optional[Any]			,	**_a      :	str   )					->					Union[str, Any]:
    return self.tokenizer.decode(*_a			,	**_a   )




  @property
  def 						_lowercase     ( self      :	Any   )					->					Any:
    warnings.warn(
        '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'			,	_a			,	)
    return self.image_processor_class




  @property
  def 						_lowercase     ( self      :	Union[str, Any]   )					->					Any:
    warnings.warn(
        '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'			,	_a			,	)
    return self.image_processor






