



'''simple docstring'''
import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


if __name__ == "__main__":
					snake_case_ :	int         =  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_ :	Dict         =  parser.parse_args()

					snake_case_ :	List[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)







'''simple docstring'''





import argparse

from torch import nn

# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
    ProphetNetForConditionalGeneration as ProphetNetForConditionalGenerationOld,
)
from transformers_old.modeling_xlm_prophetnet import (
    XLMProphetNetForConditionalGeneration as XLMProphetNetForConditionalGenerationOld,
)

from transformers import ProphetNetForConditionalGeneration, XLMProphetNetForConditionalGeneration, logging


_UpperCamelCase											=						logging.get_logger(__name__)
logging.set_verbosity_info()

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       )      ->      Optional[int]:
  if "xprophetnet" in prophetnet_checkpoint_path:
    __lowerCamelCase       :       Union[str, Any]           = XLMProphetNetForConditionalGenerationOld.from_pretrained(_lowerCAmelCase       )
    __lowerCamelCase       ,__lowerCamelCase       :       List[str]           = XLMProphetNetForConditionalGeneration.from_pretrained(
        _lowerCAmelCase       ,output_loading_info=_lowerCAmelCase       )
  else:
    __lowerCamelCase       :       Optional[int]           = ProphetNetForConditionalGenerationOld.from_pretrained(_lowerCAmelCase       )
    __lowerCamelCase       ,__lowerCamelCase       :       List[str]           = ProphetNetForConditionalGeneration.from_pretrained(
        _lowerCAmelCase       ,output_loading_info=_lowerCAmelCase       )

  __lowerCamelCase       :       Union[str, Any]           = ['key_proj', 'value_proj', 'query_proj']

  __lowerCamelCase       :       Optional[Any]           = {
      'self_attn': 'ngram_self_attn',
      'cross_attn': 'encoder_attn',
      'cross_attn_layer_norm': 'encoder_attn_layer_norm',
      'feed_forward_layer_norm': 'final_layer_norm',
      'feed_forward': '',
      'intermediate': 'fc1',
      'output': 'fc2',
      'key_proj': 'k_proj',
      'query_proj': 'q_proj',
      'value_proj': 'v_proj',
      'word_embeddings': 'embed_tokens',
      'embeddings_layer_norm': 'emb_layer_norm',
      'relative_pos_embeddings': 'relative_linear',
      'ngram_embeddings': 'ngram_input_embed',
      'position_embeddings': 'embed_positions',
  }

  for key in loading_info["missing_keys"]:
    __lowerCamelCase       :       Optional[int]           = key.split('.'       )

    if attributes[0] == "lm_head":
      __lowerCamelCase       :       Dict           = prophet
      __lowerCamelCase       :       List[Any]           = prophet_old
    else:
      __lowerCamelCase       :       Any           = prophet.prophetnet
      __lowerCamelCase       :       Any           = prophet_old.model

    __lowerCamelCase       :       Optional[Any]           = False
    for attribute in attributes:
      if attribute in mapping:
        __lowerCamelCase       :       Any           = mapping[attribute]
        if not hasattr(_lowerCAmelCase       ,_lowerCAmelCase       ) and len(_lowerCAmelCase       ) > 0:
          __lowerCamelCase       :       int           = attribute
      elif hasattr(_lowerCAmelCase       ,_lowerCAmelCase       ):
        __lowerCamelCase       :       Optional[int]           = attribute

      if attribute == "weight":
        assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
        __lowerCamelCase       :       List[Any]           = old_model.weight
        logger.info(F'{attribute} is initialized.'       )
        __lowerCamelCase       :       List[Any]           = True
        break
      elif attribute == "bias":
        assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
        __lowerCamelCase       :       List[Any]           = old_model.bias
        logger.info(F'{attribute} is initialized'       )
        __lowerCamelCase       :       Dict           = True
        break
      elif attribute in special_keys and hasattr(_lowerCAmelCase       ,'in_proj_weight'       ):
        __lowerCamelCase       :       Optional[Any]           = old_model.in_proj_weight.shape[0] // 3
        __lowerCamelCase       :       Optional[Any]           = getattr(_lowerCAmelCase       ,_lowerCAmelCase       )
        param.weight.shape == old_model.in_proj_weight[:embed_dim, :].shape, "Shapes have to match"
        param.bias.shape == old_model.in_proj_bias[:embed_dim].shape, "Shapes have to match"
        if attribute == "query_proj":
          __lowerCamelCase       :       Optional[int]           = nn.Parameter(old_model.in_proj_weight[:embed_dim, :]       )
          __lowerCamelCase       :       Dict           = nn.Parameter(old_model.in_proj_bias[:embed_dim]       )

        elif attribute == "key_proj":
          __lowerCamelCase       :       List[str]           = nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]       )
          __lowerCamelCase       :       Dict           = nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]       )
        elif attribute == "value_proj":
          __lowerCamelCase       :       str           = nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]       )
          __lowerCamelCase       :       Optional[int]           = nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]       )
        __lowerCamelCase       :       Optional[int]           = True
        break
      elif attribute == "position_embeddings":
        assert (
            model.position_embeddings.weight.shape[-1] == old_model.embed_positions.weight.shape[-1]
        ), "Hidden size has to match"
        assert model.position_embeddings.weight.shape[0] == 512, "We want 512 position_embeddings."
        __lowerCamelCase       :       Optional[int]           = nn.Parameter(old_model.embed_positions.weight[:512, :]       )
        __lowerCamelCase       :       Dict           = True
        break

      if attribute.isdigit():
        __lowerCamelCase       :       List[str]           = model[int(_lowerCAmelCase       )]
        __lowerCamelCase       :       Union[str, Any]           = old_model[int(_lowerCAmelCase       )]
      else:
        __lowerCamelCase       :       Union[str, Any]           = getattr(_lowerCAmelCase       ,_lowerCAmelCase       )

        if old_attribute == "":
          __lowerCamelCase       :       str           = old_model
        else:
          if not hasattr(_lowerCAmelCase       ,_lowerCAmelCase       ):
            raise ValueError(F'{old_model} does not have {old_attribute}'       )
          __lowerCamelCase       :       str           = getattr(_lowerCAmelCase       ,_lowerCAmelCase       )

    if not is_key_init:
      raise ValueError(F'{key} was not correctly initialized!'       )

  print(F'Saving model to {pytorch_dump_folder_path}'       )
  prophet.save_pretrained(_lowerCAmelCase       )


if __name__ == "__main__":
   _UpperCamelCase											=						argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '--prophetnet_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
   )
   parser.add_argument(
       '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
   )
   _UpperCamelCase											=						parser.parse_args()
   convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)






