





import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration





def 	lowerCAmelCase_					(			__a						)	->						Any:







  """simple docstring"""





  lowerCamelCase__: List[Any]         =[
      "encoder.version",
      "decoder.version",
      "model.encoder.version",
      "model.decoder.version",
      "decoder.output_projection.weight",
      "_float_tensor",
      "encoder.embed_positions._float_tensor",
      "decoder.embed_positions._float_tensor",
  ]
  for k in ignore_keys:
    state_dict.pop(__a      ,       __a						)





def 	lowerCAmelCase_					(			__a						)	->						List[str]:







  """simple docstring"""





  lowerCamelCase__    ,   lowerCamelCase__: str         =emb.weight.shape
  lowerCamelCase__: Optional[Any]         =nn.Linear(__a      ,       __a      ,       bias=__a						)
  lowerCamelCase__: str         =emb.weight.data
  return lin_layer





def 	lowerCAmelCase_					(			__a						)	->						Any:







  """simple docstring"""





  lowerCamelCase__: str         =torch.load(__a      ,       map_location="cpu"						)
  lowerCamelCase__: List[Any]         =mam_aaa["args"] or mam_aaa["cfg"]["model"]
  lowerCamelCase__: Dict         =mam_aaa["model"]
  remove_ignore_keys_(__a						)
  lowerCamelCase__: Any         =state_dict["encoder.embed_tokens.weight"].shape[0]

  lowerCamelCase__: Dict         =MaMaaaConfig(
      vocab_size=__a      ,       max_position_embeddings=1024      ,       encoder_layers=args.encoder_layers      ,       decoder_layers=args.decoder_layers      ,       encoder_attention_heads=args.encoder_attention_heads      ,       decoder_attention_heads=args.decoder_attention_heads      ,       encoder_ffn_dim=args.encoder_ffn_embed_dim      ,       decoder_ffn_dim=args.decoder_ffn_embed_dim      ,       d_model=args.encoder_embed_dim      ,       encoder_layerdrop=args.encoder_layerdrop      ,       decoder_layerdrop=args.decoder_layerdrop      ,       dropout=args.dropout      ,       attention_dropout=args.attention_dropout      ,       activation_dropout=args.activation_dropout      ,       activation_function="relu"      ,       )

  lowerCamelCase__: Tuple         =state_dict["decoder.embed_tokens.weight"]
  lowerCamelCase__: Optional[Any]         =MaMaaaForConditionalGeneration(__a						)
  model.model.load_state_dict(__a      ,       strict=__a						)
  lowerCamelCase__: Optional[Any]         =make_linear_from_emb(model.model.shared						)

  return model


if __name__ == "__main__":
     __A							   =							argparse.ArgumentParser()
     # Required parameters
     parser.add_argument("fairseq_path", type=str, help="path to a model.pt on local filesystem.")
     parser.add_argument("pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
     __A							   =							parser.parse_args()
     __A							   =							convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
     model.save_pretrained(args.pytorch_dump_folder_path)








import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


SCREAMING_SNAKE_CASE__							:      List[str]         =    argparse.ArgumentParser("""Stable Diffusion script with intel optimization""", add_help=False)
parser.add_argument("""--dpm""", action="""store_true""", help="""Enable DPMSolver or not""")
parser.add_argument("""--steps""", default=None, type=int, help="""Num inference steps""")
SCREAMING_SNAKE_CASE__							:      Tuple         =    parser.parse_args()


SCREAMING_SNAKE_CASE__							:      int         =    """cpu"""
SCREAMING_SNAKE_CASE__							:      Optional[Any]         =    """a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings"""

SCREAMING_SNAKE_CASE__							:      List[Any]         =    """path-to-your-trained-model"""
SCREAMING_SNAKE_CASE__							:      Tuple         =    StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
  SCREAMING_SNAKE_CASE__							:      Union[str, Any]         =    DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
SCREAMING_SNAKE_CASE__							:      Optional[int]         =    pipe.to(device)

# to channels last
SCREAMING_SNAKE_CASE__							:      int         =    pipe.unet.to(memory_format=torch.channels_last)
SCREAMING_SNAKE_CASE__							:      List[Any]         =    pipe.vae.to(memory_format=torch.channels_last)
SCREAMING_SNAKE_CASE__							:      Union[str, Any]         =    pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
  SCREAMING_SNAKE_CASE__							:      List[str]         =    pipe.safety_checker.to(memory_format=torch.channels_last)

# optimize with ipex
SCREAMING_SNAKE_CASE__							:      Optional[int]         =    torch.randn(2, 4, 6_4, 6_4)
SCREAMING_SNAKE_CASE__							:      Any         =    torch.rand(1) * 9_9_9
SCREAMING_SNAKE_CASE__							:      List[Any]         =    torch.randn(2, 7_7, 7_6_8)
SCREAMING_SNAKE_CASE__							:      Optional[Any]         =    (sample, timestep, encoder_hidden_status)
try:
  SCREAMING_SNAKE_CASE__							:      Any         =    ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
  SCREAMING_SNAKE_CASE__							:      Optional[Any]         =    ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
SCREAMING_SNAKE_CASE__							:      str         =    ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
SCREAMING_SNAKE_CASE__							:      Union[str, Any]         =    ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
  SCREAMING_SNAKE_CASE__							:      Union[str, Any]         =    ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)

# compute
SCREAMING_SNAKE_CASE__							:      Optional[Any]         =    6_6_6
SCREAMING_SNAKE_CASE__							:      Any         =    torch.Generator(device).manual_seed(seed)
SCREAMING_SNAKE_CASE__							:      int         =    {"""generator""": generator}
if args.steps is not None:
  SCREAMING_SNAKE_CASE__							:      Optional[int]         =    args.steps

with torch.cpu.amp.autocast(enabled=True, dtype=torch.bfloataa):
  SCREAMING_SNAKE_CASE__							:      Dict         =    pipe(prompt, **generate_kwargs).images[0]

# save image
image.save("""generated.png""")

