'''simple docstring'''




import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


__UpperCAmelCase												=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")
__UpperCAmelCase												=parser.parse_args()


__UpperCAmelCase												="cpu"
__UpperCAmelCase												="a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings"

__UpperCAmelCase												="path-to-your-trained-model"
__UpperCAmelCase												=StableDiffusionPipeline.from_pretrained(model_id)
if args.dpm:
  __UpperCAmelCase												=DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
__UpperCAmelCase												=pipe.to(device)

# to channels last
__UpperCAmelCase												=pipe.unet.to(memory_format=torch.channels_last)
__UpperCAmelCase												=pipe.vae.to(memory_format=torch.channels_last)
__UpperCAmelCase												=pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
  __UpperCAmelCase												=pipe.safety_checker.to(memory_format=torch.channels_last)

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

# compute
__UpperCAmelCase												=6_6_6
__UpperCAmelCase												=torch.Generator(device).manual_seed(seed)
__UpperCAmelCase												={"generator": generator}
if args.steps is not None:
  __UpperCAmelCase												=args.steps

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

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

'''simple docstring'''




import collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

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


__UpperCAmelCase												=logging.get_logger(__name__)

__UpperCAmelCase												="▁"

__UpperCAmelCase												={"vocab_file": "prophetnet.tokenizer"}

__UpperCAmelCase												={
    "vocab_file": {
        "microsoft/xprophetnet-large-wiki100-cased": (
            "https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/prophetnet.tokenizer"
        ),
    }
}

__UpperCAmelCase												={
    "microsoft/xprophetnet-large-wiki100-cased": {"do_lower_case": False},
}

__UpperCAmelCase												={
    "microsoft/xprophetnet-large-wiki100-cased": 5_1_2,
}






def __lowerCAmelCase			(							UpperCamelCase__    )     ->   List[str]:
    __lowerCamelCase          =  collections.OrderedDict()
    with open(UpperCamelCase__    ,  '''r'''    ,  encoding='''utf-8'''    ) as reader:
        __lowerCamelCase          =  reader.readlines()
    for index, token in enumerate(UpperCamelCase__    ):
        __lowerCamelCase          =  token.rstrip('''\n'''    )
        __lowerCamelCase          =  index
    return vocab




class    a__				(       UpperCAmelCase__			):
     lowerCamelCase  :					Optional[Any]							=VOCAB_FILES_NAMES
     lowerCamelCase  :					Any							=PRETRAINED_VOCAB_FILES_MAP
     lowerCamelCase  :					Optional[int]							=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowerCamelCase  :					Union[str, Any]							=["input_ids", "attention_mask"]






     def __init__(			self		:		int  ,    a		:		List[str]  ,    a		:		Optional[int]="[SEP]"  ,    a		:		int="[SEP]"  ,    a		:		str="[SEP]"  ,    a		:		List[Any]="[UNK]"  ,    a		:		List[Any]="[PAD]"  ,    a		:		str="[CLS]"  ,    a		:		List[str]="[MASK]"  ,    a		:		Optional[Dict[str, Any]] = None  ,    **a		:		str  ,    ):





         """simple docstring"""



         __lowerCamelCase          =  {} if sp_model_kwargs is None else sp_model_kwargs

         super().__init__(
             bos_token=a  ,    eos_token=a  ,    sep_token=a  ,    unk_token=a  ,    pad_token=a  ,    cls_token=a  ,    mask_token=a  ,    sp_model_kwargs=self.sp_model_kwargs  ,    **a  ,    )

         try:
             import sentencepiece as spm
         except ImportError:
             logger.warning(
                 '''You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'''
                 ''' pip install sentencepiece'''   )
             raise

         __lowerCamelCase          =  spm.SentencePieceProcessor(**self.sp_model_kwargs   )
         self.sp_model.Load(str(a   )   )
         __lowerCamelCase          =  vocab_file

         # Original fairseq vocab and spm vocab must be "aligned":
         # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
         # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
         # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
         # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

         # put special tokens and [unused] tokens into the vocab
         __lowerCamelCase          =  {'''[PAD]''': 0, '''[CLS]''': 1, '''[SEP]''': 2, '''[UNK]''': 3, '''[MASK]''': 4}

         for i in range(10   ):
             __lowerCamelCase          =  f"""[unused{i}]"""
             __lowerCamelCase          =  5 + i

         # The first "real" token "," has position 15 in the embedding vocab and position 3 in the spm vocab
         __lowerCamelCase          =  12
         __lowerCamelCase          =  {v: k for k, v in self.fairseq_tokens_to_ids.items()}
         for k in self.fairseq_tokens_to_ids.keys():
             self.unique_no_split_tokens.append(a   )






     def __getstate__(			self		:		List[str]   ):





         """simple docstring"""



         __lowerCamelCase          =  self.__dict__.copy()
         __lowerCamelCase          =  None
         return state






     def __setstate__(			self		:		int  ,    a		:		List[Any]   ):





         """simple docstring"""



         __lowerCamelCase          =  d
         try:
             import sentencepiece as spm
         except ImportError:
             logger.warning(
                 '''You need to install SentencePiece to use XLMRobertaTokenizer: https://github.com/google/sentencepiece'''
                 ''' pip install sentencepiece'''   )
             raise

         # for backward compatibility
         if not hasattr(self  ,    '''sp_model_kwargs'''   ):
             __lowerCamelCase          =  {}

         __lowerCamelCase          =  spm.SentencePieceProcessor(**self.sp_model_kwargs   )
         self.sp_model.Load(self.vocab_file   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		str  ,    a		:		List[int]  ,    a		:		Optional[List[int]] = None  ,    a		:		bool = False   ):





         """simple docstring"""



         if already_has_special_tokens:
             return super().get_special_tokens_mask(
                 token_ids_a=a  ,    token_ids_a=a  ,    already_has_special_tokens=a   )

         if token_ids_a is None:
             return ([0] * len(a   )) + [1]
         return ([0] * len(a   )) + [1] + ([0] * len(a   )) + [1]






     def        SCREAMING_SNAKE_CASE__       (			self		:		List[Any]  ,    a		:		List[int]  ,    a		:		Optional[List[int]] = None   ):





         """simple docstring"""



         __lowerCamelCase          =  [self.sep_token_id]

         if token_ids_a is None:
             return len(token_ids_a + sep   ) * [0]
         return len(token_ids_a + sep + sep + token_ids_a + sep   ) * [0]






     @property
     def        SCREAMING_SNAKE_CASE__       (			self		:		Optional[int]   ):





         """simple docstring"""



         return len(self.sp_model   ) + self.fairseq_offset






     def        SCREAMING_SNAKE_CASE__       (			self		:		Optional[int]   ):





         """simple docstring"""



         __lowerCamelCase          =  {self.convert_ids_to_tokens(a   ): i for i in range(self.vocab_size   )}
         vocab.update(self.added_tokens_encoder   )
         return vocab






     def        SCREAMING_SNAKE_CASE__       (			self		:		Tuple  ,    a		:		str   ):





         """simple docstring"""



         return self.sp_model.encode(a  ,    out_type=a   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		Dict  ,    a		:		int   ):





         """simple docstring"""



         if token in self.fairseq_tokens_to_ids:
             return self.fairseq_tokens_to_ids[token]
         __lowerCamelCase          =  self.sp_model.PieceToId(a   )

         # Need to return unknown token if the SP model returned 0
         return spm_id + self.fairseq_offset if spm_id else self.unk_token_id






     def        SCREAMING_SNAKE_CASE__       (			self		:		Optional[Any]  ,    a		:		Union[str, Any]   ):





         """simple docstring"""



         if index in self.fairseq_ids_to_tokens:
             return self.fairseq_ids_to_tokens[index]
         return self.sp_model.IdToPiece(index - self.fairseq_offset   )






     def        SCREAMING_SNAKE_CASE__       (			self		:		List[Any]  ,    a		:		Tuple   ):





         """simple docstring"""



         __lowerCamelCase          =  ''''''.join(a   ).replace(a  ,    ''' '''   ).strip()
         return out_string






     def        SCREAMING_SNAKE_CASE__       (			self		:		int  ,    a		:		str  ,    a		:		Optional[str] = None   ):





         """simple docstring"""



         if not os.path.isdir(a   ):
             logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""   )
             return
         __lowerCamelCase          =  os.path.join(
             a  ,    (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']   )

         if os.path.abspath(self.vocab_file   ) != os.path.abspath(a   ) and os.path.isfile(self.vocab_file   ):
             copyfile(self.vocab_file  ,    a   )
         elif not os.path.isfile(self.vocab_file   ):
             with open(a  ,    '''wb'''   ) as fi:
                 __lowerCamelCase          =  self.sp_model.serialized_model_proto()
                 fi.write(a   )

         return (out_vocab_file,)






     def        SCREAMING_SNAKE_CASE__       (			self		:		Any  ,    a		:		List[int]  ,    a		:		Optional[List[int]] = None   ):





         """simple docstring"""



         if token_ids_a is None:
             return token_ids_a + [self.sep_token_id]
         __lowerCamelCase          =  [self.sep_token_id]
         return token_ids_a + sep + token_ids_a + sep
