


import json
import os
from typing import Optional, Tuple

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


_SCREAMING_SNAKE_CASE     :					List[str]      =       logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE     :					str      =       {'vocab_file': 'vocab.json'}

_SCREAMING_SNAKE_CASE     :					str      =       {
    'vocab_file': {
        'mgp-str': 'https://huggingface.co/alibaba-damo/mgp-str-base/blob/main/vocab.json',
    }
}

_SCREAMING_SNAKE_CASE     :					Union[str, Any]      =       {'mgp-str': 27}

class     A__   (       snake_case__      ):







    """simple docstring"""

    __magic_name__         =						VOCAB_FILES_NAMES
    __magic_name__         =						PRETRAINED_VOCAB_FILES_MAP
    __magic_name__         =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    def __init__(			self     ,   __snake_case     ,   __snake_case="[GO]"     ,   __snake_case="[GO]"     ,   __snake_case="[s]"     ,   __snake_case="[GO]"     ,   **__snake_case       ):
         super().__init__(
             unk_token=lowerCAmelCase__     ,   bos_token=lowerCAmelCase__     ,   eos_token=lowerCAmelCase__     ,   pad_token=lowerCAmelCase__     ,   **lowerCAmelCase__     ,   )

         with open(lowerCAmelCase__     ,   encoding='''utf-8'''       ) as vocab_handle:
              snake_case											=							json.load(lowerCAmelCase__       )
         snake_case											=							{v: k for k, v in self.vocab.items()}
    @property
    def 						a_     (			self       ):
         return len(self.vocab       )
    def 						a_     (			self       ):
         return dict(self.vocab     ,   **self.added_tokens_encoder       )
    def 						a_     (			self     ,   __snake_case       ):
         snake_case											=							[]
         for s in text:
              char_tokens.extend(lowerCAmelCase__       )
         return char_tokens
    def 						a_     (			self     ,   __snake_case       ):
         return self.vocab.get(lowerCAmelCase__     ,   self.vocab.get(self.unk_token       )       )
    def 						a_     (			self     ,   __snake_case       ):
         return self.decoder.get(lowerCAmelCase__       )




    def 						a_     (			self     ,   __snake_case     ,   __snake_case = None       ):




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

         with open(lowerCAmelCase__     ,   '''w'''     ,   encoding='''utf-8'''       ) as f:
              f.write(json.dumps(self.vocab     ,   indent=2     ,   sort_keys=lowerCAmelCase__     ,   ensure_ascii=lowerCAmelCase__       ) + '''\n'''       )

         return (vocab_file,)











'''simple docstring'''

import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


lowercase__		:     int			=							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')
lowercase__		:     Union[str, Any]			=							parser.parse_args()


lowercase__		:     Union[str, Any]			=							'cpu'
lowercase__		:     Tuple			=							'a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings'

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

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

# optimize with ipex
lowercase__		:     List[str]			=							torch.randn(2, 4, 64, 64)
lowercase__		:     int			=							torch.rand(1) * 9_99
lowercase__		:     Optional[int]			=							torch.randn(2, 77, 7_68)
lowercase__		:     List[str]			=							(sample, timestep, encoder_hidden_status)
try:
   lowercase__		:     Optional[Any]			=							ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
   lowercase__		:     Any			=							ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
lowercase__		:     Tuple			=							ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
lowercase__		:     Any			=							ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
   lowercase__		:     Union[str, Any]			=							ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)

# compute
lowercase__		:     Optional[Any]			=							6_66
lowercase__		:     Tuple			=							torch.Generator(device).manual_seed(seed)
lowercase__		:     Dict			=							{'generator': generator}
if args.steps is not None:
   lowercase__		:     int			=							args.steps

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

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



