





'''simple docstring'''

import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer


lowercase__		:     str			=							logging.get_logger(__name__)

lowercase__		:     Dict			=							{'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

lowercase__		:     str			=							{
    'vocab_file': {
        'squeezebert/squeezebert-uncased': (
            'https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/vocab.txt'
        ),
        'squeezebert/squeezebert-mnli': 'https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/vocab.txt',
        'squeezebert/squeezebert-mnli-headless': (
            'https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'squeezebert/squeezebert-uncased': (
            'https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/tokenizer.json'
        ),
        'squeezebert/squeezebert-mnli': (
            'https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/tokenizer.json'
        ),
        'squeezebert/squeezebert-mnli-headless': (
            'https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/tokenizer.json'
        ),
    },
}

lowercase__		:     Optional[int]			=							{
    'squeezebert/squeezebert-uncased': 5_12,
    'squeezebert/squeezebert-mnli': 5_12,
    'squeezebert/squeezebert-mnli-headless': 5_12,
}


lowercase__		:     Optional[Any]			=							{
    'squeezebert/squeezebert-uncased': {'do_lower_case': True},
    'squeezebert/squeezebert-mnli': {'do_lower_case': True},
    'squeezebert/squeezebert-mnli-headless': {'do_lower_case': True},
}
class 							__lowerCAmelCase	(       __magic_name__					):






       """simple docstring"""
       _snake_case      :	Union[str, Any]									=       VOCAB_FILES_NAMES
       _snake_case      :	List[Any]									=       PRETRAINED_VOCAB_FILES_MAP
       _snake_case      :	Dict									=       PRETRAINED_INIT_CONFIGURATION
       _snake_case      :	str									=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       _snake_case      :	int									=       SqueezeBertTokenizer





       def __init__(				self  :						Union[str, Any]			,					lowerCAmelCase__  :						Any=None			,					lowerCAmelCase__  :						Optional[Any]=None			,					lowerCAmelCase__  :						int=True			,					lowerCAmelCase__  :						Tuple="[UNK]"			,					lowerCAmelCase__  :						Union[str, Any]="[SEP]"			,					lowerCAmelCase__  :						int="[PAD]"			,					lowerCAmelCase__  :						str="[CLS]"			,					lowerCAmelCase__  :						Any="[MASK]"			,					lowerCAmelCase__  :						List[str]=True			,					lowerCAmelCase__  :						Optional[int]=None			,					**lowerCAmelCase__  :						Any			,					)       ->	Optional[int]:

            '''simple docstring'''




            super().__init__(
                lowerCAmelCase__			,					tokenizer_file=lowerCAmelCase__			,					do_lower_case=lowerCAmelCase__			,					unk_token=lowerCAmelCase__			,					sep_token=lowerCAmelCase__			,					pad_token=lowerCAmelCase__			,					cls_token=lowerCAmelCase__			,					mask_token=lowerCAmelCase__			,					tokenize_chinese_chars=lowerCAmelCase__			,					strip_accents=lowerCAmelCase__			,					**lowerCAmelCase__			,					)

            _UpperCamelCase							       =			json.loads(self.backend_tokenizer.normalizer.__getstate__()	)
            if (
                normalizer_state.get('''lowercase'''			,					lowerCAmelCase__	) != do_lower_case
                or normalizer_state.get('''strip_accents'''			,					lowerCAmelCase__	) != strip_accents
                or normalizer_state.get('''handle_chinese_chars'''			,					lowerCAmelCase__	) != tokenize_chinese_chars
            ):
                 _UpperCamelCase							       =			getattr(lowerCAmelCase__			,					normalizer_state.pop('''type'''	)	)
                 _UpperCamelCase							       =			do_lower_case
                 _UpperCamelCase							       =			strip_accents
                 _UpperCamelCase							       =			tokenize_chinese_chars
                 _UpperCamelCase							       =			normalizer_class(**lowerCAmelCase__	)

            _UpperCamelCase							       =			do_lower_case





       def snake_case__		(				self  :						Union[str, Any]			,					lowerCAmelCase__  :						Dict			,					lowerCAmelCase__  :						Any=None	)       ->	Any:

            '''simple docstring'''




            _UpperCamelCase							       =			[self.cls_token_id] + token_ids_a + [self.sep_token_id]

            if token_ids_a:
                 output += token_ids_a + [self.sep_token_id]

            return output





       def snake_case__		(				self  :						List[Any]			,					lowerCAmelCase__  :						List[int]			,					lowerCAmelCase__  :						Optional[List[int]] = None	)       ->	List[int]:

            '''simple docstring'''




            _UpperCamelCase							       =			[self.sep_token_id]
            _UpperCamelCase							       =			[self.cls_token_id]
            if token_ids_a is None:
                 return len(cls + token_ids_a + sep	) * [0]
            return len(cls + token_ids_a + sep	) * [0] + len(token_ids_a + sep	) * [1]







       def snake_case__		(				self  :						int			,					lowerCAmelCase__  :						str			,					lowerCAmelCase__  :						Optional[str] = None	)       ->	Tuple[str]:

            '''simple docstring'''




            _UpperCamelCase							       =			self._tokenizer.model.save(lowerCAmelCase__			,					name=lowerCAmelCase__	)
            return tuple(lowerCAmelCase__	)










'''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')



