






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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


A											=     logging.get_logger(__name__)
A											=     {'vocab_file': 'spiece.model'}

A											=     {
    'vocab_file': {
        'albert-base-v1': 'https://huggingface.co/albert-base-v1/resolve/main/spiece.model',
        'albert-large-v1': 'https://huggingface.co/albert-large-v1/resolve/main/spiece.model',
        'albert-xlarge-v1': 'https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model',
        'albert-xxlarge-v1': 'https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model',
        'albert-base-v2': 'https://huggingface.co/albert-base-v2/resolve/main/spiece.model',
        'albert-large-v2': 'https://huggingface.co/albert-large-v2/resolve/main/spiece.model',
        'albert-xlarge-v2': 'https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model',
        'albert-xxlarge-v2': 'https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model',
    }
}

A											=     {
    'albert-base-v1': 512,
    'albert-large-v1': 512,
    'albert-xlarge-v1': 512,
    'albert-xxlarge-v1': 512,
    'albert-base-v2': 512,
    'albert-large-v2': 512,
    'albert-xlarge-v2': 512,
    'albert-xxlarge-v2': 512,
}

A											=     '▁'


class SCREAMING_SNAKE_CASE  (		__snake_case							):






			"""simple docstring"""




			__A						  =  VOCAB_FILES_NAMES
			__A						  =  PRETRAINED_VOCAB_FILES_MAP
			__A						  =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES


			def __init__(    self ,				__UpperCamelCase ,				__UpperCamelCase=True ,				__UpperCamelCase=True ,				__UpperCamelCase=False ,				__UpperCamelCase="[CLS]" ,				__UpperCamelCase="[SEP]" ,				__UpperCamelCase="<unk>" ,				__UpperCamelCase="[SEP]" ,				__UpperCamelCase="<pad>" ,				__UpperCamelCase="[CLS]" ,				__UpperCamelCase="[MASK]" ,				__UpperCamelCase = None ,				**__UpperCamelCase ,				):

				"""simple docstring"""


				snake_case_      		= (
				    AddedToken(__UpperCamelCase ,				lstrip=__UpperCamelCase ,				rstrip=__UpperCamelCase ,				normalized=__UpperCamelCase							)
				    if isinstance(__UpperCamelCase ,				__UpperCamelCase							)
				    else mask_token
				)

				snake_case_      		= {} if sp_model_kwargs is None else sp_model_kwargs

				super().__init__(
				    do_lower_case=__UpperCamelCase ,				remove_space=__UpperCamelCase ,				keep_accents=__UpperCamelCase ,				bos_token=__UpperCamelCase ,				eos_token=__UpperCamelCase ,				unk_token=__UpperCamelCase ,				sep_token=__UpperCamelCase ,				pad_token=__UpperCamelCase ,				cls_token=__UpperCamelCase ,				mask_token=__UpperCamelCase ,				sp_model_kwargs=self.sp_model_kwargs ,				**__UpperCamelCase ,				)

				snake_case_      		= do_lower_case
				snake_case_      		= remove_space
				snake_case_      		= keep_accents
				snake_case_      		= vocab_file

				snake_case_      		= spm.SentencePieceProcessor(**self.sp_model_kwargs							)
				self.sp_model.Load(__UpperCamelCase							)


			@property
			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


				return len(self.sp_model							)


			def 			__lowerCAmelCase (    self							):

				"""simple docstring"""


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


			def __getstate__(    self							):

				"""simple docstring"""


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


			def __setstate__(    self ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= d

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

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


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase							):

				"""simple docstring"""


				if self.remove_space:
					snake_case_      		= ' '.join(inputs.strip().split()							)
				else:
					snake_case_      		= inputs
				snake_case_      		= outputs.replace('``' ,				'"'							).replace('\'\'' ,				'"'							)

				if not self.keep_accents:
					snake_case_      		= unicodedata.normalize('NFKD' ,				__UpperCamelCase							)
					snake_case_      		= ''.join([c for c in outputs if not unicodedata.combining(__UpperCamelCase							)]							)
				if self.do_lower_case:
					snake_case_      		= outputs.lower()

				return outputs


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= self.preprocess_text(__UpperCamelCase							)
				snake_case_      		= self.sp_model.encode(__UpperCamelCase ,				out_type=__UpperCamelCase							)
				snake_case_      		= []
				for piece in pieces:
					if len(__UpperCamelCase							) > 1 and piece[-1] == str(','							) and piece[-2].isdigit():
						snake_case_      		= self.sp_model.EncodeAsPieces(piece[:-1].replace(__UpperCamelCase ,				''							)							)
						if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
							if len(cur_pieces[0]							) == 1:
								snake_case_      		= cur_pieces[1:]
							else:
								snake_case_      		= cur_pieces[0][1:]
						cur_pieces.append(piece[-1]							)
						new_pieces.extend(__UpperCamelCase							)
					else:
						new_pieces.append(__UpperCamelCase							)

				return new_pieces


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase							):

				"""simple docstring"""


				return self.sp_model.PieceToId(__UpperCamelCase							)


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase							):

				"""simple docstring"""


				return self.sp_model.IdToPiece(__UpperCamelCase							)


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase							):

				"""simple docstring"""


				snake_case_      		= []
				snake_case_      		= ''
				snake_case_      		= False
				for token in tokens:
					# make sure that special tokens are not decoded using sentencepiece model
					if token in self.all_special_tokens:
						if not prev_is_special:
							out_string += " "
						out_string += self.sp_model.decode(__UpperCamelCase							) + token
						snake_case_      		= True
						snake_case_      		= []
					else:
						current_sub_tokens.append(__UpperCamelCase							)
						snake_case_      		= False
				out_string += self.sp_model.decode(__UpperCamelCase							)
				return out_string.strip()


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase = None							):

				"""simple docstring"""


				snake_case_      		= [self.sep_token_id]
				snake_case_      		= [self.cls_token_id]
				if token_ids_a is None:
					return cls + token_ids_a + sep
				return cls + token_ids_a + sep + token_ids_a + sep


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase = None ,				__UpperCamelCase = False							):

				"""simple docstring"""


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

				if token_ids_a is not None:
					return [1] + ([0] * len(__UpperCamelCase							)) + [1] + ([0] * len(__UpperCamelCase							)) + [1]
				return [1] + ([0] * len(__UpperCamelCase							)) + [1]


			def 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase = None							):

				"""simple docstring"""


				snake_case_      		= [self.sep_token_id]
				snake_case_      		= [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 			__lowerCAmelCase (    self ,				__UpperCamelCase ,				__UpperCamelCase = None							):

				"""simple docstring"""


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

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

				return (out_vocab_file,)



import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


A											=     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')
A											=     parser.parse_args()


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

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

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

# optimize with ipex
A											=     torch.randn(2, 4, 64, 64)
A											=     torch.rand(1) * 999
A											=     torch.randn(2, 77, 768)
A											=     (sample, timestep, encoder_hidden_status)
try:
		A											=     ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True, sample_input=input_example)
except Exception:
		A											=     ipex.optimize(pipe.unet.eval(), dtype=torch.bfloataa, inplace=True)
A											=     ipex.optimize(pipe.vae.eval(), dtype=torch.bfloataa, inplace=True)
A											=     ipex.optimize(pipe.text_encoder.eval(), dtype=torch.bfloataa, inplace=True)
if pipe.requires_safety_checker:
		A											=     ipex.optimize(pipe.safety_checker.eval(), dtype=torch.bfloataa, inplace=True)

# compute
A											=     666
A											=     torch.Generator(device).manual_seed(seed)
A											=     {'generator': generator}
if args.steps is not None:
		A											=     args.steps

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

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