




'''simple docstring'''




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

import sentencepiece as spm

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


_lowercase		:				Union[str, Any]	       =		logging.get_logger(__name__)

_lowercase		:				Any	       =		'''▁'''

_lowercase		:				List[str]	       =		{'''vocab_file''': '''sentencepiece.bpe.model'''}

_lowercase		:				Tuple	       =		{
    '''vocab_file''': {
        '''facebook/xglm-564M''': '''https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model''',
    }
}

_lowercase		:				Optional[int]	       =		{
    '''facebook/xglm-564M''': 2048,
}





class 					__magic_name__		(					_UpperCAmelCase):
			UpperCamelCase__       = VOCAB_FILES_NAMES
			UpperCamelCase__       = PRETRAINED_VOCAB_FILES_MAP
			UpperCamelCase__       = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			UpperCamelCase__       = ['input_ids', 'attention_mask']







			def __init__(			self       :						Dict       ,    lowercase_       :						List[str]       ,    lowercase_       :						Any="<s>"       ,    lowercase_       :						Union[str, Any]="</s>"       ,    lowercase_       :						Optional[Any]="</s>"       ,    lowercase_       :						List[str]="<s>"       ,    lowercase_       :						Optional[int]="<unk>"       ,    lowercase_       :						Union[str, Any]="<pad>"       ,    lowercase_       :						Optional[Dict[str, Any]] = None       ,    **lowercase_       :						List[Any]       ,    ):
										lowercase_      :    Optional[int]       		= {} if sp_model_kwargs is None else sp_model_kwargs

										# Compatibility with the original tokenizer
										lowercase_      :    int       		= 7
										lowercase_      :    List[Any]       		= [f'''<madeupword{i}>''' for i in range(self.num_madeup_words					)]

										lowercase_      :    Union[str, Any]       		= kwargs.get("""additional_special_tokens"""       ,    []					)
										kwargs["additional_special_tokens"] += [
										    word for word in madeup_words if word not in kwargs["additional_special_tokens"]
										]

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

										lowercase_      :    Tuple       		= spm.SentencePieceProcessor(**self.sp_model_kwargs					)
										self.sp_model.Load(str(__UpperCamelCase					)					)
										lowercase_      :    Union[str, Any]       		= 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'

										# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
										lowercase_      :    Union[str, Any]       		= 1

										# Mimic fairseq token-to-id alignment for the first 4 token
										lowercase_      :    Dict       		= {"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}

										lowercase_      :    Optional[Any]       		= len(self.sp_model					)
										lowercase_      :    List[Any]       		= {f'''<madeupword{i}>''': sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words					)}
										self.fairseq_tokens_to_ids.update(__UpperCamelCase					)

										lowercase_      :    List[Any]       		= {v: k for k, v in self.fairseq_tokens_to_ids.items()}







			def __getstate__(			self       :						Optional[Any]					):
										lowercase_      :    int       		= self.__dict__.copy()
										lowercase_      :    int       		= None
										lowercase_      :    Tuple       		= self.sp_model.serialized_model_proto()
										return state







			def __setstate__(			self       :						Dict       ,    lowercase_       :						Optional[Any]					):
										lowercase_      :    Optional[Any]       		= d

										# for backward compatibility
										if not hasattr(self       ,    """sp_model_kwargs"""					):
																	lowercase_      :    Union[str, Any]       		= {}

										lowercase_      :    Tuple       		= spm.SentencePieceProcessor(**self.sp_model_kwargs					)
										self.sp_model.LoadFromSerializedProto(self.sp_model_proto					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[int]       ,    lowercase_       :						List[int]       ,    lowercase_       :						Optional[List[int]] = None					):






										if token_ids_a is None:
																	return [self.sep_token_id] + token_ids_a
										lowercase_      :    List[str]       		= [self.sep_token_id]
										return sep + token_ids_a + sep + sep + token_ids_a







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Dict       ,    lowercase_       :						List[int]       ,    lowercase_       :						Optional[List[int]] = None       ,    lowercase_       :						bool = False					):






										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 None:
																	return [1] + ([0] * len(__UpperCamelCase					))
										return [1] + ([0] * len(__UpperCamelCase					)) + [1, 1] + ([0] * len(__UpperCamelCase					))







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Any       ,    lowercase_       :						List[int]       ,    lowercase_       :						Optional[List[int]] = None					):
										lowercase_      :    int       		= [self.sep_token_id]

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







			@property
			def 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[Any]					):
										return len(self.sp_model					) + self.fairseq_offset + self.num_madeup_words







			def 			SCREAMING_SNAKE_CASE_  (			self       :						str					):
										lowercase_      :    Optional[int]       		= {self.convert_ids_to_tokens(__UpperCamelCase					): i for i in range(self.vocab_size					)}
										vocab.update(self.added_tokens_encoder					)
										return vocab







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[int]       ,    lowercase_       :						str					):
										return self.sp_model.encode(__UpperCamelCase       ,    out_type=__UpperCamelCase					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						List[Any]       ,    lowercase_       :						Dict					):






										if token in self.fairseq_tokens_to_ids:
																	return self.fairseq_tokens_to_ids[token]
										lowercase_      :    Any       		= self.sp_model.PieceToId(__UpperCamelCase					)

										# 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       :						Tuple       ,    lowercase_       :						Union[str, Any]					):






										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       :						Optional[Any]       ,    lowercase_       :						List[str]					):
										lowercase_      :    str       		= """""".join(__UpperCamelCase					).replace(__UpperCamelCase       ,    """ """					).strip()
										return out_string







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Any       ,    lowercase_       :						str       ,    lowercase_       :						Optional[str] = None					):






										if not os.path.isdir(__UpperCamelCase					):
																	logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''					)
																	return
										lowercase_      :    Any       		= 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:
																								lowercase_      :    List[str]       		= self.sp_model.serialized_model_proto()
																								fi.write(__UpperCamelCase					)

										return (out_vocab_file,)






'''simple docstring'''




import argparse

import intel_extension_for_pytorch as ipex
import torch

from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline


_lowercase		:				Optional[Any]	       =		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		:				Dict	       =		parser.parse_args()


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

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

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

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

# compute
_lowercase		:				int	       =		666
_lowercase		:				Any	       =		torch.Generator(device).manual_seed(seed)
_lowercase		:				int	       =		{"generator": generator}
if args.steps is not None:
						_lowercase		:				Optional[int]	       =		args.steps

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

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





