import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

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


UpperCamelCase  							=						logging.get_logger(__name__)

UpperCamelCase  							=						"▁"

UpperCamelCase  							=						{
    "vocab_file": "vocab.json",
    "spm_file": "sentencepiece.bpe.model",
}

UpperCamelCase  							=						{
    "vocab_file": {
        "facebook/s2t-small-librispeech-asr": (
            "https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/vocab.json"
        ),
    },
    "spm_file": {
        "facebook/s2t-small-librispeech-asr": (
            "https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/sentencepiece.bpe.model"
        )
    },
}

UpperCamelCase  							=						{
    "facebook/s2t-small-librispeech-asr": 1024,
}

UpperCamelCase  							=						["pt", "fr", "ru", "nl", "ro", "it", "es", "de"]

UpperCamelCase  							=						{"mustc": MUSTC_LANGS}



class   snake_case_       (		_lowerCamelCase       ):
   __A	:		Dict         =				VOCAB_FILES_NAMES
   __A	:		int         =				PRETRAINED_VOCAB_FILES_MAP
   __A	:		Tuple         =				MAX_MODEL_INPUT_SIZES
   __A	:		Any         =				["input_ids", "attention_mask"]

   __A	:		List[str]         =				[]






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

       super().__init__(
           bos_token=lowerCAmelCase_	,						eos_token=lowerCAmelCase_	,						unk_token=lowerCAmelCase_	,						pad_token=lowerCAmelCase_	,						do_upper_case=lowerCAmelCase_	,						do_lower_case=lowerCAmelCase_	,						tgt_lang=lowerCAmelCase_	,						lang_codes=lowerCAmelCase_	,						sp_model_kwargs=self.sp_model_kwargs	,						**lowerCAmelCase_	,						)
       lowercase__ :   Optional[int]    					=				do_upper_case
       lowercase__ :   Any    					=				do_lower_case

       lowercase__ :   Dict    					=				load_json(lowerCAmelCase_  )
       lowercase__ :   Optional[int]    					=				{v: k for k, v in self.encoder.items()}
       lowercase__ :   str    					=				spm_file
       lowercase__ :   Any    					=				load_spm(lowerCAmelCase_	,						self.sp_model_kwargs  )

       if lang_codes is not None:
           lowercase__ :   str    					=				lang_codes
           lowercase__ :   Tuple    					=				LANGUAGES[lang_codes]
           lowercase__ :   Dict    					=				[F'''<lang:{lang}>''' for lang in self.langs]
           lowercase__ :   Tuple    					=				{lang: self.sp_model.PieceToId(F'''<lang:{lang}>'''  ) for lang in self.langs}

           lowercase__ :   Any    					=				self.lang_tokens
           lowercase__ :   Tuple    					=				tgt_lang if tgt_lang is not None else self.langs[0]

           self.set_tgt_lang_special_tokens(self._tgt_lang  )
       else:
           lowercase__ :   List[str]    					=				{}






   @property
   def       __UpperCamelCase     (      self				:     str  )					->						Optional[int]:
       return len(self.encoder  )






   @property
   def       __UpperCamelCase     (      self				:     str  )					->						int:
       return self._tgt_lang






   @tgt_lang.setter
   def       __UpperCamelCase     (      self				:     Any	,						lowercase_				:     Optional[int]  )					->						Optional[int]:
       lowercase__ :   Union[str, Any]    					=				new_tgt_lang
       self.set_tgt_lang_special_tokens(lowerCAmelCase_  )






   def       __UpperCamelCase     (      self				:     Optional[Any]	,						lowercase_				:     str  )					->						Dict:
       lowercase__ :   Optional[Any]    					=				self.lang_code_to_id[tgt_lang]
       lowercase__ :   Optional[Any]    					=				[lang_code_id]






   def       __UpperCamelCase     (      self				:     List[Any]	,						lowercase_				:     str  )					->						str:
       return self.sp_model.encode(lowerCAmelCase_	,						out_type=lowerCAmelCase_  )






   def       __UpperCamelCase     (      self				:     str	,						lowercase_				:     Union[str, Any]  )					->						List[str]:
       return self.encoder.get(lowerCAmelCase_	,						self.encoder[self.unk_token]  )






   def       __UpperCamelCase     (      self				:     Union[str, Any]	,						lowercase_				:     Any  )					->						Union[str, Any]:
       return self.decoder.get(lowerCAmelCase_	,						self.unk_token  )






   def       __UpperCamelCase     (      self				:     int	,						lowercase_				:     Tuple  )					->						int:
       lowercase__ :   Any    					=				[]
       lowercase__ :   Optional[Any]    					=				""
       for token in tokens:
           # make sure that special tokens are not decoded using sentencepiece model
           if token in self.all_special_tokens:
               lowercase__ :   Tuple    					=				self.sp_model.decode(lowerCAmelCase_  )
               out_string += (decoded.upper() if self.do_upper_case else decoded) + token + " "
               lowercase__ :   Dict    					=				[]
           else:
               current_sub_tokens.append(lowerCAmelCase_  )
       lowercase__ :   Dict    					=				self.sp_model.decode(lowerCAmelCase_  )
       out_string += decoded.upper() if self.do_upper_case else decoded
       return out_string.strip()






   def       __UpperCamelCase     (      self				:     Optional[int]	,						lowercase_				:     Union[str, Any]	,						lowercase_				:     Optional[Any]=None  )					->						Optional[int]:






       if token_ids_a is None:
           return self.prefix_tokens + token_ids_a + [self.eos_token_id]
       # We don't expect to process pairs, but leave the pair logic for API consistency
       return self.prefix_tokens + token_ids_a + token_ids_a + [self.eos_token_id]






   def       __UpperCamelCase     (      self				:     Union[str, Any]	,						lowercase_				:     Any	,						lowercase_				:     int = None	,						lowercase_				:     List[Any] = False  )					->						List[str]:






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

       lowercase__ :   List[str]    					=				[1] * len(self.prefix_tokens  )
       lowercase__ :   Optional[Any]    					=				[1]
       if token_ids_a is None:
           return prefix_ones + ([0] * len(lowerCAmelCase_  )) + suffix_ones
       return prefix_ones + ([0] * len(lowerCAmelCase_  )) + ([0] * len(lowerCAmelCase_  )) + suffix_ones






   def       __UpperCamelCase     (      self				:     Dict  )					->						Union[str, Any]:
       lowercase__ :   List[str]    					=				self.encoder.copy()
       vocab.update(self.added_tokens_encoder  )
       return vocab






   def __getstate__(      self				:     str  )					->						Union[str, Any]:
       lowercase__ :   Optional[Any]    					=				self.__dict__.copy()
       lowercase__ :   List[Any]    					=				None
       return state






   def __setstate__(      self				:     int	,						lowercase_				:     List[str]  )					->						Optional[int]:
       lowercase__ :   Any    					=				d

       # for backward compatibility
       if not hasattr(self	,						"sp_model_kwargs"  ):
           lowercase__ :   Any    					=				{}

       lowercase__ :   Union[str, Any]    					=				load_spm(self.spm_file	,						self.sp_model_kwargs  )







   def       __UpperCamelCase     (      self				:     Tuple	,						lowercase_				:     Tuple	,						lowercase_				:     str = None  )					->						Union[str, Any]:
       lowercase__ :   Dict    					=				Path(lowerCAmelCase_  )
       assert save_dir.is_dir(), F'''{save_directory} should be a directory'''
       lowercase__ :   Tuple    					=				save_dir / (
           (filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["vocab_file"]
       )
       lowercase__ :   Optional[Any]    					=				save_dir / (
           (filename_prefix + "-" if filename_prefix else "") + self.vocab_files_names["spm_file"]
       )

       save_json(self.encoder	,						lowerCAmelCase_  )

       if os.path.abspath(self.spm_file  ) != os.path.abspath(lowerCAmelCase_  ) and os.path.isfile(self.spm_file  ):
           copyfile(self.spm_file	,						lowerCAmelCase_  )
       elif not os.path.isfile(self.spm_file  ):
           with open(lowerCAmelCase_	,						"wb"  ) as fi:
               lowercase__ :   List[Any]    					=				self.sp_model.serialized_model_proto()
               fi.write(lowerCAmelCase_  )

       return (str(lowerCAmelCase_  ), str(lowerCAmelCase_  ))



def 			lowercase_				(			_lowerCamelCase							:       Any		,	_lowerCamelCase							:       List[Any]):
    lowercase__ :   Any    					=				sentencepiece.SentencePieceProcessor(**__A)
    spm.Load(str(__A))
    return spm

def 			lowercase_				(			_lowerCamelCase							:       str):
    with open(__A		,	"r") as f:
        return json.load(__A)

def 			lowercase_				(			_lowerCamelCase							:       Optional[int]		,	_lowerCamelCase							:       Optional[int]):
    with open(__A		,	"w") as f:
        json.dump(__A		,	__A		,	indent=2)





'''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    :							Tuple      =					parser.parse_args()


lowercase    :							Optional[int]      =					"cpu"
lowercase    :							Optional[Any]      =					"a lovely <dicoo> in red dress and hat, in the snowly and brightly night, with many brighly buildings"

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

# to channels last
lowercase    :							Optional[Any]      =					pipe.unet.to(memory_format=torch.channels_last)
lowercase    :							int      =					pipe.vae.to(memory_format=torch.channels_last)
lowercase    :							Optional[Any]      =					pipe.text_encoder.to(memory_format=torch.channels_last)
if pipe.requires_safety_checker:
					lowercase    :							Optional[int]      =					pipe.safety_checker.to(memory_format=torch.channels_last)

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

# compute
lowercase    :							List[str]      =					666
lowercase    :							Tuple      =					torch.Generator(device).manual_seed(seed)
lowercase    :							Union[str, Any]      =					{"generator": generator}
if args.steps is not None:
					lowercase    :							Dict      =					args.steps

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

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

