






"""simple docstring"""



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

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
    from .tokenization_mbartaa import MBartaaTokenizer
else:
    _UpperCamelCase						:    Tuple		=						None


_UpperCamelCase						:    str		=						logging.get_logger(__name__)

_UpperCamelCase						:    Tuple		=						{'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

_UpperCamelCase						:    str		=						{
    'vocab_file': {
        'facebook/mbart-large-50-one-to-many-mmt': (
            'https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model'
        ),
    },
    'tokenizer_file': {
        'facebook/mbart-large-50-one-to-many-mmt': (
            'https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/tokenizer.json'
        ),
    },
}

_UpperCamelCase						:    Optional[int]		=						{
    'facebook/mbart-large-50-one-to-many-mmt': 1_0_2_4,
}

# fmt: off
_UpperCamelCase						:    List[Any]		=						['ar_AR', 'cs_CZ', 'de_DE', 'en_XX', 'es_XX', 'et_EE', 'fi_FI', 'fr_XX', 'gu_IN', 'hi_IN', 'it_IT', 'ja_XX', 'kk_KZ', 'ko_KR', 'lt_LT', 'lv_LV', 'my_MM', 'ne_NP', 'nl_XX', 'ro_RO', 'ru_RU', 'si_LK', 'tr_TR', 'vi_VN', 'zh_CN', 'af_ZA', 'az_AZ', 'bn_IN', 'fa_IR', 'he_IL', 'hr_HR', 'id_ID', 'ka_GE', 'km_KH', 'mk_MK', 'ml_IN', 'mn_MN', 'mr_IN', 'pl_PL', 'ps_AF', 'pt_XX', 'sv_SE', 'sw_KE', 'ta_IN', 'te_IN', 'th_TH', 'tl_XX', 'uk_UA', 'ur_PK', 'xh_ZA', 'gl_ES', 'sl_SI']

class 			a	(  a_      ):
    UpperCAmelCase_					:	Any      	=VOCAB_FILES_NAMES
    UpperCAmelCase_					:	str      	=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    UpperCAmelCase_					:	Union[str, Any]      	=PRETRAINED_VOCAB_FILES_MAP
    UpperCAmelCase_					:	Optional[int]      	=["input_ids", "attention_mask"]
    UpperCAmelCase_					:	Optional[Any]      	=MBartaaTokenizer

    UpperCAmelCase_					:	List[int]      	=[]
    UpperCAmelCase_					:	List[int]      	=[]







    def __init__(     self				,						_lowerCamelCase=None				,						_lowerCamelCase=None				,						_lowerCamelCase=None				,						_lowerCamelCase=None				,						_lowerCamelCase="</s>"				,						_lowerCamelCase="</s>"				,						_lowerCamelCase="<s>"				,						_lowerCamelCase="<unk>"				,						_lowerCamelCase="<pad>"				,						_lowerCamelCase="<mask>"				,						**_lowerCamelCase				,						):
      # Mask token behave like a normal word, i.e. include the space before it
      lowercase				       =			AddedToken(_lowerCamelCase				,						lstrip=_lowerCamelCase				,						rstrip=_lowerCamelCase			) if isinstance(_lowerCamelCase				,						_lowerCamelCase			) else mask_token

      lowercase				       =			kwargs.get('additional_special_tokens'				,						[]			)
      kwargs["additional_special_tokens"] += [
          code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
      ]

      super().__init__(
          _lowerCamelCase				,						src_lang=_lowerCamelCase				,						tgt_lang=_lowerCamelCase				,						tokenizer_file=_lowerCamelCase				,						eos_token=_lowerCamelCase				,						sep_token=_lowerCamelCase				,						cls_token=_lowerCamelCase				,						unk_token=_lowerCamelCase				,						pad_token=_lowerCamelCase				,						mask_token=_lowerCamelCase				,						**_lowerCamelCase				,						)

      lowercase				       =			vocab_file
      lowercase				       =			False if not self.vocab_file else True

      lowercase				       =			{
          lang_code: self.convert_tokens_to_ids(_lowerCamelCase			) for lang_code in FAIRSEQ_LANGUAGE_CODES
      }

      lowercase				       =			src_lang if src_lang is not None else 'en_XX'
      lowercase				       =			tgt_lang
      lowercase				       =			self.lang_code_to_id[self._src_lang]
      self.set_src_lang_special_tokens(self._src_lang			)







    @property
    def      UpperCamelCase_ (     self			):
      return self._src_lang







    @src_lang.setter
    def      UpperCamelCase_ (     self				,						_lowerCamelCase			):
      lowercase				       =			new_src_lang
      self.set_src_lang_special_tokens(self._src_lang			)







    def      UpperCamelCase_ (     self				,						_lowerCamelCase				,						_lowerCamelCase = None			):
      if token_ids_a is None:
        return self.prefix_tokens + token_ids_a + self.suffix_tokens
      # 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.suffix_tokens







    def      UpperCamelCase_ (     self				,						_lowerCamelCase				,						_lowerCamelCase = "en_XX"				,						_lowerCamelCase = None				,						_lowerCamelCase = "ro_RO"				,						**_lowerCamelCase				,						):
      lowercase				       =			src_lang
      lowercase				       =			tgt_lang
      return super().prepare_seqaseq_batch(_lowerCamelCase				,						_lowerCamelCase				,						**_lowerCamelCase			)







    def      UpperCamelCase_ (     self			):
      return self.set_src_lang_special_tokens(self.src_lang			)







    def      UpperCamelCase_ (     self			):
      return self.set_tgt_lang_special_tokens(self.tgt_lang			)







    def      UpperCamelCase_ (     self				,						_lowerCamelCase			):
      lowercase				       =			self.convert_tokens_to_ids(_lowerCamelCase			)
      lowercase				       =			[self.cur_lang_code_id]
      lowercase				       =			[self.eos_token_id]

      lowercase				       =			self.convert_ids_to_tokens(self.prefix_tokens			)
      lowercase				       =			self.convert_ids_to_tokens(self.suffix_tokens			)

      lowercase				       =			processors.TemplateProcessing(
          single=prefix_tokens_str + ['$A'] + suffix_tokens_str				,						pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str				,						special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str				,						self.prefix_tokens + self.suffix_tokens			)			)				,						)







    def      UpperCamelCase_ (     self				,						_lowerCamelCase			):
      lowercase				       =			self.convert_tokens_to_ids(_lowerCamelCase			)
      lowercase				       =			[self.cur_lang_code_id]
      lowercase				       =			[self.eos_token_id]

      lowercase				       =			self.convert_ids_to_tokens(self.prefix_tokens			)
      lowercase				       =			self.convert_ids_to_tokens(self.suffix_tokens			)

      lowercase				       =			processors.TemplateProcessing(
          single=prefix_tokens_str + ['$A'] + suffix_tokens_str				,						pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str				,						special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str				,						self.prefix_tokens + self.suffix_tokens			)			)				,						)







    def      UpperCamelCase_ (     self				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						_lowerCamelCase				,						**_lowerCamelCase			):
      if src_lang is None or tgt_lang is None:
        raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model'			)
      lowercase				       =			src_lang
      lowercase				       =			self(_lowerCamelCase				,						add_special_tokens=_lowerCamelCase				,						return_tensors=_lowerCamelCase				,						**_lowerCamelCase			)
      lowercase				       =			self.convert_tokens_to_ids(_lowerCamelCase			)
      lowercase				       =			tgt_lang_id
      return inputs







    def      UpperCamelCase_ (     self				,						_lowerCamelCase				,						_lowerCamelCase = None			):
      if not self.can_save_slow_tokenizer:
        raise ValueError(
            'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
            'tokenizer.'			)

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

      if os.path.abspath(self.vocab_file			) != os.path.abspath(_lowerCamelCase			):
        copyfile(self.vocab_file				,						_lowerCamelCase			)

      return (out_vocab_file,)






'''simple docstring'''






from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available




@dataclass
class 			a  (  _SCREAMING_SNAKE_CASE							):
 _lowerCAmelCase			       =						{
     """sld_warmup_steps""": 1_5,
     """sld_guidance_scale""": 2_0,
     """sld_threshold""": 0.0,
     """sld_momentum_scale""": 0.0,
     """sld_mom_beta""": 0.0,
 }
 _lowerCAmelCase			       =						{
     """sld_warmup_steps""": 1_0,
     """sld_guidance_scale""": 1_0_0_0,
     """sld_threshold""": 0.01,
     """sld_momentum_scale""": 0.3,
     """sld_mom_beta""": 0.4,
 }
 _lowerCAmelCase			       =						{
     """sld_warmup_steps""": 7,
     """sld_guidance_scale""": 2_0_0_0,
     """sld_threshold""": 0.025,
     """sld_momentum_scale""": 0.5,
     """sld_mom_beta""": 0.7,
 }
 _lowerCAmelCase			       =						{
     """sld_warmup_steps""": 0,
     """sld_guidance_scale""": 5_0_0_0,
     """sld_threshold""": 1.0,
     """sld_momentum_scale""": 0.5,
     """sld_mom_beta""": 0.7,
 }





@dataclass
class 			a  (  _SCREAMING_SNAKE_CASE							):
 _lowerCAmelCase			       =						42
 _lowerCAmelCase			       =						42
 _lowerCAmelCase			       =						42
 _lowerCAmelCase			       =						42


if is_transformers_available() and is_torch_available():
 from .pipeline_stable_diffusion_safe import StableDiffusionPipelineSafe
 from .safety_checker import SafeStableDiffusionSafetyChecker

