

import json
import os
from typing import List, Optional, Tuple

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


A_			  =				logging.get_logger(__name__)

A_			  =				{
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
}

A_			  =				{
    'vocab_file': {
        'microsoft/biogpt': 'https://huggingface.co/microsoft/biogpt/resolve/main/vocab.json',
    },
    'merges_file': {'microsoft/biogpt': 'https://huggingface.co/microsoft/biogpt/resolve/main/merges.txt'},
}

A_			  =				{
    'microsoft/biogpt': 1024,
}


def  __a    (				SCREAMING_SNAKE_CASE ) ->       List[str]:






    '''simple docstring'''
    __UpperCAmelCase			   =     set()
    __UpperCAmelCase			   =     word[0]
    for char in word[1:]:
        pairs.add((prev_char, char) )
        __UpperCAmelCase			   =     char
    return pairs
class    A_				( lowerCAmelCase__  ):


    '''simple docstring'''






    a__       							=    VOCAB_FILES_NAMES
    a__       							=    PRETRAINED_VOCAB_FILES_MAP
    a__       							=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    a__       							=    ["input_ids", "attention_mask"]


    def __init__(self       , lowercase__       , lowercase__       , lowercase__="<unk>"       , lowercase__="<s>"       , lowercase__="</s>"       , lowercase__="</s>"       , lowercase__="<pad>"       , **lowercase__       , )       -> Union[str, Any]:
        super().__init__(
            bos_token=_A       , eos_token=_A       , sep_token=_A       , unk_token=_A       , pad_token=_A       , **_A       , )

        try:
            import sacremoses
        except ImportError:
            raise ImportError(
                '''You need to install sacremoses to use BioGptTokenizer. '''
                '''See https://pypi.org/project/sacremoses/ for installation.'''					)

        __UpperCAmelCase			   =     '''en'''
        __UpperCAmelCase			   =     sacremoses
        # cache of sm.MosesTokenizer instance
        __UpperCAmelCase			   =     {}
        __UpperCAmelCase			   =     {}
        with open(_A       , encoding='''utf-8'''					) as vocab_handle:
            __UpperCAmelCase			   =     json.load(_A					)
        __UpperCAmelCase			   =     {v: k for k, v in self.encoder.items()}
        with open(_A       , encoding='''utf-8'''					) as merges_handle:
            __UpperCAmelCase			   =     merges_handle.read().split('''\n'''					)[:-1]
        __UpperCAmelCase			   =     [tuple(merge.split()[:2]					) for merge in merges]
        __UpperCAmelCase			   =     dict(zip(_A       , range(len(_A					)					)					)					)
        __UpperCAmelCase			   =     {}


    @property
    def        lowerCAmelCase_  (self					)       -> Optional[Any]:
        return len(self.encoder					)


    def        lowerCAmelCase_  (self					)       -> List[str]:
        return dict(self.encoder       , **self.added_tokens_encoder					)


    def        lowerCAmelCase_  (self       , lowercase__       , lowercase__					)       -> Union[str, Any]:




        if lang not in self.cache_moses_tokenizer:
            __UpperCAmelCase			   =     self.sm.MosesTokenizer(lang=_A					)
            __UpperCAmelCase			   =     moses_tokenizer
        return self.cache_moses_tokenizer[lang].tokenize(
            _A       , aggressive_dash_splits=_A       , return_str=_A       , escape=_A					)


    def        lowerCAmelCase_  (self       , lowercase__       , lowercase__					)       -> str:




        if lang not in self.cache_moses_detokenizer:
            __UpperCAmelCase			   =     self.sm.MosesDetokenizer(lang=_A					)
            __UpperCAmelCase			   =     moses_detokenizer
        return self.cache_moses_detokenizer[lang].detokenize(_A					)


    def        lowerCAmelCase_  (self       , lowercase__					)       -> Optional[int]:
        __UpperCAmelCase			   =     tuple(token[:-1]					) + (token[-1] + '''</w>''',)
        if token in self.cache:
            return self.cache[token]
        __UpperCAmelCase			   =     get_pairs(_A					)

        if not pairs:
            return token + "</w>"

        while True:
            __UpperCAmelCase			   =     min(_A       , key=lambda lowercase__					: self.bpe_ranks.get(_A       , float('''inf'''					)					)					)
            if bigram not in self.bpe_ranks:
                break
            __UpperCAmelCase			   =     bigram
            __UpperCAmelCase			   =     []
            __UpperCAmelCase			   =     0
            while i < len(_A					):
                try:
                    __UpperCAmelCase			   =     word.index(_A       , _A					)
                except ValueError:
                    new_word.extend(word[i:]					)
                    break
                else:
                    new_word.extend(word[i:j]					)
                    __UpperCAmelCase			   =     j

                if word[i] == first and i < len(_A					) - 1 and word[i + 1] == second:
                    new_word.append(first + second					)
                    i += 2
                else:
                    new_word.append(word[i]					)
                    i += 1
            __UpperCAmelCase			   =     tuple(_A					)
            __UpperCAmelCase			   =     new_word
            if len(_A					) == 1:
                break
            else:
                __UpperCAmelCase			   =     get_pairs(_A					)
        __UpperCAmelCase			   =     ''' '''.join(_A					)
        if word == "\n  </w>":
            __UpperCAmelCase			   =     '''\n</w>'''
        __UpperCAmelCase			   =     word
        return word


    def        lowerCAmelCase_  (self       , lowercase__       , lowercase__=False					)       -> str:




        if bypass_tokenizer:
            __UpperCAmelCase			   =     text.split()
        else:
            __UpperCAmelCase			   =     self.moses_tokenize(_A       , self.lang					)

        __UpperCAmelCase			   =     []
        for token in text:
            if token:
                split_tokens.extend(list(self.bpe(_A					).split(''' '''					)					)					)

        return split_tokens


    def        lowerCAmelCase_  (self       , lowercase__					)       -> Union[str, Any]:
        return self.encoder.get(_A       , self.encoder.get(self.unk_token					)					)


    def        lowerCAmelCase_  (self       , lowercase__					)       -> List[str]:
        return self.decoder.get(_A       , self.unk_token					)


    def        lowerCAmelCase_  (self       , lowercase__					)       -> Optional[Any]:
        __UpperCAmelCase			   =     [t.replace(''' '''       , ''''''					).replace('''</w>'''       , ''' '''					) for t in tokens]
        __UpperCAmelCase			   =     ''''''.join(_A					).split()
        # detokenize
        __UpperCAmelCase			   =     self.moses_detokenize(_A       , self.lang					)
        return text


    def        lowerCAmelCase_  (self       , lowercase__       , lowercase__ = None					)       -> List[Any]:




        if token_ids_a is None:
            return [self.sep_token_id] + token_ids_a
        __UpperCAmelCase			   =     [self.sep_token_id]
        return sep + token_ids_a + sep + token_ids_a


    def        lowerCAmelCase_  (self       , lowercase__       , lowercase__ = None       , lowercase__ = False					)       -> Optional[int]:




        if already_has_special_tokens:
            return super().get_special_tokens_mask(
                token_ids_a=_A       , token_ids_a=_A       , already_has_special_tokens=_A					)
        # no bos used in fairseq
        if token_ids_a is not None:
            return [1] + ([0] * len(_A					)) + [1] + ([0] * len(_A					))
        return [1] + ([0] * len(_A					))


    def        lowerCAmelCase_  (self       , lowercase__       , lowercase__ = None					)       -> int:
        __UpperCAmelCase			   =     [self.sep_token_id]

        # no bos used in fairseq
        if token_ids_a is None:
            return len(token_ids_a + sep					) * [0]
        return len(token_ids_a + sep					) * [0] + len(token_ids_a + sep					) * [1]


    def        lowerCAmelCase_  (self       , lowercase__       , lowercase__ = None					)       -> str:




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

        with open(_A       , '''w'''       , encoding='''utf-8'''					) as f:
            f.write(json.dumps(self.encoder       , indent=2       , sort_keys=_A       , ensure_ascii=_A					) + '''\n'''					)

        __UpperCAmelCase			   =     0
        with open(_A       , '''w'''       , encoding='''utf-8'''					) as writer:
            for bpe_tokens, token_index in sorted(self.bpe_ranks.items()       , key=lambda lowercase__					: kv[1]					):
                if index != token_index:
                    logger.warning(
                        F'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
                        ''' Please check that the tokenizer is not corrupted!'''					)
                    __UpperCAmelCase			   =     token_index
                writer.write(''' '''.join(_A					) + '''\n'''					)
                index += 1

        return vocab_file, merge_file


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






    def __setstate__(self       , lowercase__					)       -> List[Any]:
        __UpperCAmelCase			   =     d

        try:
            import sacremoses
        except ImportError:
            raise ImportError(
                '''You need to install sacremoses to use XLMTokenizer. '''
                '''See https://pypi.org/project/sacremoses/ for installation.'''					)

        __UpperCAmelCase			   =     sacremoses







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_				( _a  ):


    '''simple docstring'''






    a__       							=    {
        "sld_warmup_steps": 15,
        "sld_guidance_scale": 20,
        "sld_threshold": 0.0,
        "sld_momentum_scale": 0.0,
        "sld_mom_beta": 0.0,
    }
    a__       							=    {
        "sld_warmup_steps": 10,
        "sld_guidance_scale": 10_00,
        "sld_threshold": 0.01,
        "sld_momentum_scale": 0.3,
        "sld_mom_beta": 0.4,
    }
    a__       							=    {
        "sld_warmup_steps": 7,
        "sld_guidance_scale": 20_00,
        "sld_threshold": 0.0_25,
        "sld_momentum_scale": 0.5,
        "sld_mom_beta": 0.7,
    }
    a__       							=    {
        "sld_warmup_steps": 0,
        "sld_guidance_scale": 50_00,
        "sld_threshold": 1.0,
        "sld_momentum_scale": 0.5,
        "sld_mom_beta": 0.7,
    }






@dataclass
class    A_				( _a  ):


    '''simple docstring'''






    a__       							=    42
    a__       							=    42
    a__       							=    42
    a__       							=    42


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




