





"""simple docstring"""




import json
from typing import List, Optional, Tuple

from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tokenization_utils import JiebaPreTokenizer


UpperCamelCase_									=logging.get_logger(__name__)

UpperCamelCase_									={"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

UpperCamelCase_									={
    """vocab_file""": {
        """junnyu/roformer_chinese_small""": """https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/vocab.txt""",
        """junnyu/roformer_chinese_base""": """https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/vocab.txt""",
        """junnyu/roformer_chinese_char_small""": (
            """https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/vocab.txt"""
        ),
        """junnyu/roformer_chinese_char_base""": (
            """https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/vocab.txt"""
        ),
        """junnyu/roformer_small_discriminator""": (
            """https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/vocab.txt"""
        ),
        """junnyu/roformer_small_generator""": (
            """https://huggingface.co/junnyu/roformer_small_generator/resolve/main/vocab.txt"""
        ),
    }
}

UpperCamelCase_									={
    """junnyu/roformer_chinese_small""": 1_536,
    """junnyu/roformer_chinese_base""": 1_536,
    """junnyu/roformer_chinese_char_small""": 512,
    """junnyu/roformer_chinese_char_base""": 512,
    """junnyu/roformer_small_discriminator""": 128,
    """junnyu/roformer_small_generator""": 128,
}


UpperCamelCase_									={
    """junnyu/roformer_chinese_small""": {"""do_lower_case""": True},
    """junnyu/roformer_chinese_base""": {"""do_lower_case""": True},
    """junnyu/roformer_chinese_char_small""": {"""do_lower_case""": True},
    """junnyu/roformer_chinese_char_base""": {"""do_lower_case""": True},
    """junnyu/roformer_small_discriminator""": {"""do_lower_case""": True},
    """junnyu/roformer_small_generator""": {"""do_lower_case""": True},
}




class 			_a						(				_lowerCAmelCase    ):
				UpperCamelCase								=   VOCAB_FILES_NAMES
				UpperCamelCase								=   PRETRAINED_VOCAB_FILES_MAP
				UpperCamelCase								=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				UpperCamelCase								=   PRETRAINED_INIT_CONFIGURATION
				UpperCamelCase								=   RoFormerTokenizer






				def __init__(			self		:   Union[str, Any],			lowerCAmelCase__		:   Union[str, Any]=None,			lowerCAmelCase__		:   str=None,			lowerCAmelCase__		:   Dict=True,			lowerCAmelCase__		:   str="[UNK]",			lowerCAmelCase__		:   Optional[int]="[SEP]",			lowerCAmelCase__		:   List[str]="[PAD]",			lowerCAmelCase__		:   Optional[int]="[CLS]",			lowerCAmelCase__		:   int="[MASK]",			lowerCAmelCase__		:   str=True,			lowerCAmelCase__		:   Any=None,			**lowerCAmelCase__		:   Any,			)     ->				Optional[Any]:




										'''simple docstring'''



										super().__init__(
										    lowerCAmelCase__,			tokenizer_file=lowerCAmelCase__,			do_lower_case=lowerCAmelCase__,			unk_token=lowerCAmelCase__,			sep_token=lowerCAmelCase__,			pad_token=lowerCAmelCase__,			cls_token=lowerCAmelCase__,			mask_token=lowerCAmelCase__,			tokenize_chinese_chars=lowerCAmelCase__,			strip_accents=lowerCAmelCase__,			**lowerCAmelCase__,			)

										_UpperCamelCase      : Dict         =   json.loads(self.backend_tokenizer.normalizer.__getstate__()							)
										if (
										    pre_tok_state.get('''lowercase''',			lowerCAmelCase__							) != do_lower_case
										    or pre_tok_state.get('''strip_accents''',			lowerCAmelCase__							) != strip_accents
										):
																_UpperCamelCase      : Tuple         =   getattr(lowerCAmelCase__,			pre_tok_state.pop('''type'''							)							)
																_UpperCamelCase      : Optional[int]         =   do_lower_case
																_UpperCamelCase      : List[Any]         =   strip_accents
																_UpperCamelCase      : Dict         =   pre_tok_class(**lowerCAmelCase__							)

										_UpperCamelCase      : Dict         =   do_lower_case






				def __getstate__(			self		:   Optional[int]							)     ->				List[Any]:




										'''simple docstring'''



										_UpperCamelCase      : List[Any]         =   self.__dict__.copy()
										_UpperCamelCase      : Any         =   BertPreTokenizer()
										return state






				def __setstate__(			self		:   List[Any],			lowerCAmelCase__		:   List[str]							)     ->				Any:




										'''simple docstring'''



										_UpperCamelCase      : Union[str, Any]         =   d
										_UpperCamelCase      : List[str]         =   self.__dict__['''_tokenizer'''].get_vocab()
										_UpperCamelCase      : Tuple         =   PreTokenizer.custom(JiebaPreTokenizer(lowerCAmelCase__							)							)






				def     snake_case     (			self		:   Any,			lowerCAmelCase__		:   Dict,			lowerCAmelCase__		:   Optional[int]=None							)     ->				Tuple:




										'''simple docstring'''



										_UpperCamelCase      : List[Any]         =   [self.cls_token_id] + token_ids_a + [self.sep_token_id]

										if token_ids_a:
																output += token_ids_a + [self.sep_token_id]

										return output






				def     snake_case     (			self		:   Union[str, Any],			lowerCAmelCase__		:   List[int],			lowerCAmelCase__		:   Optional[List[int]] = None							)     ->				List[int]:




										'''simple docstring'''



										_UpperCamelCase      : Optional[int]         =   [self.sep_token_id]
										_UpperCamelCase      : Optional[Any]         =   [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     snake_case     (			self		:   str,			lowerCAmelCase__		:   str,			lowerCAmelCase__		:   Optional[str] = None							)     ->				Tuple[str]:




										'''simple docstring'''



										_UpperCamelCase      : Union[str, Any]         =   self._tokenizer.model.save(lowerCAmelCase__,			name=lowerCAmelCase__							)
										return tuple(lowerCAmelCase__							)






				def     snake_case     (			self		:   Optional[Any],			lowerCAmelCase__		:   Optional[Any],			lowerCAmelCase__		:   Tuple=None,			lowerCAmelCase__		:   List[Any]=None,			lowerCAmelCase__		:   Union[str, Any]=False,			**lowerCAmelCase__		:   List[Any],			)     ->				Union[str, Any]:




										'''simple docstring'''



										_UpperCamelCase      : Optional[Any]         =   BertPreTokenizer()
										return super().save_pretrained(lowerCAmelCase__,			lowerCAmelCase__,			lowerCAmelCase__,			lowerCAmelCase__,			**lowerCAmelCase__							)










"""simple docstring"""




import numpy as np
import torch
from imwatermark import WatermarkEncoder


# Copied from https://github.com/Stability-AI/generative-models/blob/613af104c6b85184091d42d374fef420eddb356d/scripts/demo/streamlit_helpers.py#L66
UpperCamelCase_									=0b1_0_1_1_0_0_1_1_1_1_1_0_1_1_0_0_1_0_0_1_0_0_0_0_0_1_1_1_1_0_1_1_1_0_1_1_0_0_0_1_1_0_0_1_1_1_1_0
# bin(x)[2:] gives bits of x as str, use int to convert them to 0/1
UpperCamelCase_									=[int(bit) for bit in bin(WATERMARK_MESSAGE)[2:]]




class 			_a						:






				def __init__(			self		:   str							)     ->				str:




										'''simple docstring'''



										_UpperCamelCase      : str         =   WATERMARK_BITS
										_UpperCamelCase      : Optional[int]         =   WatermarkEncoder()

										self.encoder.set_watermark('''bits''',			self.watermark							)






				def     snake_case     (			self		:   Dict,			lowerCAmelCase__		:   torch.FloatTensor							)     ->				int:




										'''simple docstring'''



										if images.shape[-1] < 2_5_6:
																return images

										_UpperCamelCase      : Union[str, Any]         =   (2_5_5 * (images / 2 + 0.5)).cpu().permute(0,			2,			3,			1							).float().numpy()

										_UpperCamelCase      : List[str]         =   [self.encoder.encode(lowerCAmelCase__,			'''dwtDct'''							) for image in images]

										_UpperCamelCase      : Dict         =   torch.from_numpy(np.array(lowerCAmelCase__							)							).permute(0,			3,			1,			2							)

										_UpperCamelCase      : Optional[int]         =   torch.clamp(2 * (images / 2_5_5 - 0.5),			min=-1.0,			max=1.0							)
										return images



