





"""simple docstring"""




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

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


if is_sentencepiece_available():
						from .tokenization_fnet import FNetTokenizer
else:
						UpperCamelCase_									=None

UpperCamelCase_									=logging.get_logger(__name__)
UpperCamelCase_									={"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

UpperCamelCase_									={
    """vocab_file""": {
        """google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/spiece.model""",
        """google/fnet-large""": """https://huggingface.co/google/fnet-large/resolve/main/spiece.model""",
    },
    """tokenizer_file""": {
        """google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/tokenizer.json""",
        """google/fnet-large""": """https://huggingface.co/google/fnet-large/resolve/main/tokenizer.json""",
    },
}

UpperCamelCase_									={
    """google/fnet-base""": 512,
    """google/fnet-large""": 512,
}

UpperCamelCase_									="""▁"""




class 			_a						(				_lowerCAmelCase    ):
				UpperCamelCase								=   VOCAB_FILES_NAMES
				UpperCamelCase								=   PRETRAINED_VOCAB_FILES_MAP
				UpperCamelCase								=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				UpperCamelCase								=   ['''input_ids''', '''token_type_ids''']
				UpperCamelCase								=   FNetTokenizer






				def __init__(			self		:   int,			lowerCAmelCase__		:   List[str]=None,			lowerCAmelCase__		:   Union[str, Any]=None,			lowerCAmelCase__		:   Optional[Any]=False,			lowerCAmelCase__		:   Union[str, Any]=True,			lowerCAmelCase__		:   List[Any]=True,			lowerCAmelCase__		:   Optional[Any]="<unk>",			lowerCAmelCase__		:   Optional[int]="[SEP]",			lowerCAmelCase__		:   int="<pad>",			lowerCAmelCase__		:   Tuple="[CLS]",			lowerCAmelCase__		:   Dict="[MASK]",			**lowerCAmelCase__		:   List[Any],			)     ->				Tuple:




										'''simple docstring'''



										_UpperCamelCase      : Optional[Any]         =   (
										    AddedToken(lowerCAmelCase__,			lstrip=lowerCAmelCase__,			rstrip=lowerCAmelCase__,			normalized=lowerCAmelCase__							)
										    if isinstance(lowerCAmelCase__,			lowerCAmelCase__							)
										    else mask_token
										)

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

										_UpperCamelCase      : int         =   do_lower_case
										_UpperCamelCase      : Optional[int]         =   remove_space
										_UpperCamelCase      : List[str]         =   keep_accents
										_UpperCamelCase      : List[Any]         =   vocab_file
										_UpperCamelCase      : Any         =   False if not self.vocab_file else True






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




										'''simple docstring'''



										_UpperCamelCase      : Any         =   [self.sep_token_id]
										_UpperCamelCase      : Optional[Any]         =   [self.cls_token_id]
										if token_ids_a is None:
																return cls + token_ids_a + sep
										return cls + token_ids_a + sep + token_ids_a + sep






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




										'''simple docstring'''



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




										'''simple docstring'''



										if not os.path.isdir(lowerCAmelCase__							):
																logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""							)
																return
										_UpperCamelCase      : Optional[int]         =   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"""




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



