


"""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
_lowerCamelCase     =  0b1011_0011_1110_1100_1001_0000_0111_1011_1011_0001_1001_1110
# bin(x)[2:] gives bits of x as str, use int to convert them to 0/1
_lowerCamelCase     =  [int(bit) for bit in bin(WATERMARK_MESSAGE)[2:]]


class 			lowerCamelCase_		:





				"""simple docstring"""







				def __init__(						self     ):
								SCREAMING_SNAKE_CASE__     							=				WATERMARK_BITS
								SCREAMING_SNAKE_CASE__     							=				WatermarkEncoder()

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






				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__     ):
								# can't encode images that are smaller than 256
								if images.shape[-1] < 256:
												return images

								SCREAMING_SNAKE_CASE__     							=				(255 * (images / 2 + 0.5)).cpu().permute(0       ,		2       ,		3       ,		1     ).float().numpy()

								SCREAMING_SNAKE_CASE__     							=				[self.encoder.encode(UpperCAmelCase__       ,		"dwtDct"     ) for image in images]

								SCREAMING_SNAKE_CASE__     							=				torch.from_numpy(np.array(UpperCAmelCase__     )     ).permute(0       ,		3       ,		1       ,		2     )

								SCREAMING_SNAKE_CASE__     							=				torch.clamp(2 * (images / 255 - 0.5)       ,		min=-1.0       ,		max=1.0     )
								return images




"""simple docstring"""






import json
from typing import Iterator, List, Union

from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import TemplateProcessing


class 			lowerCamelCase_		(			lowercase   ):





				"""simple docstring"""







				def __init__(						self       ,		UpperCAmelCase__ = "▁"       ,		UpperCAmelCase__ = True       ,		UpperCAmelCase__ = "<unk>"       ,		UpperCAmelCase__ = "</s>"       ,		UpperCAmelCase__ = "<pad>"       ,		):
								SCREAMING_SNAKE_CASE__     							=				{
								    "pad": {"id": 0, "token": pad_token},
								    "eos": {"id": 1, "token": eos_token},
								    "unk": {"id": 2, "token": unk_token},
								}

								SCREAMING_SNAKE_CASE__     							=				[None] * len(self.special_tokens     )
								for token_dict in self.special_tokens.values():
												SCREAMING_SNAKE_CASE__     							=				token_dict["token"]

								SCREAMING_SNAKE_CASE__     							=				Tokenizer(Unigram()     )

								SCREAMING_SNAKE_CASE__     							=				normalizers.Sequence(
								    [
								        normalizers.Nmt(),
								        normalizers.NFKC(),
								        normalizers.Replace(Regex(" {2,}"     )       ,		" "     ),
								        normalizers.Lowercase(),
								    ]     )
								SCREAMING_SNAKE_CASE__     							=				pre_tokenizers.Sequence(
								    [
								        pre_tokenizers.Metaspace(replacement=UpperCAmelCase__       ,		add_prefix_space=UpperCAmelCase__     ),
								        pre_tokenizers.Digits(individual_digits=UpperCAmelCase__     ),
								        pre_tokenizers.Punctuation(),
								    ]     )
								SCREAMING_SNAKE_CASE__     							=				decoders.Metaspace(replacement=UpperCAmelCase__       ,		add_prefix_space=UpperCAmelCase__     )

								SCREAMING_SNAKE_CASE__     							=				TemplateProcessing(
								    single=f'''$A {self.special_tokens["eos"]["token"]}'''       ,		special_tokens=[(self.special_tokens["eos"]["token"], self.special_tokens["eos"]["id"])]       ,		)

								SCREAMING_SNAKE_CASE__     							=				{
								    "model": "SentencePieceUnigram",
								    "replacement": replacement,
								    "add_prefix_space": add_prefix_space,
								}

								super().__init__(UpperCAmelCase__       ,		UpperCAmelCase__     )



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__ = 8000       ,		UpperCAmelCase__ = True       ,		):
								SCREAMING_SNAKE_CASE__     							=				trainers.UnigramTrainer(
								    vocab_size=UpperCAmelCase__       ,		special_tokens=self.special_tokens_list       ,		show_progress=UpperCAmelCase__       ,		)

								if isinstance(UpperCAmelCase__       ,		UpperCAmelCase__     ):
												SCREAMING_SNAKE_CASE__     							=				[files]
								self._tokenizer.train(UpperCAmelCase__       ,		trainer=UpperCAmelCase__     )

								self.add_unk_id()



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__ = 8000       ,		UpperCAmelCase__ = True       ,		):
								SCREAMING_SNAKE_CASE__     							=				trainers.UnigramTrainer(
								    vocab_size=UpperCAmelCase__       ,		special_tokens=self.special_tokens_list       ,		show_progress=UpperCAmelCase__       ,		)

								self._tokenizer.train_from_iterator(UpperCAmelCase__       ,		trainer=UpperCAmelCase__     )

								self.add_unk_id()






				def   lowerCAmelCase__		(						self     ):
								SCREAMING_SNAKE_CASE__     							=				json.loads(self._tokenizer.to_str()     )

								SCREAMING_SNAKE_CASE__     							=				self.special_tokens["unk"]["id"]

								SCREAMING_SNAKE_CASE__     							=				Tokenizer.from_str(json.dumps(UpperCAmelCase__     )     )
