
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     lowercase (		_SCREAMING_SNAKE_CASE       ):



		def __init__(			self    ,  A_ = "▁"    ,  A_ = True    ,  A_ = "<unk>"    ,  A_ = "</s>"    ,  A_ = "<pad>"    ,  )  -> Optional[int]:
				"""simple docstring"""
				UpperCamelCase          =					{
				    'pad': {'id': 0, 'token': pad_token},
				    'eos': {'id': 1, 'token': eos_token},
				    'unk': {'id': 2, 'token': unk_token},
				}

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

				UpperCamelCase          =					Tokenizer(Unigram()	)

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

				UpperCamelCase          =					TemplateProcessing(
				    single=F'''$A {self.special_tokens['eos']['token']}'''    ,  special_tokens=[(self.special_tokens['eos']['token'], self.special_tokens['eos']['id'])]    ,  )

				UpperCamelCase          =					{
				    'model': 'SentencePieceUnigram',
				    'replacement': replacement,
				    'add_prefix_space': add_prefix_space,
				}

				super().__init__(A_    ,  A_	)



		def __UpperCamelCase			(			self    ,  A_    ,  A_ = 8_000    ,  A_ = True    ,  )  -> List[str]:
				"""simple docstring"""
				UpperCamelCase          =					trainers.UnigramTrainer(
				    vocab_size=A_    ,  special_tokens=self.special_tokens_list    ,  show_progress=A_    ,  )

				if isinstance(A_    ,  A_	):
						UpperCamelCase          =					[files]
				self._tokenizer.train(A_    ,  trainer=A_	)

				self.add_unk_id()



		def __UpperCamelCase			(			self    ,  A_    ,  A_ = 8_000    ,  A_ = True    ,  )  -> Any:
				"""simple docstring"""
				UpperCamelCase          =					trainers.UnigramTrainer(
				    vocab_size=A_    ,  special_tokens=self.special_tokens_list    ,  show_progress=A_    ,  )

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

				self.add_unk_id()



		def __UpperCamelCase			(			self	)  -> Optional[Any]:
				"""simple docstring"""
				UpperCamelCase          =					json.loads(self._tokenizer.to_str()	)

				UpperCamelCase          =					self.special_tokens['unk']['id']

				UpperCamelCase          =					Tokenizer.from_str(json.dumps(A_	)	)


from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
					if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.25.0")):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
					from .pipeline_unclip import UnCLIPPipeline
					from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
					from .text_proj import UnCLIPTextProjModel
