

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 				UpperCAmelCase_		(			__lowercase   ):
							"""simple docstring"""







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

												_a	:    int         =		[None] * len(self.special_tokens       )
												for token_dict in self.special_tokens.values():
																	_a	:    Union[str, Any]         =		token_dict['''token''']

												_a	:    Optional[int]         =		Tokenizer(Unigram()       )

												_a	:    List[str]         =		normalizers.Sequence(
												    [
												        normalizers.Nmt(),
												        normalizers.NFKC(),
												        normalizers.Replace(Regex(''' {2,}'''       )       ,							''' '''       ),
												        normalizers.Lowercase(),
												    ]       )
												_a	:    Dict         =		pre_tokenizers.Sequence(
												    [
												        pre_tokenizers.Metaspace(replacement=_a       ,							add_prefix_space=_a       ),
												        pre_tokenizers.Digits(individual_digits=_a       ),
												        pre_tokenizers.Punctuation(),
												    ]       )
												_a	:    Tuple         =		decoders.Metaspace(replacement=_a       ,							add_prefix_space=_a       )

												_a	:    Union[str, Any]         =		TemplateProcessing(
												    single=F"""$A {self.special_tokens['eos']['token']}"""       ,							special_tokens=[(self.special_tokens['''eos''']['''token'''], self.special_tokens['''eos''']['''id'''])]       ,							)

												_a	:    Dict         =		{
												    '''model''': '''SentencePieceUnigram''',
												    '''replacement''': replacement,
												    '''add_prefix_space''': add_prefix_space,
												}

												super().__init__(_a       ,							_a       )


							def 				__lowercase				(			self       ,							_a       ,							_a = 8_0_0_0       ,							_a = True       ,							)    ->    List[str]:
												_a	:    List[Any]         =		trainers.UnigramTrainer(
												    vocab_size=_a       ,							special_tokens=self.special_tokens_list       ,							show_progress=_a       ,							)

												if isinstance(_a       ,							_a       ):
																	_a	:    Union[str, Any]         =		[files]
												self._tokenizer.train(_a       ,							trainer=_a       )

												self.add_unk_id()


							def 				__lowercase				(			self       ,							_a       ,							_a = 8_0_0_0       ,							_a = True       ,							)    ->    Dict:
												_a	:    Any         =		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 				__lowercase				(			self       )    ->    Union[str, Any]:
												_a	:    Union[str, Any]         =		json.loads(self._tokenizer.to_str()       )

												_a	:    Optional[Any]         =		self.special_tokens['''unk''']['''id''']

												_a	:    Any         =		Tokenizer.from_str(json.dumps(_a       )       )






from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


a__				=		logging.get_logger(__name__)

a__				=		{
    '''google/bit-50''': '''https://huggingface.co/google/bit-50/resolve/main/config.json''',
}

class 				UpperCAmelCase_		(			__lowercase				,       __lowercase   ):
							"""simple docstring"""







							UpperCAmelCase__       :						Any	    =    "bit"
							UpperCAmelCase__       :						Optional[int]	    =    ["preactivation", "bottleneck"]
							UpperCAmelCase__       :						Optional[Any]	    =    ["SAME", "VALID"]


							def __init__(			self       ,							_a=3       ,							_a=6_4       ,							_a=[2_5_6, 5_1_2, 1_0_2_4, 2_0_4_8]       ,							_a=[3, 4, 6, 3]       ,							_a="preactivation"       ,							_a="relu"       ,							_a=None       ,							_a=3_2       ,							_a=0.0       ,							_a=False       ,							_a=3_2       ,							_a=1       ,							_a=None       ,							_a=None       ,							**_a       ,							)    ->    Union[str, Any]:
												super().__init__(**_a       )
												if layer_type not in self.layer_types:
																	raise ValueError(F"""layer_type={layer_type} is not one of {','.join(self.layer_types       )}"""       )
												if global_padding is not None:
																	if global_padding.upper() in self.supported_padding:
																						_a	:    Any         =		global_padding.upper()
																	else:
																						raise ValueError(F"""Padding strategy {global_padding} not supported"""       )
												_a	:    Optional[int]         =		num_channels
												_a	:    List[Any]         =		embedding_size
												_a	:    Any         =		hidden_sizes
												_a	:    int         =		depths
												_a	:    Dict         =		layer_type
												_a	:    int         =		hidden_act
												_a	:    Optional[Any]         =		global_padding
												_a	:    Optional[Any]         =		num_groups
												_a	:    Union[str, Any]         =		drop_path_rate
												_a	:    Tuple         =		embedding_dynamic_padding
												_a	:    Union[str, Any]         =		output_stride
												_a	:    Any         =		width_factor

												_a	:    Any         =		['''stem'''] + [F"""stage{idx}""" for idx in range(1       ,							len(_a       ) + 1       )]
												_a      ,					_a	:    List[str]         =		get_aligned_output_features_output_indices(
												    out_features=_a       ,							out_indices=_a       ,							stage_names=self.stage_names       )



