




"""simple docstring"""




from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder


# @@protoc_insertion_point(imports)

lowerCAmelCase							:    str  							=							_symbol_database.Default()


lowerCAmelCase							:    Dict  							=							_descriptor_pool.Default().AddSerializedFile(
    b"""\n\x19sentencepiece_model.proto\x12\rsentencepiece\"\x80\x0c\n\x0bTrainerSpec\x12\r\n\x05input\x18\x01 \x03(\t\x12\x14\n\x0cinput_format\x18\x07 \x01(\t\x12\x14\n\x0cmodel_prefix\x18\x02 \x01(\t\x12\x41\n\nmodel_type\x18\x03 \x01(\x0e\x32$.sentencepiece.TrainerSpec.ModelType:\x07UNIGRAM\x12\x18\n\nvocab_size\x18\x04 \x01(\x05:\x04\x38\x30\x30\x30\x12\x17\n\x0f\x61\x63\x63\x65pt_language\x18\x05 \x03(\t\x12 \n\x15self_test_sample_size\x18\x06 \x01(\x05:\x01\x30\x12*\n\x1b\x65nable_differential_privacy\x18\x32 \x01(\x08:\x05\x66\x61lse\x12+\n differential_privacy_noise_level\x18\x33 \x01(\x02:\x01\x30\x12\x32\n\'differential_privacy_clipping_threshold\x18\x34 \x01(\x04:\x01\x30\x12\"\n\x12\x63haracter_coverage\x18\n \x01(\x02:\x06\x30.9995\x12\x1e\n\x13input_sentence_size\x18\x0b \x01(\x04:\x01\x30\x12$\n\x16shuffle_input_sentence\x18\x13 \x01(\x08:\x04true\x12 \n\x14mining_sentence_size\x18\x0c \x01(\x05\x42\x02\x18\x01\x12\"\n\x16training_sentence_size\x18\r \x01(\x05\x42\x02\x18\x01\x12(\n\x17seed_sentencepiece_size\x18\x0e \x01(\x05:\x07\x31\x30\x30\x30\x30\x30\x30\x12\x1e\n\x10shrinking_factor\x18\x0f \x01(\x02:\x04\x30.75\x12!\n\x13max_sentence_length\x18\x12 \x01(\x05:\x04\x34\x31\x39\x32\x12\x17\n\x0bnum_threads\x18\x10 \x01(\x05:\x02\x31\x36\x12\x1d\n\x12num_sub_iterations\x18\x11 \x01(\x05:\x01\x32\x12$\n\x18max_sentencepiece_length\x18\x14 \x01(\x05:\x02\x31\x36\x12%\n\x17split_by_unicode_script\x18\x15 \x01(\x08:\x04true\x12\x1d\n\x0fsplit_by_number\x18\x17 \x01(\x08:\x04true\x12!\n\x13split_by_whitespace\x18\x16 \x01(\x08:\x04true\x12)\n\x1atreat_whitespace_as_suffix\x18\x18 \x01(\x08:\x05\x66\x61lse\x12+\n\x1c\x61llow_whitespace_only_pieces\x18\x1a \x01(\x08:\x05\x66\x61lse\x12\x1b\n\x0csplit_digits\x18\x19 \x01(\x08:\x05\x66\x61lse\x12#\n\x19pretokenization_delimiter\x18\x35 \x01(\t:\x00\x12\x17\n\x0f\x63ontrol_symbols\x18\x1e \x03(\t\x12\x1c\n\x14user_defined_symbols\x18\x1f \x03(\t\x12\x16\n\x0erequired_chars\x18$ \x01(\t\x12\x1c\n\rbyte_fallback\x18# \x01(\x08:\x05\x66\x61lse\x12+\n\x1dvocabulary_output_piece_score\x18  \x01(\x08:\x04true\x12\x1e\n\x10hard_vocab_limit\x18! \x01(\x08:\x04true\x12\x1c\n\ruse_all_vocab\x18\" \x01(\x08:\x05\x66\x61lse\x12\x11\n\x06unk_id\x18( \x01(\x05:\x01\x30\x12\x11\n\x06\x62os_id\x18) \x01(\x05:\x01\x31\x12\x11\n\x06\x65os_id\x18* \x01(\x05:\x01\x32\x12\x12\n\x06pad_id\x18+ \x01(\x05:\x02-1\x12\x18\n\tunk_piece\x18- \x01(\t:\x05<unk>\x12\x16\n\tbos_piece\x18. \x01(\t:\x03<s>\x12\x17\n\teos_piece\x18/ \x01(\t:\x04</s>\x12\x18\n\tpad_piece\x18\x30 \x01(\t:\x05<pad>\x12\x1a\n\x0bunk_surface\x18, \x01(\t:\x05 \xe2\x81\x87 \x12+\n\x1ctrain_extremely_large_corpus\x18\x31 \x01(\x08:\x05\x66\x61lse\"5\n\tModelType\x12\x0b\n\x07UNIGRAM\x10\x01\x12\x07\n\x03\x42PE\x10\x02\x12\x08\n\x04WORD\x10\x03\x12\x08\n\x04\x43HAR\x10\x04*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\"\xd1\x01\n\x0eNormalizerSpec\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x1c\n\x14precompiled_charsmap\x18\x02 \x01(\x0c\x12\x1e\n\x10\x61\x64\x64_dummy_prefix\x18\x03 \x01(\x08:\x04true\x12&\n\x18remove_extra_whitespaces\x18\x04 \x01(\x08:\x04true\x12 \n\x12\x65scape_whitespaces\x18\x05 \x01(\x08:\x04true\x12\x1e\n\x16normalization_rule_tsv\x18\x06 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\"y\n\x0cSelfTestData\x12\x33\n\x07samples\x18\x01 \x03(\x0b\x32\".sentencepiece.SelfTestData.Sample\x1a)\n\x06Sample\x12\r\n\x05input\x18\x01 \x01(\t\x12\x10\n\x08\x65xpected\x18\x02 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\"\xfe\x03\n\nModelProto\x12\x37\n\x06pieces\x18\x01 \x03(\x0b\x32\'.sentencepiece.ModelProto.SentencePiece\x12\x30\n\x0ctrainer_spec\x18\x02 \x01(\x0b\x32\x1a.sentencepiece.TrainerSpec\x12\x36\n\x0fnormalizer_spec\x18\x03 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x12\x33\n\x0eself_test_data\x18\x04 \x01(\x0b\x32\x1b.sentencepiece.SelfTestData\x12\x38\n\x11\x64\x65normalizer_spec\x18\x05 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x1a\xd2\x01\n\rSentencePiece\x12\r\n\x05piece\x18\x01 \x01(\t\x12\r\n\x05score\x18\x02 \x01(\x02\x12\x42\n\x04type\x18\x03 \x01(\x0e\x32,.sentencepiece.ModelProto.SentencePiece.Type:\x06NORMAL\"T\n\x04Type\x12\n\n\x06NORMAL\x10\x01\x12\x0b\n\x07UNKNOWN\x10\x02\x12\x0b\n\x07\x43ONTROL\x10\x03\x12\x10\n\x0cUSER_DEFINED\x10\x04\x12\x08\n\x04\x42YTE\x10\x06\x12\n\n\x06UNUSED\x10\x05*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\x42\x02H\x03"""
)

lowerCAmelCase							:    Union[str, Any]  							=							globals()
_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, """sentencepiece_model_pb2""", _globals)
if _descriptor._USE_C_DESCRIPTORS is False:
					lowerCAmelCase							:    Union[str, Any]  							=							None
					lowerCAmelCase							:    Optional[Any]  							=							b"""H\003"""
					# (generated by protobuf compiler, but `_TRAINERSPEC` is not defined)
					# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._options = None
					# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._serialized_options = b"\030\001"
					# _TRAINERSPEC.fields_by_name["training_sentence_size"]._options = None
					# _TRAINERSPEC.fields_by_name["training_sentence_size"]._serialized_options = b"\030\001"
					lowerCAmelCase							:    Any  							=							45
					lowerCAmelCase							:    Any  							=							1581
					lowerCAmelCase							:    Union[str, Any]  							=							1517
					lowerCAmelCase							:    List[str]  							=							1570
					lowerCAmelCase							:    List[str]  							=							1584
					lowerCAmelCase							:    Tuple  							=							1793
					lowerCAmelCase							:    Optional[int]  							=							1795
					lowerCAmelCase							:    Optional[Any]  							=							1916
					lowerCAmelCase							:    int  							=							1864
					lowerCAmelCase							:    List[str]  							=							1905
					lowerCAmelCase							:    List[str]  							=							1919
					lowerCAmelCase							:    Union[str, Any]  							=							2429
					lowerCAmelCase							:    int  							=							2208
					lowerCAmelCase							:    List[Any]  							=							2418
					lowerCAmelCase							:    List[str]  							=							2323
					lowerCAmelCase							:    Any  							=							2407
# @@protoc_insertion_point(module_scope)









"""simple docstring"""




from typing import List, Optional, TypeVar

from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal


lowerCAmelCase							:    List[Any]  							=							logging.get_logger(__name__)


lowerCAmelCase							:    int  							=							TypeVar("""DatasetType""", Dataset, IterableDataset)



def 					a__				(       snake_case__ ,				snake_case__ = None ,				snake_case__ = None ,				snake_case__ = None ,				snake_case__ = None ,				snake_case__ = "first_exhausted" ,				)		->      DatasetType:
			from .arrow_dataset import Dataset
			from .iterable_dataset import IterableDataset

			if not datasets:
						raise ValueError("""Unable to interleave an empty list of datasets."""       )
			for i, dataset in enumerate(snake_case__       ):
						if not isinstance(snake_case__ ,				(Dataset, IterableDataset)       ):
									if isinstance(snake_case__ ,				(DatasetDict, IterableDatasetDict)       ):
												if not dataset:
															raise ValueError(
															    F'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} '
															    """is an empty dataset dictionary."""       )
												raise ValueError(
												    F'Dataset at position {i} has at least one split: {list(snake_case__       )}\n'
												    F'Please pick one to interleave with the other datasets, for example: dataset[\'{next(iter(snake_case__       )       )}\']'       )
									raise ValueError(
									    F'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(snake_case__       ).__name__}.'       )
						if i == 0:
									lowerCamelCase   ,     lowerCamelCase       =      (
									    (Dataset, IterableDataset) if isinstance(snake_case__ ,				snake_case__       ) else (IterableDataset, Dataset)
									)
						elif not isinstance(snake_case__ ,				snake_case__       ):
									raise ValueError(
									    F'Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects.'       )
			if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
						raise ValueError(F'{stopping_strategy} is not supported. Please enter a valid stopping_strategy.'       )
			if dataset_type is Dataset:
						return _interleave_map_style_datasets(
						    snake_case__ ,				snake_case__ ,				snake_case__ ,				info=snake_case__ ,				split=snake_case__ ,				stopping_strategy=snake_case__       )
			else:
						return _interleave_iterable_datasets(
						    snake_case__ ,				snake_case__ ,				snake_case__ ,				info=snake_case__ ,				split=snake_case__ ,				stopping_strategy=snake_case__       )



def 					a__				(       snake_case__ ,				snake_case__ = None ,				snake_case__ = None ,				snake_case__ = 0 ,				)		->      DatasetType:

			if not dsets:
						raise ValueError("""Unable to concatenate an empty list of datasets."""       )
			for i, dataset in enumerate(snake_case__       ):
						if not isinstance(snake_case__ ,				(Dataset, IterableDataset)       ):
									if isinstance(snake_case__ ,				(DatasetDict, IterableDatasetDict)       ):
												if not dataset:
															raise ValueError(
															    F'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} '
															    """is an empty dataset dictionary."""       )
												raise ValueError(
												    F'Dataset at position {i} has at least one split: {list(snake_case__       )}\n'
												    F'Please pick one to interleave with the other datasets, for example: dataset[\'{next(iter(snake_case__       )       )}\']'       )
									raise ValueError(
									    F'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(snake_case__       ).__name__}.'       )
						if i == 0:
									lowerCamelCase   ,     lowerCamelCase       =      (
									    (Dataset, IterableDataset) if isinstance(snake_case__ ,				snake_case__       ) else (IterableDataset, Dataset)
									)
						elif not isinstance(snake_case__ ,				snake_case__       ):
									raise ValueError(
									    F'Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects.'       )
			if dataset_type is Dataset:
						return _concatenate_map_style_datasets(snake_case__ ,				info=snake_case__ ,				split=snake_case__ ,				axis=snake_case__       )
			else:
						return _concatenate_iterable_datasets(snake_case__ ,				info=snake_case__ ,				split=snake_case__ ,				axis=snake_case__       )



