

"""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


__UpperCamelCase    : int				 =						logging.get_logger(__name__)


__UpperCamelCase    : Optional[int]				 =						TypeVar('''DatasetType''', Dataset, IterableDataset)

def     __SCREAMING_SNAKE_CASE    ( A_		,	A_ = None		,	A_ = None		,	A_ = None		,	A_ = None		,	A_ = "first_exhausted"		,	):
	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(A_					):
		if not isinstance(A_		,	(Dataset, IterableDataset)					):
			if isinstance(A_		,	(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(A_					)}\n'
				    f'Please pick one to interleave with the other datasets, for example: dataset[\'{next(iter(A_					)					)}\']'					)
			raise ValueError(
			    f'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(A_					).__name__}.'					)
		if i == 0:
			lowerCAmelCase__							,lowerCAmelCase__      :		List[str]    							=      (
			    (Dataset, IterableDataset) if isinstance(A_		,	A_					) else (IterableDataset, Dataset)
			)
		elif not isinstance(A_		,	A_					):
			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(
		    A_		,	A_		,	A_		,	info=A_		,	split=A_		,	stopping_strategy=A_					)
	else:
		return _interleave_iterable_datasets(
		    A_		,	A_		,	A_		,	info=A_		,	split=A_		,	stopping_strategy=A_					)





def     __SCREAMING_SNAKE_CASE    ( A_		,	A_ = None		,	A_ = None		,	A_ = 0		,	):

	if not dsets:
		raise ValueError('''Unable to concatenate an empty list of datasets.'''					)
	for i, dataset in enumerate(A_					):
		if not isinstance(A_		,	(Dataset, IterableDataset)					):
			if isinstance(A_		,	(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(A_					)}\n'
				    f'Please pick one to interleave with the other datasets, for example: dataset[\'{next(iter(A_					)					)}\']'					)
			raise ValueError(
			    f'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(A_					).__name__}.'					)
		if i == 0:
			lowerCAmelCase__							,lowerCAmelCase__      :		List[str]    							=      (
			    (Dataset, IterableDataset) if isinstance(A_		,	A_					) else (IterableDataset, Dataset)
			)
		elif not isinstance(A_		,	A_					):
			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(A_		,	info=A_		,	split=A_		,	axis=A_					)
	else:
		return _concatenate_iterable_datasets(A_		,	info=A_		,	split=A_		,	axis=A_					)







"""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_albert import AlbertTokenizer
else:
							__UpperCamelCase    : Any				 =						None

__UpperCamelCase    : Optional[int]				 =						logging.get_logger(__name__)
__UpperCamelCase    : List[Any]				 =						{'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

__UpperCamelCase    : str				 =						{
    '''vocab_file''': {
        '''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/spiece.model''',
        '''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/spiece.model''',
        '''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model''',
        '''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model''',
        '''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/spiece.model''',
        '''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/spiece.model''',
        '''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model''',
        '''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model''',
    },
    '''tokenizer_file''': {
        '''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/tokenizer.json''',
        '''albert-large-v1''': '''https://huggingface.co/albert-large-v1/resolve/main/tokenizer.json''',
        '''albert-xlarge-v1''': '''https://huggingface.co/albert-xlarge-v1/resolve/main/tokenizer.json''',
        '''albert-xxlarge-v1''': '''https://huggingface.co/albert-xxlarge-v1/resolve/main/tokenizer.json''',
        '''albert-base-v2''': '''https://huggingface.co/albert-base-v2/resolve/main/tokenizer.json''',
        '''albert-large-v2''': '''https://huggingface.co/albert-large-v2/resolve/main/tokenizer.json''',
        '''albert-xlarge-v2''': '''https://huggingface.co/albert-xlarge-v2/resolve/main/tokenizer.json''',
        '''albert-xxlarge-v2''': '''https://huggingface.co/albert-xxlarge-v2/resolve/main/tokenizer.json''',
    },
}

__UpperCamelCase    : Any				 =						{
    '''albert-base-v1''': 5_1_2,
    '''albert-large-v1''': 5_1_2,
    '''albert-xlarge-v1''': 5_1_2,
    '''albert-xxlarge-v1''': 5_1_2,
    '''albert-base-v2''': 5_1_2,
    '''albert-large-v2''': 5_1_2,
    '''albert-xlarge-v2''': 5_1_2,
    '''albert-xxlarge-v2''': 5_1_2,
}

__UpperCamelCase    : Union[str, Any]				 =						'''▁'''







class 							SCREAMING_SNAKE_CASE   (						a_			):






					"""simple docstring"""



					lowercase__												=			VOCAB_FILES_NAMES
					lowercase__												=			PRETRAINED_VOCAB_FILES_MAP
					lowercase__												=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					lowercase__												=			AlbertTokenizer







					def __init__(			self     :							Union[str, Any]  ,lowercase_     :							List[str]=None  ,lowercase_     :							Union[str, Any]=None  ,lowercase_     :							str=True  ,lowercase_     :							Optional[Any]=True  ,lowercase_     :							str=False  ,lowercase_     :							Tuple="[CLS]"  ,lowercase_     :							Optional[int]="[SEP]"  ,lowercase_     :							Optional[Any]="<unk>"  ,lowercase_     :							List[Any]="[SEP]"  ,lowercase_     :							Optional[int]="<pad>"  ,lowercase_     :							List[Any]="[CLS]"  ,lowercase_     :							Optional[int]="[MASK]"  ,**lowercase_     :							Optional[int]  ,):
						# Mask token behave like a normal word, i.e. include the space before it and
						# is included in the raw text, there should be a match in a non-normalized sentence.
						lowerCAmelCase__      :		Optional[Any]    							=      (
						    AddedToken(lowercase_  ,lstrip=lowercase_  ,rstrip=lowercase_  ,normalized=lowercase_						)
						    if isinstance(lowercase_  ,lowercase_						)
						    else mask_token
						)

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

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







					def 			__lowerCAmelCase					(			self     :							Dict  ,lowercase_     :							List[int]  ,lowercase_     :							Optional[List[int]] = None						):
						lowerCAmelCase__      :		List[Any]    							=      [self.sep_token_id]
						lowerCAmelCase__      :		Dict    							=      [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 			__lowerCAmelCase					(			self     :							Union[str, Any]  ,lowercase_     :							List[int]  ,lowercase_     :							Optional[List[int]] = None						):
						lowerCAmelCase__      :		List[str]    							=      [self.sep_token_id]
						lowerCAmelCase__      :		List[Any]    							=      [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 			__lowerCAmelCase					(			self     :							Optional[int]  ,lowercase_     :							str  ,lowercase_     :							Optional[str] = None						):
						if not self.can_save_slow_tokenizer:
							raise ValueError(
							    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
							    '''tokenizer.'''						)

						if not os.path.isdir(lowercase_						):
							logger.error(F'Vocabulary path ({save_directory}) should be a directory'						)
							return
						lowerCAmelCase__      :		Union[str, Any]    							=      os.path.join(
						    lowercase_  ,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']						)

						if os.path.abspath(self.vocab_file						) != os.path.abspath(lowercase_						):
							copyfile(self.vocab_file  ,lowercase_						)

						return (out_vocab_file,)




