




'''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


_snake_case : Union[str, Any]  				=						logging.get_logger(__name__)


_snake_case : List[str]  				=						TypeVar('DatasetType', Dataset, IterableDataset)
def 						snake_case_       (UpperCamelCase			:    List[DatasetType]					,		UpperCamelCase			:    Optional[List[float]] = None					,		UpperCamelCase			:    Optional[int] = None					,		UpperCamelCase			:    Optional[DatasetInfo] = None					,		UpperCamelCase			:    Optional[NamedSplit] = None					,		UpperCamelCase			:    Literal["first_exhausted", "all_exhausted"] = "first_exhausted"					,		):





							'''simple docstring'''


							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(UpperCamelCase ):
														if not isinstance(UpperCamelCase					,		(Dataset, IterableDataset) ):
																					if isinstance(UpperCamelCase					,		(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(UpperCamelCase )}\n'
																												    f'Please pick one to interleave with the other datasets, for example: dataset[\'{next(iter(UpperCamelCase ) )}\']' )
																					raise ValueError(
																					    f'Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(UpperCamelCase ).__name__}.' )
														if i == 0:
																					_a	,				_a      	=							(
																					    (Dataset, IterableDataset) if isinstance(UpperCamelCase					,		UpperCamelCase ) else (IterableDataset, Dataset)
																					)
														elif not isinstance(UpperCamelCase					,		UpperCamelCase ):
																					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(
														    UpperCamelCase					,		UpperCamelCase					,		UpperCamelCase					,		info=UpperCamelCase					,		split=UpperCamelCase					,		stopping_strategy=UpperCamelCase )
							else:
														return _interleave_iterable_datasets(
														    UpperCamelCase					,		UpperCamelCase					,		UpperCamelCase					,		info=UpperCamelCase					,		split=UpperCamelCase					,		stopping_strategy=UpperCamelCase )

def 						snake_case_       (UpperCamelCase			:    List[DatasetType]					,		UpperCamelCase			:    Optional[DatasetInfo] = None					,		UpperCamelCase			:    Optional[NamedSplit] = None					,		UpperCamelCase			:    int = 0					,		):





							'''simple docstring'''


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







'''simple docstring'''


import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer


_snake_case : Tuple  				=						logging.get_logger(__name__)

_snake_case : Optional[int]  				=						{'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

_snake_case : List[Any]  				=						{
    'vocab_file': {
        'squeezebert/squeezebert-uncased': (
            'https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/vocab.txt'
        ),
        'squeezebert/squeezebert-mnli': 'https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/vocab.txt',
        'squeezebert/squeezebert-mnli-headless': (
            'https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/vocab.txt'
        ),
    },
    'tokenizer_file': {
        'squeezebert/squeezebert-uncased': (
            'https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/tokenizer.json'
        ),
        'squeezebert/squeezebert-mnli': (
            'https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/tokenizer.json'
        ),
        'squeezebert/squeezebert-mnli-headless': (
            'https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/tokenizer.json'
        ),
    },
}

_snake_case : Union[str, Any]  				=						{
    'squeezebert/squeezebert-uncased': 512,
    'squeezebert/squeezebert-mnli': 512,
    'squeezebert/squeezebert-mnli-headless': 512,
}


_snake_case : Tuple  				=						{
    'squeezebert/squeezebert-uncased': {'do_lower_case': True},
    'squeezebert/squeezebert-mnli': {'do_lower_case': True},
    'squeezebert/squeezebert-mnli-headless': {'do_lower_case': True},
}

class      A       (						_a       ):
							lowercase_							     =							VOCAB_FILES_NAMES
							lowercase_							     =							PRETRAINED_VOCAB_FILES_MAP
							lowercase_							     =							PRETRAINED_INIT_CONFIGURATION
							lowercase_							     =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowercase_							     =							SqueezeBertTokenizer







							def __init__(    self			:    str				,     lowerCAmelCase_			:    str=None				,     lowerCAmelCase_			:    List[str]=None				,     lowerCAmelCase_			:    str=True				,     lowerCAmelCase_			:    List[str]="[UNK]"				,     lowerCAmelCase_			:    Union[str, Any]="[SEP]"				,     lowerCAmelCase_			:    Optional[Any]="[PAD]"				,     lowerCAmelCase_			:    Any="[CLS]"				,     lowerCAmelCase_			:    List[str]="[MASK]"				,     lowerCAmelCase_			:    int=True				,     lowerCAmelCase_			:    List[Any]=None				,     **lowerCAmelCase_			:    Optional[int]				,     )     ->				int:


														"""simple docstring"""


														super().__init__(
														    lowerCAmelCase_				,     tokenizer_file=lowerCAmelCase_				,     do_lower_case=lowerCAmelCase_				,     unk_token=lowerCAmelCase_				,     sep_token=lowerCAmelCase_				,     pad_token=lowerCAmelCase_				,     cls_token=lowerCAmelCase_				,     mask_token=lowerCAmelCase_				,     tokenize_chinese_chars=lowerCAmelCase_				,     strip_accents=lowerCAmelCase_				,     **lowerCAmelCase_				,     )

														_a      	=							json.loads(self.backend_tokenizer.normalizer.__getstate__()   )
														if (
														    normalizer_state.get('''lowercase'''				,     lowerCAmelCase_   ) != do_lower_case
														    or normalizer_state.get('''strip_accents'''				,     lowerCAmelCase_   ) != strip_accents
														    or normalizer_state.get('''handle_chinese_chars'''				,     lowerCAmelCase_   ) != tokenize_chinese_chars
														):
																					_a      	=							getattr(lowerCAmelCase_				,     normalizer_state.pop('''type'''   )   )
																					_a      	=							do_lower_case
																					_a      	=							strip_accents
																					_a      	=							tokenize_chinese_chars
																					_a      	=							normalizer_class(**lowerCAmelCase_   )

														_a      	=							do_lower_case







							def   __lowerCAmelCase     (    self			:    Union[str, Any]				,     lowerCAmelCase_			:    int				,     lowerCAmelCase_			:    Optional[Any]=None   )     ->				List[str]:


														"""simple docstring"""


														_a      	=							[self.cls_token_id] + token_ids_a + [self.sep_token_id]

														if token_ids_a:
																					output += token_ids_a + [self.sep_token_id]

														return output







							def   __lowerCAmelCase     (    self			:    Any				,     lowerCAmelCase_			:    List[int]				,     lowerCAmelCase_			:    Optional[List[int]] = None   )     ->				List[int]:


														"""simple docstring"""


														_a      	=							[self.sep_token_id]
														_a      	=							[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[Any]				,     lowerCAmelCase_			:    str				,     lowerCAmelCase_			:    Optional[str] = None   )     ->				Tuple[str]:


														"""simple docstring"""


														_a      	=							self._tokenizer.model.save(lowerCAmelCase_				,     name=lowerCAmelCase_   )
														return tuple(lowerCAmelCase_   )

