



'''simple docstring'''



# flake8: noqa
# Lint as: python3

from typing import Dict, List, Optional, Type

from .. import config
from ..utils import logging
from .formatting import (
    ArrowFormatter,
    CustomFormatter,
    Formatter,
    PandasFormatter,
    PythonFormatter,
    TensorFormatter,
    format_table,
    query_table,
)
from .np_formatter import NumpyFormatter


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

lowerCAmelCase	:		Dict[Optional[str], Type[Formatter]]				=   {}
lowerCAmelCase	:		Dict[Optional[str], str]				=   {}
lowerCAmelCase	:		Dict[Optional[str], Exception]				=   {}



def   __lowerCAmelCase					(							lowerCamelCase   :						type				,		lowerCamelCase   :						Optional[str]				,		lowerCamelCase   :						Optional[List[str]] = None				,		):






	'''simple docstring'''




	__lowerCAmelCase												=  aliases if aliases is not None else []
	if format_type in _FORMAT_TYPES:
		logger.warning(
		    f'''Overwriting format type \'{format_type}\' ({_FORMAT_TYPES[format_type].__name__} -> {formatter_cls.__name__})'''					)
	__lowerCAmelCase												=  formatter_cls
	for alias in set(aliases + [format_type]					):
		if alias in _FORMAT_TYPES_ALIASES:
			logger.warning(
			    f'''Overwriting format type alias \'{alias}\' ({_FORMAT_TYPES_ALIASES[alias]} -> {format_type})'''					)
		__lowerCAmelCase												=  format_type



def   __lowerCAmelCase					(							lowerCamelCase   :						Exception				,		lowerCamelCase   :						Optional[str]				,		lowerCamelCase   :						Optional[List[str]] = None					):






	'''simple docstring'''




	__lowerCAmelCase												=  aliases if aliases is not None else []
	for alias in set(aliases + [format_type]					):
		__lowerCAmelCase												=  unavailable_error


# Here we define all the available formatting functions that can be used by `Dataset.set_format`
_register_formatter(PythonFormatter, None, aliases=['''python'''])
_register_formatter(ArrowFormatter, '''arrow''', aliases=['''pa''', '''pyarrow'''])
_register_formatter(NumpyFormatter, '''numpy''', aliases=['''np'''])
_register_formatter(PandasFormatter, '''pandas''', aliases=['''pd'''])
_register_formatter(CustomFormatter, '''custom''')

if config.TORCH_AVAILABLE:
	from .torch_formatter import TorchFormatter

	_register_formatter(TorchFormatter, '''torch''', aliases=['''pt''', '''pytorch'''])
else:
	lowerCAmelCase	:		Optional[int]				=   ValueError('''PyTorch needs to be installed to be able to return PyTorch tensors.''')
	_register_unavailable_formatter(_torch_error, '''torch''', aliases=['''pt''', '''pytorch'''])

if config.TF_AVAILABLE:
	from .tf_formatter import TFFormatter

	_register_formatter(TFFormatter, '''tensorflow''', aliases=['''tf'''])
else:
	lowerCAmelCase	:		str				=   ValueError('''Tensorflow needs to be installed to be able to return Tensorflow tensors.''')
	_register_unavailable_formatter(_tf_error, '''tensorflow''', aliases=['''tf'''])

if config.JAX_AVAILABLE:
	from .jax_formatter import JaxFormatter

	_register_formatter(JaxFormatter, '''jax''', aliases=[])
else:
	lowerCAmelCase	:		Any				=   ValueError('''JAX needs to be installed to be able to return JAX arrays.''')
	_register_unavailable_formatter(_jax_error, '''jax''', aliases=[])



def   __lowerCAmelCase					(							lowerCamelCase   :						Optional[str]					):






	'''simple docstring'''




	if format_type in _FORMAT_TYPES_ALIASES:
		return _FORMAT_TYPES_ALIASES[format_type]
	else:
		return format_type




def   __lowerCAmelCase					(							lowerCamelCase   :						Optional[str]				,		**lowerCamelCase   :						Tuple					):






	'''simple docstring'''




	__lowerCAmelCase												=  get_format_type_from_alias(lowerCamelCase					)
	if format_type in _FORMAT_TYPES:
		return _FORMAT_TYPES[format_type](**lowerCamelCase					)
	if format_type in _FORMAT_TYPES_ALIASES_UNAVAILABLE:
		raise _FORMAT_TYPES_ALIASES_UNAVAILABLE[format_type]
	else:
		raise ValueError(
		    f'''Return type should be None or selected in {list(type for type in _FORMAT_TYPES.keys() if type != None					)}, but got \'{format_type}\''''					)




'''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


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

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

lowerCAmelCase	:		str				=   {
    '''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'''
        ),
    },
}

lowerCAmelCase	:		Optional[Any]				=   {
    '''squeezebert/squeezebert-uncased''': 5_1_2,
    '''squeezebert/squeezebert-mnli''': 5_1_2,
    '''squeezebert/squeezebert-mnli-headless''': 5_1_2,
}


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






class 					UpperCAmelCase__	(		UpperCamelCase__ ):
					a      :     Dict        =							VOCAB_FILES_NAMES
					a      :     Any        =							PRETRAINED_VOCAB_FILES_MAP
					a      :     Dict        =							PRETRAINED_INIT_CONFIGURATION
					a      :     str        =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					a      :     Optional[Any]        =							SqueezeBertTokenizer



					def __init__(    self			,				UpperCamelCase=None			,				UpperCamelCase=None			,				UpperCamelCase=True			,				UpperCamelCase="[UNK]"			,				UpperCamelCase="[SEP]"			,				UpperCamelCase="[PAD]"			,				UpperCamelCase="[CLS]"			,				UpperCamelCase="[MASK]"			,				UpperCamelCase=True			,				UpperCamelCase=None			,				**UpperCamelCase			,				)     ->			List[Any]:
						super().__init__(
						    UpperCamelCase			,				tokenizer_file=UpperCamelCase			,				do_lower_case=UpperCamelCase			,				unk_token=UpperCamelCase			,				sep_token=UpperCamelCase			,				pad_token=UpperCamelCase			,				cls_token=UpperCamelCase			,				mask_token=UpperCamelCase			,				tokenize_chinese_chars=UpperCamelCase			,				strip_accents=UpperCamelCase			,				**UpperCamelCase			,				)

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

						__lowerCAmelCase												=  do_lower_case



					def        UpperCAmelCase_ (    self			,				UpperCamelCase			,				UpperCamelCase=None     )     ->			str:
						__lowerCAmelCase												=  [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        UpperCAmelCase_ (    self			,				UpperCamelCase			,				UpperCamelCase = None     )     ->			List[int]:
						__lowerCAmelCase												=  [self.sep_token_id]
						__lowerCAmelCase												=  [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        UpperCAmelCase_ (    self			,				UpperCamelCase			,				UpperCamelCase = None     )     ->			Tuple[str]:
						__lowerCAmelCase												=  self._tokenizer.model.save(UpperCamelCase			,				name=UpperCamelCase     )
						return tuple(UpperCamelCase     )