
"""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_mobilebert import MobileBertTokenizer


lowerCamelCase__        =		logging.get_logger(__name__)

lowerCamelCase__        =		{"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}

lowerCamelCase__        =		{
    """vocab_file""": {"""mobilebert-uncased""": """https://huggingface.co/google/mobilebert-uncased/resolve/main/vocab.txt"""},
    """tokenizer_file""": {
        """mobilebert-uncased""": """https://huggingface.co/google/mobilebert-uncased/resolve/main/tokenizer.json"""
    },
}

lowerCamelCase__        =		{"""mobilebert-uncased""": 512}


lowerCamelCase__        =		{}




class 			A__	(     _lowerCamelCase):
		A_						:	Optional[int]           =     VOCAB_FILES_NAMES
		A_						:	Dict           =     PRETRAINED_VOCAB_FILES_MAP
		A_						:	List[Any]           =     PRETRAINED_INIT_CONFIGURATION
		A_						:	Optional[Any]           =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		A_						:	str           =     MobileBertTokenizer

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

			__lowerCAmelCase      :						List[Any]						      =				json.loads(self.backend_tokenizer.normalizer.__getstate__()			)
			if (
			    normalizer_state.get('lowercase'	,		_SCREAMING_SNAKE_CASE			) != do_lower_case
			    or normalizer_state.get('strip_accents'	,		_SCREAMING_SNAKE_CASE			) != strip_accents
			    or normalizer_state.get('handle_chinese_chars'	,		_SCREAMING_SNAKE_CASE			) != tokenize_chinese_chars
			):
				__lowerCAmelCase      :						str						      =				getattr(_SCREAMING_SNAKE_CASE	,		normalizer_state.pop('type'			)			)
				__lowerCAmelCase      :						Any						      =				do_lower_case
				__lowerCAmelCase      :						Tuple						      =				strip_accents
				__lowerCAmelCase      :						Optional[int]						      =				tokenize_chinese_chars
				__lowerCAmelCase      :						Union[str, Any]						      =				normalizer_class(**_SCREAMING_SNAKE_CASE			)

			__lowerCAmelCase      :						List[Any]						      =				do_lower_case

		def 							__lowerCamelCase							(				self	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE=None			):
			__lowerCAmelCase      :						str						      =				[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	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE = None			):
			__lowerCAmelCase      :						Dict						      =				[self.sep_token_id]
			__lowerCAmelCase      :						Tuple						      =				[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	,		_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE = None			):
			__lowerCAmelCase      :						Optional[Any]						      =				self._tokenizer.model.save(_SCREAMING_SNAKE_CASE	,		name=_SCREAMING_SNAKE_CASE			)
			return tuple(_SCREAMING_SNAKE_CASE			)

"""simple docstring"""







lowerCamelCase__        =		"""0.21.0"""

from .accelerator import Accelerator
from .big_modeling import (
    cpu_offload,
    cpu_offload_with_hook,
    disk_offload,
    dispatch_model,
    init_empty_weights,
    init_on_device,
    load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batches
from .launchers import debug_launcher, notebook_launcher
from .state import PartialState
from .utils import (
    DeepSpeedPlugin,
    DistributedDataParallelKwargs,
    DistributedType,
    FullyShardedDataParallelPlugin,
    GradScalerKwargs,
    InitProcessGroupKwargs,
    find_executable_batch_size,
    infer_auto_device_map,
    is_rich_available,
    load_checkpoint_in_model,
    synchronize_rng_states,
)


if is_rich_available():
							from .utils import rich