





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''': 5_1_2}


lowerCAmelCase_    =    {}



class  __lowerCAmelCase	(      lowercase__					):
			lowerCamelCase_					:					Any	      =				VOCAB_FILES_NAMES
			lowerCamelCase_					:					int	      =				PRETRAINED_VOCAB_FILES_MAP
			lowerCamelCase_					:					Tuple	      =				PRETRAINED_INIT_CONFIGURATION
			lowerCamelCase_					:					List[str]	      =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCamelCase_					:					Optional[Any]	      =				MobileBertTokenizer






			def __init__(self ,							__magic_name__=None ,							__magic_name__=None ,							__magic_name__=True ,							__magic_name__="[UNK]" ,							__magic_name__="[SEP]" ,							__magic_name__="[PAD]" ,							__magic_name__="[CLS]" ,							__magic_name__="[MASK]" ,							__magic_name__=True ,							__magic_name__=None ,							**__magic_name__ ,							)					->       Any:


							'''simple docstring'''



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

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

							snake_case_						:   str           =  do_lower_case






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__=None   )					->       Optional[Any]:


							'''simple docstring'''



							snake_case_						:   Tuple           =  [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 ,							__magic_name__ ,							__magic_name__ = None   )					->       Tuple:


							'''simple docstring'''



							snake_case_						:   List[str]           =  [self.sep_token_id]
							snake_case_						:   str           =  [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 ,							__magic_name__ ,							__magic_name__ = None   )					->       Tuple:


							'''simple docstring'''



							snake_case_						:   int           =  self._tokenizer.model.save(_a ,							name=_a   )
							return tuple(_a   )











from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase_    =    {'''configuration_xglm''': ['''XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XGLMConfig''']}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCAmelCase_    =    ['''XGLMTokenizer''']

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCAmelCase_    =    ['''XGLMTokenizerFast''']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCAmelCase_    =    [
	    '''XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''XGLMForCausalLM''',
	    '''XGLMModel''',
	    '''XGLMPreTrainedModel''',
	]


try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCAmelCase_    =    [
	    '''FlaxXGLMForCausalLM''',
	    '''FlaxXGLMModel''',
	    '''FlaxXGLMPreTrainedModel''',
	]


try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCAmelCase_    =    [
	    '''TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''TFXGLMForCausalLM''',
	    '''TFXGLMModel''',
	    '''TFXGLMPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_xglm import XGLMTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_xglm_fast import XGLMTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_xglm import (
		    TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFXGLMForCausalLM,
		    TFXGLMModel,
		    TFXGLMPreTrainedModel,
		)


else:
	import sys

	lowerCAmelCase_    =    _LazyModule(__name__, globals()['''__file__'''], _import_structure)
