




'''simple docstring'''



from typing import TYPE_CHECKING

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


lowerCamelCase_			=						{
    'configuration_rembert': ['REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RemBertConfig', 'RemBertOnnxConfig']
}

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

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

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase_			=						[
     'REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
     'RemBertForCausalLM',
     'RemBertForMaskedLM',
     'RemBertForMultipleChoice',
     'RemBertForQuestionAnswering',
     'RemBertForSequenceClassification',
     'RemBertForTokenClassification',
     'RemBertLayer',
     'RemBertModel',
     'RemBertPreTrainedModel',
     'load_tf_weights_in_rembert',
 ]


try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCamelCase_			=						[
     'TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
     'TFRemBertForCausalLM',
     'TFRemBertForMaskedLM',
     'TFRemBertForMultipleChoice',
     'TFRemBertForQuestionAnswering',
     'TFRemBertForSequenceClassification',
     'TFRemBertForTokenClassification',
     'TFRemBertLayer',
     'TFRemBertModel',
     'TFRemBertPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_rembert import REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RemBertConfig, RemBertOnnxConfig

 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_rembert import RemBertTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_rembert_fast import RemBertTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_rembert import (
      REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      RemBertForCausalLM,
      RemBertForMaskedLM,
      RemBertForMultipleChoice,
      RemBertForQuestionAnswering,
      RemBertForSequenceClassification,
      RemBertForTokenClassification,
      RemBertLayer,
      RemBertModel,
      RemBertPreTrainedModel,
      load_tf_weights_in_rembert,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_rembert import (
      TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFRemBertForCausalLM,
      TFRemBertForMaskedLM,
      TFRemBertForMultipleChoice,
      TFRemBertForQuestionAnswering,
      TFRemBertForSequenceClassification,
      TFRemBertForTokenClassification,
      TFRemBertLayer,
      TFRemBertModel,
      TFRemBertPreTrainedModel,
  )


else:
 import sys

 lowerCamelCase_			=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)







'''simple docstring'''



lowerCamelCase_			=						'Tobias Carryer'

from time import time







class 		lowercase_   :


   """simple docstring"""

   def __init__(   self :     Tuple  ,				__lowerCamelCase :     int  ,				__lowerCamelCase :     List[Any]  ,				__lowerCamelCase :     Optional[int]  ,				__lowerCamelCase :     Dict=int(time()   )   ):  # noqa: B008

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				multiplier
      _SCREAMING_SNAKE_CASE	=				increment
      _SCREAMING_SNAKE_CASE	=				modulo
      _SCREAMING_SNAKE_CASE	=				seed



   def 							lowerCAmelCase_      (   self :     List[str]   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				(self.multiplier * self.seed + self.increment) % self.modulo
      return self.seed


if __name__ == "__main__":
 # Show the LCG in action.
 lowerCamelCase_			=						LinearCongruentialGenerator(1_66_45_25, 10_13_90_42_23, 2 << 31)
 while True:
  print(lcg.next_number())

