



"""simple docstring"""






from typing import TYPE_CHECKING

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


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

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

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

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case   					=				[
         '''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:
     snake_case   					=				[
         '''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

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












def   __lowerCamelCase   (				__a  :int       )						->							Dict:






		"""simple docstring"""

		A__     =  len(__a       )
		A__     =  sum(__a       )

		A__     =  [[False for x in range(s + 1       )] for y in range(n + 1       )]

		for i in range(1       ,	n + 1       ):
				A__     =  True

		for i in range(1       ,	s + 1       ):
				A__     =  False

		for i in range(1       ,	n + 1       ):
				for j in range(1       ,	s + 1       ):
						A__     =  dp[i][j - 1]

						if arr[i - 1] <= j:
								A__     =  dp[i][j] or dp[i - 1][j - arr[i - 1]]

		for j in range(int(s / 2       )       ,	-1       ,	-1       ):
				if dp[n][j] is True:
						A__     =  s - 2 * j
						break

		return diff



