


'''simple docstring'''



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,
)


__A		={
    'configuration_xlm_roberta': [
        'XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'XLMRobertaConfig',
        'XLMRobertaOnnxConfig',
    ],
}

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

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

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__A		=[
		    'XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'XLMRobertaForCausalLM',
		    'XLMRobertaForMaskedLM',
		    'XLMRobertaForMultipleChoice',
		    'XLMRobertaForQuestionAnswering',
		    'XLMRobertaForSequenceClassification',
		    'XLMRobertaForTokenClassification',
		    'XLMRobertaModel',
		    'XLMRobertaPreTrainedModel',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__A		=[
		    'TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'TFXLMRobertaForCausalLM',
		    'TFXLMRobertaForMaskedLM',
		    'TFXLMRobertaForMultipleChoice',
		    'TFXLMRobertaForQuestionAnswering',
		    'TFXLMRobertaForSequenceClassification',
		    'TFXLMRobertaForTokenClassification',
		    'TFXLMRobertaModel',
		    'TFXLMRobertaPreTrainedModel',
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__A		=[
		    'FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'FlaxXLMRobertaForMaskedLM',
		    'FlaxXLMRobertaForCausalLM',
		    'FlaxXLMRobertaForMultipleChoice',
		    'FlaxXLMRobertaForQuestionAnswering',
		    'FlaxXLMRobertaForSequenceClassification',
		    'FlaxXLMRobertaForTokenClassification',
		    'FlaxXLMRobertaModel',
		    'FlaxXLMRobertaPreTrainedModel',
		]

if TYPE_CHECKING:
		from .configuration_xlm_roberta import (
		    XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    XLMRobertaConfig,
		    XLMRobertaOnnxConfig,
		)

		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_xlm_roberta import XLMRobertaTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_xlm_roberta_fast import XLMRobertaTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_xlm_roberta import (
				    XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
				    XLMRobertaForCausalLM,
				    XLMRobertaForMaskedLM,
				    XLMRobertaForMultipleChoice,
				    XLMRobertaForQuestionAnswering,
				    XLMRobertaForSequenceClassification,
				    XLMRobertaForTokenClassification,
				    XLMRobertaModel,
				    XLMRobertaPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_xlm_roberta import (
				    TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFXLMRobertaForCausalLM,
				    TFXLMRobertaForMaskedLM,
				    TFXLMRobertaForMultipleChoice,
				    TFXLMRobertaForQuestionAnswering,
				    TFXLMRobertaForSequenceClassification,
				    TFXLMRobertaForTokenClassification,
				    TFXLMRobertaModel,
				    TFXLMRobertaPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_xlm_roberta import (
				    FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
				    FlaxXLMRobertaForCausalLM,
				    FlaxXLMRobertaForMaskedLM,
				    FlaxXLMRobertaForMultipleChoice,
				    FlaxXLMRobertaForQuestionAnswering,
				    FlaxXLMRobertaForSequenceClassification,
				    FlaxXLMRobertaForTokenClassification,
				    FlaxXLMRobertaModel,
				    FlaxXLMRobertaPreTrainedModel,
				)

else:
		import sys

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








'''simple docstring'''



def       _UpperCamelCase (						UpperCamelCase__   ):
			UpperCAmelCase__					:   List[str]       				=       len(UpperCamelCase__   )
			UpperCAmelCase__					:   List[Any]       				=       sum(UpperCamelCase__   )

			UpperCAmelCase__					:   List[str]       				=       [[False for x in range(s + 1   )] for y in range(n + 1   )]

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

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

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

									if arr[i - 1] <= j:
												UpperCAmelCase__					:   str       				=       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:
									UpperCAmelCase__					:   int       				=       s - 2 * j
									break

			return diff