
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__)

class      A__							:






				def __init__(						self					:      Dict    ,     a					:      list[int]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Union[str, Any]         =		len(a   )
							lowerCAmelCase__      :      Optional[Any]         =		[0] * len_array

							if len_array > 0:
										lowerCAmelCase__      :      List[Any]         =		array[0]

							for i in range(1    ,     a   ):
										lowerCAmelCase__      :      Optional[int]         =		self.prefix_sum[i - 1] + array[i]






				def 							_lowerCamelCase		(						self					:      int    ,     a					:      int    ,     a					:      int   ):






							'''simple docstring'''







							if start == 0:
										return self.prefix_sum[end]

							return self.prefix_sum[end] - self.prefix_sum[start - 1]






				def 							_lowerCamelCase		(						self					:      Optional[Any]    ,     a					:      int   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Any         =		{0}
							for sum_item in self.prefix_sum:
										if sum_item - target_sum in sums:
													return True

										sums.add(a   )

							return False


if __name__ == "__main__":
					import doctest

					doctest.testmod()