
'''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,
)


_lowercase				:						Tuple												= {
    "configuration_big_bird": ["BIG_BIRD_PRETRAINED_CONFIG_ARCHIVE_MAP", "BigBirdConfig", "BigBirdOnnxConfig"],
}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowercase				:						Tuple												= ["BigBirdTokenizer"]

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowercase				:						int												= ["BigBirdTokenizerFast"]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowercase				:						List[str]												= [
							    "BIG_BIRD_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "BigBirdForCausalLM",
							    "BigBirdForMaskedLM",
							    "BigBirdForMultipleChoice",
							    "BigBirdForPreTraining",
							    "BigBirdForQuestionAnswering",
							    "BigBirdForSequenceClassification",
							    "BigBirdForTokenClassification",
							    "BigBirdLayer",
							    "BigBirdModel",
							    "BigBirdPreTrainedModel",
							    "load_tf_weights_in_big_bird",
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowercase				:						Any												= [
							    "FlaxBigBirdForCausalLM",
							    "FlaxBigBirdForMaskedLM",
							    "FlaxBigBirdForMultipleChoice",
							    "FlaxBigBirdForPreTraining",
							    "FlaxBigBirdForQuestionAnswering",
							    "FlaxBigBirdForSequenceClassification",
							    "FlaxBigBirdForTokenClassification",
							    "FlaxBigBirdModel",
							    "FlaxBigBirdPreTrainedModel",
							]

if TYPE_CHECKING:
							from .configuration_big_bird import BIG_BIRD_PRETRAINED_CONFIG_ARCHIVE_MAP, BigBirdConfig, BigBirdOnnxConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_big_bird import BigBirdTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_big_bird_fast import BigBirdTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_big_bird import (
														    BIG_BIRD_PRETRAINED_MODEL_ARCHIVE_LIST,
														    BigBirdForCausalLM,
														    BigBirdForMaskedLM,
														    BigBirdForMultipleChoice,
														    BigBirdForPreTraining,
														    BigBirdForQuestionAnswering,
														    BigBirdForSequenceClassification,
														    BigBirdForTokenClassification,
														    BigBirdLayer,
														    BigBirdModel,
														    BigBirdPreTrainedModel,
														    load_tf_weights_in_big_bird,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_big_bird import (
														    FlaxBigBirdForCausalLM,
														    FlaxBigBirdForMaskedLM,
														    FlaxBigBirdForMultipleChoice,
														    FlaxBigBirdForPreTraining,
														    FlaxBigBirdForQuestionAnswering,
														    FlaxBigBirdForSequenceClassification,
														    FlaxBigBirdForTokenClassification,
														    FlaxBigBirdModel,
														    FlaxBigBirdPreTrainedModel,
														)

else:
							import sys

							_lowercase				:						List[Any]												= _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





'''simple docstring'''







from itertools import combinations, product
def       snake_case_	(       __SCREAMING_SNAKE_CASE      :				int   ,   __SCREAMING_SNAKE_CASE      :				int   ,   __SCREAMING_SNAKE_CASE      :				int   ,   __SCREAMING_SNAKE_CASE      :				int							):



						"""simple docstring"""





						if xa == ya == 0 or xa == ya == 0:
												return False
						lowercase_			:  Optional[Any]									= xa * xa + ya * ya
						lowercase_			:  List[str]									= xa * xa + ya * ya
						lowercase_			:  Any									= (xa - xa) * (xa - xa) + (ya - ya) * (ya - ya)
						return (
						    a_square + b_square == c_square
						    or a_square + c_square == b_square
						    or b_square + c_square == a_square
						)
def       snake_case_	(       __SCREAMING_SNAKE_CASE      :				int = 50							):



						"""simple docstring"""





						return sum(
						    1
						    for pta, pta in combinations(product(range(limit + 1							)   ,   repeat=2							)   ,   2							)
						    if is_right(*__SCREAMING_SNAKE_CASE   ,   *__SCREAMING_SNAKE_CASE							)							)


if __name__ == "__main__":
							print(f"""{solution() = }""")



