




'''simple docstring'''
from typing import TYPE_CHECKING

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


_SCREAMING_SNAKE_CASE =	{
    "configuration_bart": ["BART_PRETRAINED_CONFIG_ARCHIVE_MAP", "BartConfig", "BartOnnxConfig"],
    "tokenization_bart": ["BartTokenizer"],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_SCREAMING_SNAKE_CASE =	["BartTokenizerFast"]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_SCREAMING_SNAKE_CASE =	[
							    "BART_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "BartForCausalLM",
							    "BartForConditionalGeneration",
							    "BartForQuestionAnswering",
							    "BartForSequenceClassification",
							    "BartModel",
							    "BartPretrainedModel",
							    "PretrainedBartModel",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_SCREAMING_SNAKE_CASE =	[
							    "TFBartForConditionalGeneration",
							    "TFBartForSequenceClassification",
							    "TFBartModel",
							    "TFBartPretrainedModel",
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_SCREAMING_SNAKE_CASE =	[
							    "FlaxBartDecoderPreTrainedModel",
							    "FlaxBartForCausalLM",
							    "FlaxBartForConditionalGeneration",
							    "FlaxBartForQuestionAnswering",
							    "FlaxBartForSequenceClassification",
							    "FlaxBartModel",
							    "FlaxBartPreTrainedModel",
							]

if TYPE_CHECKING:
							from .configuration_bart import BART_PRETRAINED_CONFIG_ARCHIVE_MAP, BartConfig, BartOnnxConfig
							from .tokenization_bart import BartTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_bart_fast import BartTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_bart import (
														    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
														    BartForCausalLM,
														    BartForConditionalGeneration,
														    BartForQuestionAnswering,
														    BartForSequenceClassification,
														    BartModel,
														    BartPretrainedModel,
														    PretrainedBartModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_bart import (
														    TFBartForConditionalGeneration,
														    TFBartForSequenceClassification,
														    TFBartModel,
														    TFBartPretrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_bart import (
														    FlaxBartDecoderPreTrainedModel,
														    FlaxBartForCausalLM,
														    FlaxBartForConditionalGeneration,
														    FlaxBartForQuestionAnswering,
														    FlaxBartForSequenceClassification,
														    FlaxBartModel,
														    FlaxBartPreTrainedModel,
														)

else:
							import sys

							_SCREAMING_SNAKE_CASE =	_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)










'''simple docstring'''
from __future__ import annotations

import sys





def  lowerCamelCase(       SCREAMING_SNAKE_CASE_					,SCREAMING_SNAKE_CASE_    )		->		tuple[int, int]:
			# base cases
			if abs(SCREAMING_SNAKE_CASE_    ) == 1:
						return a, 0
			elif abs(SCREAMING_SNAKE_CASE_    ) == 1:
						return 0, b

			A_		,   A_       		=   a, b
			A_		,   A_       		=   1, 0
			A_		,   A_       		=   0, 1

			while remainder != 0:
						A_       		=   old_remainder // remainder
						A_		,   A_       		=   remainder, old_remainder - quotient * remainder
						A_		,   A_       		=   coeff_a, old_coeff_a - quotient * coeff_a
						A_		,   A_       		=   coeff_b, old_coeff_b - quotient * coeff_b

			# sign correction for negative numbers
			if a < 0:
						A_       		=   -old_coeff_a
			if b < 0:
						A_       		=   -old_coeff_b

			return old_coeff_a, old_coeff_b





def  lowerCamelCase(       )		->		Tuple:
			if len(sys.argv    ) < 3:
						print('2 integer arguments required'    )
						return 1
			A_       		=   int(sys.argv[1]    )
			A_       		=   int(sys.argv[2]    )
			print(extended_euclidean_algorithm(SCREAMING_SNAKE_CASE_					,SCREAMING_SNAKE_CASE_    )    )
			return 0


if __name__ == "__main__":
							raise SystemExit(main())




