

from typing import TYPE_CHECKING

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


a_						     =    {"""configuration_opt""": ["""OPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """OPTConfig"""]}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a_						     =    [
			    """OPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
			    """OPTForCausalLM""",
			    """OPTModel""",
			    """OPTPreTrainedModel""",
			    """OPTForSequenceClassification""",
			    """OPTForQuestionAnswering""",
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a_						     =    ["""TFOPTForCausalLM""", """TFOPTModel""", """TFOPTPreTrainedModel"""]

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			a_						     =    [
			    """FlaxOPTForCausalLM""",
			    """FlaxOPTModel""",
			    """FlaxOPTPreTrainedModel""",
			]


if TYPE_CHECKING:
			from .configuration_opt import OPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OPTConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_opt import (
						    OPT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    OPTForCausalLM,
						    OPTForQuestionAnswering,
						    OPTForSequenceClassification,
						    OPTModel,
						    OPTPreTrainedModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_opt import TFOPTForCausalLM, TFOPTModel, TFOPTPreTrainedModel

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_opt import FlaxOPTForCausalLM, FlaxOPTModel, FlaxOPTPreTrainedModel

else:
			import sys

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



from __future__ import annotations

def  lowerCamelCase__ (					_a							,			_a):
	if b == 0:
		return (1, 0)
	((SCREAMING_SNAKE_CASE)     ,(SCREAMING_SNAKE_CASE))   :      Tuple				     =       extended_euclid(_a							,			a % b)
	SCREAMING_SNAKE_CASE   :      Dict				     =       a // b
	return (y, x - k * y)

def  lowerCamelCase__ (					_a							,			_a							,			_a							,			_a):
	((SCREAMING_SNAKE_CASE)     ,(SCREAMING_SNAKE_CASE))   :      str				     =       extended_euclid(_a							,			_a)
	SCREAMING_SNAKE_CASE   :      str				     =       na * na
	SCREAMING_SNAKE_CASE   :      int				     =       ra * x * na + ra * y * na
	return (n % m + m) % m

def  lowerCamelCase__ (					_a							,			_a):
	((SCREAMING_SNAKE_CASE)     ,(SCREAMING_SNAKE_CASE))   :      List[str]				     =       extended_euclid(_a							,			_a)
	if b < 0:
		SCREAMING_SNAKE_CASE   :      int				     =       (b % n + n) % n
	return b

def  lowerCamelCase__ (					_a							,			_a							,			_a							,			_a):
	SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Dict				     =       invert_modulo(_a							,			_a), invert_modulo(_a							,			_a)
	SCREAMING_SNAKE_CASE   :      Optional[int]				     =       na * na
	SCREAMING_SNAKE_CASE   :      Optional[Any]				     =       ra * x * na + ra * y * na
	return (n % m + m) % m


if __name__ == "__main__":
			from doctest import testmod

			testmod(name='chinese_remainder_theorem', verbose=True)
			testmod(name='chinese_remainder_theorem2', verbose=True)
			testmod(name='invert_modulo', verbose=True)
			testmod(name='extended_euclid', verbose=True)