






from typing import TYPE_CHECKING

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


_a    :   Dict        =			{"""configuration_opt""": ["""OPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """OPTConfig"""]}

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

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

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_a    :   Any        =			[
	    """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    :   Any        =			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)











from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean


def 				snake_case__							(							UpperCAmelCase							:  int       ):

    # A local function to see if a dot lands in the circle.
    def is_in_circle(UpperCAmelCase							:  float    ,			UpperCAmelCase							:  float       ) -> bool:
        lowerCAmelCase__   :Optional[int]      							=				sqrt((x**2) + (y**2)       )
        # Our circle has a radius of 1, so a distance
        # greater than 1 would land outside the circle.
        return distance_from_centre <= 1

    # The proportion of guesses that landed in the circle
    lowerCAmelCase__   :Union[str, Any]      							=				mean(
        int(is_in_circle(uniform(-1.0    ,			1.0       )    ,			uniform(-1.0    ,			1.0       )       )       )
        for _ in range(UpperCAmelCase       )       )
    # The ratio of the area for circle to square is pi/4.
    lowerCAmelCase__   :List[Any]      							=				proportion * 4
    print(F'''The estimated value of pi is {pi_estimate}'''       )
    print(F'''The numpy value of pi is {pi}'''       )
    print(F'''The total error is {abs(pi - pi_estimate       )}'''       )


def 				snake_case__							(							UpperCAmelCase							:  int    ,			UpperCAmelCase							:  Callable[[float], float]    ,			UpperCAmelCase							:  float = 0.0    ,			UpperCAmelCase							:  float = 1.0    ,			):
    return mean(
        function_to_integrate(uniform(UpperCAmelCase    ,			UpperCAmelCase       )       ) for _ in range(UpperCAmelCase       )       ) * (max_value - min_value)


def 				snake_case__							(							UpperCAmelCase							:  int    ,			UpperCAmelCase							:  float = 0.0    ,			UpperCAmelCase							:  float = 1.0       ):

    def identity_function(UpperCAmelCase							:  float       ) -> float:
        return x

    lowerCAmelCase__   :List[str]      							=				area_under_curve_estimator(
        UpperCAmelCase    ,			UpperCAmelCase    ,			UpperCAmelCase    ,			UpperCAmelCase       )
    lowerCAmelCase__   :Tuple      							=				(max_value * max_value - min_value * min_value) / 2

    print("******************"       )
    print(F'''Estimating area under y=x where x varies from {min_value} to {max_value}'''       )
    print(F'''Estimated value is {estimated_value}'''       )
    print(F'''Expected value is {expected_value}'''       )
    print(F'''Total error is {abs(estimated_value - expected_value       )}'''       )
    print("******************"       )


def 				snake_case__							(							UpperCAmelCase							:  int       ):

    def function_to_integrate(UpperCAmelCase							:  float       ) -> float:
        return sqrt(4.0 - x * x       )

    lowerCAmelCase__   :int      							=				area_under_curve_estimator(
        UpperCAmelCase    ,			UpperCAmelCase    ,			0.0    ,			2.0       )

    print("******************"       )
    print("Estimating pi using area_under_curve_estimator"       )
    print(F'''Estimated value is {estimated_value}'''       )
    print(F'''Expected value is {pi}'''       )
    print(F'''Total error is {abs(estimated_value - pi       )}'''       )
    print("******************"       )


if __name__ == "__main__":
 import doctest

 doctest.testmod()



