
"""simple docstring"""


from typing import TYPE_CHECKING

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


_lowercase				 =					{
    '''configuration_electra''': ['''ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ElectraConfig''', '''ElectraOnnxConfig'''],
    '''tokenization_electra''': ['''ElectraTokenizer'''],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase				 =					['''ElectraTokenizerFast''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase				 =					[
		    '''ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''ElectraForCausalLM''',
		    '''ElectraForMaskedLM''',
		    '''ElectraForMultipleChoice''',
		    '''ElectraForPreTraining''',
		    '''ElectraForQuestionAnswering''',
		    '''ElectraForSequenceClassification''',
		    '''ElectraForTokenClassification''',
		    '''ElectraModel''',
		    '''ElectraPreTrainedModel''',
		    '''load_tf_weights_in_electra''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase				 =					[
		    '''TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''TFElectraForMaskedLM''',
		    '''TFElectraForMultipleChoice''',
		    '''TFElectraForPreTraining''',
		    '''TFElectraForQuestionAnswering''',
		    '''TFElectraForSequenceClassification''',
		    '''TFElectraForTokenClassification''',
		    '''TFElectraModel''',
		    '''TFElectraPreTrainedModel''',
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase				 =					[
		    '''FlaxElectraForCausalLM''',
		    '''FlaxElectraForMaskedLM''',
		    '''FlaxElectraForMultipleChoice''',
		    '''FlaxElectraForPreTraining''',
		    '''FlaxElectraForQuestionAnswering''',
		    '''FlaxElectraForSequenceClassification''',
		    '''FlaxElectraForTokenClassification''',
		    '''FlaxElectraModel''',
		    '''FlaxElectraPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_electra import ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP, ElectraConfig, ElectraOnnxConfig
		from .tokenization_electra import ElectraTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_electra_fast import ElectraTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_electra import (
				    ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ElectraForCausalLM,
				    ElectraForMaskedLM,
				    ElectraForMultipleChoice,
				    ElectraForPreTraining,
				    ElectraForQuestionAnswering,
				    ElectraForSequenceClassification,
				    ElectraForTokenClassification,
				    ElectraModel,
				    ElectraPreTrainedModel,
				    load_tf_weights_in_electra,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_electra import (
				    TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFElectraForMaskedLM,
				    TFElectraForMultipleChoice,
				    TFElectraForPreTraining,
				    TFElectraForQuestionAnswering,
				    TFElectraForSequenceClassification,
				    TFElectraForTokenClassification,
				    TFElectraModel,
				    TFElectraPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_electra import (
				    FlaxElectraForCausalLM,
				    FlaxElectraForMaskedLM,
				    FlaxElectraForMultipleChoice,
				    FlaxElectraForPreTraining,
				    FlaxElectraForQuestionAnswering,
				    FlaxElectraForSequenceClassification,
				    FlaxElectraForTokenClassification,
				    FlaxElectraModel,
				    FlaxElectraPreTrainedModel,
				)

else:
		import sys

		_lowercase				 =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
'''simple docstring'''


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





def    lowerCAmelCase       (		UpperCamelCase__					: int							):


	"""simple docstring"""

	# A local function to see if a dot lands in the circle.
	def is_in_circle(UpperCamelCase__					: float     ,       UpperCamelCase__					: float							) -> bool:
		__UpperCAmelCase      					=      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
	__UpperCAmelCase      					=      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.
	__UpperCAmelCase      					=      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    lowerCAmelCase       (		UpperCamelCase__					: int     ,       UpperCamelCase__					: Callable[[float], float]     ,       UpperCamelCase__					: float = 0.0     ,       UpperCamelCase__					: float = 1.0     ,       ):


	"""simple docstring"""
	return mean(
	    function_to_integrate(uniform(UpperCamelCase__     ,       UpperCamelCase__							)							) for _ in range(UpperCamelCase__							)							) * (max_value - min_value)





def    lowerCAmelCase       (		UpperCamelCase__					: int     ,       UpperCamelCase__					: float = 0.0     ,       UpperCamelCase__					: float = 1.0							):


	"""simple docstring"""

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

	__UpperCAmelCase      					=      area_under_curve_estimator(
	    UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__     ,       UpperCamelCase__							)
	__UpperCAmelCase      					=      (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    lowerCAmelCase       (		UpperCamelCase__					: int							):


	"""simple docstring"""

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

	__UpperCAmelCase      					=      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()
