






from __future__ import annotations

import typing
from collections import Counter





def 	lowerCAmelCase_		(lowercase__						:       int       )		->    typing.Counter[int]:


				'''simple docstring'''




				lowerCAmelCase__      			=						Counter()
				for base in range(1     ,					max_perimeter + 1       ):
								for perpendicular in range(lowercase__     ,					max_perimeter + 1       ):
												lowerCAmelCase__      			=						(base * base + perpendicular * perpendicular) ** 0.5
												if hypotenuse == int(lowercase__       ):
																lowerCAmelCase__      			=						int(base + perpendicular + hypotenuse       )
																if perimeter > max_perimeter:
																				continue
																triplets[perimeter] += 1
				return triplets





def 	lowerCAmelCase_		(lowercase__						:       int = 10_00       )		->    int:


				'''simple docstring'''




				lowerCAmelCase__      			=						pythagorean_triple(lowercase__       )
				return triplets.most_common(1       )[0][0]


if __name__ == "__main__":
			print(F'''Perimeter {solution()} has maximum solutions''')













from typing import TYPE_CHECKING

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


_UpperCAmelCase			:		List[str]   				=    {
    "configuration_electra": ["ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP", "ElectraConfig", "ElectraOnnxConfig"],
    "tokenization_electra": ["ElectraTokenizer"],
}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_UpperCAmelCase			:		Dict   				=    ["ElectraTokenizerFast"]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_UpperCAmelCase			:		Optional[Any]   				=    [
			    "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:
			_UpperCAmelCase			:		Union[str, Any]   				=    [
			    "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:
			_UpperCAmelCase			:		Optional[int]   				=    [
			    "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

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





