




from typing import TYPE_CHECKING

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


__UpperCAmelCase     							=						{
    'configuration_rembert': ['REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RemBertConfig', 'RemBertOnnxConfig']
}

try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCAmelCase     							=						['RemBertTokenizer']

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

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCAmelCase     							=						[
		    'REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'RemBertForCausalLM',
		    'RemBertForMaskedLM',
		    'RemBertForMultipleChoice',
		    'RemBertForQuestionAnswering',
		    'RemBertForSequenceClassification',
		    'RemBertForTokenClassification',
		    'RemBertLayer',
		    'RemBertModel',
		    'RemBertPreTrainedModel',
		    'load_tf_weights_in_rembert',
		]


try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCAmelCase     							=						[
		    'TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'TFRemBertForCausalLM',
		    'TFRemBertForMaskedLM',
		    'TFRemBertForMultipleChoice',
		    'TFRemBertForQuestionAnswering',
		    'TFRemBertForSequenceClassification',
		    'TFRemBertForTokenClassification',
		    'TFRemBertLayer',
		    'TFRemBertModel',
		    'TFRemBertPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_rembert import REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RemBertConfig, RemBertOnnxConfig

		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_rembert import RemBertTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_rembert_fast import RemBertTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_rembert import (
				    REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    RemBertForCausalLM,
				    RemBertForMaskedLM,
				    RemBertForMultipleChoice,
				    RemBertForQuestionAnswering,
				    RemBertForSequenceClassification,
				    RemBertForTokenClassification,
				    RemBertLayer,
				    RemBertModel,
				    RemBertPreTrainedModel,
				    load_tf_weights_in_rembert,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_rembert import (
				    TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFRemBertForCausalLM,
				    TFRemBertForMaskedLM,
				    TFRemBertForMultipleChoice,
				    TFRemBertForQuestionAnswering,
				    TFRemBertForSequenceClassification,
				    TFRemBertForTokenClassification,
				    TFRemBertLayer,
				    TFRemBertModel,
				    TFRemBertPreTrainedModel,
				)


else:
		import sys

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














from collections import defaultdict
from math import ceil, sqrt




def 					lowercase__				(					__snake_case   :							int = 1_000_000					,	__snake_case   :							int = 10  ):


					'''simple docstring'''


					UpperCAmelCase_   :				defaultdict						=						defaultdict(__snake_case  )

					for outer_width in range(3					,	(t_limit // 4) + 2  ):
										if outer_width * outer_width > t_limit:
															UpperCAmelCase_   :				Union[str, Any]						=						max(
															    ceil(sqrt(outer_width * outer_width - t_limit  )  )					,	1  )
										else:
															UpperCAmelCase_   :				int						=						1

										hole_width_lower_bound += (outer_width - hole_width_lower_bound) % 2

										for hole_width in range(__snake_case					,	outer_width - 1					,	2  ):
															count[outer_width * outer_width - hole_width * hole_width] += 1

					return sum(1 for n in count.values() if 1 <= n <= 10  )


if __name__ == "__main__":
		print(F'{solution() = }')






