
'''simple docstring'''


from typing import TYPE_CHECKING

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


__lowercase							:      Dict		 =   {
    '''configuration_deberta''': ['''DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DebertaConfig''', '''DebertaOnnxConfig'''],
    '''tokenization_deberta''': ['''DebertaTokenizer'''],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowercase							:      List[Any]		 =   ['''DebertaTokenizerFast''']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowercase							:      List[str]		 =   [
					    '''DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''DebertaForMaskedLM''',
					    '''DebertaForQuestionAnswering''',
					    '''DebertaForSequenceClassification''',
					    '''DebertaForTokenClassification''',
					    '''DebertaModel''',
					    '''DebertaPreTrainedModel''',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowercase							:      Tuple		 =   [
					    '''TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''TFDebertaForMaskedLM''',
					    '''TFDebertaForQuestionAnswering''',
					    '''TFDebertaForSequenceClassification''',
					    '''TFDebertaForTokenClassification''',
					    '''TFDebertaModel''',
					    '''TFDebertaPreTrainedModel''',
					]


if TYPE_CHECKING:
					from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
					from .tokenization_deberta import DebertaTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_deberta_fast import DebertaTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_deberta import (
										    DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
										    DebertaForMaskedLM,
										    DebertaForQuestionAnswering,
										    DebertaForSequenceClassification,
										    DebertaForTokenClassification,
										    DebertaModel,
										    DebertaPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_deberta import (
										    TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFDebertaForMaskedLM,
										    TFDebertaForQuestionAnswering,
										    TFDebertaForSequenceClassification,
										    TFDebertaForTokenClassification,
										    TFDebertaModel,
										    TFDebertaPreTrainedModel,
										)


else:
					import sys

					__lowercase							:      str		 =   _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)










"""simple docstring"""


from collections import defaultdict
from math import ceil, sqrt



def 					lowerCAmelCase				(    __UpperCamelCase = 1_0_0_0_0_0_0		,       __UpperCamelCase = 1_0 ):




  """simple docstring"""







  __A									= defaultdict(__UpperCamelCase )

  for outer_width in range(3		,       (t_limit // 4) + 2 ):
    if outer_width * outer_width > t_limit:
      __A									= max(
          ceil(sqrt(outer_width * outer_width - t_limit ) )		,       1 )
    else:
      __A									= 1

    hole_width_lower_bound += (outer_width - hole_width_lower_bound) % 2

    for hole_width in range(__UpperCamelCase		,       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 <= 1_0 )


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




