






from __future__ import annotations


def      lowerCamelCase__	(   lowercase ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE				:					Any		  =			[0] * max_one

  for i in range(0  ,   __UpperCAmelCase ):
    SCREAMING_SNAKE_CASE				:					Optional[Any]		  =			i

  for i in range(2  ,   __UpperCAmelCase ):
    if totients[i] == i:
      for j in range(__UpperCAmelCase  ,   __UpperCAmelCase  ,   __UpperCAmelCase ):
        totients[j] -= totients[j] // i

  return totients


def      lowerCamelCase__	(   lowercase  ,   lowercase ):

  """simple docstring"""






  return sorted(str(__UpperCAmelCase ) ) == sorted(str(__UpperCAmelCase ) )


def      lowerCamelCase__	(   lowercase = 10000000 ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE				:					str		  =			1  # i
  SCREAMING_SNAKE_CASE				:					List[str]		  =			0  # φ(i)
  SCREAMING_SNAKE_CASE				:					Any		  =			get_totients(max_n + 1 )

  for i in range(2  ,   max_n + 1 ):
    SCREAMING_SNAKE_CASE				:					Dict		  =			totients[i]

    if i * min_denominator < min_numerator * t and has_same_digits(__UpperCAmelCase  ,   __UpperCAmelCase ):
      SCREAMING_SNAKE_CASE				:					str		  =			i
      SCREAMING_SNAKE_CASE				:					List[str]		  =			t

  return min_numerator


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











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


UpperCamelCase_	       =   {
    "configuration_esm": ["ESM_PRETRAINED_CONFIG_ARCHIVE_MAP", "EsmConfig"],
    "tokenization_esm": ["EsmTokenizer"],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_	       =   [
							    "ESM_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "EsmForMaskedLM",
							    "EsmForSequenceClassification",
							    "EsmForTokenClassification",
							    "EsmModel",
							    "EsmPreTrainedModel",
							]
							UpperCamelCase_	       =   ["EsmForProteinFolding", "EsmFoldPreTrainedModel"]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_	       =   [
							    "TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "TFEsmForMaskedLM",
							    "TFEsmForSequenceClassification",
							    "TFEsmForTokenClassification",
							    "TFEsmModel",
							    "TFEsmPreTrainedModel",
							]

if TYPE_CHECKING:
							from .configuration_esm import ESM_PRETRAINED_CONFIG_ARCHIVE_MAP, EsmConfig
							from .tokenization_esm import EsmTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_esm import (
														    ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    EsmForMaskedLM,
														    EsmForSequenceClassification,
														    EsmForTokenClassification,
														    EsmModel,
														    EsmPreTrainedModel,
														)
														from .modeling_esmfold import EsmFoldPreTrainedModel, EsmForProteinFolding

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_esm import (
														    TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFEsmForMaskedLM,
														    TFEsmForSequenceClassification,
														    TFEsmForTokenClassification,
														    TFEsmModel,
														    TFEsmPreTrainedModel,
														)


else:
							import sys

							UpperCamelCase_	       =   _LazyModule(__name__, globals()["__file__"], _import_structure)





