code: 
  
  
  """simple docstring"""
  
  
  
  
  from collections import namedtuple
  
  __magic_name__	:   int          =							namedtuple('from_to', 'from_ to')
  
  __magic_name__	:   List[Any]          =							{
      'millimeter': 'mm',
      'centimeter': 'cm',
      'meter': 'm',
      'kilometer': 'km',
      'inch': 'in',
      'inche': 'in',  # Trailing 's' has been stripped off
      'feet': 'ft',
      'foot': 'ft',
      'yard': 'yd',
      'mile': 'mi',
  }
  
  __magic_name__	:   Any          =							{
      'mm': from_to(0.0_0_1, 1_0_0_0),
      'cm': from_to(0.0_1, 1_0_0),
      'm': from_to(1, 1),
      'km': from_to(1_0_0_0, 0.0_0_1),
      'in': from_to(0.0_2_5_4, 3_9.3_7_0_1),
      'ft': from_to(0.3_0_4_8, 3.2_8_0_8_4),
      'yd': from_to(0.9_1_4_4, 1.0_9_3_6_1),
      'mi': from_to(1_6_0_9.3_4, 0.0_0_0_6_2_1_3_7_1),
  }
  
  
  
  
  
  def 			a_   (  lowercase__   :float, lowercase__   :str, lowercase__   :str    ):
  			__lowerCamelCase        =			from_type.lower().rstrip("""s"""    )
  			__lowerCamelCase        =			TYPE_CONVERSION.get(lowercase__, lowercase__    )
  			__lowerCamelCase        =			to_type.lower().rstrip("""s"""    )
  			__lowerCamelCase        =			TYPE_CONVERSION.get(lowercase__, lowercase__    )
  			if new_from not in METRIC_CONVERSION:
  						__lowerCamelCase        =			(
  						    f'Invalid \'from_type\' value: {from_type!r}.\n'
  						    f'Conversion abbreviations are: {", ".join(lowercase__    )}'
  						)
  						raise ValueError(lowercase__    )
  			if new_to not in METRIC_CONVERSION:
  						__lowerCamelCase        =			(
  						    f'Invalid \'to_type\' value: {to_type!r}.\n'
  						    f'Conversion abbreviations are: {", ".join(lowercase__    )}'
  						)
  						raise ValueError(lowercase__    )
  			return value * METRIC_CONVERSION[new_from].from_ * METRIC_CONVERSION[new_to].to
  
  
  if __name__ == "__main__":
  					import doctest
  
  					doctest.testmod()
  
  
  
  
  
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
  
  
  __magic_name__	:   Optional[Any]          =							{
      'configuration_flaubert': ['FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FlaubertConfig', 'FlaubertOnnxConfig'],
      'tokenization_flaubert': ['FlaubertTokenizer'],
  }
  
  try:
  					if not is_torch_available():
  										raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  					pass
  else:
  					__magic_name__	:   List[Any]          =							[
  					    'FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
  					    'FlaubertForMultipleChoice',
  					    'FlaubertForQuestionAnswering',
  					    'FlaubertForQuestionAnsweringSimple',
  					    'FlaubertForSequenceClassification',
  					    'FlaubertForTokenClassification',
  					    'FlaubertModel',
  					    'FlaubertWithLMHeadModel',
  					    'FlaubertPreTrainedModel',
  					]
  
  try:
  					if not is_tf_available():
  										raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  					pass
  else:
  					__magic_name__	:   Tuple          =							[
  					    'TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
  					    'TFFlaubertForMultipleChoice',
  					    'TFFlaubertForQuestionAnsweringSimple',
  					    'TFFlaubertForSequenceClassification',
  					    'TFFlaubertForTokenClassification',
  					    'TFFlaubertModel',
  					    'TFFlaubertPreTrainedModel',
  					    'TFFlaubertWithLMHeadModel',
  					]
  
  
  if TYPE_CHECKING:
  					from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertOnnxConfig
  					from .tokenization_flaubert import FlaubertTokenizer
  
  					try:
  										if not is_torch_available():
  															raise OptionalDependencyNotAvailable()
  					except OptionalDependencyNotAvailable:
  										pass
  					else:
  										from .modeling_flaubert import (
  										    FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
  										    FlaubertForMultipleChoice,
  										    FlaubertForQuestionAnswering,
  										    FlaubertForQuestionAnsweringSimple,
  										    FlaubertForSequenceClassification,
  										    FlaubertForTokenClassification,
  										    FlaubertModel,
  										    FlaubertPreTrainedModel,
  										    FlaubertWithLMHeadModel,
  										)
  
  					try:
  										if not is_tf_available():
  															raise OptionalDependencyNotAvailable()
  					except OptionalDependencyNotAvailable:
  										pass
  					else:
  										from .modeling_tf_flaubert import (
  										    TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
  										    TFFlaubertForMultipleChoice,
  										    TFFlaubertForQuestionAnsweringSimple,
  										    TFFlaubertForSequenceClassification,
  										    TFFlaubertForTokenClassification,
  										    TFFlaubertModel,
  										    TFFlaubertPreTrainedModel,
  										    TFFlaubertWithLMHeadModel,
  										)
  
  else:
  					import sys
  
  					__magic_name__	:   List[str]          =							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
  
  
  
  
  