


"""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()








"""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__)




