



"""simple docstring"""






from collections import namedtuple

_lowercase			:			Dict										=						namedtuple("from_to", "from_ to")

_lowercase			:			List[Any]										=						{
    "cubicmeter": from_to(1, 1),
    "litre": from_to(0.001, 1_0_0_0),
    "kilolitre": from_to(1, 1),
    "gallon": from_to(0.00_454, 264.172),
    "cubicyard": from_to(0.76_455, 1.30_795),
    "cubicfoot": from_to(0.028, 35.3_147),
    "cup": from_to(0.000_236_588, 4_226.75),
}






def 					snake_case__       (		__lowerCamelCase    :   float							,		__lowerCamelCase    :   str							,		__lowerCamelCase    :   str     ):
			"""simple docstring"""
			if from_type not in METRIC_CONVERSION:
						raise ValueError(
						    f'''Invalid \'from_type\' value: {from_type!r}  Supported values are:\n'''
						    + ''', '''.join(__lowerCamelCase     )     )
			if to_type not in METRIC_CONVERSION:
						raise ValueError(
						    f'''Invalid \'to_type\' value: {to_type!r}.  Supported values are:\n'''
						    + ''', '''.join(__lowerCamelCase     )     )
			return value * METRIC_CONVERSION[from_type].from_ * METRIC_CONVERSION[to_type].to


if __name__ == "__main__":
		import doctest

		doctest.testmod()









"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


_lowercase			:			Any										=						{
    "configuration_trocr": ["TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP", "TrOCRConfig"],
    "processing_trocr": ["TrOCRProcessor"],
}


try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase			:			List[Any]										=						[
		    "TROCR_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "TrOCRForCausalLM",
		    "TrOCRPreTrainedModel",
		]


if TYPE_CHECKING:
		from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
		from .processing_trocr import TrOCRProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel

else:
		import sys

		_lowercase			:			Tuple										=						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




