"""simple docstring"""






from datetime import date

import requests

UpperCamelCase     					=     'https://api.carbonintensity.org.uk/intensity'

def  lowerCAmelCase_      ()				-> str:
						a_							:			str     		=					requests.get(_SCREAMING_SNAKE_CASE  ).json()["data"][0]
						return last_half_hour["intensity"]["actual"]

def  lowerCAmelCase_      (_SCREAMING_SNAKE_CASE	:Union[str, Any]		,			_SCREAMING_SNAKE_CASE	:int  )				-> list:
						return requests.get(F'''{BASE_URL}/{start}/{end}'''  ).json()["data"]


if __name__ == "__main__":
				for entry in fetch_from_to(start=date(20_20, 10, 1), end=date(20_20, 10, 3)):
								print('from {from} to {to}: {intensity[actual]}'.format(**entry))
				print(F'{fetch_last_half_hour() = }')





"""simple docstring"""






from typing import TYPE_CHECKING

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


UpperCamelCase     					=     {'configuration_levit': ['LEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LevitConfig', 'LevitOnnxConfig']}

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCamelCase     					=     ['LevitFeatureExtractor']
				UpperCamelCase     					=     ['LevitImageProcessor']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				UpperCamelCase     					=     [
				    'LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'LevitForImageClassification',
				    'LevitForImageClassificationWithTeacher',
				    'LevitModel',
				    'LevitPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_levit import LEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, LevitConfig, LevitOnnxConfig

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .feature_extraction_levit import LevitFeatureExtractor
								from .image_processing_levit import LevitImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_levit import (
								    LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    LevitForImageClassification,
								    LevitForImageClassificationWithTeacher,
								    LevitModel,
								    LevitPreTrainedModel,
								)
else:
				import sys

				UpperCamelCase     					=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




