
import torch

from transformers import PreTrainedModel

from .custom_configuration import CustomConfig, NoSuperInitConfig





class 							UpperCAmelCase   (						__SCREAMING_SNAKE_CASE						):




	'''simple docstring'''





	snake_case_      					=  CustomConfig






	def __init__( self      :				Optional[int]							,A      :				List[str]				):
					super().__init__(A				)
					__A         =				torch.nn.Linear(config.hidden_size							,config.hidden_size				)






	def        UpperCamelCase_						( self      :				Union[str, Any]							,A      :				Optional[int]				):
					return self.linear(A				)







	def        UpperCamelCase_						( self      :				Optional[int]							,A      :				int				):
					pass





class 							UpperCAmelCase   (						__SCREAMING_SNAKE_CASE						):




	'''simple docstring'''





	snake_case_      					=  NoSuperInitConfig






	def __init__( self      :				Optional[int]							,A      :				Union[str, Any]				):
					super().__init__(A				)
					__A         =				torch.nn.Linear(config.attribute							,config.attribute				)






	def        UpperCamelCase_						( self      :				str							,A      :				Any				):
					return self.linear(A				)







	def        UpperCamelCase_						( self      :				Tuple							,A      :				int				):
					pass


from datetime import date

import requests

SCREAMING_SNAKE_CASE							:str											=       'https://api.carbonintensity.org.uk/intensity'


def 		UpperCAmelCase    (			)	->			str:







				"""simple docstring"""



				__A         =				requests.get(a_		).json()["data"][0]
				return last_half_hour["intensity"]["actual"]


def 		UpperCAmelCase    (			a_	,     a_		)	->			list:







				"""simple docstring"""



				return requests.get(F'''{BASE_URL}/{start}/{end}'''		).json()["data"]


if __name__ == "__main__":
					for entry in fetch_from_to(start=date(2020, 10, 1), end=date(2020, 10, 3)):
										print('from {from} to {to}: {intensity[actual]}'.format(**entry))
					print(f'''{fetch_last_half_hour() = }''')
