






from typing import List, Optional, Tuple, Union

from . import logging
from .import_utils import is_torch_available, is_torch_version


if is_torch_available():
			import torch

A_	:	str				=				logging.get_logger(__name__)  # pylint: disable=invalid-name

try:
			from torch._dynamo import allow_in_graph as maybe_allow_in_graph
except (ImportError, ModuleNotFoundError):

			def 						snake_case      (cls						) ->       List[Any]:
					return cls

def 						snake_case      (UpperCAmelCase__			,					UpperCAmelCase__ = None			,					UpperCAmelCase__ = None			,					UpperCAmelCase__ = None			,					UpperCAmelCase__ = None			,					) ->       List[Any]:
		UpperCamelCase_:      Dict					   =       device
		UpperCamelCase_:      Tuple					   =       shape[0]

		UpperCamelCase_:      Union[str, Any]					   =       layout or torch.strided
		UpperCamelCase_:      Dict					   =       device or torch.device('cpu'						)

		if generator is not None:
				UpperCamelCase_:      Optional[int]					   =       generator.device.type if not isinstance(UpperCAmelCase__			,					UpperCAmelCase__						) else generator[0].device.type
				if gen_device_type != device.type and gen_device_type == "cpu":
						UpperCamelCase_:      List[str]					   =       "cpu"
						if device != "mps":
								logger.info(
								    F'''The passed generator was created on \'cpu\' even though a tensor on {device} was expected.'''
								    F''' Tensors will be created on \'cpu\' and then moved to {device}. Note that one can probably'''
								    F''' slighly speed up this function by passing a generator that was created on the {device} device.'''						)
				elif gen_device_type != device.type and gen_device_type == "cuda":
						raise ValueError(F'''Cannot generate a {device} tensor from a generator of type {gen_device_type}.'''						)

		if isinstance(UpperCAmelCase__			,					UpperCAmelCase__						):
				UpperCamelCase_:      Tuple					   =       (1,) + shape[1:]
				UpperCamelCase_:      str					   =       [
				    torch.randn(UpperCAmelCase__			,					generator=generator[i]			,					device=UpperCAmelCase__			,					dtype=UpperCAmelCase__			,					layout=UpperCAmelCase__						)
				    for i in range(UpperCAmelCase__						)
				]
				UpperCamelCase_:      Dict					   =       torch.cat(UpperCAmelCase__			,					dim=0						).to(UpperCAmelCase__						)
		else:
				UpperCamelCase_:      Optional[Any]					   =       torch.randn(UpperCAmelCase__			,					generator=UpperCAmelCase__			,					device=UpperCAmelCase__			,					dtype=UpperCAmelCase__			,					layout=UpperCAmelCase__						).to(UpperCAmelCase__						)

		return latents

def 						snake_case      (UpperCAmelCase__						) ->       Optional[int]:



		if is_torch_version('<'			,					'2.0.0'						) or not hasattr(UpperCAmelCase__			,					'_dynamo'						):
				return False
		return isinstance(UpperCAmelCase__			,					torch._dynamo.eval_frame.OptimizedModule						)







from datetime import date

import requests

A_	:	Any				=				'https://api.carbonintensity.org.uk/intensity'

def 						snake_case      () ->       str:
  UpperCamelCase_:      Any					   =       requests.get(UpperCAmelCase__						).json()['data'][0]
  return last_half_hour["intensity"]["actual"]

def 						snake_case      (UpperCAmelCase__			,					UpperCAmelCase__						) ->       list:
  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() = }''')