from datetime import date

import requests

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


def 					__lowerCAmelCase			(  ):



							"""simple docstring"""


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


def 					__lowerCAmelCase			(  _A ,_A					):



							"""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() = }''')



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


A_:      int        =			{
    'configuration_bart': ['BART_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BartConfig', 'BartOnnxConfig'],
    'tokenization_bart': ['BartTokenizer'],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				A_:      List[str]        =			['BartTokenizerFast']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				A_:      int        =			[
				    'BART_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'BartForCausalLM',
				    'BartForConditionalGeneration',
				    'BartForQuestionAnswering',
				    'BartForSequenceClassification',
				    'BartModel',
				    'BartPretrainedModel',
				    'PretrainedBartModel',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				A_:      Any        =			[
				    'TFBartForConditionalGeneration',
				    'TFBartForSequenceClassification',
				    'TFBartModel',
				    'TFBartPretrainedModel',
				]

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				A_:      str        =			[
				    'FlaxBartDecoderPreTrainedModel',
				    'FlaxBartForCausalLM',
				    'FlaxBartForConditionalGeneration',
				    'FlaxBartForQuestionAnswering',
				    'FlaxBartForSequenceClassification',
				    'FlaxBartModel',
				    'FlaxBartPreTrainedModel',
				]

if TYPE_CHECKING:
				from .configuration_bart import BART_PRETRAINED_CONFIG_ARCHIVE_MAP, BartConfig, BartOnnxConfig
				from .tokenization_bart import BartTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_bart_fast import BartTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_bart import (
								    BART_PRETRAINED_MODEL_ARCHIVE_LIST,
								    BartForCausalLM,
								    BartForConditionalGeneration,
								    BartForQuestionAnswering,
								    BartForSequenceClassification,
								    BartModel,
								    BartPretrainedModel,
								    PretrainedBartModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_bart import (
								    TFBartForConditionalGeneration,
								    TFBartForSequenceClassification,
								    TFBartModel,
								    TFBartPretrainedModel,
								)

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_bart import (
								    FlaxBartDecoderPreTrainedModel,
								    FlaxBartForCausalLM,
								    FlaxBartForConditionalGeneration,
								    FlaxBartForQuestionAnswering,
								    FlaxBartForSequenceClassification,
								    FlaxBartModel,
								    FlaxBartPreTrainedModel,
								)

else:
				import sys

				A_:      Optional[Any]        =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


