
from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE							:Optional[int]											=       {
    'configuration_layoutlm': ['LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LayoutLMConfig', 'LayoutLMOnnxConfig'],
    'tokenization_layoutlm': ['LayoutLMTokenizer'],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE							:List[Any]											=       ['LayoutLMTokenizerFast']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE							:List[Any]											=       [
					    'LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'LayoutLMForMaskedLM',
					    'LayoutLMForSequenceClassification',
					    'LayoutLMForTokenClassification',
					    'LayoutLMForQuestionAnswering',
					    'LayoutLMModel',
					    'LayoutLMPreTrainedModel',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE							:List[str]											=       [
					    'TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'TFLayoutLMForMaskedLM',
					    'TFLayoutLMForSequenceClassification',
					    'TFLayoutLMForTokenClassification',
					    'TFLayoutLMForQuestionAnswering',
					    'TFLayoutLMMainLayer',
					    'TFLayoutLMModel',
					    'TFLayoutLMPreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig, LayoutLMOnnxConfig
					from .tokenization_layoutlm import LayoutLMTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_layoutlm_fast import LayoutLMTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_layoutlm import (
										    LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
										    LayoutLMForMaskedLM,
										    LayoutLMForQuestionAnswering,
										    LayoutLMForSequenceClassification,
										    LayoutLMForTokenClassification,
										    LayoutLMModel,
										    LayoutLMPreTrainedModel,
										)
					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_layoutlm import (
										    TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFLayoutLMForMaskedLM,
										    TFLayoutLMForQuestionAnswering,
										    TFLayoutLMForSequenceClassification,
										    TFLayoutLMForTokenClassification,
										    TFLayoutLMMainLayer,
										    TFLayoutLMModel,
										    TFLayoutLMPreTrainedModel,
										)

else:
					import sys

					SCREAMING_SNAKE_CASE							:str											=       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


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() = }''')
