




from typing import TYPE_CHECKING

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


UpperCamelCase__						=		{
    'configuration_layoutlm': ['LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LayoutLMConfig', 'LayoutLMOnnxConfig'],
    'tokenization_layoutlm': ['LayoutLMTokenizer'],
}

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

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

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase__						=		[
							    '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

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








from datetime import date

import requests

a 			=							'https://api.carbonintensity.org.uk/intensity'
def    a__    (	):
   lowercase__    :						Union[str, Any]       =			requests.get(A       ).json()["""data"""][0]
   return last_half_hour["intensity"]["actual"]
def    a__    (	A		,					A       ):
   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() = }')


