from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


a_					      = {
    'configuration_esm': ['ESM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'EsmConfig'],
    'tokenization_esm': ['EsmTokenizer'],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a_					      = [
       'ESM_PRETRAINED_MODEL_ARCHIVE_LIST',
       'EsmForMaskedLM',
       'EsmForSequenceClassification',
       'EsmForTokenClassification',
       'EsmModel',
       'EsmPreTrainedModel',
   ]
   a_					      = ['EsmForProteinFolding', 'EsmFoldPreTrainedModel']

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a_					      = [
       'TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TFEsmForMaskedLM',
       'TFEsmForSequenceClassification',
       'TFEsmForTokenClassification',
       'TFEsmModel',
       'TFEsmPreTrainedModel',
   ]

if TYPE_CHECKING:
   from .configuration_esm import ESM_PRETRAINED_CONFIG_ARCHIVE_MAP, EsmConfig
   from .tokenization_esm import EsmTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_esm import (
          ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
          EsmForMaskedLM,
          EsmForSequenceClassification,
          EsmForTokenClassification,
          EsmModel,
          EsmPreTrainedModel,
      )
      from .modeling_esmfold import EsmFoldPreTrainedModel, EsmForProteinFolding

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_esm import (
          TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFEsmForMaskedLM,
          TFEsmForSequenceClassification,
          TFEsmForTokenClassification,
          TFEsmModel,
          TFEsmPreTrainedModel,
      )


else:
   import sys

   a_					      = _LazyModule(__name__, globals()['__file__'], _import_structure)




from datetime import date

import requests

a_					      = 'https://api.carbonintensity.org.uk/intensity'





def      __lowercase  (		):
 UpperCamelCase_      :		Dict				=      requests.get(lowerCamelCase			).json()['data'][0]
 return last_half_hour["intensity"]["actual"]





def      __lowercase  (		lowerCamelCase      :  Union[str, Any]						,						lowerCamelCase      :  Union[str, Any]			):
 return requests.get(F"{BASE_URL}/{start}/{end}"			).json()["data"]


if __name__ == "__main__":
   for entry in fetch_from_to(start=date(2_020, 10, 1), end=date(2_020, 10, 3)):
      print('from {from} to {to}: {intensity[actual]}'.format(**entry))
   print(F"""{fetch_last_half_hour() = }""")



