from ...configuration_utils import PretrainedConfig
from ...utils import is_detectrona_available, logging


a_					      = logging.get_logger(__name__)

a_					      = {
    'layoutlmv2-base-uncased': 'https://huggingface.co/microsoft/layoutlmv2-base-uncased/resolve/main/config.json',
    'layoutlmv2-large-uncased': 'https://huggingface.co/microsoft/layoutlmv2-large-uncased/resolve/main/config.json',
    # See all LayoutLMv2 models at https://huggingface.co/models?filter=layoutlmv2
}

# soft dependency
if is_detectrona_available():
   import detectrona





class 	_lowercase		(      snake_case_ ):
 lowercase         =     'layoutlmv2'

 def __init__(				self			:							Tuple     ,   snake_case			:							str=3_0_5_2_2     ,   snake_case			:							Optional[int]=7_6_8     ,   snake_case			:							Any=1_2     ,   snake_case			:							Optional[int]=1_2     ,   snake_case			:							int=3_0_7_2     ,   snake_case			:							Tuple="gelu"     ,   snake_case			:							List[str]=0.1     ,   snake_case			:							Union[str, Any]=0.1     ,   snake_case			:							str=5_1_2     ,   snake_case			:							Optional[int]=2     ,   snake_case			:							List[str]=0.02     ,   snake_case			:							Optional[Any]=1e-12     ,   snake_case			:							Tuple=0     ,   snake_case			:							Dict=1_0_2_4     ,   snake_case			:							List[Any]=1_2_8     ,   snake_case			:							Any=3_2     ,   snake_case			:							List[str]=True     ,   snake_case			:							Optional[int]=2_5_6     ,   snake_case			:							List[Any]=6_4     ,   snake_case			:							Union[str, Any]=True     ,   snake_case			:							Any=[7, 7, 2_5_6]     ,   snake_case			:							List[str]=1_2_8     ,   snake_case			:							Union[str, Any]=1_2_8     ,   snake_case			:							int=True     ,   snake_case			:							int=True     ,   snake_case			:							int=False     ,   snake_case			:							List[Any]=None     ,   **snake_case			:							Optional[Any]     ,   )     ->		List[Any]:

  """simple docstring"""





  super().__init__(
      vocab_size=snake_case     ,   hidden_size=snake_case     ,   num_hidden_layers=snake_case     ,   num_attention_heads=snake_case     ,   intermediate_size=snake_case     ,   hidden_act=snake_case     ,   hidden_dropout_prob=snake_case     ,   attention_probs_dropout_prob=snake_case     ,   max_position_embeddings=snake_case     ,   type_vocab_size=snake_case     ,   initializer_range=snake_case     ,   layer_norm_eps=snake_case     ,   pad_token_id=snake_case     ,   **snake_case     ,   )
  UpperCamelCase_      :		Optional[int]				=      max_ad_position_embeddings
  UpperCamelCase_      :		str				=      max_rel_pos
  UpperCamelCase_      :		Any				=      rel_pos_bins
  UpperCamelCase_      :		Dict				=      fast_qkv
  UpperCamelCase_      :		int				=      max_rel_ad_pos
  UpperCamelCase_      :		List[Any]				=      rel_ad_pos_bins
  UpperCamelCase_      :		Dict				=      convert_sync_batchnorm
  UpperCamelCase_      :		Optional[Any]				=      image_feature_pool_shape
  UpperCamelCase_      :		Optional[int]				=      coordinate_size
  UpperCamelCase_      :		List[str]				=      shape_size
  UpperCamelCase_      :		int				=      has_relative_attention_bias
  UpperCamelCase_      :		Optional[int]				=      has_spatial_attention_bias
  UpperCamelCase_      :		Union[str, Any]				=      has_visual_segment_embedding
  UpperCamelCase_      :		str				=      (
      detectrona_config_args if detectrona_config_args is not None else self.get_default_detectrona_config()
  )

 @classmethod
 def 		SCREAMING_SNAKE_CASE__     (				self			:							Optional[int]    )     ->		Optional[int]:

  """simple docstring"""





  return {
      "MODEL.MASK_ON": True,
      "MODEL.PIXEL_STD": [57.375, 57.120, 58.395],
      "MODEL.BACKBONE.NAME": "build_resnet_fpn_backbone",
      "MODEL.FPN.IN_FEATURES": ["res2", "res3", "res4", "res5"],
      "MODEL.ANCHOR_GENERATOR.SIZES": [[3_2], [6_4], [1_2_8], [2_5_6], [5_1_2]],
      "MODEL.RPN.IN_FEATURES": ["p2", "p3", "p4", "p5", "p6"],
      "MODEL.RPN.PRE_NMS_TOPK_TRAIN": 2_0_0_0,
      "MODEL.RPN.PRE_NMS_TOPK_TEST": 1_0_0_0,
      "MODEL.RPN.POST_NMS_TOPK_TRAIN": 1_0_0_0,
      "MODEL.POST_NMS_TOPK_TEST": 1_0_0_0,
      "MODEL.ROI_HEADS.NAME": "StandardROIHeads",
      "MODEL.ROI_HEADS.NUM_CLASSES": 5,
      "MODEL.ROI_HEADS.IN_FEATURES": ["p2", "p3", "p4", "p5"],
      "MODEL.ROI_BOX_HEAD.NAME": "FastRCNNConvFCHead",
      "MODEL.ROI_BOX_HEAD.NUM_FC": 2,
      "MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION": 1_4,
      "MODEL.ROI_MASK_HEAD.NAME": "MaskRCNNConvUpsampleHead",
      "MODEL.ROI_MASK_HEAD.NUM_CONV": 4,
      "MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION": 7,
      "MODEL.RESNETS.DEPTH": 1_0_1,
      "MODEL.RESNETS.SIZES": [[3_2], [6_4], [1_2_8], [2_5_6], [5_1_2]],
      "MODEL.RESNETS.ASPECT_RATIOS": [[0.5, 1.0, 2.0]],
      "MODEL.RESNETS.OUT_FEATURES": ["res2", "res3", "res4", "res5"],
      "MODEL.RESNETS.NUM_GROUPS": 3_2,
      "MODEL.RESNETS.WIDTH_PER_GROUP": 8,
      "MODEL.RESNETS.STRIDE_IN_1X1": False,
  }


 def 		SCREAMING_SNAKE_CASE__     (				self			:							Dict    )     ->		Optional[int]:

  """simple docstring"""





  UpperCamelCase_      :		Optional[int]				=      detectrona.config.get_cfg()
  for k, v in self.detectrona_config_args.items():
   UpperCamelCase_      :		Optional[int]				=      k.split('.'    )
   UpperCamelCase_      :		int				=      detectrona_config
   for attribute in attributes[:-1]:
    UpperCamelCase_      :		Optional[Any]				=      getattr(snake_case     ,   snake_case    )
   setattr(snake_case     ,   attributes[-1]     ,   snake_case    )

  return detectrona_config




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() = }""")



