from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE:						Tuple    	=       logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE:						Optional[int]    	=       {
    '''google/electra-small-generator''': '''https://huggingface.co/google/electra-small-generator/resolve/main/config.json''',
    '''google/electra-base-generator''': '''https://huggingface.co/google/electra-base-generator/resolve/main/config.json''',
    '''google/electra-large-generator''': '''https://huggingface.co/google/electra-large-generator/resolve/main/config.json''',
    '''google/electra-small-discriminator''': (
        '''https://huggingface.co/google/electra-small-discriminator/resolve/main/config.json'''
    ),
    '''google/electra-base-discriminator''': (
        '''https://huggingface.co/google/electra-base-discriminator/resolve/main/config.json'''
    ),
    '''google/electra-large-discriminator''': (
        '''https://huggingface.co/google/electra-large-discriminator/resolve/main/config.json'''
    ),
}

class 	_SCREAMING_SNAKE_CASE							(     _lowercase							):






		"""simple docstring"""







		A_						 =      '''electra'''



		def __init__(			self    :				Tuple  ,					__SCREAMING_SNAKE_CASE    :				Any=30_522  ,					__SCREAMING_SNAKE_CASE    :				Optional[int]=128  ,					__SCREAMING_SNAKE_CASE    :				List[Any]=256  ,					__SCREAMING_SNAKE_CASE    :				Any=12  ,					__SCREAMING_SNAKE_CASE    :				List[str]=4  ,					__SCREAMING_SNAKE_CASE    :				int=1_024  ,					__SCREAMING_SNAKE_CASE    :				str="gelu"  ,					__SCREAMING_SNAKE_CASE    :				Dict=0.1  ,					__SCREAMING_SNAKE_CASE    :				str=0.1  ,					__SCREAMING_SNAKE_CASE    :				Union[str, Any]=512  ,					__SCREAMING_SNAKE_CASE    :				str=2  ,					__SCREAMING_SNAKE_CASE    :				Any=0.0_2  ,					__SCREAMING_SNAKE_CASE    :				int=1e-12  ,					__SCREAMING_SNAKE_CASE    :				Optional[Any]="first"  ,					__SCREAMING_SNAKE_CASE    :				Tuple=True  ,					__SCREAMING_SNAKE_CASE    :				str="gelu"  ,					__SCREAMING_SNAKE_CASE    :				List[str]=0.1  ,					__SCREAMING_SNAKE_CASE    :				Union[str, Any]=0  ,					__SCREAMING_SNAKE_CASE    :				List[str]="absolute"  ,					__SCREAMING_SNAKE_CASE    :				List[str]=True  ,					__SCREAMING_SNAKE_CASE    :				Union[str, Any]=None  ,					**__SCREAMING_SNAKE_CASE    :				int  ,					):






					"""simple docstring"""
					super().__init__(pad_token_id=__SCREAMING_SNAKE_CASE  ,					**__SCREAMING_SNAKE_CASE   )

					_snake_case              =      vocab_size
					_snake_case              =      embedding_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              =      summary_type
					_snake_case              =      summary_use_proj
					_snake_case              =      summary_activation
					_snake_case              =      summary_last_dropout
					_snake_case              =      position_embedding_type
					_snake_case              =      use_cache
					_snake_case              =      classifier_dropout

class 	_SCREAMING_SNAKE_CASE							(     _lowercase							):






		"""simple docstring"""







		@property
		def 				_a				(			self    :				Dict   ):






					"""simple docstring"""
					if self.task == "multiple-choice":
								_snake_case              =      {0: "batch", 1: "choice", 2: "sequence"}
					else:
								_snake_case              =      {0: "batch", 1: "sequence"}
					return OrderedDict(
					    [
					        ("input_ids", dynamic_axis),
					        ("attention_mask", dynamic_axis),
					        ("token_type_ids", dynamic_axis),
					    ]   )


def    UpperCAmelCase__(						lowerCamelCase_							,	lowerCamelCase_  ):



			"""simple docstring"""





			_snake_case              =      sorted(lowerCamelCase_							,	key=lambda lowerCamelCase_  : value[2]							,	reverse=lowerCamelCase_  )

			# Create a list of size equal to the maximum deadline
			# and initialize it with -1
			_snake_case              =      max(lowerCamelCase_							,	key=lambda lowerCamelCase_  : value[1]  )[1]
			_snake_case              =      [-1] * max_deadline

			# Finding the maximum profit and the count of jobs
			_snake_case              =      0
			_snake_case              =      0
			for job in jobs:
						# Find a free time slot for this job
						# (Note that we start from the last possible slot)
						for i in range(job[1] - 1							,	-1							,	-1  ):
									if time_slots[i] == -1:
												_snake_case              =      job[0]
												count += 1
												max_profit += job[2]
												break
			return [count, max_profit]


if __name__ == "__main__":
			import doctest

			doctest.testmod()

