





from collections import OrderedDict
from typing import Mapping

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


_lowerCamelCase   				=			logging.get_logger(__name__)

_lowerCamelCase   				=			{
    'YituTech/conv-bert-base': 'https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json',
    'YituTech/conv-bert-medium-small': (
        'https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json'
    ),
    'YituTech/conv-bert-small': 'https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json',
    # See all ConvBERT models at https://huggingface.co/models?filter=convbert
}




class   UpperCamelCase_       (     UpperCamelCase__    ):
							lowerCamelCase_           =   "convbert"


							def __init__(							self    :List[Any]							,				__A    :List[Any]=3_0522							,				__A    :int=768							,				__A    :Dict=12							,				__A    :int=12							,				__A    :int=3072							,				__A    :Any="gelu"							,				__A    :List[str]=0.1							,				__A    :Any=0.1							,				__A    :Union[str, Any]=512							,				__A    :List[str]=2							,				__A    :Optional[int]=0.0_2							,				__A    :Union[str, Any]=1E-12							,				__A    :Tuple=1							,				__A    :Tuple=0							,				__A    :int=2							,				__A    :Union[str, Any]=768							,				__A    :int=2							,				__A    :str=9							,				__A    :Tuple=1							,				__A    :Optional[int]=None							,				**__A    :Optional[Any]							,				)   ->   int:



									"""simple docstring"""



									super().__init__(
									    pad_token_id=__A							,				bos_token_id=__A							,				eos_token_id=__A							,				**__A							,				)

									SCREAMING_SNAKE_CASE__			=      vocab_size
									SCREAMING_SNAKE_CASE__			=      hidden_size
									SCREAMING_SNAKE_CASE__			=      num_hidden_layers
									SCREAMING_SNAKE_CASE__			=      num_attention_heads
									SCREAMING_SNAKE_CASE__			=      intermediate_size
									SCREAMING_SNAKE_CASE__			=      hidden_act
									SCREAMING_SNAKE_CASE__			=      hidden_dropout_prob
									SCREAMING_SNAKE_CASE__			=      attention_probs_dropout_prob
									SCREAMING_SNAKE_CASE__			=      max_position_embeddings
									SCREAMING_SNAKE_CASE__			=      type_vocab_size
									SCREAMING_SNAKE_CASE__			=      initializer_range
									SCREAMING_SNAKE_CASE__			=      layer_norm_eps
									SCREAMING_SNAKE_CASE__			=      embedding_size
									SCREAMING_SNAKE_CASE__			=      head_ratio
									SCREAMING_SNAKE_CASE__			=      conv_kernel_size
									SCREAMING_SNAKE_CASE__			=      num_groups
									SCREAMING_SNAKE_CASE__			=      classifier_dropout




class   UpperCamelCase_       (     UpperCamelCase__    ):


							@property
							def      _snake_case  (							self    :Union[str, Any]		)   ->   Mapping[str, Mapping[int, str]]:



									"""simple docstring"""



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






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							list[list[float]]    ):
		SCREAMING_SNAKE_CASE__			=      []
		for data in source_data:
				for i, el in enumerate(UpperCamelCase__    ):
						if len(UpperCamelCase__    ) < i + 1:
								data_lists.append([]    )
						data_lists[i].append(float(UpperCamelCase__    )    )
		return data_lists






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							list[list[float]]					,   UpperCamelCase__:							list[int]    ):
		SCREAMING_SNAKE_CASE__			=      []
		for dlist, weight in zip(UpperCamelCase__					,   UpperCamelCase__    ):
				SCREAMING_SNAKE_CASE__			=      min(UpperCamelCase__    )
				SCREAMING_SNAKE_CASE__			=      max(UpperCamelCase__    )

				SCREAMING_SNAKE_CASE__			=      []
				# for weight 0 score is 1 - actual score
				if weight == 0:
						for item in dlist:
								try:
										score.append(1 - ((item - mind) / (maxd - mind))    )
								except ZeroDivisionError:
										score.append(1    )

				elif weight == 1:
						for item in dlist:
								try:
										score.append((item - mind) / (maxd - mind)    )
								except ZeroDivisionError:
										score.append(0    )

        # weight not 0 or 1
				else:
						SCREAMING_SNAKE_CASE__			=      f'''Invalid weight of {weight:f} provided'''
						raise ValueError(UpperCamelCase__    )

				score_lists.append(UpperCamelCase__    )

		return score_lists






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							list[list[float]]    ):
		SCREAMING_SNAKE_CASE__			=      [0 for i in range(len(score_lists[0]    )    )]

		for slist in score_lists:
				for j, ele in enumerate(UpperCamelCase__    ):
						SCREAMING_SNAKE_CASE__			=      final_scores[j] + ele

		return final_scores






def 				SCREAMING_SNAKE_CASE__							(       UpperCamelCase__:							list[list[float]]					,   UpperCamelCase__:							list[int]    ):
		SCREAMING_SNAKE_CASE__			=      get_data(UpperCamelCase__    )
		SCREAMING_SNAKE_CASE__			=      calculate_each_score(UpperCamelCase__					,   UpperCamelCase__    )
		SCREAMING_SNAKE_CASE__			=      generate_final_scores(UpperCamelCase__    )

		# append scores to source data
		for i, ele in enumerate(UpperCamelCase__    ):
				source_data[i].append(UpperCamelCase__    )

		return source_data