
from typing import List

import numpy as np





def __A						(   a_ :							Tuple     )->							int:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Tuple				= {key: len(a_     ) for key, value in gen_kwargs.items() if isinstance(a_		,		a_     )}
		if len(set(lists_lengths.values()     )     ) > 1:
				raise RuntimeError(
				    (
				        '''Sharding is ambiguous for this dataset: '''
				        + '''we found several data sources lists of different lengths, and we don\'t know over which list we should parallelize:\n'''
				        + '''\n'''.join(F"\t- key {key} has length {length}" for key, length in lists_lengths.items()     )
				        + '''\nTo fix this, check the \'gen_kwargs\' and make sure to use lists only for data sources, '''
				        + '''and use tuples otherwise. In the end there should only be one single list, or several lists with the same length.'''
				    )     )
		SCREAMING_SNAKE_CASE       :     List[str]				= max(lists_lengths.values()		,		default=0     )
		return max(1		,		a_     )





def __A						(   a_ :							Tuple		,		a_ :							Any     )->							List[range]:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Dict				= []
		for group_idx in range(a_     ):
				SCREAMING_SNAKE_CASE       :     List[str]				= num_shards // max_num_jobs + (group_idx < (num_shards % max_num_jobs))
				if num_shards_to_add == 0:
						break
				SCREAMING_SNAKE_CASE       :     Optional[Any]				= shards_indices_per_group[-1].stop if shards_indices_per_group else 0
				SCREAMING_SNAKE_CASE       :     Dict				= range(a_		,		start + num_shards_to_add     )
				shards_indices_per_group.append(a_     )
		return shards_indices_per_group





def __A						(   a_ :							int		,		a_ :							Any     )->							List[dict]:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     Any				= _number_of_shards_in_gen_kwargs(a_     )
		if num_shards == 1:
				return [dict(a_     )]
		else:
				SCREAMING_SNAKE_CASE       :     List[str]				= _distribute_shards(num_shards=a_		,		max_num_jobs=a_     )
				return [
				    {
				        key: [value[shard_idx] for shard_idx in shard_indices_per_group[group_idx]]
				        if isinstance(a_		,		a_     )
				        else value
				        for key, value in gen_kwargs.items()
				    }
				    for group_idx in range(len(a_     )     )
				]





def __A						(   a_ :							Dict     )->							dict:




		'''simple docstring'''






		return {
		    key: [value for gen_kwargs in gen_kwargs_list for value in gen_kwargs[key]]
		    if isinstance(gen_kwargs_list[0][key]		,		a_     )
		    else gen_kwargs_list[0][key]
		    for key in gen_kwargs_list[0]
		}





def __A						(   a_ :							Optional[Any]		,		a_ :							Optional[Any]     )->							dict:




		'''simple docstring'''






		SCREAMING_SNAKE_CASE       :     str				= {len(a_     ) for value in gen_kwargs.values() if isinstance(a_		,		a_     )}
		SCREAMING_SNAKE_CASE       :     Optional[int]				= {}
		for size in list_sizes:
				SCREAMING_SNAKE_CASE       :     Tuple				= list(range(a_     )     )
				rng.shuffle(indices_per_size[size]     )
		# Now let's copy the gen_kwargs and shuffle the lists based on their sizes
		SCREAMING_SNAKE_CASE       :     str				= dict(a_     )
		for key, value in shuffled_kwargs.items():
				if isinstance(a_		,		a_     ):
						SCREAMING_SNAKE_CASE       :     List[Any]				= [value[i] for i in indices_per_size[len(a_     )]]
		return shuffled_kwargs







"""simple docstring"""






from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase__ :       Tuple						     =			logging.get_logger(__name__)

lowerCamelCase__ :       str						     =			{
    "studio-ousia/luke-base": "https://huggingface.co/studio-ousia/luke-base/resolve/main/config.json",
    "studio-ousia/luke-large": "https://huggingface.co/studio-ousia/luke-large/resolve/main/config.json",
}




class 				lowercase__(				_UpperCAmelCase   ):
	'''simple docstring'''

	UpperCamelCase				=  """luke"""





	def __init__(						self    :Optional[int]		, lowerCamelCase_    :Union[str, Any]=5_02_67		, lowerCamelCase_    :int=50_00_00		, lowerCamelCase_    :Tuple=7_68		, lowerCamelCase_    :List[str]=2_56		, lowerCamelCase_    :Dict=12		, lowerCamelCase_    :Optional[int]=12		, lowerCamelCase_    :Optional[Any]=30_72		, lowerCamelCase_    :List[Any]="gelu"		, lowerCamelCase_    :Any=0.1		, lowerCamelCase_    :Union[str, Any]=0.1		, lowerCamelCase_    :List[str]=5_12		, lowerCamelCase_    :Optional[int]=2		, lowerCamelCase_    :Tuple=0.0_2		, lowerCamelCase_    :Optional[int]=1E-12		, lowerCamelCase_    :List[Any]=True		, lowerCamelCase_    :List[str]=None		, lowerCamelCase_    :str=1		, lowerCamelCase_    :Any=0		, lowerCamelCase_    :str=2		, **lowerCamelCase_    :List[Any]		, )							->			Optional[int]:







			'''simple docstring'''




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

			SCREAMING_SNAKE_CASE       :     Dict				= vocab_size
			SCREAMING_SNAKE_CASE       :     List[str]				= entity_vocab_size
			SCREAMING_SNAKE_CASE       :     str				= hidden_size
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= entity_emb_size
			SCREAMING_SNAKE_CASE       :     List[Any]				= num_hidden_layers
			SCREAMING_SNAKE_CASE       :     Optional[int]				= num_attention_heads
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= hidden_act
			SCREAMING_SNAKE_CASE       :     Optional[Any]				= intermediate_size
			SCREAMING_SNAKE_CASE       :     Union[str, Any]				= hidden_dropout_prob
			SCREAMING_SNAKE_CASE       :     Dict				= attention_probs_dropout_prob
			SCREAMING_SNAKE_CASE       :     Dict				= max_position_embeddings
			SCREAMING_SNAKE_CASE       :     Tuple				= type_vocab_size
			SCREAMING_SNAKE_CASE       :     str				= initializer_range
			SCREAMING_SNAKE_CASE       :     Dict				= layer_norm_eps
			SCREAMING_SNAKE_CASE       :     Optional[int]				= use_entity_aware_attention
			SCREAMING_SNAKE_CASE       :     List[str]				= classifier_dropout





