
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__          =		{
    'google/bigbird-roberta-base': 'https://huggingface.co/google/bigbird-roberta-base/resolve/main/config.json',
    'google/bigbird-roberta-large': 'https://huggingface.co/google/bigbird-roberta-large/resolve/main/config.json',
    'google/bigbird-base-trivia-itc': 'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/config.json',
    # See all BigBird models at https://huggingface.co/models?filter=big_bird
}







class      A__							(     lowerCAmelCase_	):
				lowercase			    =  'big_bird'






				def __init__(						self					:      Dict    ,     a					:      List[Any]=50_358    ,     a					:      Union[str, Any]=768    ,     a					:      Union[str, Any]=12    ,     a					:      List[str]=12    ,     a					:      int=3_072    ,     a					:      Optional[int]="gelu_new"    ,     a					:      Dict=0.1    ,     a					:      str=0.1    ,     a					:      int=4_096    ,     a					:      List[str]=2    ,     a					:      Union[str, Any]=0.0_2    ,     a					:      Union[str, Any]=1E-12    ,     a					:      Optional[int]=True    ,     a					:      Dict=0    ,     a					:      Any=1    ,     a					:      Optional[Any]=2    ,     a					:      Any=66    ,     a					:      Optional[Any]="block_sparse"    ,     a					:      List[Any]=True    ,     a					:      str=False    ,     a					:      List[Any]=64    ,     a					:      List[str]=3    ,     a					:      Optional[int]=None    ,     **a					:      List[str]    ,     ):






							'''simple docstring'''







							super().__init__(
							    pad_token_id=__a    ,     bos_token_id=__a    ,     eos_token_id=__a    ,     sep_token_id=__a    ,     **__a    ,     )

							lowerCAmelCase__      :      int         =		vocab_size
							lowerCAmelCase__      :      Tuple         =		max_position_embeddings
							lowerCAmelCase__      :      int         =		hidden_size
							lowerCAmelCase__      :      Optional[Any]         =		num_hidden_layers
							lowerCAmelCase__      :      Optional[Any]         =		num_attention_heads
							lowerCAmelCase__      :      Dict         =		intermediate_size
							lowerCAmelCase__      :      Tuple         =		hidden_act
							lowerCAmelCase__      :      str         =		hidden_dropout_prob
							lowerCAmelCase__      :      Union[str, Any]         =		attention_probs_dropout_prob
							lowerCAmelCase__      :      Any         =		initializer_range
							lowerCAmelCase__      :      Optional[int]         =		type_vocab_size
							lowerCAmelCase__      :      str         =		layer_norm_eps
							lowerCAmelCase__      :      List[Any]         =		use_cache

							lowerCAmelCase__      :      Union[str, Any]         =		rescale_embeddings
							lowerCAmelCase__      :      Tuple         =		attention_type
							lowerCAmelCase__      :      Any         =		use_bias
							lowerCAmelCase__      :      List[Any]         =		block_size
							lowerCAmelCase__      :      Optional[Any]         =		num_random_blocks
							lowerCAmelCase__      :      List[Any]         =		classifier_dropout







class      A__							(     lowerCAmelCase_	):






				@property
				def 							_lowerCamelCase		(						self					:      Optional[Any]   ):






							'''simple docstring'''







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



'''simple docstring'''



import torch

from transformers import AutoModel





class 							__UpperCamelCase				(		torch.nn.Module					):




	def __init__(   self						,				__a="sayef/fsner-bert-base-uncased"							):

						'''simple docstring'''





						super(__a						,				self							).__init__()

						__a			:       Tuple					    =  AutoModel.from_pretrained(__a						,				return_dict=__a							)
						__a			:       int					    =  torch.nn.CosineSimilarity(3						,				1E-0_8							)
						__a			:       Union[str, Any]					    =  torch.nn.Softmax(dim=1							)




	def       __UpperCAmelCase							(   self						,				**__a							):

						'''simple docstring'''





						return self.bert(**__a							).last_hidden_state




	def       __UpperCAmelCase							(   self						,				__a							):

						'''simple docstring'''





						return token_embeddings.sum(2						,				keepdim=__a							)




	def       __UpperCAmelCase							(   self						,				__a						,				__a						,				__a=1							):

						'''simple docstring'''





						return self.softmax(T * self.cos(__a						,				__a							)							)





	def       __UpperCAmelCase							(   self						,				__a						,				__a							):

						'''simple docstring'''





						__a			:       str					    =  W_supports['sizes'].tolist()
						__a			:       Union[str, Any]					    =  W_supports['start_token_id'].item()
						__a			:       Any					    =  W_supports['end_token_id'].item()

						del W_supports["sizes"]
						del W_supports["start_token_id"]
						del W_supports["end_token_id"]

						__a			:       Tuple					    =  self.BERT(**__a							)
						__a			:       str					    =  self.BERT(**__a							)

						__a			:       Any					    =  None
						__a			:       Dict					    =  None

						__a			:       Dict					    =  W_supports['input_ids'] == start_token_id
						__a			:       Union[str, Any]					    =  W_supports['input_ids'] == end_token_id

						for i, size in enumerate(__a							):
											if i == 0:
																__a			:       Optional[int]					    =  0
											else:
																__a			:       Union[str, Any]					    =  support_sizes[i - 1]

											__a			:       int					    =  S[s : s + size][start_token_masks[s : s + size]]
											__a			:       Union[str, Any]					    =  S[s : s + size][end_token_masks[s : s + size]]

											__a			:       Tuple					    =  torch.matmul(q[i]						,				s_start.T							).sum(1							).softmax(0							)
											__a			:       Dict					    =  torch.matmul(q[i]						,				s_end.T							).sum(1							).softmax(0							)

											if p_starts is not None:
																__a			:       str					    =  torch.vstack((p_starts, p_start)							)
																__a			:       str					    =  torch.vstack((p_ends, p_end)							)
											else:
																__a			:       List[str]					    =  p_start
																__a			:       int					    =  p_end

						return p_starts, p_ends



