
import pytest

from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested

from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_     )				->		Any:  # picklable for multiprocessing
			return i + 1
@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
def  lowerCAmelCase__			(  )				->		int:
			with parallel_backend('spark'     ):
						assert ParallelBackendConfig.backend_name == "spark"

			lowerCAmelCase__      :      Any         =		[1, 2, 3]
			with pytest.raises(_SCREAMING_SNAKE_CASE     ):
						with parallel_backend('unsupported backend'     ):
									map_nested(_SCREAMING_SNAKE_CASE       ,							_SCREAMING_SNAKE_CASE       ,							num_proc=2     )

			with pytest.raises(_SCREAMING_SNAKE_CASE     ):
						with parallel_backend('unsupported backend'     ):
									map_nested(_SCREAMING_SNAKE_CASE       ,							_SCREAMING_SNAKE_CASE       ,							num_proc=-1     )







@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
@pytest.mark.parametrize('num_proc'       ,							[2, -1]     )
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_     )				->		Dict:
			lowerCAmelCase__      :      Optional[Any]         =		[1, 2]
			lowerCAmelCase__      :      List[str]         =		{'a': 1, 'b': 2}
			lowerCAmelCase__      :      Optional[Any]         =		{'a': [1, 2], 'b': [3, 4]}
			lowerCAmelCase__      :      Union[str, Any]         =		{'a': {'1': 1}, 'b': 2}
			lowerCAmelCase__      :      int         =		{'a': 1, 'b': 2, 'c': 3, 'd': 4}
			lowerCAmelCase__      :      List[Any]         =		[2, 3]
			lowerCAmelCase__      :      List[Any]         =		{'a': 2, 'b': 3}
			lowerCAmelCase__      :      int         =		{'a': [2, 3], 'b': [4, 5]}
			lowerCAmelCase__      :      str         =		{'a': {'1': 2}, 'b': 3}
			lowerCAmelCase__      :      List[Any]         =		{'a': 2, 'b': 3, 'c': 4, 'd': 5}

			with parallel_backend('spark'     ):
						assert map_nested(_SCREAMING_SNAKE_CASE       ,							_SCREAMING_SNAKE_CASE       ,							num_proc=_SCREAMING_SNAKE_CASE     ) == expected_map_nested_sa
						assert map_nested(_SCREAMING_SNAKE_CASE       ,							_SCREAMING_SNAKE_CASE       ,							num_proc=_SCREAMING_SNAKE_CASE     ) == expected_map_nested_sa
						assert map_nested(_SCREAMING_SNAKE_CASE       ,							_SCREAMING_SNAKE_CASE       ,							num_proc=_SCREAMING_SNAKE_CASE     ) == expected_map_nested_sa
						assert map_nested(_SCREAMING_SNAKE_CASE       ,							_SCREAMING_SNAKE_CASE       ,							num_proc=_SCREAMING_SNAKE_CASE     ) == expected_map_nested_sa
						assert map_nested(_SCREAMING_SNAKE_CASE       ,							_SCREAMING_SNAKE_CASE       ,							num_proc=_SCREAMING_SNAKE_CASE     ) == expected_map_nested_sa



'''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



