


'''simple docstring'''






import os

import pytest

from datasets import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
)


__A	:	List[str]     =     pytest.mark.integration





@pytest.mark.parametrize("""path"""				,      ["""paws""", """csv"""] )
def      UpperCAmelCase      (		lowerCamelCase_  :List[Any]				,      lowerCamelCase_  :Optional[int] ):







		'''simple docstring'''
		inspect_dataset(lowerCamelCase_				,      lowerCamelCase_ )
		snake_case_		:							Any			       =						path + """.py"""
		assert script_name in os.listdir(lowerCamelCase_ )
		assert "__pycache__" not in os.listdir(lowerCamelCase_ )





@pytest.mark.filterwarnings("""ignore:inspect_metric is deprecated:FutureWarning""" )
@pytest.mark.filterwarnings("""ignore:metric_module_factory is deprecated:FutureWarning""" )
@pytest.mark.parametrize("""path"""				,      ["""accuracy"""] )
def      UpperCAmelCase      (		lowerCamelCase_  :Dict				,      lowerCamelCase_  :int ):







		'''simple docstring'''
		inspect_metric(lowerCamelCase_				,      lowerCamelCase_ )
		snake_case_		:							Union[str, Any]			       =						path + """.py"""
		assert script_name in os.listdir(lowerCamelCase_ )
		assert "__pycache__" not in os.listdir(lowerCamelCase_ )





@pytest.mark.parametrize(
    """path, config_name, expected_splits"""				,      [
        ("""squad""", """plain_text""", ["""train""", """validation"""]),
        ("""dalle-mini/wit""", """dalle-mini--wit""", ["""train"""]),
        ("""paws""", """labeled_final""", ["""train""", """test""", """validation"""]),
    ]				,      )
def      UpperCAmelCase      (		lowerCamelCase_  :Optional[Any]				,      lowerCamelCase_  :List[Any]				,      lowerCamelCase_  :int ):







		'''simple docstring'''
		snake_case_		:							Optional[Any]			       =						get_dataset_config_info(lowerCamelCase_				,      config_name=lowerCamelCase_ )
		assert info.config_name == config_name
		assert list(info.splits.keys() ) == expected_splits





@pytest.mark.parametrize(
    """path, config_name, expected_exception"""				,      [
        ("""paws""", None, ValueError),
    ]				,      )
def      UpperCAmelCase      (		lowerCamelCase_  :str				,      lowerCamelCase_  :Any				,      lowerCamelCase_  :Optional[Any] ):







		'''simple docstring'''
		with pytest.raises(lowerCamelCase_ ):
				get_dataset_config_info(lowerCamelCase_				,      config_name=lowerCamelCase_ )





@pytest.mark.parametrize(
    """path, expected"""				,      [
        ("""squad""", """plain_text"""),
        ("""acronym_identification""", """default"""),
        ("""lhoestq/squad""", """plain_text"""),
        ("""lhoestq/test""", """default"""),
        ("""lhoestq/demo1""", """lhoestq--demo1"""),
        ("""dalle-mini/wit""", """dalle-mini--wit"""),
    ]				,      )
def      UpperCAmelCase      (		lowerCamelCase_  :List[str]				,      lowerCamelCase_  :Union[str, Any] ):







		'''simple docstring'''
		snake_case_		:							int			       =						get_dataset_config_names(lowerCamelCase_ )
		assert expected in config_names





@pytest.mark.parametrize(
    """path, expected_configs, expected_splits_in_first_config"""				,      [
        ("""squad""", ["""plain_text"""], ["""train""", """validation"""]),
        ("""dalle-mini/wit""", ["""dalle-mini--wit"""], ["""train"""]),
        ("""paws""", ["""labeled_final""", """labeled_swap""", """unlabeled_final"""], ["""train""", """test""", """validation"""]),
    ]				,      )
def      UpperCAmelCase      (		lowerCamelCase_  :Any				,      lowerCamelCase_  :Tuple				,      lowerCamelCase_  :Tuple ):







		'''simple docstring'''
		snake_case_		:							int			       =						get_dataset_infos(lowerCamelCase_ )
		assert list(infos.keys() ) == expected_configs
		snake_case_		:							List[str]			       =						expected_configs[0]
		assert expected_config in infos
		snake_case_		:							Any			       =						infos[expected_config]
		assert info.config_name == expected_config
		assert list(info.splits.keys() ) == expected_splits_in_first_config





@pytest.mark.parametrize(
    """path, expected_config, expected_splits"""				,      [
        ("""squad""", """plain_text""", ["""train""", """validation"""]),
        ("""dalle-mini/wit""", """dalle-mini--wit""", ["""train"""]),
        ("""paws""", """labeled_final""", ["""train""", """test""", """validation"""]),
    ]				,      )
def      UpperCAmelCase      (		lowerCamelCase_  :List[str]				,      lowerCamelCase_  :List[str]				,      lowerCamelCase_  :Tuple ):







		'''simple docstring'''
		snake_case_		:							int			       =						get_dataset_infos(lowerCamelCase_ )
		assert expected_config in infos
		snake_case_		:							Tuple			       =						infos[expected_config]
		assert info.config_name == expected_config
		assert list(info.splits.keys() ) == expected_splits






@pytest.mark.parametrize(
    """path, config_name, expected_exception"""				,      [
        ("""paws""", None, ValueError),
    ]				,      )
def      UpperCAmelCase      (		lowerCamelCase_  :Optional[Any]				,      lowerCamelCase_  :Any				,      lowerCamelCase_  :str ):







		'''simple docstring'''
		with pytest.raises(lowerCamelCase_ ):
				get_dataset_split_names(lowerCamelCase_				,      config_name=lowerCamelCase_ )



'''simple docstring'''






import torch

from transformers import AutoModel
class 						__UpperCamelCase	(						torch.nn.Module							):





							def __init__(  self		:Union[str, Any]  ,_UpperCamelCase		:Tuple="sayef/fsner-bert-base-uncased"			):
									super(_UpperCamelCase  ,self			).__init__()

									snake_case_		:							Dict			       =						AutoModel.from_pretrained(_UpperCamelCase  ,return_dict=_UpperCamelCase			)
									snake_case_		:							Dict			       =						torch.nn.CosineSimilarity(3  ,1E-0_8			)
									snake_case_		:							int			       =						torch.nn.Softmax(dim=1			)





							def 		a__   (  self		:Optional[Any]  ,**_UpperCamelCase		:Optional[Any]			):
									return self.bert(**_UpperCamelCase			).last_hidden_state





							def 		a__   (  self		:List[Any]  ,_UpperCamelCase		:Dict			):
									return token_embeddings.sum(2  ,keepdim=_UpperCamelCase			)





							def 		a__   (  self		:Optional[Any]  ,_UpperCamelCase		:List[str]  ,_UpperCamelCase		:List[str]  ,_UpperCamelCase		:List[str]=1			):
									return self.softmax(T * self.cos(_UpperCamelCase  ,_UpperCamelCase			)			)





							def 		a__   (  self		:Optional[Any]  ,_UpperCamelCase		:Optional[Any]  ,_UpperCamelCase		:int			):
									snake_case_		:							Union[str, Any]			       =						W_supports["""sizes"""].tolist()
									snake_case_		:							Dict			       =						W_supports["""start_token_id"""].item()
									snake_case_		:							int			       =						W_supports["""end_token_id"""].item()

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

									snake_case_		:							int			       =						self.BERT(**_UpperCamelCase			)
									snake_case_		:							int			       =						self.BERT(**_UpperCamelCase			)

									snake_case_		:							Any			       =						None
									snake_case_		:							Any			       =						None

									snake_case_		:							Union[str, Any]			       =						W_supports["""input_ids"""] == start_token_id
									snake_case_		:							Tuple			       =						W_supports["""input_ids"""] == end_token_id

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

											snake_case_		:							Dict			       =						S[s : s + size][start_token_masks[s : s + size]]
											snake_case_		:							Optional[Any]			       =						S[s : s + size][end_token_masks[s : s + size]]

											snake_case_		:							int			       =						torch.matmul(q[i]  ,s_start.T			).sum(1			).softmax(0			)
											snake_case_		:							List[str]			       =						torch.matmul(q[i]  ,s_end.T			).sum(1			).softmax(0			)

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

									return p_starts, p_ends