
'''simple docstring'''







import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


_lowercase :  Dict    							=       5_0_0_0_0
_lowercase :  Union[str, Any]    							=       5_0_0_0

_lowercase							,  _lowercase :  List[str]    							=       os.path.split(__file__)
_lowercase :  Any    							=       os.path.join(RESULTS_BASEPATH, "results", RESULTS_FILENAME.replace(".py", ".json"))
@get_duration
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: datasets.Dataset      ,      __SCREAMING_SNAKE_CASE					: str							):



						"""simple docstring"""





						for i in range(__SCREAMING_SNAKE_CASE							):
												lowercase_	:     int													=       dataset[i]
@get_duration
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: datasets.Dataset      ,      __SCREAMING_SNAKE_CASE					: str      ,      __SCREAMING_SNAKE_CASE					: str							):



						"""simple docstring"""





						for i in range(0      ,      len(__SCREAMING_SNAKE_CASE							)      ,      __SCREAMING_SNAKE_CASE							):
												lowercase_	:     int													=       dataset[i : i + batch_size]
@get_duration
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: datasets.Dataset      ,      __SCREAMING_SNAKE_CASE					: Dict      ,      __SCREAMING_SNAKE_CASE					: Tuple							):



						"""simple docstring"""





						with dataset.formatted_as(type=__SCREAMING_SNAKE_CASE							):
												for i in range(__SCREAMING_SNAKE_CASE							):
																		lowercase_	:     Union[str, Any]													=       dataset[i]
@get_duration
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: datasets.Dataset      ,      __SCREAMING_SNAKE_CASE					: Union[str, Any]      ,      __SCREAMING_SNAKE_CASE					: Tuple      ,      __SCREAMING_SNAKE_CASE					: Optional[Any]							):



						"""simple docstring"""





						with dataset.formatted_as(type=__SCREAMING_SNAKE_CASE							):
												for i in range(0      ,      __SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE							):
																		lowercase_	:     List[str]													=       dataset[i : i + batch_size]
def 					snake_case_		(				):



						"""simple docstring"""





						lowercase_	:     Any													=       {'''num examples''': SPEED_TEST_N_EXAMPLES}
						lowercase_	:     Optional[int]													=       [
						    (read, {'''length''': SMALL_TEST}),
						    (read, {'''length''': SPEED_TEST_N_EXAMPLES}),
						    (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 10}),
						    (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 100}),
						    (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 1000}),
						    (read_formatted, {'''type''': '''numpy''', '''length''': SMALL_TEST}),
						    (read_formatted, {'''type''': '''pandas''', '''length''': SMALL_TEST}),
						    (read_formatted, {'''type''': '''torch''', '''length''': SMALL_TEST}),
						    (read_formatted, {'''type''': '''tensorflow''', '''length''': SMALL_TEST}),
						    (read_formatted_batch, {'''type''': '''numpy''', '''length''': SMALL_TEST, '''batch_size''': 10}),
						    (read_formatted_batch, {'''type''': '''numpy''', '''length''': SMALL_TEST, '''batch_size''': 1000}),
						]

						lowercase_	:     List[str]													=       [
						    (read, {'''length''': SMALL_TEST}),
						    (read, {'''length''': SPEED_TEST_N_EXAMPLES}),
						    (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 10}),
						    (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 100}),
						    (read_batch, {'''length''': SPEED_TEST_N_EXAMPLES, '''batch_size''': 1000}),
						    (read_formatted, {'''type''': '''numpy''', '''length''': SMALL_TEST}),
						    (read_formatted_batch, {'''type''': '''numpy''', '''length''': SMALL_TEST, '''batch_size''': 10}),
						    (read_formatted_batch, {'''type''': '''numpy''', '''length''': SMALL_TEST, '''batch_size''': 1000}),
						]
						with tempfile.TemporaryDirectory() as tmp_dir:
												print('''generating dataset'''							)
												lowercase_	:     Dict													=       datasets.Features(
												    {'''list''': datasets.Sequence(datasets.Value('''float32'''							)							), '''numbers''': datasets.Value('''float32'''							)}							)
												lowercase_	:     Dict													=       generate_example_dataset(
												    os.path.join(__SCREAMING_SNAKE_CASE      ,      '''dataset.arrow'''							)      ,      __SCREAMING_SNAKE_CASE      ,      num_examples=__SCREAMING_SNAKE_CASE      ,      seq_shapes={'''list''': (100,)}      ,      )
												print('''first set of iterations'''							)
												for func, kwargs in functions:
																		print(func.__name__      ,      str(__SCREAMING_SNAKE_CASE							)							)
																		lowercase_	:     Optional[Any]													=       func(__SCREAMING_SNAKE_CASE      ,      **__SCREAMING_SNAKE_CASE							)

												print('''shuffling dataset'''							)
												lowercase_	:     int													=       dataset.shuffle()
												print('''Second set of iterations (after shuffling'''							)
												for func, kwargs in functions_shuffled:
																		print('''shuffled '''      ,      func.__name__      ,      str(__SCREAMING_SNAKE_CASE							)							)
																		lowercase_	:     Union[str, Any]													=       func(
																		    __SCREAMING_SNAKE_CASE      ,      **__SCREAMING_SNAKE_CASE							)

						with open(__SCREAMING_SNAKE_CASE      ,      '''wb'''							) as f:
												f.write(json.dumps(__SCREAMING_SNAKE_CASE							).encode('''utf-8'''							)							)


if __name__ == "__main__":  # useful to run the profiler
							benchmark_iterating()





'''simple docstring'''







import torch

from transformers import AutoModel






class  lowerCAmelCase__   (  torch.nn.Module     ):






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


											"""simple docstring"""





											super(__SCREAMING_SNAKE_CASE				,							self		).__init__()

											lowercase_	:     Tuple													=       AutoModel.from_pretrained(__SCREAMING_SNAKE_CASE				,							return_dict=__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Optional[Any]													=       torch.nn.CosineSimilarity(3				,							1E-0_8		)
											lowercase_	:     Optional[Any]													=       torch.nn.Softmax(dim=1		)






					def 	_snake_case    (   self				,							**__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											return self.bert(**__SCREAMING_SNAKE_CASE		).last_hidden_state






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											return token_embeddings.sum(2				,							keepdim=__SCREAMING_SNAKE_CASE		)






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE=1		):


											"""simple docstring"""





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






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     Optional[Any]													=       W_supports['''sizes'''].tolist()
											lowercase_	:     Dict													=       W_supports['''start_token_id'''].item()
											lowercase_	:     List[Any]													=       W_supports['''end_token_id'''].item()

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

											lowercase_	:     List[str]													=       self.BERT(**__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Optional[int]													=       self.BERT(**__SCREAMING_SNAKE_CASE		)

											lowercase_	:     str													=       None
											lowercase_	:     Dict													=       None

											lowercase_	:     Tuple													=       W_supports['''input_ids'''] == start_token_id
											lowercase_	:     Any													=       W_supports['''input_ids'''] == end_token_id

											for i, size in enumerate(__SCREAMING_SNAKE_CASE		):
																	if i == 0:
																							lowercase_	:     List[str]													=       0
																	else:
																							lowercase_	:     List[Any]													=       support_sizes[i - 1]

																	lowercase_	:     str													=       S[s : s + size][start_token_masks[s : s + size]]
																	lowercase_	:     Optional[int]													=       S[s : s + size][end_token_masks[s : s + size]]

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

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

											return p_starts, p_ends



