




import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


__lowerCAmelCase		:    Optional[Any]	      =		50_0000

__lowerCAmelCase      ,					__lowerCAmelCase		:    Dict	      =		os.path.split(__file__)
__lowerCAmelCase		:    str	      =		os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))




@get_duration
def       __magic_name__    (			A		: datasets.Dataset       ):






			'''simple docstring'''







			a   =      dataset.select(range(0,  len(A       ),  2       )       )




@get_duration
def       __magic_name__    (			A		: datasets.Dataset       ):






			'''simple docstring'''







			a   =      dataset.sort("numbers"       )




@get_duration
def       __magic_name__    (			A		: datasets.Dataset       ):






			'''simple docstring'''







			a   =      dataset.shuffle()




@get_duration
def       __magic_name__    (			A		: datasets.Dataset       ):






			'''simple docstring'''







			a   =      dataset.train_test_split(0.1       )




@get_duration
def       __magic_name__    (			A		: datasets.Dataset,  A		: List[str]=10       ):






			'''simple docstring'''







			for shard_id in range(A       ):
						a   =      dataset.shard(A,  A       )




def       __magic_name__    (			):






			'''simple docstring'''







			a   =      {"num examples": SPEED_TEST_N_EXAMPLES}
			a   =      (select, sort, shuffle, train_test_split, shard)
			with tempfile.TemporaryDirectory() as tmp_dir:
						print("generating dataset"       )
						a   =      datasets.Features({"text": datasets.Value("string"       ), "numbers": datasets.Value("float32"       )}       )
						a   =      generate_example_dataset(
						    os.path.join(A,  "dataset.arrow"       ),  A,  num_examples=A       )
						print("Functions"       )
						for func in functions:
									print(func.__name__       )
									a   =      func(A       )

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


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












import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
		import torch

		from transformers import CamembertModel




@require_torch
@require_sentencepiece
@require_tokenizers
class    snake_case__							(unittest.TestCase       ):
				"""simple docstring"""



				@slow
				def 							__UpperCAmelCase							(				self    :					Optional[Any]							) ->					List[str]:
							a   =      CamembertModel.from_pretrained("camembert-base"							)
							model.to(__lowerCamelCase							)

							a   =      torch.tensor(
							    [[5, 1_21, 11, 6_60, 16, 7_30, 2_55_43, 1_10, 83, 6]] ,				device=__lowerCamelCase ,				dtype=torch.long ,				)  # J'aime le camembert !
							with torch.no_grad():
										a   =      model(__lowerCamelCase							)["last_hidden_state"]
							a   =      torch.Size((1, 10, 7_68)							)
							self.assertEqual(output.shape ,				__lowerCamelCase							)
							# compare the actual values for a slice.
							a   =      torch.tensor(
							    [[[-0.0_254, 0.0_235, 0.1_027], [0.0_606, -0.1_811, -0.0_418], [-0.1_561, -0.1_127, 0.2_687]]] ,				device=__lowerCamelCase ,				dtype=torch.float ,				)
							# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
							# camembert.eval()
							# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

							self.assertTrue(torch.allclose(output[:, :3, :3] ,				__lowerCamelCase ,				atol=1e-4							)							)






