


'''simple docstring'''



import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


__A	=50_00_00

__A	, __A	=os.path.split(__file__)
__A	=os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))







@get_duration
def     _UpperCamelCase      (		UpperCamelCase__ ,      **UpperCamelCase__					):

			'''simple docstring'''





			UpperCAmelCase__  :					Tuple        =     dataset.map(**UpperCamelCase__					)







@get_duration
def     _UpperCamelCase      (		UpperCamelCase__ ,      **UpperCamelCase__					):

			'''simple docstring'''





			UpperCAmelCase__  :					int        =     dataset.filter(**UpperCamelCase__					)







def     _UpperCamelCase      (		):

			'''simple docstring'''





			UpperCAmelCase__  :					List[str]        =     {"""num examples""": SPEED_TEST_N_EXAMPLES}
			with tempfile.TemporaryDirectory() as tmp_dir:
						UpperCAmelCase__  :					int        =     datasets.Features({"""text""": datasets.Value("""string"""					), """numbers""": datasets.Value("""float32"""					)}					)
						UpperCAmelCase__  :					List[str]        =     generate_example_dataset(
						    os.path.join(UpperCamelCase__ ,      """dataset.arrow"""					) ,      UpperCamelCase__ ,      num_examples=UpperCamelCase__					)

						UpperCAmelCase__  :					List[Any]        =     transformers.AutoTokenizer.from_pretrained("""bert-base-cased""" ,      use_fast=UpperCamelCase__					)

						def tokenize(UpperCamelCase__					):
									return tokenizer(examples["""text"""]					)

						UpperCAmelCase__  :					Optional[int]        =     map(UpperCamelCase__					)

						UpperCAmelCase__  :					Union[str, Any]        =     map(UpperCamelCase__ ,      batched=UpperCamelCase__					)

						UpperCAmelCase__  :					Optional[Any]        =     map(UpperCamelCase__ ,      function=lambda UpperCamelCase__					: None ,      batched=UpperCamelCase__					)

						with dataset.formatted_as(type="""numpy"""					):
									UpperCAmelCase__  :					int        =     map(UpperCamelCase__ ,      function=lambda UpperCamelCase__					: None ,      batched=UpperCamelCase__					)

						with dataset.formatted_as(type="""pandas"""					):
									UpperCAmelCase__  :					Dict        =     map(UpperCamelCase__ ,      function=lambda UpperCamelCase__					: None ,      batched=UpperCamelCase__					)

						with dataset.formatted_as(type="""torch""" ,      columns="""numbers"""					):
									UpperCAmelCase__  :					Optional[Any]        =     map(UpperCamelCase__ ,      function=lambda UpperCamelCase__					: None ,      batched=UpperCamelCase__					)

						with dataset.formatted_as(type="""tensorflow""" ,      columns="""numbers"""					):
									UpperCAmelCase__  :					Optional[Any]        =     map(UpperCamelCase__ ,      function=lambda UpperCamelCase__					: None ,      batched=UpperCamelCase__					)

						UpperCAmelCase__  :					Optional[int]        =     map(UpperCamelCase__ ,      function=UpperCamelCase__ ,      batched=UpperCamelCase__					)

						UpperCAmelCase__  :					Dict        =     filter(UpperCamelCase__					)

						# Activate later when tokenizer support batched inputs
						# with dataset.formatted_as(type='numpy'):
						#     times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)

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


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



'''simple docstring'''



import unittest

from transformers import AutoTokenizer, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow


if is_flax_available():
		import jax.numpy as jnp

		from transformers import FlaxXLMRobertaModel


@require_sentencepiece
@require_tokenizers
@require_flax
class 		_snake_case					(     unittest.TestCase	):




					@slow
					def 				snake_case__						(      self):
								UpperCAmelCase__  :					Union[str, Any]        =     FlaxXLMRobertaModel.from_pretrained("""xlm-roberta-base""")
								UpperCAmelCase__  :					List[str]        =     AutoTokenizer.from_pretrained("""xlm-roberta-base""")
								UpperCAmelCase__  :					Dict        =     """The dog is cute and lives in the garden house"""
								UpperCAmelCase__  :					Dict        =     jnp.array([tokenizer.encode(_lowerCamelCase)])

								UpperCAmelCase__  :					int        =     (1, 12, 768)  # batch_size, sequence_length, embedding_vector_dim
								UpperCAmelCase__  :					Any        =     jnp.array(
								    [[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]])

								UpperCAmelCase__  :					Any        =     model(_lowerCamelCase)["""last_hidden_state"""]
								self.assertEqual(output.shape							,							_lowerCamelCase)
								# compare the actual values for a slice of last dim
								self.assertTrue(jnp.allclose(output[:, :, -1]							,							_lowerCamelCase							,							atol=1e-3))