"""simple docstring"""




import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser





def 		__lowerCamelCase							(UpperCAmelCase__       :							str					):
					SCREAMING_SNAKE_CASE           =    {}
					SCREAMING_SNAKE_CASE           =    tokenizer(example["content"] ,	truncation=UpperCAmelCase__					)["""input_ids"""]
					SCREAMING_SNAKE_CASE           =    len(example["content"]					) / len(output["input_ids"]					)
					return output


_lowerCamelCase   :				Tuple     			=						HfArgumentParser(PretokenizationArguments)
_lowerCamelCase   :				Dict     			=						parser.parse_args()
if args.num_workers is None:
							_lowerCamelCase   :				List[str]     			=						multiprocessing.cpu_count()
_lowerCamelCase   :				int     			=						AutoTokenizer.from_pretrained(args.tokenizer_dir)

_lowerCamelCase   :				int     			=						time.time()
_lowerCamelCase   :				Optional[Any]     			=						load_dataset(args.dataset_name, split='''train''')
print(f"""Dataset loaded in {time.time()-t_start:.2f}s""")

_lowerCamelCase   :				List[str]     			=						time.time()
_lowerCamelCase   :				Optional[Any]     			=						ds.map(
    tokenize,
    num_proc=args.num_workers,
    remove_columns=[
        '''repo_name''',
        '''path''',
        '''copies''',
        '''size''',
        '''content''',
        '''license''',
        '''hash''',
        '''line_mean''',
        '''line_max''',
        '''alpha_frac''',
        '''autogenerated''',
    ],
)
print(f"""Dataset tokenized in {time.time()-t_start:.2f}s""")

_lowerCamelCase   :				Tuple     			=						time.time()
ds.push_to_hub(args.tokenized_data_repo)
print(f"""Data pushed to the hub in {time.time()-t_start:.2f}s""")





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   lowercase      (       unittest.TestCase       ):




							@slow
							def 		__snake_case(  self				:   Optional[int] )   ->			List[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    FlaxXLMRobertaModel.from_pretrained("xlm-roberta-base" )
												SCREAMING_SNAKE_CASE           =    AutoTokenizer.from_pretrained("xlm-roberta-base" )
												SCREAMING_SNAKE_CASE           =    "The dog is cute and lives in the garden house"
												SCREAMING_SNAKE_CASE           =    jnp.array([tokenizer.encode(_UpperCamelCase )] )

												SCREAMING_SNAKE_CASE           =    (1, 12, 768)  # batch_size, sequence_length, embedding_vector_dim
												SCREAMING_SNAKE_CASE           =    jnp.array(
												    [[-0.0_1_0_1, 0.1_2_1_8, -0.0_8_0_3, 0.0_8_0_1, 0.1_3_2_7, 0.0_7_7_6, -0.1_2_1_5, 0.2_3_8_3, 0.3_3_3_8, 0.3_1_0_6, 0.0_3_0_0, 0.0_2_5_2]] )

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




