





import unittest

import numpy as np

from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
		from transformers.models.roberta.modeling_flax_roberta import (
		    FlaxRobertaForCausalLM,
		    FlaxRobertaForMaskedLM,
		    FlaxRobertaForMultipleChoice,
		    FlaxRobertaForQuestionAnswering,
		    FlaxRobertaForSequenceClassification,
		    FlaxRobertaForTokenClassification,
		    FlaxRobertaModel,
		)


class  __UpperCamelCase       (		unittest.TestCase		):






							def __init__(     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__=13   ,      lowerCAmelCase__=7   ,      lowerCAmelCase__=True   ,      lowerCAmelCase__=True   ,      lowerCAmelCase__=True   ,      lowerCAmelCase__=True   ,      lowerCAmelCase__=99   ,      lowerCAmelCase__=32   ,      lowerCAmelCase__=5   ,      lowerCAmelCase__=4   ,      lowerCAmelCase__=37   ,      lowerCAmelCase__="gelu"   ,      lowerCAmelCase__=0.1   ,      lowerCAmelCase__=0.1   ,      lowerCAmelCase__=512   ,      lowerCAmelCase__=16   ,      lowerCAmelCase__=2   ,      lowerCAmelCase__=0.02   ,      lowerCAmelCase__=4   ,      )			->				List[Any]:
								a   :	Union[str, Any]			=       parent
								a   :	str			=       batch_size
								a   :	int			=       seq_length
								a   :	Optional[int]			=       is_training
								a   :	Tuple			=       use_attention_mask
								a   :	List[Any]			=       use_token_type_ids
								a   :	Optional[int]			=       use_labels
								a   :	Union[str, Any]			=       vocab_size
								a   :	Tuple			=       hidden_size
								a   :	Union[str, Any]			=       num_hidden_layers
								a   :	str			=       num_attention_heads
								a   :	Any			=       intermediate_size
								a   :	Tuple			=       hidden_act
								a   :	List[Any]			=       hidden_dropout_prob
								a   :	str			=       attention_probs_dropout_prob
								a   :	Dict			=       max_position_embeddings
								a   :	Any			=       type_vocab_size
								a   :	List[str]			=       type_sequence_label_size
								a   :	Any			=       initializer_range
								a   :	str			=       num_choices






							def 					__a     (     self   )			->				int:
								a   :	Union[str, Any]			=       ids_tensor([self.batch_size, self.seq_length]   ,      self.vocab_size   )

								a   :	Optional[Any]			=       None
								if self.use_attention_mask:
									a   :	str			=       random_attention_mask([self.batch_size, self.seq_length]   )

								a   :	Dict			=       None
								if self.use_token_type_ids:
									a   :	int			=       ids_tensor([self.batch_size, self.seq_length]   ,      self.type_vocab_size   )

								a   :	Any			=       RobertaConfig(
								    vocab_size=self.vocab_size   ,      hidden_size=self.hidden_size   ,      num_hidden_layers=self.num_hidden_layers   ,      num_attention_heads=self.num_attention_heads   ,      intermediate_size=self.intermediate_size   ,      hidden_act=self.hidden_act   ,      hidden_dropout_prob=self.hidden_dropout_prob   ,      attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,      max_position_embeddings=self.max_position_embeddings   ,      type_vocab_size=self.type_vocab_size   ,      is_decoder=_a   ,      initializer_range=self.initializer_range   ,      )

								return config, input_ids, token_type_ids, attention_mask






							def 					__a     (     self   )			->				Dict:
								a   :	List[str]			=       self.prepare_config_and_inputs()
								a, a, a, a   :	Tuple			=       config_and_inputs
								a   :	Optional[int]			=       {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
								return config, inputs_dict






							def 					__a     (     self   )			->				Optional[int]:
								a   :	Optional[Any]			=       self.prepare_config_and_inputs()
								a, a, a, a   :	List[str]			=       config_and_inputs

								a   :	List[str]			=       True
								a   :	Optional[int]			=       floats_tensor([self.batch_size, self.seq_length, self.hidden_size]   )
								a   :	str			=       ids_tensor([self.batch_size, self.seq_length]   ,      vocab_size=2   )

								return (
								    config,
								    input_ids,
								    token_type_ids,
								    encoder_hidden_states,
								    encoder_attention_mask,
								)


@require_flax
class  __UpperCamelCase       (		__SCREAMING_SNAKE_CASE	, unittest.TestCase		):
							lowerCamelCase     :      Tuple         =True

							lowerCamelCase     :      Optional[Any]         =(
							    (
							        FlaxRobertaModel,
							        FlaxRobertaForCausalLM,
							        FlaxRobertaForMaskedLM,
							        FlaxRobertaForSequenceClassification,
							        FlaxRobertaForTokenClassification,
							        FlaxRobertaForMultipleChoice,
							        FlaxRobertaForQuestionAnswering,
							    )
							    if is_flax_available()
							    else ()
							)






							def 					__a     (     self   )			->				Union[str, Any]:
								a   :	List[Any]			=       FlaxRobertaModelTester(self   )






							@slow
							def 					__a     (     self   )			->				Dict:
								for model_class_name in self.all_model_classes:
									a   :	str			=       model_class_name.from_pretrained("roberta-base"   ,      from_pt=_a   )
									a   :	List[Any]			=       model(np.ones((1, 1)   )   )
									self.assertIsNotNone(_a   )









"""simple docstring"""




import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					List[str]      )				->int:






	'''simple docstring'''






	a   :	int			=       {}
	a   :	Union[str, Any]			=       tokenizer(example["content"]					, truncation=_lowercase      )["input_ids"]
	a   :	Any			=       len(example["content"]      ) / len(output["input_ids"]      )
	return output


a		:				int  			=    HfArgumentParser(PretokenizationArguments)
a		:				Optional[int]  			=    parser.parse_args()
if args.num_workers is None:
		a		:				Tuple  			=    multiprocessing.cpu_count()
a		:				Optional[int]  			=    AutoTokenizer.from_pretrained(args.tokenizer_dir)

a		:				Dict  			=    time.time()
a		:				Tuple  			=    load_dataset(args.dataset_name, split='''train''')
print(F'''Dataset loaded in {time.time()-t_start:.2f}s''')

a		:				Dict  			=    time.time()
a		:				Tuple  			=    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''')

a		:				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''')


