

'''simple docstring'''



import unittest

import numpy as np

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

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
  import jax.numpy as jnp

  from transformers.models.albert.modeling_flax_albert import (
      FlaxAlbertForMaskedLM,
      FlaxAlbertForMultipleChoice,
      FlaxAlbertForPreTraining,
      FlaxAlbertForQuestionAnswering,
      FlaxAlbertForSequenceClassification,
      FlaxAlbertForTokenClassification,
      FlaxAlbertModel,
  )







class 		lowerCAmelCase__  ( unittest.TestCase   ):



 """simple docstring"""



 def __init__(						self   :			List[Any]   ,				__SCREAMING_SNAKE_CASE   :			List[str]   ,				__SCREAMING_SNAKE_CASE   :			str=13   ,				__SCREAMING_SNAKE_CASE   :			Tuple=7   ,				__SCREAMING_SNAKE_CASE   :			Union[str, Any]=True   ,				__SCREAMING_SNAKE_CASE   :			Any=True   ,				__SCREAMING_SNAKE_CASE   :			str=True   ,				__SCREAMING_SNAKE_CASE   :			int=True   ,				__SCREAMING_SNAKE_CASE   :			Any=99   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]=32   ,				__SCREAMING_SNAKE_CASE   :			int=5   ,				__SCREAMING_SNAKE_CASE   :			int=4   ,				__SCREAMING_SNAKE_CASE   :			List[str]=37   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]="gelu"   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]=0.1   ,				__SCREAMING_SNAKE_CASE   :			Dict=0.1   ,				__SCREAMING_SNAKE_CASE   :			List[Any]=512   ,				__SCREAMING_SNAKE_CASE   :			int=16   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]=2   ,				__SCREAMING_SNAKE_CASE   :			List[str]=0.02   ,				__SCREAMING_SNAKE_CASE   :			int=4   ,				)    ->					Optional[int]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = parent
      __SCREAMING_SNAKE_CASE      = batch_size
      __SCREAMING_SNAKE_CASE      = seq_length
      __SCREAMING_SNAKE_CASE      = is_training
      __SCREAMING_SNAKE_CASE      = use_attention_mask
      __SCREAMING_SNAKE_CASE      = use_token_type_ids
      __SCREAMING_SNAKE_CASE      = use_labels
      __SCREAMING_SNAKE_CASE      = vocab_size
      __SCREAMING_SNAKE_CASE      = hidden_size
      __SCREAMING_SNAKE_CASE      = num_hidden_layers
      __SCREAMING_SNAKE_CASE      = num_attention_heads
      __SCREAMING_SNAKE_CASE      = intermediate_size
      __SCREAMING_SNAKE_CASE      = hidden_act
      __SCREAMING_SNAKE_CASE      = hidden_dropout_prob
      __SCREAMING_SNAKE_CASE      = attention_probs_dropout_prob
      __SCREAMING_SNAKE_CASE      = max_position_embeddings
      __SCREAMING_SNAKE_CASE      = type_vocab_size
      __SCREAMING_SNAKE_CASE      = type_sequence_label_size
      __SCREAMING_SNAKE_CASE      = initializer_range
      __SCREAMING_SNAKE_CASE      = num_choices
 def  UpperCAmelCase__						(						self   :			Dict     )    ->					Optional[int]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = ids_tensor([self.batch_size, self.seq_length]   ,				self.vocab_size     )

      __SCREAMING_SNAKE_CASE      = None
      if self.use_attention_mask:
           __SCREAMING_SNAKE_CASE      = random_attention_mask([self.batch_size, self.seq_length]     )

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

      __SCREAMING_SNAKE_CASE      = AlbertConfig(
          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=__SCREAMING_SNAKE_CASE   ,				initializer_range=self.initializer_range   ,				)

      return config, input_ids, token_type_ids, attention_mask


 def  UpperCAmelCase__						(						self   :			Any     )    ->					int:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = self.prepare_config_and_inputs()
      __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = config_and_inputs
      __SCREAMING_SNAKE_CASE      = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
      return config, inputs_dict







@require_flax
class 		lowerCAmelCase__  ( a					,   unittest.TestCase   ):



 """simple docstring"""



 lowerCAmelCase__       = (
     (
         FlaxAlbertModel,
         FlaxAlbertForPreTraining,
         FlaxAlbertForMaskedLM,
         FlaxAlbertForMultipleChoice,
         FlaxAlbertForQuestionAnswering,
         FlaxAlbertForSequenceClassification,
         FlaxAlbertForTokenClassification,
         FlaxAlbertForQuestionAnswering,
     )
     if is_flax_available()
     else ()
 )
 def  UpperCAmelCase__						(						self   :			List[Any]     )    ->					Dict:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = FlaxAlbertModelTester(self     )


 @slow
 def  UpperCAmelCase__						(						self   :			int     )    ->					str:


      """simple docstring"""




      for model_class_name in self.all_model_classes:
           __SCREAMING_SNAKE_CASE      = model_class_name.from_pretrained("""albert-base-v2"""     )
           __SCREAMING_SNAKE_CASE      = model(np.ones((1, 1)     )     )
           self.assertIsNotNone(__SCREAMING_SNAKE_CASE     )







@require_flax
class 		lowerCAmelCase__  ( unittest.TestCase   ):



 """simple docstring"""



 @slow
 def  UpperCAmelCase__						(						self   :			Union[str, Any]     )    ->					Dict:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = FlaxAlbertModel.from_pretrained("""albert-base-v2"""     )
      __SCREAMING_SNAKE_CASE      = np.array([[0, 345, 232, 328, 740, 140, 1_695, 69, 6_078, 1_588, 2]]     )
      __SCREAMING_SNAKE_CASE      = np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]     )
      __SCREAMING_SNAKE_CASE      = model(__SCREAMING_SNAKE_CASE   ,				attention_mask=__SCREAMING_SNAKE_CASE     )[0]
      __SCREAMING_SNAKE_CASE      = (1, 11, 768)
      self.assertEqual(output.shape   ,				__SCREAMING_SNAKE_CASE     )
      __SCREAMING_SNAKE_CASE      = np.array(
          [[[-0.6513, 1.5035, -0.2766], [-0.6515, 1.5046, -0.2780], [-0.6512, 1.5049, -0.2784]]]     )

      self.assertTrue(jnp.allclose(output[:, 1:4, 1:4]   ,				__SCREAMING_SNAKE_CASE   ,				atol=1E-4     )     )









'''simple docstring'''



import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser







def   a__			(    a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = {}
     __SCREAMING_SNAKE_CASE      = tokenizer(example["""content"""] ,       truncation=a__		)["""input_ids"""]
     __SCREAMING_SNAKE_CASE      = len(example["""content"""]		) / len(output["""input_ids"""]		)
     return output


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

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

UpperCAmelCase					:			List[str]       =	time.time()
UpperCAmelCase					:			List[str]       =	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""")

UpperCAmelCase					:			List[Any]       =	time.time()
ds.push_to_hub(args.tokenized_data_repo)



print(f"""Data pushed to the hub in {time.time()-t_start:.2f}s""")






