


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   __lowercase				(  unittest.TestCase  ):





      def __init__(		self						:					Optional[Any]					,       __lowerCamelCase						:					Any					,       __lowerCamelCase						:					Union[str, Any]=1_3					,       __lowerCamelCase						:					Tuple=7					,       __lowerCamelCase						:					int=True					,       __lowerCamelCase						:					Union[str, Any]=True					,       __lowerCamelCase						:					List[Any]=True					,       __lowerCamelCase						:					Tuple=True					,       __lowerCamelCase						:					Optional[Any]=9_9					,       __lowerCamelCase						:					List[Any]=3_2					,       __lowerCamelCase						:					Any=5					,       __lowerCamelCase						:					Dict=4					,       __lowerCamelCase						:					str=3_7					,       __lowerCamelCase						:					Dict="gelu"					,       __lowerCamelCase						:					Optional[int]=0.1					,       __lowerCamelCase						:					Optional[Any]=0.1					,       __lowerCamelCase						:					int=5_1_2					,       __lowerCamelCase						:					Tuple=1_6					,       __lowerCamelCase						:					Union[str, Any]=2					,       __lowerCamelCase						:					int=0.02					,       __lowerCamelCase						:					Any=4					,       )    ->   Dict:






         """simple docstring"""







         UpperCAmelCase							=     parent
         UpperCAmelCase							=     batch_size
         UpperCAmelCase							=     seq_length
         UpperCAmelCase							=     is_training
         UpperCAmelCase							=     use_attention_mask
         UpperCAmelCase							=     use_token_type_ids
         UpperCAmelCase							=     use_labels
         UpperCAmelCase							=     vocab_size
         UpperCAmelCase							=     hidden_size
         UpperCAmelCase							=     num_hidden_layers
         UpperCAmelCase							=     num_attention_heads
         UpperCAmelCase							=     intermediate_size
         UpperCAmelCase							=     hidden_act
         UpperCAmelCase							=     hidden_dropout_prob
         UpperCAmelCase							=     attention_probs_dropout_prob
         UpperCAmelCase							=     max_position_embeddings
         UpperCAmelCase							=     type_vocab_size
         UpperCAmelCase							=     type_sequence_label_size
         UpperCAmelCase							=     initializer_range
         UpperCAmelCase							=     num_choices





      def        _lowercase				(		self						:					int       )    ->   str:






         """simple docstring"""







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

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

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

         UpperCAmelCase							=     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=__lowerCamelCase					,       initializer_range=self.initializer_range					,       )

         return config, input_ids, token_type_ids, attention_mask





      def        _lowercase				(		self						:					Dict       )    ->   Optional[int]:






         """simple docstring"""







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






@require_flax
class   __lowercase				(  __snake_case ,					unittest.TestCase  ):
      UpperCamelCase								=       (
          (
              FlaxAlbertModel,
              FlaxAlbertForPreTraining,
              FlaxAlbertForMaskedLM,
              FlaxAlbertForMultipleChoice,
              FlaxAlbertForQuestionAnswering,
              FlaxAlbertForSequenceClassification,
              FlaxAlbertForTokenClassification,
              FlaxAlbertForQuestionAnswering,
          )
          if is_flax_available()
          else ()
      )





      def        _lowercase				(		self						:					Union[str, Any]       )    ->   List[str]:






         """simple docstring"""







         UpperCAmelCase							=     FlaxAlbertModelTester(self       )





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






         """simple docstring"""







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






@require_flax
class   __lowercase				(  unittest.TestCase  ):





      @slow
      def        _lowercase				(		self						:					Optional[int]       )    ->   Union[str, Any]:






         """simple docstring"""







         UpperCAmelCase							=     FlaxAlbertModel.from_pretrained("""albert-base-v2"""       )
         UpperCAmelCase							=     np.array([[0, 3_4_5, 2_3_2, 3_2_8, 7_4_0, 1_4_0, 1_6_9_5, 6_9, 6_0_7_8, 1_5_8_8, 2]]       )
         UpperCAmelCase							=     np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]       )
         UpperCAmelCase							=     model(__lowerCamelCase					,       attention_mask=__lowerCamelCase       )[0]
         UpperCAmelCase							=     (1, 1_1, 7_6_8)
         self.assertEqual(output.shape					,       __lowerCamelCase       )
         UpperCAmelCase							=     np.array(
             [[[-0.6_513, 1.5_035, -0.2_766], [-0.6_515, 1.5_046, -0.2_780], [-0.6_512, 1.5_049, -0.2_784]]]       )

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







import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser
def   _UpperCamelCase				(						lowerCAmelCase_   )						->int:
   UpperCAmelCase							=     {}
   UpperCAmelCase							=     tokenizer(example["""content"""]  ,						truncation=lowerCAmelCase_   )["""input_ids"""]
   UpperCAmelCase							=     len(example["""content"""]   ) / len(output["""input_ids"""]   )
   return output


__a     =			HfArgumentParser(PretokenizationArguments)
__a     =			parser.parse_args()
if args.num_workers is None:
 __a     =			multiprocessing.cpu_count()
__a     =			AutoTokenizer.from_pretrained(args.tokenizer_dir)

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

__a     =			time.time()
__a     =			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     =			time.time()
ds.push_to_hub(args.tokenized_data_repo)







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



