import unittest

import numpy as np

from transformers import DistilBertConfig, 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.distilbert.modeling_flax_distilbert import (
        FlaxDistilBertForMaskedLM,
        FlaxDistilBertForMultipleChoice,
        FlaxDistilBertForQuestionAnswering,
        FlaxDistilBertForSequenceClassification,
        FlaxDistilBertForTokenClassification,
        FlaxDistilBertModel,
    )




class 	__snake_case	( unittest.TestCase		):





      '''simple docstring'''






      def __init__(					self     :				List[str]    ,  _UpperCamelCase     :				Tuple    ,  _UpperCamelCase     :				Optional[int]=13    ,  _UpperCamelCase     :				str=7    ,  _UpperCamelCase     :				Any=True    ,  _UpperCamelCase     :				Tuple=True    ,  _UpperCamelCase     :				str=True    ,  _UpperCamelCase     :				Optional[Any]=True    ,  _UpperCamelCase     :				Dict=99    ,  _UpperCamelCase     :				Optional[int]=32    ,  _UpperCamelCase     :				Tuple=5    ,  _UpperCamelCase     :				Union[str, Any]=4    ,  _UpperCamelCase     :				str=37    ,  _UpperCamelCase     :				str="gelu"    ,  _UpperCamelCase     :				List[str]=0.1    ,  _UpperCamelCase     :				List[Any]=0.1    ,  _UpperCamelCase     :				Dict=512    ,  _UpperCamelCase     :				List[Any]=16    ,  _UpperCamelCase     :				Optional[int]=2    ,  _UpperCamelCase     :				Tuple=0.0_2    ,  _UpperCamelCase     :				Tuple=4    ,  )				->List[str]:


          """simple docstring"""


          _lowerCamelCase     :		Tuple 						=						parent
          _lowerCamelCase     :		int 						=						batch_size
          _lowerCamelCase     :		str 						=						seq_length
          _lowerCamelCase     :		Optional[Any] 						=						is_training
          _lowerCamelCase     :		Tuple 						=						use_attention_mask
          _lowerCamelCase     :		Any 						=						use_token_type_ids
          _lowerCamelCase     :		Optional[int] 						=						use_labels
          _lowerCamelCase     :		Union[str, Any] 						=						vocab_size
          _lowerCamelCase     :		Optional[Any] 						=						hidden_size
          _lowerCamelCase     :		str 						=						num_hidden_layers
          _lowerCamelCase     :		List[str] 						=						num_attention_heads
          _lowerCamelCase     :		List[str] 						=						intermediate_size
          _lowerCamelCase     :		Optional[Any] 						=						hidden_act
          _lowerCamelCase     :		Dict 						=						hidden_dropout_prob
          _lowerCamelCase     :		str 						=						attention_probs_dropout_prob
          _lowerCamelCase     :		List[Any] 						=						max_position_embeddings
          _lowerCamelCase     :		List[str] 						=						type_vocab_size
          _lowerCamelCase     :		Tuple 						=						type_sequence_label_size
          _lowerCamelCase     :		Optional[int] 						=						initializer_range
          _lowerCamelCase     :		Dict 						=						num_choices



      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[Any])				->str:


          """simple docstring"""


          _lowerCamelCase     :		Tuple 						=						ids_tensor([self.batch_size, self.seq_length]    ,  self.vocab_size)

          _lowerCamelCase     :		Any 						=						None
          if self.use_attention_mask:
              _lowerCamelCase     :		int 						=						random_attention_mask([self.batch_size, self.seq_length])

          _lowerCamelCase     :		List[str] 						=						DistilBertConfig(
              vocab_size=self.vocab_size    ,  dim=self.hidden_size    ,  n_layers=self.num_hidden_layers    ,  n_heads=self.num_attention_heads    ,  hidden_dim=self.intermediate_size    ,  hidden_act=self.hidden_act    ,  dropout=self.hidden_dropout_prob    ,  attention_dropout=self.attention_probs_dropout_prob    ,  max_position_embeddings=self.max_position_embeddings    ,  initializer_range=self.initializer_range    ,  tie_weights_=_UpperCamelCase    ,  )

          return config, input_ids, attention_mask




      def 	_SCREAMING_SNAKE_CASE    (					self     :				Any)				->List[str]:


          """simple docstring"""


          _lowerCamelCase     :		str 						=						self.prepare_config_and_inputs()
          _lowerCamelCase						,							_lowerCamelCase						,							_lowerCamelCase     :		Tuple 						=						config_and_inputs
          _lowerCamelCase     :		Union[str, Any] 						=						{"""input_ids""": input_ids, """attention_mask""": attention_mask}
          return config, inputs_dict





@require_flax
class 	__snake_case	( __lowerCAmelCase ,  unittest.TestCase		):





      '''simple docstring'''






      _snake_case    		=       (
          (
              FlaxDistilBertModel,
              FlaxDistilBertForMaskedLM,
              FlaxDistilBertForMultipleChoice,
              FlaxDistilBertForQuestionAnswering,
              FlaxDistilBertForSequenceClassification,
              FlaxDistilBertForTokenClassification,
              FlaxDistilBertForQuestionAnswering,
          )
          if is_flax_available()
          else ()
      )



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[int])				->Optional[Any]:


          """simple docstring"""


          _lowerCamelCase     :		Optional[Any] 						=						FlaxDistilBertModelTester(self)




      @slow
      def 	_SCREAMING_SNAKE_CASE    (					self     :				Dict)				->List[str]:


          """simple docstring"""


          for model_class_name in self.all_model_classes:
              _lowerCamelCase     :		Tuple 						=						model_class_name.from_pretrained("""distilbert-base-uncased""")
              _lowerCamelCase     :		List[str] 						=						model(np.ones((1, 1)))
              self.assertIsNotNone(_UpperCamelCase)





@require_flax
class 	__snake_case	( unittest.TestCase		):





      '''simple docstring'''






      @slow
      def 	_SCREAMING_SNAKE_CASE    (					self     :				Dict)				->List[Any]:


          """simple docstring"""


          _lowerCamelCase     :		Optional[Any] 						=						FlaxDistilBertModel.from_pretrained("""distilbert-base-uncased""")
          _lowerCamelCase     :		List[Any] 						=						np.array([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]])
          _lowerCamelCase     :		Dict 						=						np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
          _lowerCamelCase     :		Union[str, Any] 						=						model(_UpperCamelCase    ,  attention_mask=_UpperCamelCase)[0]
          _lowerCamelCase     :		Optional[Any] 						=						(1, 11, 768)
          self.assertEqual(output.shape    ,  _UpperCamelCase)
          _lowerCamelCase     :		Dict 						=						np.array([[[-0.1_6_3_9, 0.3_2_9_9, 0.1_6_4_8], [-0.1_7_4_6, 0.3_2_8_9, 0.1_7_1_0], [-0.1_8_8_4, 0.3_3_5_7, 0.1_8_1_0]]])

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





import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser







def 						A__  (					__A	):







    '''simple docstring'''
    _lowerCamelCase     :		Tuple 						=						{}
    _lowerCamelCase     :		List[Any] 						=						tokenizer(example["""content"""]   , truncation=__A	)["""input_ids"""]
    _lowerCamelCase     :		Tuple 						=						len(example["""content"""]	) / len(output["""input_ids"""]	)
    return output


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

lowerCAmelCase			:							str     =time.time()
lowerCAmelCase			:							Union[str, Any]     =load_dataset(args.dataset_name, split="train")
print(F"""Dataset loaded in {time.time()-t_start:.2f}s""")

lowerCAmelCase			:							Dict     =time.time()
lowerCAmelCase			:							Dict     =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""")




