



import unittest

import numpy as np

from transformers import RobertaPreLayerNormConfig, 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():
   import jax.numpy as jnp

   from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
       FlaxRobertaPreLayerNormForCausalLM,
       FlaxRobertaPreLayerNormForMaskedLM,
       FlaxRobertaPreLayerNormForMultipleChoice,
       FlaxRobertaPreLayerNormForQuestionAnswering,
       FlaxRobertaPreLayerNormForSequenceClassification,
       FlaxRobertaPreLayerNormForTokenClassification,
       FlaxRobertaPreLayerNormModel,
   )


class   snake_case_ (unittest.TestCase  ):




       """simple docstring"""






       def __init__(     self     ,lowercase     ,lowercase=13     ,lowercase=7     ,lowercase=True     ,lowercase=True     ,lowercase=True     ,lowercase=True     ,lowercase=99     ,lowercase=32     ,lowercase=5     ,lowercase=4     ,lowercase=37     ,lowercase="gelu"     ,lowercase=0.1     ,lowercase=0.1     ,lowercase=512     ,lowercase=16     ,lowercase=2     ,lowercase=0.02     ,lowercase=4     ,):

             """simple docstring"""





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



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	List[str]				      =		ids_tensor([self.batch_size, self.seq_length]     ,self.vocab_size)

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

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

             UpperCAmelCase_  :	str				      =		RobertaPreLayerNormConfig(
                 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=lowercase     ,initializer_range=self.initializer_range     ,)

             return config, input_ids, token_type_ids, attention_mask



       def  A_   (     self):

             """simple docstring"""





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



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	int				      =		self.prepare_config_and_inputs()
             UpperCAmelCase_      ,      UpperCAmelCase_      ,      UpperCAmelCase_      ,      UpperCAmelCase_  :	Optional[int]				      =		config_and_inputs

             UpperCAmelCase_  :	List[str]				      =		True
             UpperCAmelCase_  :	Dict				      =		floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
             UpperCAmelCase_  :	List[Any]				      =		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
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class   snake_case_ (lowercase__ , unittest.TestCase  ):




       """simple docstring"""






       _lowerCamelCase			    =				True

       _lowerCamelCase			    =				(
           (
               FlaxRobertaPreLayerNormModel,
               FlaxRobertaPreLayerNormForCausalLM,
               FlaxRobertaPreLayerNormForMaskedLM,
               FlaxRobertaPreLayerNormForSequenceClassification,
               FlaxRobertaPreLayerNormForTokenClassification,
               FlaxRobertaPreLayerNormForMultipleChoice,
               FlaxRobertaPreLayerNormForQuestionAnswering,
           )
           if is_flax_available()
           else ()
       )



       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	str				      =		FlaxRobertaPreLayerNormModelTester(self)



       @slow
       def  A_   (     self):

             """simple docstring"""





             for model_class_name in self.all_model_classes:
                   UpperCAmelCase_  :	str				      =		model_class_name.from_pretrained("andreasmadsen/efficient_mlm_m0.40"     ,from_pt=lowercase)
                   UpperCAmelCase_  :	Dict				      =		model(np.ones((1, 1)))
                   self.assertIsNotNone(lowercase)


@require_flax
class   snake_case_ (unittest.TestCase  ):




       """simple docstring"""






       @slow
       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	List[Any]				      =		FlaxRobertaPreLayerNormForMaskedLM.from_pretrained("andreasmadsen/efficient_mlm_m0.40"     ,from_pt=lowercase)

             UpperCAmelCase_  :	Optional[int]				      =		np.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]     ,dtype=jnp.intaa)
             UpperCAmelCase_  :	Dict				      =		model(lowercase)[0]
             UpperCAmelCase_  :	Tuple				      =		[1, 11, 50265]
             self.assertEqual(list(output.shape)     ,lowercase)
             # compare the actual values for a slice.
             UpperCAmelCase_  :	Optional[int]				      =		np.array(
                 [[[40.4880, 18.0199, -5.2367], [-1.8877, -4.0885, 10.7085], [-2.2613, -5.6110, 7.2665]]]     ,dtype=np.floataa)
             self.assertTrue(np.allclose(output[:, :3, :3]     ,lowercase     ,atol=1E-4))



       @slow
       def  A_   (     self):

             """simple docstring"""





             UpperCAmelCase_  :	Optional[int]				      =		FlaxRobertaPreLayerNormModel.from_pretrained("andreasmadsen/efficient_mlm_m0.40"     ,from_pt=lowercase)

             UpperCAmelCase_  :	Optional[Any]				      =		np.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]     ,dtype=jnp.intaa)
             UpperCAmelCase_  :	List[str]				      =		model(lowercase)[0]
             # compare the actual values for a slice.
             UpperCAmelCase_  :	int				      =		np.array(
                 [[[0.0208, -0.0356, 0.0237], [-0.1569, -0.0411, -0.2626], [0.1879, 0.0125, -0.0089]]]     ,dtype=np.floataa)
             self.assertTrue(np.allclose(output[:, :3, :3]     ,lowercase     ,atol=1E-4))





import argparse
import logging
import pickle
import random
import time

import numpy as np

from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer


logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
__lowerCamelCase         =			logging.getLogger(__name__)







def    _snake_case (       )     ->     Dict:






      '''simple docstring'''







      UpperCAmelCase_  :	Any				      =		argparse.ArgumentParser(
          description="Preprocess the data to avoid re-doing it several times by (tokenization + token_to_ids)."    )
      parser.add_argument("--file_path"					,  type=__snake_case					,  default="data/dump.txt"					,  help="The path to the data."    )
      parser.add_argument("--tokenizer_type"					,  type=__snake_case					,  default="bert"					,  choices=["bert", "roberta", "gpt2"]    )
      parser.add_argument("--tokenizer_name"					,  type=__snake_case					,  default="bert-base-uncased"					,  help="The tokenizer to use."    )
      parser.add_argument("--dump_file"					,  type=__snake_case					,  default="data/dump"					,  help="The dump file prefix."    )
      UpperCAmelCase_  :	str				      =		parser.parse_args()

      logger.info(F"""Loading Tokenizer ({args.tokenizer_name})"""    )
      if args.tokenizer_type == "bert":
            UpperCAmelCase_  :	int				      =		BertTokenizer.from_pretrained(args.tokenizer_name    )
            UpperCAmelCase_  :	Optional[Any]				      =		tokenizer.special_tokens_map["cls_token"]  # `[CLS]`
            UpperCAmelCase_  :	Dict				      =		tokenizer.special_tokens_map["sep_token"]  # `[SEP]`
      elif args.tokenizer_type == "roberta":
            UpperCAmelCase_  :	str				      =		RobertaTokenizer.from_pretrained(args.tokenizer_name    )
            UpperCAmelCase_  :	int				      =		tokenizer.special_tokens_map["cls_token"]  # `<s>`
            UpperCAmelCase_  :	Optional[Any]				      =		tokenizer.special_tokens_map["sep_token"]  # `</s>`
      elif args.tokenizer_type == "gpt2":
            UpperCAmelCase_  :	Tuple				      =		GPTaTokenizer.from_pretrained(args.tokenizer_name    )
            UpperCAmelCase_  :	List[Any]				      =		tokenizer.special_tokens_map["bos_token"]  # `<|endoftext|>`
            UpperCAmelCase_  :	List[str]				      =		tokenizer.special_tokens_map["eos_token"]  # `<|endoftext|>`

      logger.info(F"""Loading text from {args.file_path}"""    )
      with open(args.file_path					,  "r"					,  encoding="utf8"    ) as fp:
            UpperCAmelCase_  :	Union[str, Any]				      =		fp.readlines()

      logger.info("Start encoding"    )
      logger.info(F"""{len(__snake_case    )} examples to process."""    )

      UpperCAmelCase_  :	List[str]				      =		[]
      UpperCAmelCase_  :	str				      =		0
      UpperCAmelCase_  :	Union[str, Any]				      =		1_0_0_0_0
      UpperCAmelCase_  :	int				      =		time.time()
      for text in data:
            UpperCAmelCase_  :	str				      =		F"""{bos} {text.strip()} {sep}"""
            UpperCAmelCase_  :	Any				      =		tokenizer.encode(__snake_case					,  add_special_tokens=__snake_case    )
            rslt.append(__snake_case    )

            iter += 1
            if iter % interval == 0:
                  UpperCAmelCase_  :	List[str]				      =		time.time()
                  logger.info(F"""{iter} examples processed. - {(end-start):.2f}s/{interval}expl"""    )
                  UpperCAmelCase_  :	Optional[Any]				      =		time.time()
      logger.info("Finished binarization"    )
      logger.info(F"""{len(__snake_case    )} examples processed."""    )

      UpperCAmelCase_  :	List[Any]				      =		F"""{args.dump_file}.{args.tokenizer_name}.pickle"""
      UpperCAmelCase_  :	Optional[int]				      =		tokenizer.vocab_size
      if vocab_size < (1 << 1_6):
            UpperCAmelCase_  :	List[Any]				      =		[np.uintaa(__snake_case    ) for d in rslt]
      else:
            UpperCAmelCase_  :	str				      =		[np.intaa(__snake_case    ) for d in rslt]
      random.shuffle(rslt_    )
      logger.info(F"""Dump to {dp_file}"""    )
      with open(__snake_case					,  "wb"    ) as handle:
            pickle.dump(rslt_					,  __snake_case					,  protocol=pickle.HIGHEST_PROTOCOL    )


if __name__ == "__main__":
   main()
