


'''simple docstring'''







import unittest

import numpy as np

from transformers import RoFormerConfig, 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.roformer.modeling_flax_roformer import (
           FlaxRoFormerForMaskedLM,
           FlaxRoFormerForMultipleChoice,
           FlaxRoFormerForQuestionAnswering,
           FlaxRoFormerForSequenceClassification,
           FlaxRoFormerForTokenClassification,
           FlaxRoFormerModel,
       )


class  lowerCAmelCase_						(       unittest.TestCase      ):





  """simple docstring"""



  def __init__(					self     :							Tuple				,							SCREAMING_SNAKE_CASE__     :							Tuple				,							SCREAMING_SNAKE_CASE__     :							Union[str, Any]=1_3				,							SCREAMING_SNAKE_CASE__     :							Dict=7				,							SCREAMING_SNAKE_CASE__     :							List[Any]=True				,							SCREAMING_SNAKE_CASE__     :							Optional[int]=True				,							SCREAMING_SNAKE_CASE__     :							str=True				,							SCREAMING_SNAKE_CASE__     :							Any=True				,							SCREAMING_SNAKE_CASE__     :							Any=9_9				,							SCREAMING_SNAKE_CASE__     :							List[Any]=3_2				,							SCREAMING_SNAKE_CASE__     :							Any=5				,							SCREAMING_SNAKE_CASE__     :							str=4				,							SCREAMING_SNAKE_CASE__     :							List[str]=3_7				,							SCREAMING_SNAKE_CASE__     :							Dict="gelu"				,							SCREAMING_SNAKE_CASE__     :							str=0.1				,							SCREAMING_SNAKE_CASE__     :							Dict=0.1				,							SCREAMING_SNAKE_CASE__     :							Optional[int]=5_1_2				,							SCREAMING_SNAKE_CASE__     :							Tuple=1_6				,							SCREAMING_SNAKE_CASE__     :							Any=2				,							SCREAMING_SNAKE_CASE__     :							List[Any]=0.0_2				,							SCREAMING_SNAKE_CASE__     :							int=4				,							):





       '''simple docstring'''

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

  def __a						(					self     :							str						):





       '''simple docstring'''

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

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

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

       __a       =   RoFormerConfig(
           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 __a						(					self     :							Tuple						):





       '''simple docstring'''

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


@require_flax
class  lowerCAmelCase_						(       snake_case__		,    unittest.TestCase      ):





  """simple docstring"""



  a_       :Optional[int]												=True

  a_       :int												=(
      (
          FlaxRoFormerModel,
          FlaxRoFormerForMaskedLM,
          FlaxRoFormerForSequenceClassification,
          FlaxRoFormerForTokenClassification,
          FlaxRoFormerForMultipleChoice,
          FlaxRoFormerForQuestionAnswering,
      )
      if is_flax_available()
      else ()
  )

  def __a						(					self     :							Optional[Any]						):





       '''simple docstring'''

       __a       =   FlaxRoFormerModelTester(self						)


  @slow
  def __a						(					self     :							Dict						):





       '''simple docstring'''

       for model_class_name in self.all_model_classes:
            __a       =   model_class_name.from_pretrained("""junnyu/roformer_chinese_small"""				,							from_pt=SCREAMING_SNAKE_CASE__						)
            __a       =   model(np.ones((1, 1)						)						)
            self.assertIsNotNone(SCREAMING_SNAKE_CASE__						)


@require_flax
class  lowerCAmelCase_						(       unittest.TestCase      ):





  """simple docstring"""



  @slow
  def __a						(					self     :							Tuple						):





       '''simple docstring'''

       __a       =   FlaxRoFormerForMaskedLM.from_pretrained("""junnyu/roformer_chinese_base"""						)
       __a       =   jnp.array([[0, 1, 2, 3, 4, 5]]						)
       __a       =   model(SCREAMING_SNAKE_CASE__						)[0]

       __a       =   5_0_0_0_0

       __a       =   (1, 6, vocab_size)
       self.assertEqual(output.shape				,							SCREAMING_SNAKE_CASE__						)

       __a       =   jnp.array(
           [[[-0.1_2_0_5, -1.0_2_6_5, 0.2_9_2_2], [-1.5_1_3_4, 0.1_9_7_4, 0.1_5_1_9], [-5.0_1_3_5, -3.9_0_0_3, -0.8_4_0_4]]]						)

       self.assertTrue(jnp.allclose(output[:, :3, :3]				,							SCREAMING_SNAKE_CASE__				,							atol=1E-4						)						)










'''simple docstring'''







import os
from shutil import copyfile
from typing import List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


SCREAMING_SNAKE_CASE_      =			logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_      =			{'vocab_file': 'sentencepiece.model'}

SCREAMING_SNAKE_CASE_      =			{
    'vocab_file': {
        'google/rembert': 'https://huggingface.co/google/rembert/resolve/main/sentencepiece.model',
    },
}

SCREAMING_SNAKE_CASE_      =			{
    'google/rembert': 2_56,
}


class  lowerCAmelCase_						(       snake_case__      ):





  """simple docstring"""



  a_       :Tuple												=VOCAB_FILES_NAMES
  a_       :Tuple												=PRETRAINED_VOCAB_FILES_MAP
  a_       :Optional[int]												=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES

  def __init__(					self     :							Union[str, Any]				,							SCREAMING_SNAKE_CASE__     :							str				,							SCREAMING_SNAKE_CASE__     :							Tuple=False				,							SCREAMING_SNAKE_CASE__     :							Optional[Any]=True				,							SCREAMING_SNAKE_CASE__     :							Union[str, Any]=True				,							SCREAMING_SNAKE_CASE__     :							int="[CLS]"				,							SCREAMING_SNAKE_CASE__     :							Optional[int]="[SEP]"				,							SCREAMING_SNAKE_CASE__     :							Optional[Any]="[UNK]"				,							SCREAMING_SNAKE_CASE__     :							Any="[SEP]"				,							SCREAMING_SNAKE_CASE__     :							Dict="[PAD]"				,							SCREAMING_SNAKE_CASE__     :							Dict="[CLS]"				,							SCREAMING_SNAKE_CASE__     :							Dict="[MASK]"				,							**SCREAMING_SNAKE_CASE__     :							List[str]				,							):





       '''simple docstring'''

       super().__init__(
           do_lower_case=SCREAMING_SNAKE_CASE__				,							remove_space=SCREAMING_SNAKE_CASE__				,							keep_accents=SCREAMING_SNAKE_CASE__				,							bos_token=SCREAMING_SNAKE_CASE__				,							eos_token=SCREAMING_SNAKE_CASE__				,							unk_token=SCREAMING_SNAKE_CASE__				,							sep_token=SCREAMING_SNAKE_CASE__				,							pad_token=SCREAMING_SNAKE_CASE__				,							cls_token=SCREAMING_SNAKE_CASE__				,							mask_token=SCREAMING_SNAKE_CASE__				,							**SCREAMING_SNAKE_CASE__				,							)

       __a       =   do_lower_case
       __a       =   remove_space
       __a       =   keep_accents
       __a       =   vocab_file

       __a       =   spm.SentencePieceProcessor()
       self.sp_model.Load(SCREAMING_SNAKE_CASE__						)

  @property
  def __a						(					self     :							List[str]						):





       '''simple docstring'''

       return len(self.sp_model						)

  def __a						(					self     :							str						):





       '''simple docstring'''

       __a       =   {self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__						): i for i in range(self.vocab_size						)}
       vocab.update(self.added_tokens_encoder						)
       return vocab

  def __getstate__(					self     :							List[Any]						):





       '''simple docstring'''

       __a       =   self.__dict__.copy()
       __a       =   None
       return state

  def __setstate__(					self     :							Dict				,							SCREAMING_SNAKE_CASE__     :							Optional[int]						):





       '''simple docstring'''

       __a       =   d
       __a       =   spm.SentencePieceProcessor()
       self.sp_model.Load(self.vocab_file						)

  def __a						(					self     :							List[Any]				,							SCREAMING_SNAKE_CASE__     :							Tuple				,							SCREAMING_SNAKE_CASE__     :							List[str]=False						):





       '''simple docstring'''

       __a       =   self.sp_model.EncodeAsPieces(SCREAMING_SNAKE_CASE__						)
       return pieces

  def __a						(					self     :							Optional[Any]				,							SCREAMING_SNAKE_CASE__     :							int						):





       '''simple docstring'''

       return self.sp_model.PieceToId(SCREAMING_SNAKE_CASE__						)

  def __a						(					self     :							List[Any]				,							SCREAMING_SNAKE_CASE__     :							List[Any]						):





       '''simple docstring'''

       return self.sp_model.IdToPiece(SCREAMING_SNAKE_CASE__						)

  def __a						(					self     :							List[str]				,							SCREAMING_SNAKE_CASE__     :							str						):





       '''simple docstring'''

       __a       =   self.sp_model.decode_pieces(SCREAMING_SNAKE_CASE__						)
       return out_string

  def __a						(					self     :							Tuple				,							SCREAMING_SNAKE_CASE__     :							List[int]				,							SCREAMING_SNAKE_CASE__     :							Optional[List[int]] = None						):





       '''simple docstring'''

       __a       =   [self.sep_token_id]
       __a       =   [self.cls_token_id]
       if token_ids_a is None:
            return cls + token_ids_a + sep
       return cls + token_ids_a + sep + token_ids_a + sep

  def __a						(					self     :							Any				,							SCREAMING_SNAKE_CASE__     :							List[int]				,							SCREAMING_SNAKE_CASE__     :							Optional[List[int]] = None				,							SCREAMING_SNAKE_CASE__     :							bool = False						):





       '''simple docstring'''

       if already_has_special_tokens:
            if token_ids_a is not None:
                 raise ValueError(
                     """You should not supply a second sequence if the provided sequence of """
                     """ids is already formatted with special tokens for the model."""						)
            return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

       if token_ids_a is not None:
            return [1] + ([0] * len(SCREAMING_SNAKE_CASE__						)) + [1] + ([0] * len(SCREAMING_SNAKE_CASE__						)) + [1]
       return [1] + ([0] * len(SCREAMING_SNAKE_CASE__						)) + [1]

  def __a						(					self     :							Union[str, Any]				,							SCREAMING_SNAKE_CASE__     :							List[int]				,							SCREAMING_SNAKE_CASE__     :							Optional[List[int]] = None						):





       '''simple docstring'''

       __a       =   [self.sep_token_id]
       __a       =   [self.cls_token_id]

       if token_ids_a is None:
            return len(cls + token_ids_a + sep						) * [0]
       return len(cls + token_ids_a + sep						) * [0] + len(token_ids_a + sep						) * [1]


  def __a						(					self     :							int				,							SCREAMING_SNAKE_CASE__     :							str				,							SCREAMING_SNAKE_CASE__     :							Optional[str] = None						):





       '''simple docstring'''

       if not os.path.isdir(SCREAMING_SNAKE_CASE__						):
            logger.error("""Vocabulary path ({}) should be a directory""".format(SCREAMING_SNAKE_CASE__						)						)
            return
       __a       =   os.path.join(
           SCREAMING_SNAKE_CASE__				,							(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]						)

       if os.path.abspath(self.vocab_file						) != os.path.abspath(SCREAMING_SNAKE_CASE__						):
            copyfile(self.vocab_file				,							SCREAMING_SNAKE_CASE__						)

       return (out_vocab_file,)






