


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       __magic_name__	(     unittest.TestCase):


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





      '''simple docstring'''






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


     def      UpperCAmelCase__	(      self							:     Dict			)  ->      Tuple:





      '''simple docstring'''






      UpperCamelCase__					:    Optional[int]          =   ids_tensor([self.batch_size, self.seq_length]							,	self.vocab_size			)

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

      UpperCamelCase__					:    List[Any]          =   None
      if self.use_token_type_ids:
       UpperCamelCase__					:    Tuple          =   ids_tensor([self.batch_size, self.seq_length]							,	self.type_vocab_size			)

      UpperCamelCase__					:    Union[str, Any]          =   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=lowerCamelCase__							,	initializer_range=self.initializer_range							,	)

      return config, input_ids, token_type_ids, attention_mask


     def      UpperCAmelCase__	(      self							:     List[Any]			)  ->      Tuple:





      '''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      UpperCAmelCase__	(      self							:     Optional[Any]			)  ->      List[Any]:





      '''simple docstring'''






      UpperCamelCase__					:    List[Any]          =   self.prepare_config_and_inputs()
      UpperCamelCase__       ,				UpperCamelCase__       ,				UpperCamelCase__       ,				UpperCamelCase__					:    Union[str, Any]          =   config_and_inputs

      UpperCamelCase__					:    int          =   True
      UpperCamelCase__					:    List[Any]          =   floats_tensor([self.batch_size, self.seq_length, self.hidden_size]			)
      UpperCamelCase__					:    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       __magic_name__	(     __lowerCAmelCase					,				unittest.TestCase):
     A:		List[Any]			=      True

     A:		Optional[Any]			=      (
         (
             FlaxRobertaPreLayerNormModel,
             FlaxRobertaPreLayerNormForCausalLM,
             FlaxRobertaPreLayerNormForMaskedLM,
             FlaxRobertaPreLayerNormForSequenceClassification,
             FlaxRobertaPreLayerNormForTokenClassification,
             FlaxRobertaPreLayerNormForMultipleChoice,
             FlaxRobertaPreLayerNormForQuestionAnswering,
         )
         if is_flax_available()
         else ()
     )


     def      UpperCAmelCase__	(      self							:     int			)  ->      Any:





      '''simple docstring'''






      UpperCamelCase__					:    Any          =   FlaxRobertaPreLayerNormModelTester(self			)


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





      '''simple docstring'''






      for model_class_name in self.all_model_classes:
       UpperCamelCase__					:    Optional[int]          =   model_class_name.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''							,	from_pt=lowerCamelCase__			)
       UpperCamelCase__					:    Dict          =   model(np.ones((1, 1)			)			)
       self.assertIsNotNone(lowerCamelCase__			)



@require_flax
class       __magic_name__	(     unittest.TestCase):


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





      '''simple docstring'''






      UpperCamelCase__					:    Union[str, Any]          =   FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''							,	from_pt=lowerCamelCase__			)

      UpperCamelCase__					:    Optional[Any]          =   np.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]							,	dtype=jnp.intaa			)
      UpperCamelCase__					:    List[str]          =   model(lowerCamelCase__			)[0]
      UpperCamelCase__					:    int          =   [1, 11, 50265]
      self.assertEqual(list(output.shape			)							,	lowerCamelCase__			)
      # compare the actual values for a slice.
      UpperCamelCase__					:    Union[str, Any]          =   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]							,	lowerCamelCase__							,	atol=1E-4			)			)


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





      '''simple docstring'''






      UpperCamelCase__					:    Union[str, Any]          =   FlaxRobertaPreLayerNormModel.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''							,	from_pt=lowerCamelCase__			)

      UpperCamelCase__					:    Dict          =   np.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]							,	dtype=jnp.intaa			)
      UpperCamelCase__					:    str          =   model(lowerCamelCase__			)[0]
      # compare the actual values for a slice.
      UpperCamelCase__					:    Dict          =   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]							,	lowerCamelCase__							,	atol=1E-4			)			)






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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__UpperCamelCase       :       Tuple						       =				logging.get_logger(__name__)

__UpperCamelCase       :       Union[str, Any]						       =				{"vocab_file": "sentencepiece.bpe.model"}

__UpperCamelCase       :       List[Any]						       =				{
    "vocab_file": {
        "moussaKam/mbarthez": "https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model",
        "moussaKam/barthez": "https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model",
        "moussaKam/barthez-orangesum-title": (
            "https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"
        ),
    },
}

__UpperCamelCase       :       int						       =				{
    "moussaKam/mbarthez": 1024,
    "moussaKam/barthez": 1024,
    "moussaKam/barthez-orangesum-title": 1024,
}

__UpperCamelCase       :       int						       =				"▁"

class       __magic_name__	(     __lowerCAmelCase):
     A:		Tuple			=      VOCAB_FILES_NAMES
     A:		Optional[int]			=      PRETRAINED_VOCAB_FILES_MAP
     A:		str			=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     A:		Union[str, Any]			=      ["input_ids", "attention_mask"]


     def __init__(      self							:     Union[str, Any]							,	lowerCamelCase__							:     Optional[int]							,	lowerCamelCase__							:     Tuple="<s>"							,	lowerCamelCase__							:     Tuple="</s>"							,	lowerCamelCase__							:     int="</s>"							,	lowerCamelCase__							:     Optional[Any]="<s>"							,	lowerCamelCase__							:     List[str]="<unk>"							,	lowerCamelCase__							:     str="<pad>"							,	lowerCamelCase__							:     int="<mask>"							,	lowerCamelCase__							:     Optional[Dict[str, Any]] = None							,	**lowerCamelCase__							:     Any							,	)  ->      None:





      '''simple docstring'''






      UpperCamelCase__					:    str          =   AddedToken(lowerCamelCase__							,	lstrip=lowerCamelCase__							,	rstrip=lowerCamelCase__			) if isinstance(lowerCamelCase__							,	lowerCamelCase__			) else mask_token

      UpperCamelCase__					:    Optional[int]          =   {} if sp_model_kwargs is None else sp_model_kwargs

      super().__init__(
          bos_token=lowerCamelCase__							,	eos_token=lowerCamelCase__							,	unk_token=lowerCamelCase__							,	sep_token=lowerCamelCase__							,	cls_token=lowerCamelCase__							,	pad_token=lowerCamelCase__							,	mask_token=lowerCamelCase__							,	sp_model_kwargs=self.sp_model_kwargs							,	**lowerCamelCase__							,	)

      UpperCamelCase__					:    Any          =   vocab_file
      UpperCamelCase__					:    str          =   spm.SentencePieceProcessor(**self.sp_model_kwargs			)
      self.sp_model.Load(str(lowerCamelCase__			)			)

      UpperCamelCase__					:    Any          =   {'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

      UpperCamelCase__					:    Tuple          =   len(self.sp_model			) - 1
      UpperCamelCase__					:    str          =   {v: k for k, v in self.fairseq_tokens_to_ids.items()}


     def      UpperCAmelCase__	(      self							:     List[str]							,	lowerCamelCase__							:     List[int]							,	lowerCamelCase__							:     Optional[List[int]] = None			)  ->      List[int]:





      '''simple docstring'''






      if token_ids_a is None:
       return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
      UpperCamelCase__					:    List[str]          =   [self.cls_token_id]
      UpperCamelCase__					:    Optional[Any]          =   [self.sep_token_id]
      return cls + token_ids_a + sep + sep + token_ids_a + sep


     def      UpperCAmelCase__	(      self							:     Dict							,	lowerCamelCase__							:     List[int]							,	lowerCamelCase__							:     Optional[List[int]] = None							,	lowerCamelCase__							:     bool = False			)  ->      List[int]:





      '''simple docstring'''






      if already_has_special_tokens:
       return super().get_special_tokens_mask(
           token_ids_a=lowerCamelCase__							,	token_ids_a=lowerCamelCase__							,	already_has_special_tokens=lowerCamelCase__			)

      if token_ids_a is None:
       return [1] + ([0] * len(lowerCamelCase__			)) + [1]
      return [1] + ([0] * len(lowerCamelCase__			)) + [1, 1] + ([0] * len(lowerCamelCase__			)) + [1]


     def      UpperCAmelCase__	(      self							:     List[str]							,	lowerCamelCase__							:     List[int]							,	lowerCamelCase__							:     Optional[List[int]] = None			)  ->      List[int]:





      '''simple docstring'''






      UpperCamelCase__					:    int          =   [self.sep_token_id]
      UpperCamelCase__					:    List[str]          =   [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 + sep + token_ids_a + sep			) * [0]


     @property
     def      UpperCAmelCase__	(      self							:     Union[str, Any]			)  ->      int:





      '''simple docstring'''






      return len(self.sp_model			)


     def      UpperCAmelCase__	(      self							:     List[str]			)  ->      Optional[int]:





      '''simple docstring'''






      UpperCamelCase__					:    Tuple          =   {self.convert_ids_to_tokens(lowerCamelCase__			): i for i in range(self.vocab_size			)}
      vocab.update(self.added_tokens_encoder			)
      return vocab


     def      UpperCAmelCase__	(      self							:     int							,	lowerCamelCase__							:     str			)  ->      List[str]:





      '''simple docstring'''






      return self.sp_model.encode(lowerCamelCase__							,	out_type=lowerCamelCase__			)


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





      '''simple docstring'''






      if token in self.fairseq_tokens_to_ids:
       return self.fairseq_tokens_to_ids[token]
      UpperCamelCase__					:    List[str]          =   self.sp_model.PieceToId(lowerCamelCase__			)

      return spm_id if spm_id else self.unk_token_id


     def      UpperCAmelCase__	(      self							:     Dict							,	lowerCamelCase__							:     int			)  ->      List[str]:





      '''simple docstring'''






      if index in self.fairseq_ids_to_tokens:
       return self.fairseq_ids_to_tokens[index]
      return self.sp_model.IdToPiece(lowerCamelCase__			)


     def      UpperCAmelCase__	(      self							:     str							,	lowerCamelCase__							:     Tuple			)  ->      List[Any]:





      '''simple docstring'''






      UpperCamelCase__					:    Union[str, Any]          =   []
      UpperCamelCase__					:    Any          =   ''''''
      UpperCamelCase__					:    str          =   False
      for token in tokens:
       # make sure that special tokens are not decoded using sentencepiece model
       if token in self.all_special_tokens:
        if not prev_is_special:
         out_string += " "
        out_string += self.sp_model.decode(lowerCamelCase__			) + token
        UpperCamelCase__					:    str          =   True
        UpperCamelCase__					:    Tuple          =   []
       else:
        current_sub_tokens.append(lowerCamelCase__			)
        UpperCamelCase__					:    Any          =   False
      out_string += self.sp_model.decode(lowerCamelCase__			)
      return out_string.strip()


     def __getstate__(      self							:     Tuple			)  ->      Dict:





      '''simple docstring'''






      UpperCamelCase__					:    str          =   self.__dict__.copy()
      UpperCamelCase__					:    int          =   None
      return state


     def __setstate__(      self							:     Tuple							,	lowerCamelCase__							:     Any			)  ->      str:





      '''simple docstring'''






      UpperCamelCase__					:    List[Any]          =   d

      # for backward compatibility
      if not hasattr(self							,	'''sp_model_kwargs'''			):
       UpperCamelCase__					:    Optional[Any]          =   {}

      UpperCamelCase__					:    Optional[int]          =   spm.SentencePieceProcessor(**self.sp_model_kwargs			)
      self.sp_model.Load(self.vocab_file			)


     def      UpperCAmelCase__	(      self							:     Optional[int]							,	lowerCamelCase__							:     str							,	lowerCamelCase__							:     Optional[str] = None			)  ->      Tuple[str]:





      '''simple docstring'''






      if not os.path.isdir(lowerCamelCase__			):
       logger.error(F"Vocabulary path ({save_directory}) should be a directory"			)
       return
      UpperCamelCase__					:    Any          =   os.path.join(
          lowerCamelCase__							,	(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']			)

      if os.path.abspath(self.vocab_file			) != os.path.abspath(lowerCamelCase__			) and os.path.isfile(self.vocab_file			):
       copyfile(self.vocab_file							,	lowerCamelCase__			)
      elif not os.path.isfile(self.vocab_file			):
       with open(lowerCamelCase__							,	'''wb'''			) as fi:
        UpperCamelCase__					:    int          =   self.sp_model.serialized_model_proto()
        fi.write(lowerCamelCase__			)

      return (out_vocab_file,)


