'''simple docstring'''






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 lowercase_       (		unittest.TestCase			):







   """simple docstring"""

   def __init__(      self      :			Optional[Any], UpperCamelCase__      :			List[str], UpperCamelCase__      :			Any=13, UpperCamelCase__      :			Optional[Any]=7, UpperCamelCase__      :			Tuple=True, UpperCamelCase__      :			int=True, UpperCamelCase__      :			str=True, UpperCamelCase__      :			str=True, UpperCamelCase__      :			Optional[int]=99, UpperCamelCase__      :			Optional[Any]=32, UpperCamelCase__      :			Dict=5, UpperCamelCase__      :			Union[str, Any]=4, UpperCamelCase__      :			List[str]=37, UpperCamelCase__      :			Dict="gelu", UpperCamelCase__      :			Optional[Any]=0.1, UpperCamelCase__      :			List[str]=0.1, UpperCamelCase__      :			Dict=5_12, UpperCamelCase__      :			str=16, UpperCamelCase__      :			Optional[int]=2, UpperCamelCase__      :			Any=0.02, UpperCamelCase__      :			Dict=4, )  ->			int:
        _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 	__UpperCAmelCase   (      self      :			Union[str, Any]     )  ->			Dict:
        _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    		=  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=UpperCamelCase__, initializer_range=self.initializer_range, )

        return config, input_ids, token_type_ids, attention_mask
   def 	__UpperCAmelCase   (      self      :			Optional[int]     )  ->			Optional[Any]:
        _A    		=  self.prepare_config_and_inputs()
        _A    		=  config_and_inputs
        _A    		=  {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
        return config, inputs_dict

   def 	__UpperCAmelCase   (      self      :			List[str]     )  ->			List[str]:
        _A    		=  self.prepare_config_and_inputs()
        _A    		=  config_and_inputs

        _A    		=  True
        _A    		=  floats_tensor([self.batch_size, self.seq_length, self.hidden_size]     )
        _A    		=  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 lowercase_       (		lowerCamelCase__    ,    unittest.TestCase			):







   """simple docstring"""

   __lowerCAmelCase         =   True

   __lowerCAmelCase         =   (
       (
           FlaxRobertaPreLayerNormModel,
           FlaxRobertaPreLayerNormForCausalLM,
           FlaxRobertaPreLayerNormForMaskedLM,
           FlaxRobertaPreLayerNormForSequenceClassification,
           FlaxRobertaPreLayerNormForTokenClassification,
           FlaxRobertaPreLayerNormForMultipleChoice,
           FlaxRobertaPreLayerNormForQuestionAnswering,
       )
       if is_flax_available()
       else ()
   )
   def 	__UpperCAmelCase   (      self      :			int     )  ->			int:
        _A    		=  FlaxRobertaPreLayerNormModelTester(self     )

   @slow
   def 	__UpperCAmelCase   (      self      :			Any     )  ->			Dict:




        for model_class_name in self.all_model_classes:
             _A    		=  model_class_name.from_pretrained('andreasmadsen/efficient_mlm_m0.40', from_pt=UpperCamelCase__     )
             _A    		=  model(np.ones((1, 1)     )     )
             self.assertIsNotNone(UpperCamelCase__     )







@require_flax
class lowercase_       (		unittest.TestCase			):







   """simple docstring"""

   @slow
   def 	__UpperCAmelCase   (      self      :			int     )  ->			List[Any]:
        _A    		=  FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('andreasmadsen/efficient_mlm_m0.40', from_pt=UpperCamelCase__     )

        _A    		=  np.array([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]], dtype=jnp.intaa     )
        _A    		=  model(UpperCamelCase__     )[0]
        _A    		=  [1, 11, 5_02_65]
        self.assertEqual(list(output.shape     ), UpperCamelCase__     )
        # compare the actual values for a slice.
        _A    		=  np.array(
            [[[40.4_880, 18.0_199, -5.2_367], [-1.8_877, -4.0_885, 10.7_085], [-2.2_613, -5.6_110, 7.2_665]]], dtype=np.floataa     )
        self.assertTrue(np.allclose(output[:, :3, :3], UpperCamelCase__, atol=1e-4     )     )

   @slow
   def 	__UpperCAmelCase   (      self      :			Dict     )  ->			List[str]:
        _A    		=  FlaxRobertaPreLayerNormModel.from_pretrained('andreasmadsen/efficient_mlm_m0.40', from_pt=UpperCamelCase__     )

        _A    		=  np.array([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]], dtype=jnp.intaa     )
        _A    		=  model(UpperCamelCase__     )[0]
        # compare the actual values for a slice.
        _A    		=  np.array(
            [[[0.0_208, -0.0_356, 0.0_237], [-0.1_569, -0.0_411, -0.2_626], [0.1_879, 0.0_125, -0.0_089]]], dtype=np.floataa     )
        self.assertTrue(np.allclose(output[:, :3, :3], UpperCamelCase__, atol=1e-4     )     )










import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer


_lowerCAmelCase				:   Optional[Any]						       =						logging.get_logger(__name__)


_lowerCAmelCase				:   List[str]						       =						{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

# See all BART models at https://huggingface.co/models?filter=bart
_lowerCAmelCase				:   int						       =						{
    '''vocab_file''': {
        '''facebook/bart-base''': '''https://huggingface.co/facebook/bart-base/resolve/main/vocab.json''',
        '''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/vocab.json''',
        '''facebook/bart-large-mnli''': '''https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json''',
        '''facebook/bart-large-cnn''': '''https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json''',
        '''facebook/bart-large-xsum''': '''https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json''',
        '''yjernite/bart_eli5''': '''https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''facebook/bart-base''': '''https://huggingface.co/facebook/bart-base/resolve/main/merges.txt''',
        '''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/merges.txt''',
        '''facebook/bart-large-mnli''': '''https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt''',
        '''facebook/bart-large-cnn''': '''https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt''',
        '''facebook/bart-large-xsum''': '''https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt''',
        '''yjernite/bart_eli5''': '''https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''facebook/bart-base''': '''https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json''',
        '''facebook/bart-large''': '''https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json''',
        '''facebook/bart-large-mnli''': '''https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json''',
        '''facebook/bart-large-cnn''': '''https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json''',
        '''facebook/bart-large-xsum''': '''https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json''',
        '''yjernite/bart_eli5''': '''https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json''',
    },
}

_lowerCAmelCase				:   Optional[int]						       =						{
    '''facebook/bart-base''': 1_024,
    '''facebook/bart-large''': 1_024,
    '''facebook/bart-large-mnli''': 1_024,
    '''facebook/bart-large-cnn''': 1_024,
    '''facebook/bart-large-xsum''': 1_024,
    '''yjernite/bart_eli5''': 1_024,
}


class        __magic_name__	(				lowerCamelCase__				):





     """simple docstring"""



     __UpperCamelCase            =  VOCAB_FILES_NAMES
     __UpperCamelCase            =  PRETRAINED_VOCAB_FILES_MAP
     __UpperCamelCase            =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __UpperCamelCase            =  ['''input_ids''', '''attention_mask''']
     __UpperCamelCase            =  BartTokenizer



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





        '''simple docstring'''




        super().__init__(
            snake_case      ,   snake_case      ,   tokenizer_file=snake_case      ,   errors=snake_case      ,   bos_token=snake_case      ,   eos_token=snake_case      ,   sep_token=snake_case      ,   cls_token=snake_case      ,   unk_token=snake_case      ,   pad_token=snake_case      ,   mask_token=snake_case      ,   add_prefix_space=snake_case      ,   trim_offsets=snake_case      ,   **snake_case      ,   )

        A_       :					Optional[Any]        =      json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()	)
        if pre_tok_state.get("add_prefix_space"      ,   snake_case	) != add_prefix_space:
           A_       :					Dict        =      getattr(snake_case      ,   pre_tok_state.pop("type"	)	)
           A_       :					List[str]        =      add_prefix_space
           A_       :					int        =      pre_tok_class(**snake_case	)

        A_       :					Any        =      add_prefix_space

        # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
        A_       :					Tuple        =      "post_processor"
        A_       :					Union[str, Any]        =      getattr(self.backend_tokenizer      ,   snake_case      ,   snake_case	)
        if tokenizer_component_instance:
           A_       :					Any        =      json.loads(tokenizer_component_instance.__getstate__()	)

           # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
           if "sep" in state:
              A_       :					List[Any]        =      tuple(state["sep"]	)
           if "cls" in state:
              A_       :					str        =      tuple(state["cls"]	)

           A_       :					int        =      False

           if state.get("add_prefix_space"      ,   snake_case	) != add_prefix_space:
              A_       :					List[Any]        =      add_prefix_space
              A_       :					Union[str, Any]        =      True

           if state.get("trim_offsets"      ,   snake_case	) != trim_offsets:
              A_       :					int        =      trim_offsets
              A_       :					str        =      True

           if changes_to_apply:
              A_       :					Tuple        =      getattr(snake_case      ,   state.pop("type"	)	)
              A_       :					Union[str, Any]        =      component_class(**snake_case	)
              setattr(self.backend_tokenizer      ,   snake_case      ,   snake_case	)



     @property
     def     SCREAMING_SNAKE_CASE		(				self							:List[Any]	):





        '''simple docstring'''




        if self._mask_token is None:
           if self.verbose:
              logger.error("Using mask_token, but it is not set yet."	)
           return None
        return str(self._mask_token	)



     @mask_token.setter
     def     SCREAMING_SNAKE_CASE		(				self							:Tuple      ,   snake_case							:Dict	):





        '''simple docstring'''




        A_       :					Optional[int]        =      AddedToken(snake_case      ,   lstrip=snake_case      ,   rstrip=snake_case	) if isinstance(snake_case      ,   snake_case	) else value
        A_       :					List[Any]        =      value



     def     SCREAMING_SNAKE_CASE		(				self							:Tuple      ,   *snake_case							:str      ,   **snake_case							:str	):





        '''simple docstring'''




        A_       :					Tuple        =      kwargs.get("is_split_into_words"      ,   snake_case	)

        if is_split_into_words and not self.add_prefix_space:
           raise ValueError(
               f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
               "to use it with pretokenized inputs."	)

        return super()._batch_encode_plus(*snake_case      ,   **snake_case	)



     def     SCREAMING_SNAKE_CASE		(				self							:Union[str, Any]      ,   *snake_case							:Any      ,   **snake_case							:Optional[Any]	):





        '''simple docstring'''




        A_       :					List[Any]        =      kwargs.get("is_split_into_words"      ,   snake_case	)

        if is_split_into_words and not self.add_prefix_space:
           raise ValueError(
               f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
               "to use it with pretokenized inputs."	)

        return super()._encode_plus(*snake_case      ,   **snake_case	)



     def     SCREAMING_SNAKE_CASE		(				self							:int      ,   snake_case							:str      ,   snake_case							:Optional[str] = None	):





        '''simple docstring'''




        A_       :					Any        =      self._tokenizer.model.save(snake_case      ,   name=snake_case	)
        return tuple(snake_case	)



     def     SCREAMING_SNAKE_CASE		(				self							:Dict      ,   snake_case							:List[str]      ,   snake_case							:Optional[int]=None	):





        '''simple docstring'''




        A_       :					str        =      [self.bos_token_id] + token_ids_a + [self.eos_token_id]
        if token_ids_a is None:
           return output

        return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]





     def     SCREAMING_SNAKE_CASE		(				self							:int      ,   snake_case							:List[int]      ,   snake_case							:Optional[List[int]] = None	):





        '''simple docstring'''




        A_       :					Dict        =      [self.sep_token_id]
        A_       :					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]
