"""simple docstring"""







from __future__ import annotations

import unittest

from transformers import RoFormerConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
       import tensorflow as tf

       from transformers import (
           TFRoFormerForCausalLM,
           TFRoFormerForMaskedLM,
           TFRoFormerForMultipleChoice,
           TFRoFormerForQuestionAnswering,
           TFRoFormerForSequenceClassification,
           TFRoFormerForTokenClassification,
           TFRoFormerModel,
       )
       from transformers.models.roformer.modeling_tf_roformer import (
           TFRoFormerSelfAttention,
           TFRoFormerSinusoidalPositionalEmbedding,
       )







class        UpperCamelCase		:
   def __init__(			self     ,__UpperCamelCase     ,__UpperCamelCase=13     ,__UpperCamelCase=7     ,__UpperCamelCase=True     ,__UpperCamelCase=True     ,__UpperCamelCase=True     ,__UpperCamelCase=True     ,__UpperCamelCase=99     ,__UpperCamelCase=32     ,__UpperCamelCase=2     ,__UpperCamelCase=4     ,__UpperCamelCase=37     ,__UpperCamelCase="gelu"     ,__UpperCamelCase=0.1     ,__UpperCamelCase=0.1     ,__UpperCamelCase=512     ,__UpperCamelCase=16     ,__UpperCamelCase=2     ,__UpperCamelCase=0.02     ,__UpperCamelCase=3     ,__UpperCamelCase=4     ,__UpperCamelCase=None     ,)	->      Tuple:


        '''simple docstring'''


        lowercase_	:				List[str]													=	parent
        lowercase_	:				Optional[Any]													=	13
        lowercase_	:				Any													=	7
        lowercase_	:				Dict													=	True
        lowercase_	:				List[Any]													=	True
        lowercase_	:				Union[str, Any]													=	True
        lowercase_	:				List[str]													=	True
        lowercase_	:				Optional[Any]													=	99
        lowercase_	:				Optional[Any]													=	32
        lowercase_	:				Tuple													=	2
        lowercase_	:				List[str]													=	4
        lowercase_	:				List[Any]													=	37
        lowercase_	:				Tuple													=	'gelu'
        lowercase_	:				str													=	0.1
        lowercase_	:				Tuple													=	0.1
        lowercase_	:				int													=	512
        lowercase_	:				str													=	16
        lowercase_	:				str													=	2
        lowercase_	:				List[str]													=	0.02
        lowercase_	:				Optional[int]													=	3
        lowercase_	:				List[Any]													=	4
        lowercase_	:				str													=	None
   def 			_UpperCAmelCase   (			self			)	->      List[Any]:


        '''simple docstring'''


        lowercase_	:				Union[str, Any]													=	ids_tensor([self.batch_size, self.seq_length]     ,self.vocab_size			)

        lowercase_	:				Optional[Any]													=	None
        if self.use_input_mask:
             lowercase_	:				Optional[Any]													=	random_attention_mask([self.batch_size, self.seq_length]			)

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

        lowercase_	:				Tuple													=	None
        lowercase_	:				Tuple													=	None
        lowercase_	:				Any													=	None
        if self.use_labels:
             lowercase_	:				Optional[int]													=	ids_tensor([self.batch_size]     ,self.type_sequence_label_size			)
             lowercase_	:				int													=	ids_tensor([self.batch_size, self.seq_length]     ,self.num_labels			)
             lowercase_	:				List[Any]													=	ids_tensor([self.batch_size]     ,self.num_choices			)

        lowercase_	:				List[str]													=	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     ,initializer_range=self.initializer_range     ,return_dict=__UpperCamelCase     ,)

        return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase			)	->      str:


        '''simple docstring'''


        lowercase_	:				List[Any]													=	TFRoFormerModel(config=__UpperCamelCase			)
        lowercase_	:				Tuple													=	{'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}

        lowercase_	:				Union[str, Any]													=	[input_ids, input_mask]
        lowercase_	:				Any													=	model(__UpperCamelCase			)

        lowercase_	:				Optional[Any]													=	model(__UpperCamelCase			)

        self.parent.assertEqual(result.last_hidden_state.shape     ,(self.batch_size, self.seq_length, self.hidden_size)			)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase			)	->      str:


        '''simple docstring'''


        lowercase_	:				Tuple													=	True
        lowercase_	:				Optional[Any]													=	TFRoFormerForCausalLM(config=__UpperCamelCase			)
        lowercase_	:				List[str]													=	{
            'input_ids': input_ids,
            'attention_mask': input_mask,
            'token_type_ids': token_type_ids,
        }
        lowercase_	:				Union[str, Any]													=	model(__UpperCamelCase			)['logits']
        self.parent.assertListEqual(
            list(prediction_scores.numpy().shape			)     ,[self.batch_size, self.seq_length, self.vocab_size]			)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase			)	->      Optional[Any]:


        '''simple docstring'''


        lowercase_	:				Tuple													=	TFRoFormerForMaskedLM(config=__UpperCamelCase			)
        lowercase_	:				List[Any]													=	{
            'input_ids': input_ids,
            'attention_mask': input_mask,
            'token_type_ids': token_type_ids,
        }
        lowercase_	:				Any													=	model(__UpperCamelCase			)
        self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.seq_length, self.vocab_size)			)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase			)	->      Optional[Any]:


        '''simple docstring'''


        lowercase_	:				Optional[int]													=	self.num_labels
        lowercase_	:				Optional[int]													=	TFRoFormerForSequenceClassification(config=__UpperCamelCase			)
        lowercase_	:				List[Any]													=	{
            'input_ids': input_ids,
            'attention_mask': input_mask,
            'token_type_ids': token_type_ids,
        }

        lowercase_	:				int													=	model(__UpperCamelCase			)
        self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.num_labels)			)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase			)	->      Union[str, Any]:


        '''simple docstring'''


        lowercase_	:				Dict													=	self.num_choices
        lowercase_	:				Tuple													=	TFRoFormerForMultipleChoice(config=__UpperCamelCase			)
        lowercase_	:				List[str]													=	tf.tile(tf.expand_dims(__UpperCamelCase     ,1			)     ,(1, self.num_choices, 1)			)
        lowercase_	:				Dict													=	tf.tile(tf.expand_dims(__UpperCamelCase     ,1			)     ,(1, self.num_choices, 1)			)
        lowercase_	:				str													=	tf.tile(tf.expand_dims(__UpperCamelCase     ,1			)     ,(1, self.num_choices, 1)			)
        lowercase_	:				Optional[Any]													=	{
            'input_ids': multiple_choice_inputs_ids,
            'attention_mask': multiple_choice_input_mask,
            'token_type_ids': multiple_choice_token_type_ids,
        }
        lowercase_	:				Optional[int]													=	model(__UpperCamelCase			)
        self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.num_choices)			)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase			)	->      Optional[int]:


        '''simple docstring'''


        lowercase_	:				List[Any]													=	self.num_labels
        lowercase_	:				Optional[int]													=	TFRoFormerForTokenClassification(config=__UpperCamelCase			)
        lowercase_	:				Tuple													=	{
            'input_ids': input_ids,
            'attention_mask': input_mask,
            'token_type_ids': token_type_ids,
        }
        lowercase_	:				List[str]													=	model(__UpperCamelCase			)
        self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.seq_length, self.num_labels)			)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase			)	->      Dict:


        '''simple docstring'''


        lowercase_	:				List[Any]													=	TFRoFormerForQuestionAnswering(config=__UpperCamelCase			)
        lowercase_	:				int													=	{
            'input_ids': input_ids,
            'attention_mask': input_mask,
            'token_type_ids': token_type_ids,
        }

        lowercase_	:				Tuple													=	model(__UpperCamelCase			)
        self.parent.assertEqual(result.start_logits.shape     ,(self.batch_size, self.seq_length)			)
        self.parent.assertEqual(result.end_logits.shape     ,(self.batch_size, self.seq_length)			)







   def 			_UpperCAmelCase   (			self			)	->      Optional[int]:


        '''simple docstring'''


        lowercase_	:				List[Any]													=	self.prepare_config_and_inputs()
        (
            (
            lowercase_
        )							,  (
            lowercase_
        )							,  (
            lowercase_
        )							,  (
            lowercase_
        )							,  (
            lowercase_
        )							,  (
            lowercase_
        )							,  (
            lowercase_
        )							,  
        )	:				List[Any]													=	config_and_inputs
        lowercase_	:				List[Any]													=	{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
        return config, inputs_dict







@require_tf
class        UpperCamelCase		(			lowercase_						,      lowercase_						,      unittest.TestCase  ):
   lowercase							=  (
       (
           TFRoFormerModel,
           TFRoFormerForCausalLM,
           TFRoFormerForMaskedLM,
           TFRoFormerForQuestionAnswering,
           TFRoFormerForSequenceClassification,
           TFRoFormerForTokenClassification,
           TFRoFormerForMultipleChoice,
       )
       if is_tf_available()
       else ()
   )
   lowercase							=  (
       {
           'feature-extraction': TFRoFormerModel,
           'fill-mask': TFRoFormerForMaskedLM,
           'question-answering': TFRoFormerForQuestionAnswering,
           'text-classification': TFRoFormerForSequenceClassification,
           'text-generation': TFRoFormerForCausalLM,
           'token-classification': TFRoFormerForTokenClassification,
           'zero-shot': TFRoFormerForSequenceClassification,
       }
       if is_tf_available()
       else {}
   )

   lowercase							=  False
   lowercase							=  False
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase			)	->      Optional[int]:


        '''simple docstring'''


        if pipeline_test_casse_name == "TextGenerationPipelineTests":
             return True

        return False
   def 			_UpperCAmelCase   (			self			)	->      Union[str, Any]:


        '''simple docstring'''


        lowercase_	:				Tuple													=	TFRoFormerModelTester(self			)
        lowercase_	:				List[Any]													=	ConfigTester(self     ,config_class=__UpperCamelCase     ,hidden_size=37			)
   def 			_UpperCAmelCase   (			self			)	->      Union[str, Any]:


        '''simple docstring'''


        self.config_tester.run_common_tests()
   def 			_UpperCAmelCase   (			self			)	->      Optional[Any]:


        '''simple docstring'''


        lowercase_	:				Optional[Any]													=	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__UpperCamelCase			)
   def 			_UpperCAmelCase   (			self			)	->      Tuple:


        '''simple docstring'''


        lowercase_	:				Optional[Any]													=	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_lm(*__UpperCamelCase			)
   def 			_UpperCAmelCase   (			self			)	->      Optional[Any]:


        '''simple docstring'''


        lowercase_	:				str													=	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_lm_head(*__UpperCamelCase			)
   def 			_UpperCAmelCase   (			self			)	->      List[Any]:


        '''simple docstring'''


        lowercase_	:				Any													=	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_multiple_choice(*__UpperCamelCase			)
   def 			_UpperCAmelCase   (			self			)	->      Union[str, Any]:


        '''simple docstring'''


        lowercase_	:				Any													=	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*__UpperCamelCase			)
   def 			_UpperCAmelCase   (			self			)	->      Optional[Any]:


        '''simple docstring'''


        lowercase_	:				Union[str, Any]													=	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*__UpperCamelCase			)
   def 			_UpperCAmelCase   (			self			)	->      Optional[Any]:


        '''simple docstring'''


        lowercase_	:				str													=	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*__UpperCamelCase			)







   @slow
   def 			_UpperCAmelCase   (			self			)	->      Optional[int]:


        '''simple docstring'''


        lowercase_	:				Union[str, Any]													=	TFRoFormerModel.from_pretrained('junnyu/roformer_chinese_base'			)
        self.assertIsNotNone(__UpperCamelCase			)







@require_tf
class        UpperCamelCase		(			unittest.TestCase  ):
   @slow
   def 			_UpperCAmelCase   (			self			)	->      str:


        '''simple docstring'''


        lowercase_	:				Tuple													=	TFRoFormerForMaskedLM.from_pretrained('junnyu/roformer_chinese_base'			)
        lowercase_	:				Optional[Any]													=	tf.constant([[0, 1, 2, 3, 4, 5]]			)
        lowercase_	:				List[Any]													=	model(__UpperCamelCase			)[0]

        # TODO Replace vocab size
        lowercase_	:				List[str]													=	5_0000

        lowercase_	:				List[Any]													=	[1, 6, vocab_size]
        self.assertEqual(output.shape     ,__UpperCamelCase			)

        print(output[:, :3, :3]			)

        # TODO Replace values below with what was printed above.
        lowercase_	:				Optional[int]													=	tf.constant(
            [
                [
                    [-0.1205_3341, -1.026_4901, 0.2922_1946],
                    [-1.513_3783, 0.19_7433, 0.1519_0607],
                    [-5.013_5403, -3.90_0256, -0.8403_8764],
                ]
            ]			)
        tf.debugging.assert_near(output[:, :3, :3]     ,__UpperCamelCase     ,atol=1e-4			)







@require_tf
class        UpperCamelCase		(			unittest.TestCase  ):
   lowercase							=  1e-4
   def 			_UpperCAmelCase   (			self			)	->      Optional[int]:


        '''simple docstring'''


        lowercase_	:				int													=	tf.constant([[4, 10]]			)
        lowercase_	:				int													=	TFRoFormerSinusoidalPositionalEmbedding(num_positions=6     ,embedding_dim=6			)

        lowercase_	:				Any													=	emba(input_ids.shape			)
        lowercase_	:				Dict													=	tf.constant(
            [[0.0000, 0.0000, 0.0000, 1.0000, 1.0000, 1.0000], [0.8415, 0.0464, 0.0022, 0.5403, 0.9989, 1.0000]]			)

        tf.debugging.assert_near(__UpperCamelCase     ,__UpperCamelCase     ,atol=self.tolerance			)







   def 			_UpperCAmelCase   (			self			)	->      Union[str, Any]:


        '''simple docstring'''


        lowercase_	:				int													=	tf.constant(
            [
                [0.0000, 0.0000, 0.0000, 0.0000, 0.0000],
                [0.8415, 0.8219, 0.8020, 0.7819, 0.7617],
                [0.9093, 0.9364, 0.9581, 0.9749, 0.9870],
            ]			)
        lowercase_	:				Optional[Any]													=	TFRoFormerSinusoidalPositionalEmbedding(num_positions=512     ,embedding_dim=512			)
        emba([2, 16, 512]			)
        lowercase_	:				str													=	emba.weight[:3, :5]

        tf.debugging.assert_near(__UpperCamelCase     ,__UpperCamelCase     ,atol=self.tolerance			)







@require_tf
class        UpperCamelCase		(			unittest.TestCase  ):
   lowercase							=  1e-4
   def 			_UpperCAmelCase   (			self			)	->      int:


        '''simple docstring'''


        lowercase_	:				Optional[Any]													=	tf.reshape(tf.range(2 * 12 * 16 * 64     ,dtype=tf.floataa			)     ,shape=(2, 12, 16, 64)			) / 100

        lowercase_	:				int													=	-tf.reshape(tf.range(2 * 12 * 16 * 64     ,dtype=tf.floataa			)     ,shape=(2, 12, 16, 64)			) / 100

        lowercase_	:				Optional[int]													=	TFRoFormerSinusoidalPositionalEmbedding(num_positions=32     ,embedding_dim=64			)
        lowercase_	:				int													=	embed_positions([2, 16, 768]			)[None, None, :, :]

        lowercase_							,  lowercase_	:				int													=	TFRoFormerSelfAttention.apply_rotary_position_embeddings(
            __UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase			)

        lowercase_	:				Tuple													=	tf.constant(
            [
                [0.0000, 0.0100, 0.0200, 0.0300, 0.0400, 0.0500, 0.0600, 0.0700],
                [-0.2012, 0.8897, 0.0263, 0.9401, 0.2074, 0.9463, 0.3481, 0.9343],
                [-1.7057, 0.6271, -1.2145, 1.3897, -0.6303, 1.7647, -0.1173, 1.8985],
                [-2.1731, -1.6397, -2.7358, 0.2854, -2.1840, 1.7183, -1.3018, 2.4871],
                [0.2717, -3.6173, -2.9206, -2.1988, -3.6638, 0.3858, -2.9155, 2.2980],
                [3.9859, -2.1580, -0.7984, -4.4904, -4.1181, -2.0252, -4.4782, 1.1253],
            ]			)
        lowercase_	:				List[str]													=	tf.constant(
            [
                [0.0000, -0.0100, -0.0200, -0.0300, -0.0400, -0.0500, -0.0600, -0.0700],
                [0.2012, -0.8897, -0.0263, -0.9401, -0.2074, -0.9463, -0.3481, -0.9343],
                [1.7057, -0.6271, 1.2145, -1.3897, 0.6303, -1.7647, 0.1173, -1.8985],
                [2.1731, 1.6397, 2.7358, -0.2854, 2.1840, -1.7183, 1.3018, -2.4871],
                [-0.2717, 3.6173, 2.9206, 2.1988, 3.6638, -0.3858, 2.9155, -2.2980],
                [-3.9859, 2.1580, 0.7984, 4.4904, 4.1181, 2.0252, 4.4782, -1.1253],
            ]			)

        tf.debugging.assert_near(query_layer[0, 0, :6, :8]     ,__UpperCamelCase     ,atol=self.tolerance			)
        tf.debugging.assert_near(key_layer[0, 0, :6, :8]     ,__UpperCamelCase     ,atol=self.tolerance			)


"""simple docstring"""







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

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
       from .tokenization_mbart import MBartTokenizer
else:
       __SCREAMING_SNAKE_CASE    	=None


__SCREAMING_SNAKE_CASE    	=logging.get_logger(__name__)


__SCREAMING_SNAKE_CASE    	={"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

__SCREAMING_SNAKE_CASE    	={
    "vocab_file": {
        "facebook/mbart-large-en-ro": (
            "https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model"
        ),
        "facebook/mbart-large-cc25": (
            "https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model"
        ),
    },
    "tokenizer_file": {
        "facebook/mbart-large-en-ro": "https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json",
        "facebook/mbart-large-cc25": "https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json",
    },
}

__SCREAMING_SNAKE_CASE    	={
    "facebook/mbart-large-en-ro": 1024,
    "facebook/mbart-large-cc25": 1024,
}

# fmt: off
__SCREAMING_SNAKE_CASE    	=["ar_AR", "cs_CZ", "de_DE", "en_XX", "es_XX", "et_EE", "fi_FI", "fr_XX", "gu_IN", "hi_IN", "it_IT", "ja_XX", "kk_KZ", "ko_KR", "lt_LT", "lv_LV", "my_MM", "ne_NP", "nl_XX", "ro_RO", "ru_RU", "si_LK", "tr_TR", "vi_VN", "zh_CN"]







class        UpperCamelCase		(			lowercase_  ):
   lowercase							=  VOCAB_FILES_NAMES
   lowercase							=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowercase							=  PRETRAINED_VOCAB_FILES_MAP
   lowercase							=  ['input_ids', 'attention_mask']
   lowercase							=  MBartTokenizer

   lowercase							=  []
   lowercase							=  []
   def __init__(			self     ,__UpperCamelCase=None     ,__UpperCamelCase=None     ,__UpperCamelCase="<s>"     ,__UpperCamelCase="</s>"     ,__UpperCamelCase="</s>"     ,__UpperCamelCase="<s>"     ,__UpperCamelCase="<unk>"     ,__UpperCamelCase="<pad>"     ,__UpperCamelCase="<mask>"     ,__UpperCamelCase=None     ,__UpperCamelCase=None     ,__UpperCamelCase=None     ,**__UpperCamelCase     ,)	->      List[str]:


        '''simple docstring'''


        lowercase_	:				str													=	AddedToken(__UpperCamelCase     ,lstrip=__UpperCamelCase     ,rstrip=__UpperCamelCase			) if isinstance(__UpperCamelCase     ,__UpperCamelCase			) else mask_token

        super().__init__(
            vocab_file=__UpperCamelCase     ,tokenizer_file=__UpperCamelCase     ,bos_token=__UpperCamelCase     ,eos_token=__UpperCamelCase     ,sep_token=__UpperCamelCase     ,cls_token=__UpperCamelCase     ,unk_token=__UpperCamelCase     ,pad_token=__UpperCamelCase     ,mask_token=__UpperCamelCase     ,src_lang=__UpperCamelCase     ,tgt_lang=__UpperCamelCase     ,additional_special_tokens=__UpperCamelCase     ,**__UpperCamelCase     ,)

        lowercase_	:				str													=	vocab_file
        lowercase_	:				Optional[Any]													=	False if not self.vocab_file else True

        lowercase_	:				List[str]													=	FAIRSEQ_LANGUAGE_CODES.copy()

        if additional_special_tokens is not None:
             # Only add those special tokens if they are not already there.
             _additional_special_tokens.extend(
                 [t for t in additional_special_tokens if t not in _additional_special_tokens]			)

        self.add_special_tokens({'additional_special_tokens': _additional_special_tokens}			)
        lowercase_	:				List[Any]													=	{
            lang_code: self.convert_tokens_to_ids(__UpperCamelCase			) for lang_code in FAIRSEQ_LANGUAGE_CODES
        }

        lowercase_	:				Dict													=	src_lang if src_lang is not None else 'en_XX'
        lowercase_	:				Union[str, Any]													=	self.convert_tokens_to_ids(self._src_lang			)
        lowercase_	:				int													=	tgt_lang
        self.set_src_lang_special_tokens(self._src_lang			)
   @property
   def 			_UpperCAmelCase   (			self			)	->      str:


        '''simple docstring'''


        return self._src_lang
   @src_lang.setter
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase			)	->      None:


        '''simple docstring'''


        lowercase_	:				Optional[int]													=	new_src_lang
        self.set_src_lang_special_tokens(self._src_lang			)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase = None			)	->      List[int]:


        '''simple docstring'''


        if token_ids_a is None:
             return self.prefix_tokens + token_ids_a + self.suffix_tokens
        # We don't expect to process pairs, but leave the pair logic for API consistency
        return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase = None			)	->      List[int]:


        '''simple docstring'''


        lowercase_	:				List[Any]													=	[self.sep_token_id]
        lowercase_	:				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]
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase     ,**__UpperCamelCase			)	->      Union[str, Any]:


        '''simple docstring'''


        if src_lang is None or tgt_lang is None:
             raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model'			)
        lowercase_	:				Dict													=	src_lang
        lowercase_	:				List[Any]													=	self(__UpperCamelCase     ,add_special_tokens=__UpperCamelCase     ,return_tensors=__UpperCamelCase     ,**__UpperCamelCase			)
        lowercase_	:				Optional[Any]													=	self.convert_tokens_to_ids(__UpperCamelCase			)
        lowercase_	:				Dict													=	tgt_lang_id
        return inputs
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase = "en_XX"     ,__UpperCamelCase = None     ,__UpperCamelCase = "ro_RO"     ,**__UpperCamelCase     ,)	->      BatchEncoding:


        '''simple docstring'''


        lowercase_	:				Union[str, Any]													=	src_lang
        lowercase_	:				List[Any]													=	tgt_lang
        return super().prepare_seqaseq_batch(__UpperCamelCase     ,__UpperCamelCase     ,**__UpperCamelCase			)
   def 			_UpperCAmelCase   (			self			)	->      Union[str, Any]:


        '''simple docstring'''


        return self.set_src_lang_special_tokens(self.src_lang			)
   def 			_UpperCAmelCase   (			self			)	->      int:


        '''simple docstring'''


        return self.set_tgt_lang_special_tokens(self.tgt_lang			)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase			)	->      None:


        '''simple docstring'''


        lowercase_	:				Any													=	self.convert_tokens_to_ids(__UpperCamelCase			)
        lowercase_	:				Optional[Any]													=	[]
        lowercase_	:				int													=	[self.eos_token_id, self.cur_lang_code]

        lowercase_	:				Any													=	self.convert_ids_to_tokens(self.prefix_tokens			)
        lowercase_	:				int													=	self.convert_ids_to_tokens(self.suffix_tokens			)

        lowercase_	:				Union[str, Any]													=	processors.TemplateProcessing(
            single=prefix_tokens_str + ['$A'] + suffix_tokens_str     ,pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str     ,special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str     ,self.prefix_tokens + self.suffix_tokens			)			)     ,)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase			)	->      None:


        '''simple docstring'''


        lowercase_	:				Optional[int]													=	self.convert_tokens_to_ids(__UpperCamelCase			)
        lowercase_	:				str													=	[]
        lowercase_	:				Dict													=	[self.eos_token_id, self.cur_lang_code]

        lowercase_	:				str													=	self.convert_ids_to_tokens(self.prefix_tokens			)
        lowercase_	:				List[Any]													=	self.convert_ids_to_tokens(self.suffix_tokens			)

        lowercase_	:				int													=	processors.TemplateProcessing(
            single=prefix_tokens_str + ['$A'] + suffix_tokens_str     ,pair=prefix_tokens_str + ['$A', '$B'] + suffix_tokens_str     ,special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str     ,self.prefix_tokens + self.suffix_tokens			)			)     ,)







   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase = None			)	->      Tuple[str]:


        '''simple docstring'''


        if not self.can_save_slow_tokenizer:
             raise ValueError(
                 'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
                 'tokenizer.'			)

        if not os.path.isdir(__UpperCamelCase			):
             logger.error(f'''Vocabulary path ({save_directory}) should be a directory.'''			)
             return
        lowercase_	:				Tuple													=	os.path.join(
            __UpperCamelCase     ,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']			)

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

        return (out_vocab_file,)

