"""simple docstring"""







import inspect
import unittest

from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch

       from transformers import (
           MODEL_MAPPING,
           SegformerForImageClassification,
           SegformerForSemanticSegmentation,
           SegformerModel,
       )
       from transformers.models.segformer.modeling_segformer import SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
       from PIL import Image

       from transformers import SegformerImageProcessor







class        UpperCamelCase		(			lowercase_  ):
   def 			_UpperCAmelCase   (			self			)	->      Any:


        '''simple docstring'''


        lowercase_	:				Any													=	self.config_class(**self.inputs_dict			)
        self.parent.assertTrue(hasattr(__UpperCamelCase     ,'hidden_sizes'			)			)
        self.parent.assertTrue(hasattr(__UpperCamelCase     ,'num_attention_heads'			)			)
        self.parent.assertTrue(hasattr(__UpperCamelCase     ,'num_encoder_blocks'			)			)







class        UpperCamelCase		:
   def __init__(			self     ,__UpperCamelCase     ,__UpperCamelCase=13     ,__UpperCamelCase=64     ,__UpperCamelCase=3     ,__UpperCamelCase=4     ,__UpperCamelCase=[2, 2, 2, 2]     ,__UpperCamelCase=[8, 4, 2, 1]     ,__UpperCamelCase=[16, 32, 64, 128]     ,__UpperCamelCase=[1, 4, 8, 16]     ,__UpperCamelCase=[1, 2, 4, 8]     ,__UpperCamelCase=True     ,__UpperCamelCase=True     ,__UpperCamelCase="gelu"     ,__UpperCamelCase=0.1     ,__UpperCamelCase=0.1     ,__UpperCamelCase=0.02     ,__UpperCamelCase=3     ,__UpperCamelCase=None     ,)	->      int:


        '''simple docstring'''


        lowercase_	:				int													=	parent
        lowercase_	:				Dict													=	batch_size
        lowercase_	:				Dict													=	image_size
        lowercase_	:				Any													=	num_channels
        lowercase_	:				Dict													=	num_encoder_blocks
        lowercase_	:				Optional[int]													=	sr_ratios
        lowercase_	:				int													=	depths
        lowercase_	:				List[str]													=	hidden_sizes
        lowercase_	:				int													=	downsampling_rates
        lowercase_	:				str													=	num_attention_heads
        lowercase_	:				Dict													=	is_training
        lowercase_	:				Tuple													=	use_labels
        lowercase_	:				Any													=	hidden_act
        lowercase_	:				int													=	hidden_dropout_prob
        lowercase_	:				Any													=	attention_probs_dropout_prob
        lowercase_	:				Optional[int]													=	initializer_range
        lowercase_	:				Dict													=	num_labels
        lowercase_	:				Dict													=	scope
   def 			_UpperCAmelCase   (			self			)	->      Any:


        '''simple docstring'''


        lowercase_	:				Optional[int]													=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

        lowercase_	:				Dict													=	None
        if self.use_labels:
             lowercase_	:				Optional[int]													=	ids_tensor([self.batch_size, self.image_size, self.image_size]     ,self.num_labels			)

        lowercase_	:				Dict													=	self.get_config()
        return config, pixel_values, labels
   def 			_UpperCAmelCase   (			self			)	->      Dict:


        '''simple docstring'''


        return SegformerConfig(
            image_size=self.image_size     ,num_channels=self.num_channels     ,num_encoder_blocks=self.num_encoder_blocks     ,depths=self.depths     ,hidden_sizes=self.hidden_sizes     ,num_attention_heads=self.num_attention_heads     ,hidden_act=self.hidden_act     ,hidden_dropout_prob=self.hidden_dropout_prob     ,attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,initializer_range=self.initializer_range     ,)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase			)	->      int:


        '''simple docstring'''


        lowercase_	:				Optional[int]													=	SegformerModel(config=__UpperCamelCase			)
        model.to(__UpperCamelCase			)
        model.eval()
        lowercase_	:				Optional[int]													=	model(__UpperCamelCase			)
        lowercase_	:				Union[str, Any]													=	self.image_size // (self.downsampling_rates[-1] * 2)
        self.parent.assertEqual(
            result.last_hidden_state.shape     ,(self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)			)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase			)	->      List[Any]:


        '''simple docstring'''


        lowercase_	:				Tuple													=	self.num_labels
        lowercase_	:				Union[str, Any]													=	SegformerForSemanticSegmentation(__UpperCamelCase			)
        model.to(__UpperCamelCase			)
        model.eval()
        lowercase_	:				Tuple													=	model(__UpperCamelCase			)
        self.parent.assertEqual(
            result.logits.shape     ,(self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4)			)
        lowercase_	:				Any													=	model(__UpperCamelCase     ,labels=__UpperCamelCase			)
        self.parent.assertEqual(
            result.logits.shape     ,(self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4)			)
        self.parent.assertGreater(result.loss     ,0.0			)
   def 			_UpperCAmelCase   (			self     ,__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase			)	->      Union[str, Any]:


        '''simple docstring'''


        lowercase_	:				Optional[int]													=	1
        lowercase_	:				int													=	SegformerForSemanticSegmentation(config=__UpperCamelCase			)
        model.to(__UpperCamelCase			)
        model.eval()
        lowercase_	:				List[str]													=	torch.randint(0     ,1     ,(self.batch_size, self.image_size, self.image_size)			).to(__UpperCamelCase			)
        lowercase_	:				Dict													=	model(__UpperCamelCase     ,labels=__UpperCamelCase			)
        self.parent.assertGreater(result.loss     ,0.0			)







   def 			_UpperCAmelCase   (			self			)	->      Tuple:


        '''simple docstring'''


        lowercase_	:				Dict													=	self.prepare_config_and_inputs()
        lowercase_							,  lowercase_							,  lowercase_	:				str													=	config_and_inputs
        lowercase_	:				str													=	{'pixel_values': pixel_values}
        return config, inputs_dict







@require_torch
class        UpperCamelCase		(			lowercase_						,      lowercase_						,      unittest.TestCase  ):
   lowercase							=  (
       (
           SegformerModel,
           SegformerForSemanticSegmentation,
           SegformerForImageClassification,
       )
       if is_torch_available()
       else ()
   )
   lowercase							=  (
       {
           'feature-extraction': SegformerModel,
           'image-classification': SegformerForImageClassification,
           'image-segmentation': SegformerForSemanticSegmentation,
       }
       if is_torch_available()
       else {}
   )

   lowercase							=  True
   lowercase							=  False
   lowercase							=  False
   lowercase							=  False
   def 			_UpperCAmelCase   (			self			)	->      Union[str, Any]:


        '''simple docstring'''


        lowercase_	:				List[Any]													=	SegformerModelTester(self			)
        lowercase_	:				int													=	SegformerConfigTester(self     ,config_class=__UpperCamelCase			)
   def 			_UpperCAmelCase   (			self			)	->      Optional[int]:


        '''simple docstring'''


        self.config_tester.run_common_tests()
   def 			_UpperCAmelCase   (			self			)	->      Dict:


        '''simple docstring'''


        lowercase_	:				Dict													=	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__UpperCamelCase			)
   def 			_UpperCAmelCase   (			self			)	->      int:


        '''simple docstring'''


        lowercase_	:				Dict													=	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_binary_image_segmentation(*__UpperCamelCase			)
   def 			_UpperCAmelCase   (			self			)	->      Tuple:


        '''simple docstring'''


        lowercase_	:				Optional[int]													=	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_segmentation(*__UpperCamelCase			)
   @unittest.skip('SegFormer does not use inputs_embeds'			)
   def 			_UpperCAmelCase   (			self			)	->      int:


        '''simple docstring'''


        pass
   @unittest.skip('SegFormer does not have get_input_embeddings method and get_output_embeddings methods'			)
   def 			_UpperCAmelCase   (			self			)	->      List[Any]:


        '''simple docstring'''


        pass
   def 			_UpperCAmelCase   (			self			)	->      List[Any]:


        '''simple docstring'''


        lowercase_							,  lowercase_	:				Optional[Any]													=	self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
             lowercase_	:				List[str]													=	model_class(__UpperCamelCase			)
             lowercase_	:				Union[str, Any]													=	inspect.signature(model.forward			)
             # signature.parameters is an OrderedDict => so arg_names order is deterministic
             lowercase_	:				Tuple													=	[*signature.parameters.keys()]

             lowercase_	:				int													=	['pixel_values']
             self.assertListEqual(arg_names[:1]     ,__UpperCamelCase			)
   def 			_UpperCAmelCase   (			self			)	->      Any:


        '''simple docstring'''


        lowercase_							,  lowercase_	:				str													=	self.model_tester.prepare_config_and_inputs_for_common()
        lowercase_	:				str													=	True

        for model_class in self.all_model_classes:
             lowercase_	:				Optional[Any]													=	True
             lowercase_	:				Dict													=	False
             lowercase_	:				Dict													=	True
             lowercase_	:				Tuple													=	model_class(__UpperCamelCase			)
             model.to(__UpperCamelCase			)
             model.eval()
             with torch.no_grad():
                  lowercase_	:				Dict													=	model(**self._prepare_for_class(__UpperCamelCase     ,__UpperCamelCase			)			)
             lowercase_	:				List[Any]													=	outputs.attentions

             lowercase_	:				int													=	sum(self.model_tester.depths			)
             self.assertEqual(len(__UpperCamelCase			)     ,__UpperCamelCase			)

             # check that output_attentions also work using config
             del inputs_dict["output_attentions"]
             lowercase_	:				Any													=	True
             lowercase_	:				List[str]													=	model_class(__UpperCamelCase			)
             model.to(__UpperCamelCase			)
             model.eval()
             with torch.no_grad():
                  lowercase_	:				Optional[Any]													=	model(**self._prepare_for_class(__UpperCamelCase     ,__UpperCamelCase			)			)
             lowercase_	:				List[Any]													=	outputs.attentions

             self.assertEqual(len(__UpperCamelCase			)     ,__UpperCamelCase			)

             # verify the first attentions (first block, first layer)
             lowercase_	:				str													=	(self.model_tester.image_size // 4) ** 2
             lowercase_	:				Union[str, Any]													=	(self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
             self.assertListEqual(
                 list(attentions[0].shape[-3:]			)     ,[self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]     ,)

             # verify the last attentions (last block, last layer)
             lowercase_	:				Tuple													=	(self.model_tester.image_size // 32) ** 2
             lowercase_	:				int													=	(self.model_tester.image_size // (32 * self.model_tester.sr_ratios[-1])) ** 2
             self.assertListEqual(
                 list(attentions[-1].shape[-3:]			)     ,[self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len]     ,)
             lowercase_	:				Any													=	len(__UpperCamelCase			)

             # Check attention is always last and order is fine
             lowercase_	:				int													=	True
             lowercase_	:				Any													=	True
             lowercase_	:				List[Any]													=	model_class(__UpperCamelCase			)
             model.to(__UpperCamelCase			)
             model.eval()
             with torch.no_grad():
                  lowercase_	:				Optional[Any]													=	model(**self._prepare_for_class(__UpperCamelCase     ,__UpperCamelCase			)			)

             self.assertEqual(out_len + 1     ,len(__UpperCamelCase			)			)

             lowercase_	:				List[str]													=	outputs.attentions

             self.assertEqual(len(__UpperCamelCase			)     ,__UpperCamelCase			)
             # verify the first attentions (first block, first layer)
             lowercase_	:				Union[str, Any]													=	(self.model_tester.image_size // 4) ** 2
             lowercase_	:				List[Any]													=	(self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
             self.assertListEqual(
                 list(self_attentions[0].shape[-3:]			)     ,[self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]     ,)
   def 			_UpperCAmelCase   (			self			)	->      List[Any]:


        '''simple docstring'''


        def check_hidden_states_output(__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase			):
             lowercase_	:				Any													=	model_class(__UpperCamelCase			)
             model.to(__UpperCamelCase			)
             model.eval()

             with torch.no_grad():
                  lowercase_	:				List[Any]													=	model(**self._prepare_for_class(__UpperCamelCase     ,__UpperCamelCase			)			)

             lowercase_	:				Tuple													=	outputs.hidden_states

             lowercase_	:				str													=	self.model_tester.num_encoder_blocks
             self.assertEqual(len(__UpperCamelCase			)     ,__UpperCamelCase			)

             # verify the first hidden states (first block)
             self.assertListEqual(
                 list(hidden_states[0].shape[-3:]			)     ,[
                     self.model_tester.hidden_sizes[0],
                     self.model_tester.image_size // 4,
                     self.model_tester.image_size // 4,
                 ]     ,)

        lowercase_							,  lowercase_	:				List[str]													=	self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
             lowercase_	:				List[str]													=	True
             check_hidden_states_output(__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase			)

             # check that output_hidden_states also work using config
             del inputs_dict["output_hidden_states"]
             lowercase_	:				List[str]													=	True

             check_hidden_states_output(__UpperCamelCase     ,__UpperCamelCase     ,__UpperCamelCase			)
   def 			_UpperCAmelCase   (			self			)	->      str:


        '''simple docstring'''


        if not self.model_tester.is_training:
             return

        lowercase_							,  lowercase_	:				Any													=	self.model_tester.prepare_config_and_inputs_for_common()
        lowercase_	:				str													=	True

        for model_class in self.all_model_classes:
             if model_class in get_values(__UpperCamelCase			):
                  continue

             lowercase_	:				str													=	model_class(__UpperCamelCase			)
             model.to(__UpperCamelCase			)
             model.train()
             lowercase_	:				Dict													=	self._prepare_for_class(__UpperCamelCase     ,__UpperCamelCase     ,return_labels=__UpperCamelCase			)
             lowercase_	:				List[Any]													=	model(**__UpperCamelCase			).loss
             loss.backward()
   @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'			)
   def 			_UpperCAmelCase   (			self			)	->      Union[str, Any]:


        '''simple docstring'''


        pass







   @slow
   def 			_UpperCAmelCase   (			self			)	->      List[Any]:


        '''simple docstring'''


        for model_name in SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             lowercase_	:				int													=	SegformerModel.from_pretrained(__UpperCamelCase			)
             self.assertIsNotNone(__UpperCamelCase			)






def 			lowercase__(       ):
     lowercase_	:				Tuple													=	Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'      )
     return image







@require_torch
class        UpperCamelCase		(			unittest.TestCase  ):
   @slow
   def 			_UpperCAmelCase   (			self			)	->      int:


        '''simple docstring'''


        lowercase_	:				Union[str, Any]													=	SegformerImageProcessor(
            image_scale=(512, 512)     ,keep_ratio=__UpperCamelCase     ,align=__UpperCamelCase     ,do_random_crop=__UpperCamelCase			)
        lowercase_	:				Dict													=	SegformerForSemanticSegmentation.from_pretrained('nvidia/segformer-b0-finetuned-ade-512-512'			).to(
            __UpperCamelCase			)

        lowercase_	:				Union[str, Any]													=	prepare_img()
        lowercase_	:				List[Any]													=	image_processor(images=__UpperCamelCase     ,return_tensors='pt'			)
        lowercase_	:				Optional[Any]													=	encoded_inputs.pixel_values.to(__UpperCamelCase			)

        with torch.no_grad():
             lowercase_	:				Tuple													=	model(__UpperCamelCase			)

        lowercase_	:				Optional[int]													=	torch.Size((1, model.config.num_labels, 128, 128)			)
        self.assertEqual(outputs.logits.shape     ,__UpperCamelCase			)

        lowercase_	:				int													=	torch.tensor(
            [
                [[-4.6310, -5.5232, -6.2356], [-5.1921, -6.1444, -6.5996], [-5.4424, -6.2790, -6.7574]],
                [[-12.1391, -13.3122, -13.9554], [-12.8732, -13.9352, -14.3563], [-12.9438, -13.8226, -14.2513]],
                [[-12.5134, -13.4686, -14.4915], [-12.8669, -14.4343, -14.7758], [-13.2523, -14.5819, -15.0694]],
            ]			).to(__UpperCamelCase			)
        self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]     ,__UpperCamelCase     ,atol=1e-4			)			)
   @slow
   def 			_UpperCAmelCase   (			self			)	->      Union[str, Any]:


        '''simple docstring'''


        lowercase_	:				Optional[int]													=	SegformerImageProcessor(
            image_scale=(512, 512)     ,keep_ratio=__UpperCamelCase     ,align=__UpperCamelCase     ,do_random_crop=__UpperCamelCase			)
        lowercase_	:				Optional[Any]													=	SegformerForSemanticSegmentation.from_pretrained(
            'nvidia/segformer-b1-finetuned-cityscapes-1024-1024'			).to(__UpperCamelCase			)

        lowercase_	:				List[Any]													=	prepare_img()
        lowercase_	:				Tuple													=	image_processor(images=__UpperCamelCase     ,return_tensors='pt'			)
        lowercase_	:				List[str]													=	encoded_inputs.pixel_values.to(__UpperCamelCase			)

        with torch.no_grad():
             lowercase_	:				str													=	model(__UpperCamelCase			)

        lowercase_	:				str													=	torch.Size((1, model.config.num_labels, 128, 128)			)
        self.assertEqual(outputs.logits.shape     ,__UpperCamelCase			)

        lowercase_	:				Optional[Any]													=	torch.tensor(
            [
                [[-13.5748, -13.9111, -12.6500], [-14.3500, -15.3683, -14.2328], [-14.7532, -16.0424, -15.6087]],
                [[-17.1651, -15.8725, -12.9653], [-17.2580, -17.3718, -14.8223], [-16.6058, -16.8783, -16.7452]],
                [[-3.6456, -3.0209, -1.4203], [-3.0797, -3.1959, -2.0000], [-1.8757, -1.9217, -1.6997]],
            ]			).to(__UpperCamelCase			)
        self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]     ,__UpperCamelCase     ,atol=1e-1			)			)







   @slow
   def 			_UpperCAmelCase   (			self			)	->      Dict:


        '''simple docstring'''


        lowercase_	:				Dict													=	SegformerImageProcessor(
            image_scale=(512, 512)     ,keep_ratio=__UpperCamelCase     ,align=__UpperCamelCase     ,do_random_crop=__UpperCamelCase			)
        lowercase_	:				List[Any]													=	SegformerForSemanticSegmentation.from_pretrained('nvidia/segformer-b0-finetuned-ade-512-512'			).to(
            __UpperCamelCase			)

        lowercase_	:				Dict													=	prepare_img()
        lowercase_	:				int													=	image_processor(images=__UpperCamelCase     ,return_tensors='pt'			)
        lowercase_	:				List[str]													=	encoded_inputs.pixel_values.to(__UpperCamelCase			)

        with torch.no_grad():
             lowercase_	:				str													=	model(__UpperCamelCase			)

        lowercase_	:				Dict													=	outputs.logits.detach().cpu()

        lowercase_	:				str													=	image_processor.post_process_semantic_segmentation(outputs=__UpperCamelCase     ,target_sizes=[(500, 300)]			)
        lowercase_	:				Union[str, Any]													=	torch.Size((500, 300)			)
        self.assertEqual(segmentation[0].shape     ,__UpperCamelCase			)

        lowercase_	:				int													=	image_processor.post_process_semantic_segmentation(outputs=__UpperCamelCase			)
        lowercase_	:				Union[str, Any]													=	torch.Size((128, 128)			)
        self.assertEqual(segmentation[0].shape     ,__UpperCamelCase			)


"""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,)

