




import copy
import unittest

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

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


if is_torch_available():
		import torch

		from transformers import (
		    MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
		    MODEL_FOR_QUESTION_ANSWERING_MAPPING,
		    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
		    MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
		    LayoutLMvaConfig,
		    LayoutLMvaForQuestionAnswering,
		    LayoutLMvaForSequenceClassification,
		    LayoutLMvaForTokenClassification,
		    LayoutLMvaModel,
		)
		from transformers.models.layoutlmva.modeling_layoutlmva import LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
		from PIL import Image

		from transformers import LayoutLMvaImageProcessor




class 			UpperCAmelCase_      :




							'''simple docstring'''



							def __init__(					self       :	Optional[int]       ,     a       :	Dict       ,     a       :	Dict=2       ,     a       :	int=3       ,     a       :	Any=4       ,     a       :	List[Any]=2       ,     a       :	List[str]=7       ,     a       :	int=True       ,     a       :	Union[str, Any]=True       ,     a       :	Union[str, Any]=True       ,     a       :	Tuple=True       ,     a       :	str=99       ,     a       :	Optional[Any]=36       ,     a       :	Dict=3       ,     a       :	Union[str, Any]=4       ,     a       :	Dict=37       ,     a       :	List[str]="gelu"       ,     a       :	Tuple=0.1       ,     a       :	str=0.1       ,     a       :	Any=512       ,     a       :	Optional[int]=16       ,     a       :	List[Any]=2       ,     a       :	str=0.02       ,     a       :	Tuple=6       ,     a       :	int=6       ,     a       :	int=3       ,     a       :	str=4       ,     a       :	Dict=None       ,     a       :	List[str]=1_000       ,     ):
										SCREAMING_SNAKE_CASE           =					parent
										SCREAMING_SNAKE_CASE           =					batch_size
										SCREAMING_SNAKE_CASE           =					num_channels
										SCREAMING_SNAKE_CASE           =					image_size
										SCREAMING_SNAKE_CASE           =					patch_size
										SCREAMING_SNAKE_CASE           =					text_seq_length
										SCREAMING_SNAKE_CASE           =					is_training
										SCREAMING_SNAKE_CASE           =					use_input_mask
										SCREAMING_SNAKE_CASE           =					use_token_type_ids
										SCREAMING_SNAKE_CASE           =					use_labels
										SCREAMING_SNAKE_CASE           =					vocab_size
										SCREAMING_SNAKE_CASE           =					hidden_size
										SCREAMING_SNAKE_CASE           =					num_hidden_layers
										SCREAMING_SNAKE_CASE           =					num_attention_heads
										SCREAMING_SNAKE_CASE           =					intermediate_size
										SCREAMING_SNAKE_CASE           =					hidden_act
										SCREAMING_SNAKE_CASE           =					hidden_dropout_prob
										SCREAMING_SNAKE_CASE           =					attention_probs_dropout_prob
										SCREAMING_SNAKE_CASE           =					max_position_embeddings
										SCREAMING_SNAKE_CASE           =					type_vocab_size
										SCREAMING_SNAKE_CASE           =					type_sequence_label_size
										SCREAMING_SNAKE_CASE           =					initializer_range
										SCREAMING_SNAKE_CASE           =					coordinate_size
										SCREAMING_SNAKE_CASE           =					shape_size
										SCREAMING_SNAKE_CASE           =					num_labels
										SCREAMING_SNAKE_CASE           =					num_choices
										SCREAMING_SNAKE_CASE           =					scope
										SCREAMING_SNAKE_CASE           =					range_bbox

										# LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
										SCREAMING_SNAKE_CASE           =					text_seq_length
										SCREAMING_SNAKE_CASE           =					(image_size // patch_size) ** 2 + 1
										SCREAMING_SNAKE_CASE           =					self.text_seq_length + self.image_seq_length





							def    _UpperCAmelCase     (					self       :	Union[str, Any]			):
										SCREAMING_SNAKE_CASE           =					ids_tensor([self.batch_size, self.text_seq_length]       ,     self.vocab_size			)

										SCREAMING_SNAKE_CASE           =					ids_tensor([self.batch_size, self.text_seq_length, 4]       ,     self.range_bbox			)
										# Ensure that bbox is legal
										for i in range(bbox.shape[0]			):
													for j in range(bbox.shape[1]			):
																if bbox[i, j, 3] < bbox[i, j, 1]:
																			SCREAMING_SNAKE_CASE           =					bbox[i, j, 3]
																			SCREAMING_SNAKE_CASE           =					bbox[i, j, 1]
																			SCREAMING_SNAKE_CASE           =					t
																if bbox[i, j, 2] < bbox[i, j, 0]:
																			SCREAMING_SNAKE_CASE           =					bbox[i, j, 2]
																			SCREAMING_SNAKE_CASE           =					bbox[i, j, 0]
																			SCREAMING_SNAKE_CASE           =					t

										SCREAMING_SNAKE_CASE           =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

										SCREAMING_SNAKE_CASE           =					None
										if self.use_input_mask:
													SCREAMING_SNAKE_CASE           =					random_attention_mask([self.batch_size, self.text_seq_length]			)

										SCREAMING_SNAKE_CASE           =					None
										if self.use_token_type_ids:
													SCREAMING_SNAKE_CASE           =					ids_tensor([self.batch_size, self.text_seq_length]       ,     self.type_vocab_size			)

										SCREAMING_SNAKE_CASE           =					None
										SCREAMING_SNAKE_CASE           =					None
										if self.use_labels:
													SCREAMING_SNAKE_CASE           =					ids_tensor([self.batch_size]       ,     self.type_sequence_label_size			)
													SCREAMING_SNAKE_CASE           =					ids_tensor([self.batch_size, self.text_seq_length]       ,     self.num_labels			)

										SCREAMING_SNAKE_CASE           =					LayoutLMvaConfig(
										    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       ,     coordinate_size=self.coordinate_size       ,     shape_size=self.shape_size       ,     input_size=self.image_size       ,     patch_size=self.patch_size       ,     )

										return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels





							def    _UpperCAmelCase     (					self       :	Any       ,     a       :	Optional[Any]       ,     a       :	List[str]       ,     a       :	str       ,     a       :	str       ,     a       :	Tuple       ,     a       :	Dict       ,     a       :	Tuple       ,     a       :	Any			):
										SCREAMING_SNAKE_CASE           =					LayoutLMvaModel(config=a			)
										model.to(a			)
										model.eval()

										# text + image
										SCREAMING_SNAKE_CASE           =					model(a       ,     pixel_values=a			)
										SCREAMING_SNAKE_CASE           =					model(
										    a       ,     bbox=a       ,     pixel_values=a       ,     attention_mask=a       ,     token_type_ids=a			)
										SCREAMING_SNAKE_CASE           =					model(a       ,     bbox=a       ,     pixel_values=a       ,     token_type_ids=a			)
										SCREAMING_SNAKE_CASE           =					model(a       ,     bbox=a       ,     pixel_values=a			)

										self.parent.assertEqual(result.last_hidden_state.shape       ,     (self.batch_size, self.seq_length, self.hidden_size)			)

										# text only
										SCREAMING_SNAKE_CASE           =					model(a			)
										self.parent.assertEqual(
										    result.last_hidden_state.shape       ,     (self.batch_size, self.text_seq_length, self.hidden_size)			)

										# image only
										SCREAMING_SNAKE_CASE           =					model(pixel_values=a			)
										self.parent.assertEqual(
										    result.last_hidden_state.shape       ,     (self.batch_size, self.image_seq_length, self.hidden_size)			)





							def    _UpperCAmelCase     (					self       :	Optional[int]       ,     a       :	List[Any]       ,     a       :	Any       ,     a       :	Union[str, Any]       ,     a       :	int       ,     a       :	Tuple       ,     a       :	Union[str, Any]       ,     a       :	Optional[int]       ,     a       :	int			):
										SCREAMING_SNAKE_CASE           =					self.num_labels
										SCREAMING_SNAKE_CASE           =					LayoutLMvaForSequenceClassification(a			)
										model.to(a			)
										model.eval()
										SCREAMING_SNAKE_CASE           =					model(
										    a       ,     bbox=a       ,     pixel_values=a       ,     attention_mask=a       ,     token_type_ids=a       ,     labels=a       ,     )
										self.parent.assertEqual(result.logits.shape       ,     (self.batch_size, self.num_labels)			)





							def    _UpperCAmelCase     (					self       :	Dict       ,     a       :	Dict       ,     a       :	Optional[Any]       ,     a       :	Union[str, Any]       ,     a       :	Any       ,     a       :	Dict       ,     a       :	int       ,     a       :	Dict       ,     a       :	int			):
										SCREAMING_SNAKE_CASE           =					self.num_labels
										SCREAMING_SNAKE_CASE           =					LayoutLMvaForTokenClassification(config=a			)
										model.to(a			)
										model.eval()
										SCREAMING_SNAKE_CASE           =					model(
										    a       ,     bbox=a       ,     pixel_values=a       ,     attention_mask=a       ,     token_type_ids=a       ,     labels=a       ,     )
										self.parent.assertEqual(result.logits.shape       ,     (self.batch_size, self.text_seq_length, self.num_labels)			)





							def    _UpperCAmelCase     (					self       :	Optional[Any]       ,     a       :	Dict       ,     a       :	Any       ,     a       :	int       ,     a       :	Optional[Any]       ,     a       :	Tuple       ,     a       :	List[str]       ,     a       :	int       ,     a       :	Optional[int]			):
										SCREAMING_SNAKE_CASE           =					LayoutLMvaForQuestionAnswering(config=a			)
										model.to(a			)
										model.eval()
										SCREAMING_SNAKE_CASE           =					model(
										    a       ,     bbox=a       ,     pixel_values=a       ,     attention_mask=a       ,     token_type_ids=a       ,     start_positions=a       ,     end_positions=a       ,     )
										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       :	List[Any]			):
										SCREAMING_SNAKE_CASE           =					self.prepare_config_and_inputs()
										(
										    (
										    SCREAMING_SNAKE_CASE  
										)     ,  (
										    SCREAMING_SNAKE_CASE  
										)     ,  (
										    SCREAMING_SNAKE_CASE  
										)     ,  (
										    SCREAMING_SNAKE_CASE  
										)     ,  (
										    SCREAMING_SNAKE_CASE  
										)     ,  (
										    SCREAMING_SNAKE_CASE  
										)     ,  (
										    SCREAMING_SNAKE_CASE  
										)     ,  (
										    SCREAMING_SNAKE_CASE  
										)     ,    
										)           =					config_and_inputs
										SCREAMING_SNAKE_CASE           =					{
										    """input_ids""": input_ids,
										    """bbox""": bbox,
										    """pixel_values""": pixel_values,
										    """token_type_ids""": token_type_ids,
										    """attention_mask""": input_mask,
										}
										return config, inputs_dict





@require_torch
class 			UpperCAmelCase_      (   A		,      A		,      unittest.TestCase       ):




							'''simple docstring'''



							a__						  =     False
							a__						  =     False
							a__						  =     False

							a__						  =     (
							    (
							        LayoutLMvaModel,
							        LayoutLMvaForSequenceClassification,
							        LayoutLMvaForTokenClassification,
							        LayoutLMvaForQuestionAnswering,
							    )
							    if is_torch_available()
							    else ()
							)
							a__						  =     (
							    {'''document-question-answering''': LayoutLMvaForQuestionAnswering, '''feature-extraction''': LayoutLMvaModel}
							    if is_torch_available()
							    else {}
							)





							def    _UpperCAmelCase     (					self       :	str       ,     a       :	int       ,     a       :	Optional[int]       ,     a       :	List[Any]       ,     a       :	Dict       ,     a       :	Tuple			):
										# `DocumentQuestionAnsweringPipeline` is expected to work with this model, but it combines the text and visual
										# embedding along the sequence dimension (dim 1), which causes an error during post-processing as `p_mask` has
										# the sequence dimension of the text embedding only.
										# (see the line `embedding_output = torch.cat([embedding_output, visual_embeddings], dim=1)`)
										return True





							def    _UpperCAmelCase     (					self       :	List[str]			):
										SCREAMING_SNAKE_CASE           =					LayoutLMvaModelTester(self			)
										SCREAMING_SNAKE_CASE           =					ConfigTester(self       ,     config_class=a       ,     hidden_size=37			)





							def    _UpperCAmelCase     (					self       :	str       ,     a       :	List[str]       ,     a       :	List[Any]       ,     a       :	List[Any]=False			):
										SCREAMING_SNAKE_CASE           =					copy.deepcopy(a			)
										if model_class in get_values(a			):
													SCREAMING_SNAKE_CASE           =					{
													    k: v.unsqueeze(1			).expand(-1       ,     self.model_tester.num_choices       ,     -1			).contiguous()
													    if isinstance(a       ,     torch.Tensor			) and v.ndim > 1
													    else v
													    for k, v in inputs_dict.items()
													}
										if return_labels:
													if model_class in get_values(a			):
																SCREAMING_SNAKE_CASE           =					torch.ones(self.model_tester.batch_size       ,     dtype=torch.long       ,     device=a			)
													elif model_class in get_values(a			):
																SCREAMING_SNAKE_CASE           =					torch.zeros(
																    self.model_tester.batch_size       ,     dtype=torch.long       ,     device=a			)
																SCREAMING_SNAKE_CASE           =					torch.zeros(
																    self.model_tester.batch_size       ,     dtype=torch.long       ,     device=a			)
													elif model_class in [
													    *get_values(a			),
													]:
																SCREAMING_SNAKE_CASE           =					torch.zeros(
																    self.model_tester.batch_size       ,     dtype=torch.long       ,     device=a			)
													elif model_class in [
													    *get_values(a			),
													]:
																SCREAMING_SNAKE_CASE           =					torch.zeros(
																    (self.model_tester.batch_size, self.model_tester.text_seq_length)       ,     dtype=torch.long       ,     device=a       ,     )

										return inputs_dict





							def    _UpperCAmelCase     (					self       :	Union[str, Any]			):
										self.config_tester.run_common_tests()





							def    _UpperCAmelCase     (					self       :	List[Any]			):
										SCREAMING_SNAKE_CASE           =					self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*a			)





							def    _UpperCAmelCase     (					self       :	int			):
										SCREAMING_SNAKE_CASE           =					self.model_tester.prepare_config_and_inputs()
										for type in ["absolute", "relative_key", "relative_key_query"]:
													SCREAMING_SNAKE_CASE           =					type
													self.model_tester.create_and_check_model(*a			)





							def    _UpperCAmelCase     (					self       :	Dict			):
										SCREAMING_SNAKE_CASE           =					self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_sequence_classification(*a			)





							def    _UpperCAmelCase     (					self       :	Union[str, Any]			):
										SCREAMING_SNAKE_CASE           =					self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_token_classification(*a			)





							def    _UpperCAmelCase     (					self       :	Optional[int]			):
										SCREAMING_SNAKE_CASE           =					self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_question_answering(*a			)







							@slow
							def    _UpperCAmelCase     (					self       :	Optional[int]			):
										for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
													SCREAMING_SNAKE_CASE           =					LayoutLMvaModel.from_pretrained(a			)
													self.assertIsNotNone(a			)





def 					lowerCamelCase_    (    ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""   )
			return image




@require_torch
class 			UpperCAmelCase_      (   unittest.TestCase       ):




							'''simple docstring'''



							@cached_property
							def    _UpperCAmelCase     (					self       :	Optional[int]			):
										return LayoutLMvaImageProcessor(apply_ocr=a			) if is_vision_available() else None







							@slow
							def    _UpperCAmelCase     (					self       :	Union[str, Any]			):
										SCREAMING_SNAKE_CASE           =					LayoutLMvaModel.from_pretrained("""microsoft/layoutlmv3-base"""			).to(a			)

										SCREAMING_SNAKE_CASE           =					self.default_image_processor
										SCREAMING_SNAKE_CASE           =					prepare_img()
										SCREAMING_SNAKE_CASE           =					image_processor(images=a       ,     return_tensors="""pt"""			).pixel_values.to(a			)

										SCREAMING_SNAKE_CASE           =					torch.tensor([[1, 2]]			)
										SCREAMING_SNAKE_CASE           =					torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]]			).unsqueeze(0			)

										# forward pass
										SCREAMING_SNAKE_CASE           =					model(
										    input_ids=input_ids.to(a			)       ,     bbox=bbox.to(a			)       ,     pixel_values=pixel_values.to(a			)       ,     )

										# verify the logits
										SCREAMING_SNAKE_CASE           =					torch.Size((1, 199, 768)			)
										self.assertEqual(outputs.last_hidden_state.shape       ,     a			)

										SCREAMING_SNAKE_CASE           =					torch.tensor(
										    [[-0.0529, 0.3618, 0.1632], [-0.1587, -0.1667, -0.0400], [-0.1557, -0.1671, -0.0505]]			).to(a			)

										self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]       ,     a       ,     atol=1E-4			)			)












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:
		__A						:							Tuple							=  None


__A						:							Any							=  logging.get_logger(__name__)


__A						:							Union[str, Any]							=  {"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

__A						:							List[Any]							=  {
    """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""",
    },
}

__A						:							int							=  {
    """facebook/mbart-large-en-ro""": 1_0_2_4,
    """facebook/mbart-large-cc25""": 1_0_2_4,
}

# fmt: off
__A						:							List[Any]							=  ["""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_      (   A       ):




							'''simple docstring'''



							a__						  =     VOCAB_FILES_NAMES
							a__						  =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							a__						  =     PRETRAINED_VOCAB_FILES_MAP
							a__						  =     ['''input_ids''', '''attention_mask''']
							a__						  =     MBartTokenizer

							a__						  =     []
							a__						  =     []





							def __init__(					self       :	str       ,     a       :	List[Any]=None       ,     a       :	Optional[int]=None       ,     a       :	str="<s>"       ,     a       :	Tuple="</s>"       ,     a       :	Optional[int]="</s>"       ,     a       :	int="<s>"       ,     a       :	List[str]="<unk>"       ,     a       :	Tuple="<pad>"       ,     a       :	Dict="<mask>"       ,     a       :	Optional[Any]=None       ,     a       :	List[Any]=None       ,     a       :	Any=None       ,     **a       :	Optional[Any]       ,     )			->					Any:
										# Mask token behave like a normal word, i.e. include the space before it
										SCREAMING_SNAKE_CASE           =					AddedToken(a       ,     lstrip=a       ,     rstrip=a			) if isinstance(a       ,     a			) else mask_token

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

										SCREAMING_SNAKE_CASE           =					vocab_file
										SCREAMING_SNAKE_CASE           =					False if not self.vocab_file else True

										SCREAMING_SNAKE_CASE           =					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}			)
										SCREAMING_SNAKE_CASE           =					{
										    lang_code: self.convert_tokens_to_ids(a			) for lang_code in FAIRSEQ_LANGUAGE_CODES
										}

										SCREAMING_SNAKE_CASE           =					src_lang if src_lang is not None else """en_XX"""
										SCREAMING_SNAKE_CASE           =					self.convert_tokens_to_ids(self._src_lang			)
										SCREAMING_SNAKE_CASE           =					tgt_lang
										self.set_src_lang_special_tokens(self._src_lang			)





							@property
							def    _UpperCAmelCase     (					self       :	int			)			->					str:
										return self._src_lang





							@src_lang.setter
							def    _UpperCAmelCase     (					self       :	List[Any]       ,     a       :	str			)			->					None:
										SCREAMING_SNAKE_CASE           =					new_src_lang
										self.set_src_lang_special_tokens(self._src_lang			)





							def    _UpperCAmelCase     (					self       :	Dict       ,     a       :	List[int]       ,     a       :	Optional[List[int]] = None			)			->					List[int]:
										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       :	Tuple       ,     a       :	List[int]       ,     a       :	Optional[List[int]] = None			)			->					List[int]:
										SCREAMING_SNAKE_CASE           =					[self.sep_token_id]
										SCREAMING_SNAKE_CASE           =					[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       :	Optional[Any]       ,     a       :	Optional[int]       ,     a       :	str       ,     a       :	Optional[str]       ,     a       :	Optional[str]       ,     **a       :	Optional[Any]			)			->					Optional[Any]:
										if src_lang is None or tgt_lang is None:
													raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model"""			)
										SCREAMING_SNAKE_CASE           =					src_lang
										SCREAMING_SNAKE_CASE           =					self(a       ,     add_special_tokens=a       ,     return_tensors=a       ,     **a			)
										SCREAMING_SNAKE_CASE           =					self.convert_tokens_to_ids(a			)
										SCREAMING_SNAKE_CASE           =					tgt_lang_id
										return inputs





							def    _UpperCAmelCase     (					self       :	str       ,     a       :	List[str]       ,     a       :	str = "en_XX"       ,     a       :	Optional[List[str]] = None       ,     a       :	str = "ro_RO"       ,     **a       :	List[str]       ,     )			->					BatchEncoding:
										SCREAMING_SNAKE_CASE           =					src_lang
										SCREAMING_SNAKE_CASE           =					tgt_lang
										return super().prepare_seqaseq_batch(a       ,     a       ,     **a			)





							def    _UpperCAmelCase     (					self       :	Optional[Any]			)			->					str:
										return self.set_src_lang_special_tokens(self.src_lang			)





							def    _UpperCAmelCase     (					self       :	str			)			->					List[str]:
										return self.set_tgt_lang_special_tokens(self.tgt_lang			)





							def    _UpperCAmelCase     (					self       :	Dict       ,     a       :	List[str]			)			->					None:
										SCREAMING_SNAKE_CASE           =					self.convert_tokens_to_ids(a			)
										SCREAMING_SNAKE_CASE           =					[]
										SCREAMING_SNAKE_CASE           =					[self.eos_token_id, self.cur_lang_code]

										SCREAMING_SNAKE_CASE           =					self.convert_ids_to_tokens(self.prefix_tokens			)
										SCREAMING_SNAKE_CASE           =					self.convert_ids_to_tokens(self.suffix_tokens			)

										SCREAMING_SNAKE_CASE           =					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       :	Dict       ,     a       :	str			)			->					None:
										SCREAMING_SNAKE_CASE           =					self.convert_tokens_to_ids(a			)
										SCREAMING_SNAKE_CASE           =					[]
										SCREAMING_SNAKE_CASE           =					[self.eos_token_id, self.cur_lang_code]

										SCREAMING_SNAKE_CASE           =					self.convert_ids_to_tokens(self.prefix_tokens			)
										SCREAMING_SNAKE_CASE           =					self.convert_ids_to_tokens(self.suffix_tokens			)

										SCREAMING_SNAKE_CASE           =					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       :	Dict       ,     a       :	str       ,     a       :	Optional[str] = None			)			->					Tuple[str]:
										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(a			):
													logger.error(f"""Vocabulary path ({save_directory}) should be a directory."""			)
													return
										SCREAMING_SNAKE_CASE           =					os.path.join(
										    a       ,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]			)

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

										return (out_vocab_file,)






