






"""simple docstring"""
import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
			import torch

if is_vision_available():
			from PIL import Image

			from transformers import DeformableDetrImageProcessor







class 			UpperCamelCase_       (unittest.TestCase     ):





	def __init__(      self					: Tuple	,	lowerCAmelCase_					: List[str]	,	lowerCAmelCase_					: int=7	,	lowerCAmelCase_					: Any=3	,	lowerCAmelCase_					: List[str]=30	,	lowerCAmelCase_					: int=400	,	lowerCAmelCase_					: Any=True	,	lowerCAmelCase_					: Any=None	,	lowerCAmelCase_					: str=True	,	lowerCAmelCase_					: Any=[0.5, 0.5, 0.5]	,	lowerCAmelCase_					: Optional[Any]=[0.5, 0.5, 0.5]	,	lowerCAmelCase_					: Optional[int]=True	,	lowerCAmelCase_					: Optional[Any]=1 / 255	,	lowerCAmelCase_					: List[str]=True	,	)						->			str:
				# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
				UpperCAmelCase_     : int			     =					size if size is not None else {"shortest_edge": 18, "longest_edge": 1_333}
				UpperCAmelCase_     : List[str]			     =					parent
				UpperCAmelCase_     : Optional[int]			     =					batch_size
				UpperCAmelCase_     : List[str]			     =					num_channels
				UpperCAmelCase_     : Union[str, Any]			     =					min_resolution
				UpperCAmelCase_     : Tuple			     =					max_resolution
				UpperCAmelCase_     : Dict			     =					do_resize
				UpperCAmelCase_     : List[Any]			     =					size
				UpperCAmelCase_     : Union[str, Any]			     =					do_normalize
				UpperCAmelCase_     : Any			     =					image_mean
				UpperCAmelCase_     : int			     =					image_std
				UpperCAmelCase_     : Tuple			     =					do_rescale
				UpperCAmelCase_     : Any			     =					rescale_factor
				UpperCAmelCase_     : Dict			     =					do_pad





	def      _SCREAMING_SNAKE_CASE      (      self					: int						)						->			Optional[int]:
				return {
				    "do_resize": self.do_resize,
				    "size": self.size,
				    "do_normalize": self.do_normalize,
				    "image_mean": self.image_mean,
				    "image_std": self.image_std,
				    "do_rescale": self.do_rescale,
				    "rescale_factor": self.rescale_factor,
				    "do_pad": self.do_pad,
				}





	def      _SCREAMING_SNAKE_CASE      (      self					: int	,	lowerCAmelCase_					: Dict	,	lowerCAmelCase_					: Tuple=False						)						->			List[str]:
				if not batched:
							UpperCAmelCase_     : Any			     =					image_inputs[0]
							if isinstance(lowerCAmelCase_	,	Image.Image						):
										UpperCAmelCase_     ,	UpperCAmelCase_     : Optional[int]			     =					image.size
							else:
										UpperCAmelCase_     ,	UpperCAmelCase_     : Optional[int]			     =					image.shape[1], image.shape[2]
							if w < h:
										UpperCAmelCase_     : Any			     =					int(self.size["shortest_edge"] * h / w						)
										UpperCAmelCase_     : Tuple			     =					self.size["shortest_edge"]
							elif w > h:
										UpperCAmelCase_     : List[str]			     =					self.size["shortest_edge"]
										UpperCAmelCase_     : List[str]			     =					int(self.size["shortest_edge"] * w / h						)
							else:
										UpperCAmelCase_     : str			     =					self.size["shortest_edge"]
										UpperCAmelCase_     : List[str]			     =					self.size["shortest_edge"]

				else:
							UpperCAmelCase_     : int			     =					[]
							for image in image_inputs:
										UpperCAmelCase_     ,	UpperCAmelCase_     : Optional[Any]			     =					self.get_expected_values([image]						)
										expected_values.append((expected_height, expected_width)						)
							UpperCAmelCase_     : Dict			     =					max(lowerCAmelCase_	,	key=lambda lowerCAmelCase_						: item[0]						)[0]
							UpperCAmelCase_     : Any			     =					max(lowerCAmelCase_	,	key=lambda lowerCAmelCase_						: item[1]						)[1]

				return expected_height, expected_width







@require_torch
@require_vision
class 			UpperCamelCase_       (__A				,						unittest.TestCase     ):
	__magic_name__	      =				DeformableDetrImageProcessor if is_vision_available() else None





	def      _SCREAMING_SNAKE_CASE      (      self					: Tuple						)						->			Optional[Any]:
				UpperCAmelCase_     : List[Any]			     =					DeformableDetrImageProcessingTester(self						)





	@property
	def      _SCREAMING_SNAKE_CASE      (      self					: Tuple						)						->			Optional[Any]:
				return self.image_processor_tester.prepare_image_processor_dict()





	def      _SCREAMING_SNAKE_CASE      (      self					: List[Any]						)						->			List[Any]:
				UpperCAmelCase_     : List[Any]			     =					self.image_processing_class(**self.image_processor_dict						)
				self.assertTrue(hasattr(lowerCAmelCase_	,	"image_mean"						)						)
				self.assertTrue(hasattr(lowerCAmelCase_	,	"image_std"						)						)
				self.assertTrue(hasattr(lowerCAmelCase_	,	"do_normalize"						)						)
				self.assertTrue(hasattr(lowerCAmelCase_	,	"do_resize"						)						)
				self.assertTrue(hasattr(lowerCAmelCase_	,	"do_rescale"						)						)
				self.assertTrue(hasattr(lowerCAmelCase_	,	"do_pad"						)						)
				self.assertTrue(hasattr(lowerCAmelCase_	,	"size"						)						)





	def      _SCREAMING_SNAKE_CASE      (      self					: Optional[Any]						)						->			Union[str, Any]:
				UpperCAmelCase_     : Optional[Any]			     =					self.image_processing_class.from_dict(self.image_processor_dict						)
				self.assertEqual(image_processor.size	,	{"shortest_edge": 18, "longest_edge": 1_333}						)
				self.assertEqual(image_processor.do_pad	,	lowerCAmelCase_						)

				UpperCAmelCase_     : Optional[Any]			     =					self.image_processing_class.from_dict(
				    self.image_processor_dict	,	size=42	,	max_size=84	,	pad_and_return_pixel_mask=lowerCAmelCase_						)
				self.assertEqual(image_processor.size	,	{"shortest_edge": 42, "longest_edge": 84}						)
				self.assertEqual(image_processor.do_pad	,	lowerCAmelCase_						)





	def      _SCREAMING_SNAKE_CASE      (      self					: List[str]						)						->			Union[str, Any]:
				pass





	def      _SCREAMING_SNAKE_CASE      (      self					: List[str]						)						->			Any:
				# Initialize image_processing
				UpperCAmelCase_     : Optional[Any]			     =					self.image_processing_class(**self.image_processor_dict						)
				# create random PIL images
				UpperCAmelCase_     : Any			     =					prepare_image_inputs(self.image_processor_tester	,	equal_resolution=lowerCAmelCase_						)
				for image in image_inputs:
							self.assertIsInstance(lowerCAmelCase_	,	Image.Image						)

				# Test not batched input
				UpperCAmelCase_     : Optional[int]			     =					image_processing(image_inputs[0]	,	return_tensors="pt"						).pixel_values

				UpperCAmelCase_     ,	UpperCAmelCase_     : str			     =					self.image_processor_tester.get_expected_values(lowerCAmelCase_						)

				self.assertEqual(
				    encoded_images.shape	,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)	,	)

				# Test batched
				UpperCAmelCase_     ,	UpperCAmelCase_     : Tuple			     =					self.image_processor_tester.get_expected_values(lowerCAmelCase_	,	batched=lowerCAmelCase_						)

				UpperCAmelCase_     : Tuple			     =					image_processing(lowerCAmelCase_	,	return_tensors="pt"						).pixel_values
				self.assertEqual(
				    encoded_images.shape	,	(
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        expected_height,
				        expected_width,
				    )	,	)





	def      _SCREAMING_SNAKE_CASE      (      self					: Optional[int]						)						->			Union[str, Any]:
				# Initialize image_processing
				UpperCAmelCase_     : Union[str, Any]			     =					self.image_processing_class(**self.image_processor_dict						)
				# create random numpy tensors
				UpperCAmelCase_     : str			     =					prepare_image_inputs(self.image_processor_tester	,	equal_resolution=lowerCAmelCase_	,	numpify=lowerCAmelCase_						)
				for image in image_inputs:
							self.assertIsInstance(lowerCAmelCase_	,	np.ndarray						)

				# Test not batched input
				UpperCAmelCase_     : Any			     =					image_processing(image_inputs[0]	,	return_tensors="pt"						).pixel_values

				UpperCAmelCase_     ,	UpperCAmelCase_     : Tuple			     =					self.image_processor_tester.get_expected_values(lowerCAmelCase_						)

				self.assertEqual(
				    encoded_images.shape	,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)	,	)

				# Test batched
				UpperCAmelCase_     : int			     =					image_processing(lowerCAmelCase_	,	return_tensors="pt"						).pixel_values

				UpperCAmelCase_     ,	UpperCAmelCase_     : Any			     =					self.image_processor_tester.get_expected_values(lowerCAmelCase_	,	batched=lowerCAmelCase_						)

				self.assertEqual(
				    encoded_images.shape	,	(
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        expected_height,
				        expected_width,
				    )	,	)





	def      _SCREAMING_SNAKE_CASE      (      self					: Union[str, Any]						)						->			Tuple:
				# Initialize image_processing
				UpperCAmelCase_     : Dict			     =					self.image_processing_class(**self.image_processor_dict						)
				# create random PyTorch tensors
				UpperCAmelCase_     : List[Any]			     =					prepare_image_inputs(self.image_processor_tester	,	equal_resolution=lowerCAmelCase_	,	torchify=lowerCAmelCase_						)
				for image in image_inputs:
							self.assertIsInstance(lowerCAmelCase_	,	torch.Tensor						)

				# Test not batched input
				UpperCAmelCase_     : Optional[Any]			     =					image_processing(image_inputs[0]	,	return_tensors="pt"						).pixel_values

				UpperCAmelCase_     ,	UpperCAmelCase_     : List[str]			     =					self.image_processor_tester.get_expected_values(lowerCAmelCase_						)

				self.assertEqual(
				    encoded_images.shape	,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)	,	)

				# Test batched
				UpperCAmelCase_     : str			     =					image_processing(lowerCAmelCase_	,	return_tensors="pt"						).pixel_values

				UpperCAmelCase_     ,	UpperCAmelCase_     : int			     =					self.image_processor_tester.get_expected_values(lowerCAmelCase_	,	batched=lowerCAmelCase_						)

				self.assertEqual(
				    encoded_images.shape	,	(
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        expected_height,
				        expected_width,
				    )	,	)





	@slow
	def      _SCREAMING_SNAKE_CASE      (      self					: Any						)						->			Dict:
				# prepare image and target
				UpperCAmelCase_     : Tuple			     =					Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"						)
				with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"	,	"r"						) as f:
							UpperCAmelCase_     : List[str]			     =					json.loads(f.read()						)

				UpperCAmelCase_     : int			     =					{"image_id": 39_769, "annotations": target}

				# encode them
				UpperCAmelCase_     : Tuple			     =					DeformableDetrImageProcessor()
				UpperCAmelCase_     : Optional[int]			     =					image_processing(images=lowerCAmelCase_	,	annotations=lowerCAmelCase_	,	return_tensors="pt"						)

				# verify pixel values
				UpperCAmelCase_     : Any			     =					torch.Size([1, 3, 800, 1_066]						)
				self.assertEqual(encoding["pixel_values"].shape	,	lowerCAmelCase_						)

				UpperCAmelCase_     : str			     =					torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]						)
				self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]	,	lowerCAmelCase_	,	atol=1e-4						)						)

				# verify area
				UpperCAmelCase_     : Optional[Any]			     =					torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8]						)
				self.assertTrue(torch.allclose(encoding["labels"][0]["area"]	,	lowerCAmelCase_						)						)
				# verify boxes
				UpperCAmelCase_     : List[Any]			     =					torch.Size([6, 4]						)
				self.assertEqual(encoding["labels"][0]["boxes"].shape	,	lowerCAmelCase_						)
				UpperCAmelCase_     : Union[str, Any]			     =					torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5]						)
				self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]	,	lowerCAmelCase_	,	atol=1e-3						)						)
				# verify image_id
				UpperCAmelCase_     : Dict			     =					torch.tensor([39_769]						)
				self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]	,	lowerCAmelCase_						)						)
				# verify is_crowd
				UpperCAmelCase_     : Union[str, Any]			     =					torch.tensor([0, 0, 0, 0, 0, 0]						)
				self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]	,	lowerCAmelCase_						)						)
				# verify class_labels
				UpperCAmelCase_     : Union[str, Any]			     =					torch.tensor([75, 75, 63, 65, 17, 17]						)
				self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]	,	lowerCAmelCase_						)						)
				# verify orig_size
				UpperCAmelCase_     : List[str]			     =					torch.tensor([480, 640]						)
				self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]	,	lowerCAmelCase_						)						)
				# verify size
				UpperCAmelCase_     : Union[str, Any]			     =					torch.tensor([800, 1_066]						)
				self.assertTrue(torch.allclose(encoding["labels"][0]["size"]	,	lowerCAmelCase_						)						)





	@slow
	def      _SCREAMING_SNAKE_CASE      (      self					: Any						)						->			Any:
				# prepare image, target and masks_path
				UpperCAmelCase_     : List[Any]			     =					Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"						)
				with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"	,	"r"						) as f:
							UpperCAmelCase_     : Tuple			     =					json.loads(f.read()						)

				UpperCAmelCase_     : Tuple			     =					{"file_name": "000000039769.png", "image_id": 39_769, "segments_info": target}

				UpperCAmelCase_     : Optional[Any]			     =					pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic"						)

				# encode them
				UpperCAmelCase_     : Union[str, Any]			     =					DeformableDetrImageProcessor(format="coco_panoptic"						)
				UpperCAmelCase_     : str			     =					image_processing(images=lowerCAmelCase_	,	annotations=lowerCAmelCase_	,	masks_path=lowerCAmelCase_	,	return_tensors="pt"						)

				# verify pixel values
				UpperCAmelCase_     : List[Any]			     =					torch.Size([1, 3, 800, 1_066]						)
				self.assertEqual(encoding["pixel_values"].shape	,	lowerCAmelCase_						)

				UpperCAmelCase_     : List[str]			     =					torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]						)
				self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]	,	lowerCAmelCase_	,	atol=1e-4						)						)

				# verify area
				UpperCAmelCase_     : Union[str, Any]			     =					torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7]						)
				self.assertTrue(torch.allclose(encoding["labels"][0]["area"]	,	lowerCAmelCase_						)						)
				# verify boxes
				UpperCAmelCase_     : Union[str, Any]			     =					torch.Size([6, 4]						)
				self.assertEqual(encoding["labels"][0]["boxes"].shape	,	lowerCAmelCase_						)
				UpperCAmelCase_     : Optional[Any]			     =					torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5]						)
				self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]	,	lowerCAmelCase_	,	atol=1e-3						)						)
				# verify image_id
				UpperCAmelCase_     : int			     =					torch.tensor([39_769]						)
				self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]	,	lowerCAmelCase_						)						)
				# verify is_crowd
				UpperCAmelCase_     : List[str]			     =					torch.tensor([0, 0, 0, 0, 0, 0]						)
				self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]	,	lowerCAmelCase_						)						)
				# verify class_labels
				UpperCAmelCase_     : int			     =					torch.tensor([17, 17, 63, 75, 75, 93]						)
				self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]	,	lowerCAmelCase_						)						)
				# verify masks
				UpperCAmelCase_     : Tuple			     =					822_873
				self.assertEqual(encoding["labels"][0]["masks"].sum().item()	,	lowerCAmelCase_						)
				# verify orig_size
				UpperCAmelCase_     : str			     =					torch.tensor([480, 640]						)
				self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]	,	lowerCAmelCase_						)						)
				# verify size
				UpperCAmelCase_     : str			     =					torch.tensor([800, 1_066]						)
				self.assertTrue(torch.allclose(encoding["labels"][0]["size"]	,	lowerCAmelCase_						)						)











"""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:
			lowerCamelCase_					=      None


lowerCamelCase_					=      logging.get_logger(__name__)


lowerCamelCase_					=      {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

lowerCamelCase_					=      {
    '''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''',
    },
}

lowerCamelCase_					=      {
    '''facebook/mbart-large-en-ro''': 1024,
    '''facebook/mbart-large-cc25''': 1024,
}

# fmt: off
lowerCamelCase_					=      ['''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     ):
	__magic_name__	      =				VOCAB_FILES_NAMES
	__magic_name__	      =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	__magic_name__	      =				PRETRAINED_VOCAB_FILES_MAP
	__magic_name__	      =				['''input_ids''', '''attention_mask''']
	__magic_name__	      =				MBartTokenizer

	__magic_name__	      =				[]
	__magic_name__	      =				[]





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

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

				UpperCAmelCase_     : Tuple			     =					vocab_file
				UpperCAmelCase_     : str			     =					False if not self.vocab_file else True

				UpperCAmelCase_     : List[Any]			     =					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}						)
				UpperCAmelCase_     : Tuple			     =					{
				    lang_code: self.convert_tokens_to_ids(lowerCAmelCase_						) for lang_code in FAIRSEQ_LANGUAGE_CODES
				}

				UpperCAmelCase_     : int			     =					src_lang if src_lang is not None else "en_XX"
				UpperCAmelCase_     : Union[str, Any]			     =					self.convert_tokens_to_ids(self._src_lang						)
				UpperCAmelCase_     : Any			     =					tgt_lang
				self.set_src_lang_special_tokens(self._src_lang						)





	@property
	def      _SCREAMING_SNAKE_CASE      (      self					: Optional[int]						)						->			str:
				return self._src_lang





	@src_lang.setter
	def      _SCREAMING_SNAKE_CASE      (      self					: Any	,	lowerCAmelCase_					: str						)						->			None:
				UpperCAmelCase_     : List[Any]			     =					new_src_lang
				self.set_src_lang_special_tokens(self._src_lang						)





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





	def      _SCREAMING_SNAKE_CASE      (      self					: List[Any]	,	lowerCAmelCase_					: List[str]	,	lowerCAmelCase_					: str = "en_XX"	,	lowerCAmelCase_					: Optional[List[str]] = None	,	lowerCAmelCase_					: str = "ro_RO"	,	**lowerCAmelCase_					: Dict	,	)						->			BatchEncoding:
				UpperCAmelCase_     : List[Any]			     =					src_lang
				UpperCAmelCase_     : Tuple			     =					tgt_lang
				return super().prepare_seqaseq_batch(lowerCAmelCase_	,	lowerCAmelCase_	,	**lowerCAmelCase_						)





	def      _SCREAMING_SNAKE_CASE      (      self					: Optional[int]						)						->			Any:
				return self.set_src_lang_special_tokens(self.src_lang						)





	def      _SCREAMING_SNAKE_CASE      (      self					: List[Any]						)						->			Dict:
				return self.set_tgt_lang_special_tokens(self.tgt_lang						)





	def      _SCREAMING_SNAKE_CASE      (      self					: Optional[Any]	,	lowerCAmelCase_					: List[Any]						)						->			None:
				UpperCAmelCase_     : int			     =					self.convert_tokens_to_ids(lowerCAmelCase_						)
				UpperCAmelCase_     : str			     =					[]
				UpperCAmelCase_     : str			     =					[self.eos_token_id, self.cur_lang_code]

				UpperCAmelCase_     : Union[str, Any]			     =					self.convert_ids_to_tokens(self.prefix_tokens						)
				UpperCAmelCase_     : List[Any]			     =					self.convert_ids_to_tokens(self.suffix_tokens						)

				UpperCAmelCase_     : Optional[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      _SCREAMING_SNAKE_CASE      (      self					: Tuple	,	lowerCAmelCase_					: str						)						->			None:
				UpperCAmelCase_     : Union[str, Any]			     =					self.convert_tokens_to_ids(lowerCAmelCase_						)
				UpperCAmelCase_     : Dict			     =					[]
				UpperCAmelCase_     : Optional[int]			     =					[self.eos_token_id, self.cur_lang_code]

				UpperCAmelCase_     : Tuple			     =					self.convert_ids_to_tokens(self.prefix_tokens						)
				UpperCAmelCase_     : Tuple			     =					self.convert_ids_to_tokens(self.suffix_tokens						)

				UpperCAmelCase_     : Optional[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      _SCREAMING_SNAKE_CASE      (      self					: str	,	lowerCAmelCase_					: str	,	lowerCAmelCase_					: 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(lowerCAmelCase_						):
							logger.error(f"""Vocabulary path ({save_directory}) should be a directory."""						)
							return
				UpperCAmelCase_     : List[str]			     =					os.path.join(
				    lowerCAmelCase_	,	(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]						)

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

				return (out_vocab_file,)



