






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 YolosImageProcessor


class 							__a							(  unittest.TestCase						):





	'''simple docstring'''



	def __init__(						self ,    UpperCamelCase__ ,    UpperCamelCase__=7 ,    UpperCamelCase__=3 ,    UpperCamelCase__=30 ,    UpperCamelCase__=400 ,    UpperCamelCase__=True ,    UpperCamelCase__=None ,    UpperCamelCase__=True ,    UpperCamelCase__=[0.5, 0.5, 0.5] ,    UpperCamelCase__=[0.5, 0.5, 0.5] ,    UpperCamelCase__=True ,    UpperCamelCase__=1 / 255 ,    UpperCamelCase__=True ,    ):
					# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
					SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   size if size is not None else {'shortest_edge': 18, 'longest_edge': 1333}
					SCREAMING_SNAKE_CASE_   :	List[str]													=   parent
					SCREAMING_SNAKE_CASE_   :	str													=   batch_size
					SCREAMING_SNAKE_CASE_   :	Any													=   num_channels
					SCREAMING_SNAKE_CASE_   :	Any													=   min_resolution
					SCREAMING_SNAKE_CASE_   :	Tuple													=   max_resolution
					SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   do_resize
					SCREAMING_SNAKE_CASE_   :	Dict													=   size
					SCREAMING_SNAKE_CASE_   :	str													=   do_normalize
					SCREAMING_SNAKE_CASE_   :	List[str]													=   image_mean
					SCREAMING_SNAKE_CASE_   :	int													=   image_std
					SCREAMING_SNAKE_CASE_   :	Any													=   do_rescale
					SCREAMING_SNAKE_CASE_   :	Dict													=   rescale_factor
					SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   do_pad


	def   __snake_case			(						self       ):
					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   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__=False       ):
					if not batched:
									SCREAMING_SNAKE_CASE_   :	Any													=   image_inputs[0]
									if isinstance(UpperCamelCase__ ,    Image.Image       ):
													SCREAMING_SNAKE_CASE_   :	Optional[int]													=   image.size
									else:
													SCREAMING_SNAKE_CASE_   :	List[Any]													=   image.shape[1], image.shape[2]
									if w < h:
													SCREAMING_SNAKE_CASE_   :	int													=   int(self.size['shortest_edge'] * h / w       )
													SCREAMING_SNAKE_CASE_   :	Optional[int]													=   self.size['shortest_edge']
									elif w > h:
													SCREAMING_SNAKE_CASE_   :	int													=   self.size['shortest_edge']
													SCREAMING_SNAKE_CASE_   :	Any													=   int(self.size['shortest_edge'] * w / h       )
									else:
													SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   self.size['shortest_edge']
													SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   self.size['shortest_edge']

					else:
									SCREAMING_SNAKE_CASE_   :	str													=   []
									for image in image_inputs:
													SCREAMING_SNAKE_CASE_   :	int													=   self.get_expected_values([image]       )
													expected_values.append((expected_height, expected_width)       )
									SCREAMING_SNAKE_CASE_   :	List[str]													=   max(UpperCamelCase__ ,    key=lambda UpperCamelCase__       : item[0]       )[0]
									SCREAMING_SNAKE_CASE_   :	Tuple													=   max(UpperCamelCase__ ,    key=lambda UpperCamelCase__       : item[1]       )[1]

					return expected_height, expected_width






@require_torch
@require_vision
class 							__a							(  __A ,	unittest.TestCase						):





	'''simple docstring'''



	UpperCAmelCase__      :				Union[str, Any]				    =						YolosImageProcessor if is_vision_available() else None


	def   __snake_case			(						self       ):
					SCREAMING_SNAKE_CASE_   :	Optional[int]													=   YolosImageProcessingTester(self       )


	@property
	def   __snake_case			(						self       ):
					return self.image_processor_tester.prepare_image_processor_dict()


	def   __snake_case			(						self       ):
					SCREAMING_SNAKE_CASE_   :	int													=   self.image_processing_class(**self.image_processor_dict       )
					self.assertTrue(hasattr(UpperCamelCase__ ,    'image_mean'       )       )
					self.assertTrue(hasattr(UpperCamelCase__ ,    'image_std'       )       )
					self.assertTrue(hasattr(UpperCamelCase__ ,    'do_normalize'       )       )
					self.assertTrue(hasattr(UpperCamelCase__ ,    'do_resize'       )       )
					self.assertTrue(hasattr(UpperCamelCase__ ,    'size'       )       )


	def   __snake_case			(						self       ):
					SCREAMING_SNAKE_CASE_   :	int													=   self.image_processing_class.from_dict(self.image_processor_dict       )
					self.assertEqual(image_processor.size ,    {'shortest_edge': 18, 'longest_edge': 1333}       )
					self.assertEqual(image_processor.do_pad ,    UpperCamelCase__       )

					SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   self.image_processing_class.from_dict(
					    self.image_processor_dict ,    size=42 ,    max_size=84 ,    pad_and_return_pixel_mask=UpperCamelCase__       )
					self.assertEqual(image_processor.size ,    {'shortest_edge': 42, 'longest_edge': 84}       )
					self.assertEqual(image_processor.do_pad ,    UpperCamelCase__       )


	def   __snake_case			(						self       ):
					pass


	def   __snake_case			(						self       ):
					# Initialize image_processing
					SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   self.image_processing_class(**self.image_processor_dict       )
					# create random PIL images
					SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   prepare_image_inputs(self.image_processor_tester ,    equal_resolution=UpperCamelCase__       )
					for image in image_inputs:
									self.assertIsInstance(UpperCamelCase__ ,    Image.Image       )

					# Test not batched input
					SCREAMING_SNAKE_CASE_   :	str													=   image_processing(image_inputs[0] ,    return_tensors='pt'       ).pixel_values

					SCREAMING_SNAKE_CASE_   :	List[str]													=   self.image_processor_tester.get_expected_values(UpperCamelCase__       )

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

					# Test batched
					SCREAMING_SNAKE_CASE_   :	Tuple													=   self.image_processor_tester.get_expected_values(UpperCamelCase__ ,    batched=UpperCamelCase__       )

					SCREAMING_SNAKE_CASE_   :	int													=   image_processing(UpperCamelCase__ ,    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   __snake_case			(						self       ):
					# Initialize image_processing
					SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   self.image_processing_class(**self.image_processor_dict       )
					# create random numpy tensors
					SCREAMING_SNAKE_CASE_   :	Tuple													=   prepare_image_inputs(self.image_processor_tester ,    equal_resolution=UpperCamelCase__ ,    numpify=UpperCamelCase__       )
					for image in image_inputs:
									self.assertIsInstance(UpperCamelCase__ ,    np.ndarray       )

					# Test not batched input
					SCREAMING_SNAKE_CASE_   :	Tuple													=   image_processing(image_inputs[0] ,    return_tensors='pt'       ).pixel_values

					SCREAMING_SNAKE_CASE_   :	Any													=   self.image_processor_tester.get_expected_values(UpperCamelCase__       )

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

					# Test batched
					SCREAMING_SNAKE_CASE_   :	List[Any]													=   image_processing(UpperCamelCase__ ,    return_tensors='pt'       ).pixel_values

					SCREAMING_SNAKE_CASE_   :	int													=   self.image_processor_tester.get_expected_values(UpperCamelCase__ ,    batched=UpperCamelCase__       )

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


	def   __snake_case			(						self       ):
					# Initialize image_processing
					SCREAMING_SNAKE_CASE_   :	Tuple													=   self.image_processing_class(**self.image_processor_dict       )
					# create random PyTorch tensors
					SCREAMING_SNAKE_CASE_   :	str													=   prepare_image_inputs(self.image_processor_tester ,    equal_resolution=UpperCamelCase__ ,    torchify=UpperCamelCase__       )
					for image in image_inputs:
									self.assertIsInstance(UpperCamelCase__ ,    torch.Tensor       )

					# Test not batched input
					SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   image_processing(image_inputs[0] ,    return_tensors='pt'       ).pixel_values

					SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   self.image_processor_tester.get_expected_values(UpperCamelCase__       )

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

					# Test batched
					SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   image_processing(UpperCamelCase__ ,    return_tensors='pt'       ).pixel_values

					SCREAMING_SNAKE_CASE_   :	Tuple													=   self.image_processor_tester.get_expected_values(UpperCamelCase__ ,    batched=UpperCamelCase__       )

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


	def   __snake_case			(						self       ):
					# Initialize image_processings
					SCREAMING_SNAKE_CASE_   :	Tuple													=   self.image_processing_class(**self.image_processor_dict       )
					SCREAMING_SNAKE_CASE_   :	List[Any]													=   self.image_processing_class(do_resize=UpperCamelCase__ ,    do_normalize=UpperCamelCase__ ,    do_rescale=UpperCamelCase__       )
					# create random PyTorch tensors
					SCREAMING_SNAKE_CASE_   :	List[str]													=   prepare_image_inputs(self.image_processor_tester ,    equal_resolution=UpperCamelCase__ ,    torchify=UpperCamelCase__       )
					for image in image_inputs:
									self.assertIsInstance(UpperCamelCase__ ,    torch.Tensor       )

					# Test whether the method "pad" and calling the image processor return the same tensors
					SCREAMING_SNAKE_CASE_   :	Any													=   image_processing_a.pad(UpperCamelCase__ ,    return_tensors='pt'       )
					SCREAMING_SNAKE_CASE_   :	str													=   image_processing_a(UpperCamelCase__ ,    return_tensors='pt'       )

					self.assertTrue(
					    torch.allclose(encoded_images_with_method['pixel_values'] ,    encoded_images['pixel_values'] ,    atol=1E-4       )       )


	@slow
	def   __snake_case			(						self       ):
					# prepare image and target
					SCREAMING_SNAKE_CASE_   :	Dict													=   Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'       )
					with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt' ,    'r'       ) as f:
									SCREAMING_SNAKE_CASE_   :	List[Any]													=   json.loads(f.read()       )

					SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   {'image_id': 39769, 'annotations': target}

					# encode them
					SCREAMING_SNAKE_CASE_   :	str													=   YolosImageProcessor.from_pretrained('hustvl/yolos-small'       )
					SCREAMING_SNAKE_CASE_   :	str													=   image_processing(images=UpperCamelCase__ ,    annotations=UpperCamelCase__ ,    return_tensors='pt'       )

					# verify pixel values
					SCREAMING_SNAKE_CASE_   :	str													=   torch.Size([1, 3, 800, 1066]       )
					self.assertEqual(encoding['pixel_values'].shape ,    UpperCamelCase__       )

					SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   torch.tensor([0.27_96, 0.31_38, 0.34_81]       )
					self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3] ,    UpperCamelCase__ ,    atol=1E-4       )       )

					# verify area
					SCREAMING_SNAKE_CASE_   :	Optional[int]													=   torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]       )
					self.assertTrue(torch.allclose(encoding['labels'][0]['area'] ,    UpperCamelCase__       )       )
					# verify boxes
					SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   torch.Size([6, 4]       )
					self.assertEqual(encoding['labels'][0]['boxes'].shape ,    UpperCamelCase__       )
					SCREAMING_SNAKE_CASE_   :	List[str]													=   torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15]       )
					self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0] ,    UpperCamelCase__ ,    atol=1E-3       )       )
					# verify image_id
					SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   torch.tensor([39769]       )
					self.assertTrue(torch.allclose(encoding['labels'][0]['image_id'] ,    UpperCamelCase__       )       )
					# verify is_crowd
					SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   torch.tensor([0, 0, 0, 0, 0, 0]       )
					self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd'] ,    UpperCamelCase__       )       )
					# verify class_labels
					SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   torch.tensor([75, 75, 63, 65, 17, 17]       )
					self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels'] ,    UpperCamelCase__       )       )
					# verify orig_size
					SCREAMING_SNAKE_CASE_   :	Any													=   torch.tensor([480, 640]       )
					self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size'] ,    UpperCamelCase__       )       )
					# verify size
					SCREAMING_SNAKE_CASE_   :	Dict													=   torch.tensor([800, 1066]       )
					self.assertTrue(torch.allclose(encoding['labels'][0]['size'] ,    UpperCamelCase__       )       )






	@slow
	def   __snake_case			(						self       ):
					# prepare image, target and masks_path
					SCREAMING_SNAKE_CASE_   :	Optional[int]													=   Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'       )
					with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt' ,    'r'       ) as f:
									SCREAMING_SNAKE_CASE_   :	Optional[int]													=   json.loads(f.read()       )

					SCREAMING_SNAKE_CASE_   :	List[Any]													=   {'file_name': '000000039769.png', 'image_id': 39769, 'segments_info': target}

					SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'       )

					# encode them
					SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   YolosImageProcessor(format='coco_panoptic'       )
					SCREAMING_SNAKE_CASE_   :	Tuple													=   image_processing(images=UpperCamelCase__ ,    annotations=UpperCamelCase__ ,    masks_path=UpperCamelCase__ ,    return_tensors='pt'       )

					# verify pixel values
					SCREAMING_SNAKE_CASE_   :	List[Any]													=   torch.Size([1, 3, 800, 1066]       )
					self.assertEqual(encoding['pixel_values'].shape ,    UpperCamelCase__       )

					SCREAMING_SNAKE_CASE_   :	Tuple													=   torch.tensor([0.27_96, 0.31_38, 0.34_81]       )
					self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3] ,    UpperCamelCase__ ,    atol=1E-4       )       )

					# verify area
					SCREAMING_SNAKE_CASE_   :	int													=   torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]       )
					self.assertTrue(torch.allclose(encoding['labels'][0]['area'] ,    UpperCamelCase__       )       )
					# verify boxes
					SCREAMING_SNAKE_CASE_   :	List[str]													=   torch.Size([6, 4]       )
					self.assertEqual(encoding['labels'][0]['boxes'].shape ,    UpperCamelCase__       )
					SCREAMING_SNAKE_CASE_   :	List[str]													=   torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25]       )
					self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0] ,    UpperCamelCase__ ,    atol=1E-3       )       )
					# verify image_id
					SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   torch.tensor([39769]       )
					self.assertTrue(torch.allclose(encoding['labels'][0]['image_id'] ,    UpperCamelCase__       )       )
					# verify is_crowd
					SCREAMING_SNAKE_CASE_   :	str													=   torch.tensor([0, 0, 0, 0, 0, 0]       )
					self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd'] ,    UpperCamelCase__       )       )
					# verify class_labels
					SCREAMING_SNAKE_CASE_   :	List[str]													=   torch.tensor([17, 17, 63, 75, 75, 93]       )
					self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels'] ,    UpperCamelCase__       )       )
					# verify masks
					SCREAMING_SNAKE_CASE_   :	List[Any]													=   822873
					self.assertEqual(encoding['labels'][0]['masks'].sum().item() ,    UpperCamelCase__       )
					# verify orig_size
					SCREAMING_SNAKE_CASE_   :	int													=   torch.tensor([480, 640]       )
					self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size'] ,    UpperCamelCase__       )       )
					# verify size
					SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   torch.tensor([800, 1066]       )
					self.assertTrue(torch.allclose(encoding['labels'][0]['size'] ,    UpperCamelCase__       )       )







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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


A        =			logging.get_logger(__name__)

A        =			{'vocab_file': 'spm_char.model'}

A        =			{
    'vocab_file': {
        'microsoft/speecht5_asr': 'https://huggingface.co/microsoft/speecht5_asr/resolve/main/spm_char.model',
        'microsoft/speecht5_tts': 'https://huggingface.co/microsoft/speecht5_tts/resolve/main/spm_char.model',
        'microsoft/speecht5_vc': 'https://huggingface.co/microsoft/speecht5_vc/resolve/main/spm_char.model',
    }
}

A        =			{
    'microsoft/speecht5_asr': 1024,
    'microsoft/speecht5_tts': 1024,
    'microsoft/speecht5_vc': 1024,
}


class 							__a							(  __A						):





		'''simple docstring'''



		UpperCAmelCase__      :				str				    =						VOCAB_FILES_NAMES
		UpperCAmelCase__      :				List[str]				    =						PRETRAINED_VOCAB_FILES_MAP
		UpperCAmelCase__      :				Optional[int]				    =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		UpperCAmelCase__      :				Dict				    =						["""input_ids""", """attention_mask"""]


		def __init__(						self ,    UpperCamelCase__ ,    UpperCamelCase__="<s>" ,    UpperCamelCase__="</s>" ,    UpperCamelCase__="<unk>" ,    UpperCamelCase__="<pad>" ,    UpperCamelCase__ = None ,    **UpperCamelCase__ ,    ):
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   {} if sp_model_kwargs is None else sp_model_kwargs

						super().__init__(
						    bos_token=UpperCamelCase__ ,    eos_token=UpperCamelCase__ ,    unk_token=UpperCamelCase__ ,    pad_token=UpperCamelCase__ ,    sp_model_kwargs=self.sp_model_kwargs ,    **UpperCamelCase__ ,    )

						SCREAMING_SNAKE_CASE_   :	Optional[Any]													=   vocab_file

						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   spm.SentencePieceProcessor(**self.sp_model_kwargs       )
						self.sp_model.Load(UpperCamelCase__       )


		@property
		def   __snake_case			(						self       ):
						return self.sp_model.get_piece_size()


		def   __snake_case			(						self       ):
						SCREAMING_SNAKE_CASE_   :	List[str]													=   {self.convert_ids_to_tokens(UpperCamelCase__       ): i for i in range(self.vocab_size       )}
						vocab.update(self.added_tokens_encoder       )
						return vocab


		def __getstate__(						self       ):
						SCREAMING_SNAKE_CASE_   :	str													=   self.__dict__.copy()
						SCREAMING_SNAKE_CASE_   :	List[str]													=   None
						return state


		def __setstate__(						self ,    UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   d

						# for backward compatibility
						if not hasattr(self ,    'sp_model_kwargs'       ):
										SCREAMING_SNAKE_CASE_   :	Any													=   {}

						SCREAMING_SNAKE_CASE_   :	List[Any]													=   spm.SentencePieceProcessor(**self.sp_model_kwargs       )
						self.sp_model.Load(self.vocab_file       )


		def   __snake_case			(						self ,    UpperCamelCase__       ):
						return self.sp_model.encode(UpperCamelCase__ ,    out_type=UpperCamelCase__       )


		def   __snake_case			(						self ,    UpperCamelCase__       ):
						return self.sp_model.piece_to_id(UpperCamelCase__       )


		def   __snake_case			(						self ,    UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   self.sp_model.IdToPiece(UpperCamelCase__       )
						return token


		def   __snake_case			(						self ,    UpperCamelCase__       ):
						SCREAMING_SNAKE_CASE_   :	int													=   []
						SCREAMING_SNAKE_CASE_   :	int													=   ''
						for token in tokens:
										# make sure that special tokens are not decoded using sentencepiece model
										if token in self.all_special_tokens:
														out_string += self.sp_model.decode(UpperCamelCase__       ) + token
														SCREAMING_SNAKE_CASE_   :	Any													=   []
										else:
														current_sub_tokens.append(UpperCamelCase__       )
						out_string += self.sp_model.decode(UpperCamelCase__       )
						return out_string.strip()


		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__=None       ):
						if token_ids_a is None:
										return token_ids_a + [self.eos_token_id]
						# We don't expect to process pairs, but leave the pair logic for API consistency
						return token_ids_a + token_ids_a + [self.eos_token_id]


		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ = None ,    UpperCamelCase__ = False       ):
						if already_has_special_tokens:
										return super().get_special_tokens_mask(
										    token_ids_a=UpperCamelCase__ ,    token_ids_a=UpperCamelCase__ ,    already_has_special_tokens=UpperCamelCase__       )

						SCREAMING_SNAKE_CASE_   :	Union[str, Any]													=   [1]
						if token_ids_a is None:
										return ([0] * len(UpperCamelCase__       )) + suffix_ones
						return ([0] * len(UpperCamelCase__       )) + ([0] * len(UpperCamelCase__       )) + suffix_ones






		def   __snake_case			(						self ,    UpperCamelCase__ ,    UpperCamelCase__ = None       ):
						if not os.path.isdir(UpperCamelCase__       ):
										logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''       )
										return
						SCREAMING_SNAKE_CASE_   :	Optional[int]													=   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__       ) and os.path.isfile(self.vocab_file       ):
										copyfile(self.vocab_file ,    UpperCamelCase__       )
						elif not os.path.isfile(self.vocab_file       ):
										with open(UpperCamelCase__ ,    'wb'       ) as fi:
														SCREAMING_SNAKE_CASE_   :	Tuple													=   self.sp_model.serialized_model_proto()
														fi.write(UpperCamelCase__       )

						return (out_vocab_file,)