


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 			A_   (			unittest.TestCase       ):




      '''simple docstring'''


      def __init__(							self:			List[str]		, a:			List[Any]		, a:			int=7		, a:			Optional[int]=3		, a:			Tuple=30		, a:			int=400		, a:			Optional[int]=True		, a:			List[Any]=None		, a:			List[Any]=True		, a:			Optional[Any]=[0.5, 0.5, 0.5]		, a:			List[str]=[0.5, 0.5, 0.5]		, a:			Dict=True		, a:			List[str]=1 / 255		, a:			Union[str, Any]=True		, ):
       __lowerCamelCase						:				Any						   =		size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 1333}
       __lowerCamelCase						:				List[Any]						   =		parent
       __lowerCamelCase						:				Tuple						   =		batch_size
       __lowerCamelCase						:				str						   =		num_channels
       __lowerCamelCase						:				str						   =		min_resolution
       __lowerCamelCase						:				Any						   =		max_resolution
       __lowerCamelCase						:				Any						   =		do_resize
       __lowerCamelCase						:				Any						   =		size
       __lowerCamelCase						:				Any						   =		do_normalize
       __lowerCamelCase						:				int						   =		image_mean
       __lowerCamelCase						:				str						   =		image_std
       __lowerCamelCase						:				List[Any]						   =		do_rescale
       __lowerCamelCase						:				List[Any]						   =		rescale_factor
       __lowerCamelCase						:				List[str]						   =		do_pad




      def 	_snake_case      (							self:			Union[str, Any]							):
       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:			Optional[int]		, a:			int		, a:			Tuple=False							):

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

       else:
        __lowerCamelCase						:				List[Any]						   =		[]
        for image in image_inputs:
         __lowerCamelCase						:				int						   =		self.get_expected_values([image]							)
         expected_values.append((expected_height, expected_width)							)
        __lowerCamelCase						:				Optional[int]						   =		max(lowercase__		, key=lambda a							: item[0]							)[0]
        __lowerCamelCase						:				Union[str, Any]						   =		max(lowercase__		, key=lambda a							: item[1]							)[1]

       return expected_height, expected_width



@require_torch
@require_vision
class 			A_   (			snake_case_   , unittest.TestCase       ):




      '''simple docstring'''


      __snake_case   			=  DeformableDetrImageProcessor if is_vision_available() else None




      def 	_snake_case      (							self:			Any							):
       __lowerCamelCase						:				Union[str, Any]						   =		DeformableDetrImageProcessingTester(self							)




      @property
      def 	_snake_case      (							self:			Tuple							):
       return self.image_processor_tester.prepare_image_processor_dict()




      def 	_snake_case      (							self:			Any							):
       __lowerCamelCase						:				str						   =		self.image_processing_class(**self.image_processor_dict							)
       self.assertTrue(hasattr(lowercase__		, 'image_mean'							)							)
       self.assertTrue(hasattr(lowercase__		, 'image_std'							)							)
       self.assertTrue(hasattr(lowercase__		, 'do_normalize'							)							)
       self.assertTrue(hasattr(lowercase__		, 'do_resize'							)							)
       self.assertTrue(hasattr(lowercase__		, 'do_rescale'							)							)
       self.assertTrue(hasattr(lowercase__		, 'do_pad'							)							)
       self.assertTrue(hasattr(lowercase__		, 'size'							)							)




      def 	_snake_case      (							self:			Tuple							):
       __lowerCamelCase						:				Optional[Any]						   =		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		, lowercase__							)

       __lowerCamelCase						:				int						   =		self.image_processing_class.from_dict(
           self.image_processor_dict		, size=42		, max_size=84		, pad_and_return_pixel_mask=lowercase__							)
       self.assertEqual(image_processor.size		, {'shortest_edge': 42, 'longest_edge': 84}							)
       self.assertEqual(image_processor.do_pad		, lowercase__							)




      def 	_snake_case      (							self:			List[str]							):
       pass




      def 	_snake_case      (							self:			List[str]							):
       __lowerCamelCase						:				str						   =		self.image_processing_class(**self.image_processor_dict							)
       # create random PIL images
       __lowerCamelCase						:				str						   =		prepare_image_inputs(self.image_processor_tester		, equal_resolution=lowercase__							)
       for image in image_inputs:
        self.assertIsInstance(lowercase__		, Image.Image							)

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

       __lowerCamelCase						:				Optional[Any]						   =		self.image_processor_tester.get_expected_values(lowercase__							)

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

       # Test batched
       __lowerCamelCase						:				Tuple						   =		self.image_processor_tester.get_expected_values(lowercase__		, batched=lowercase__							)

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

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

       __lowerCamelCase						:				Any						   =		self.image_processor_tester.get_expected_values(lowercase__							)

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

       # Test batched
       __lowerCamelCase						:				Tuple						   =		image_processing(lowercase__		, return_tensors='pt'							).pixel_values

       __lowerCamelCase						:				List[Any]						   =		self.image_processor_tester.get_expected_values(lowercase__		, batched=lowercase__							)

       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:			Union[str, Any]							):
       __lowerCamelCase						:				Any						   =		self.image_processing_class(**self.image_processor_dict							)
       # create random PyTorch tensors
       __lowerCamelCase						:				Any						   =		prepare_image_inputs(self.image_processor_tester		, equal_resolution=lowercase__		, torchify=lowercase__							)
       for image in image_inputs:
        self.assertIsInstance(lowercase__		, torch.Tensor							)

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

       __lowerCamelCase						:				str						   =		self.image_processor_tester.get_expected_values(lowercase__							)

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

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

       __lowerCamelCase						:				List[Any]						   =		self.image_processor_tester.get_expected_values(lowercase__		, batched=lowercase__							)

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




      @slow
      def 	_snake_case      (							self:			List[str]							):
       __lowerCamelCase						:				Any						   =		Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							)
       with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'		, 'r'							) as f:
        __lowerCamelCase						:				str						   =		json.loads(f.read()							)

       __lowerCamelCase						:				Union[str, Any]						   =		{"""image_id""": 3_9769, """annotations""": target}

       # encode them
       __lowerCamelCase						:				Dict						   =		DeformableDetrImageProcessor()
       __lowerCamelCase						:				Optional[Any]						   =		image_processing(images=lowercase__		, annotations=lowercase__		, return_tensors='pt'							)

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

       __lowerCamelCase						:				Optional[int]						   =		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]		, lowercase__		, atol=1e-4							)							)

       # verify area
       __lowerCamelCase						:				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']		, lowercase__							)							)
       # verify boxes
       __lowerCamelCase						:				List[Any]						   =		torch.Size([6, 4]							)
       self.assertEqual(encoding['labels'][0]['boxes'].shape		, lowercase__							)
       __lowerCamelCase						:				Tuple						   =		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]		, lowercase__		, atol=1e-3							)							)
       # verify image_id
       __lowerCamelCase						:				Tuple						   =		torch.tensor([3_9769]							)
       self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']		, lowercase__							)							)
       # verify is_crowd
       __lowerCamelCase						:				List[Any]						   =		torch.tensor([0, 0, 0, 0, 0, 0]							)
       self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']		, lowercase__							)							)
       # verify class_labels
       __lowerCamelCase						:				Optional[int]						   =		torch.tensor([75, 75, 63, 65, 17, 17]							)
       self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']		, lowercase__							)							)
       # verify orig_size
       __lowerCamelCase						:				Tuple						   =		torch.tensor([480, 640]							)
       self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']		, lowercase__							)							)
       # verify size
       __lowerCamelCase						:				Union[str, Any]						   =		torch.tensor([800, 1066]							)
       self.assertTrue(torch.allclose(encoding['labels'][0]['size']		, lowercase__							)							)




      @slow
      def 	_snake_case      (							self:			Optional[Any]							):
       __lowerCamelCase						:				Any						   =		Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							)
       with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'		, 'r'							) as f:
        __lowerCamelCase						:				Optional[int]						   =		json.loads(f.read()							)

       __lowerCamelCase						:				Optional[int]						   =		{"""file_name""": """000000039769.png""", """image_id""": 3_9769, """segments_info""": target}

       __lowerCamelCase						:				int						   =		pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'							)

       # encode them
       __lowerCamelCase						:				int						   =		DeformableDetrImageProcessor(format='coco_panoptic'							)
       __lowerCamelCase						:				int						   =		image_processing(images=lowercase__		, annotations=lowercase__		, masks_path=lowercase__		, return_tensors='pt'							)

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

       __lowerCamelCase						:				int						   =		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]		, lowercase__		, atol=1e-4							)							)

       # verify area
       __lowerCamelCase						:				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']		, lowercase__							)							)
       # verify boxes
       __lowerCamelCase						:				Tuple						   =		torch.Size([6, 4]							)
       self.assertEqual(encoding['labels'][0]['boxes'].shape		, lowercase__							)
       __lowerCamelCase						:				Tuple						   =		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]		, lowercase__		, atol=1e-3							)							)
       # verify image_id
       __lowerCamelCase						:				List[str]						   =		torch.tensor([3_9769]							)
       self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']		, lowercase__							)							)
       # verify is_crowd
       __lowerCamelCase						:				Union[str, Any]						   =		torch.tensor([0, 0, 0, 0, 0, 0]							)
       self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']		, lowercase__							)							)
       # verify class_labels
       __lowerCamelCase						:				Union[str, Any]						   =		torch.tensor([17, 17, 63, 75, 75, 93]							)
       self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']		, lowercase__							)							)
       # verify masks
       __lowerCamelCase						:				Any						   =		82_2873
       self.assertEqual(encoding['labels'][0]['masks'].sum().item()		, lowercase__							)
       # verify orig_size
       __lowerCamelCase						:				List[str]						   =		torch.tensor([480, 640]							)
       self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']		, lowercase__							)							)
       # verify size
       __lowerCamelCase						:				Union[str, Any]						   =		torch.tensor([800, 1066]							)
       self.assertTrue(torch.allclose(encoding['labels'][0]['size']		, lowercase__							)							)







'''simple docstring'''


import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import RobertaTokenizer


lowerCAmelCase_      :  Optional[Any]				     =  logging.get_logger(__name__)

lowerCAmelCase_      :  int				     =  {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

lowerCAmelCase_      :  Union[str, Any]				     =  {
    'vocab_file': {
        'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/vocab.json',
        'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/vocab.json',
        'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json',
        'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/vocab.json',
        'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json',
        'roberta-large-openai-detector': (
            'https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/merges.txt',
        'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/merges.txt',
        'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt',
        'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/merges.txt',
        'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt',
        'roberta-large-openai-detector': (
            'https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt'
        ),
    },
    'tokenizer_file': {
        'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/tokenizer.json',
        'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/tokenizer.json',
        'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json',
        'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json',
        'roberta-base-openai-detector': (
            'https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json'
        ),
        'roberta-large-openai-detector': (
            'https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json'
        ),
    },
}

lowerCAmelCase_      :  Union[str, Any]				     =  {
    'roberta-base': 512,
    'roberta-large': 512,
    'roberta-large-mnli': 512,
    'distilroberta-base': 512,
    'roberta-base-openai-detector': 512,
    'roberta-large-openai-detector': 512,
}







class      SCREAMING_SNAKE_CASE    (						snake_case_				):
      __magic_name__  :       int   							=					VOCAB_FILES_NAMES
      __magic_name__  :       List[str]   							=					PRETRAINED_VOCAB_FILES_MAP
      __magic_name__  :       str   							=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __magic_name__  :       List[str]   							=					['''input_ids''', '''attention_mask''']
      __magic_name__  :       Any   							=					RobertaTokenizer






      def __init__( self   :  Any						,						lowercase__   :  int=None						,						lowercase__   :  Union[str, Any]=None						,						lowercase__   :  List[Any]=None						,						lowercase__   :  int="replace"						,						lowercase__   :  Dict="<s>"						,						lowercase__   :  int="</s>"						,						lowercase__   :  Optional[Any]="</s>"						,						lowercase__   :  List[Any]="<s>"						,						lowercase__   :  Optional[int]="<unk>"						,						lowercase__   :  Tuple="<pad>"						,						lowercase__   :  Optional[Any]="<mask>"						,						lowercase__   :  List[Any]=False						,						lowercase__   :  Optional[Any]=True						,						**lowercase__   :  List[str]						,						):


           '''simple docstring'''

           super().__init__(
               lowercase__						,						lowercase__						,						tokenizer_file=lowercase__						,						errors=lowercase__						,						bos_token=lowercase__						,						eos_token=lowercase__						,						sep_token=lowercase__						,						cls_token=lowercase__						,						unk_token=lowercase__						,						pad_token=lowercase__						,						mask_token=lowercase__						,						add_prefix_space=lowercase__						,						trim_offsets=lowercase__						,						**lowercase__						,						)

           a_     :							Any     						=  json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()       )
           if pre_tok_state.get("""add_prefix_space"""						,						lowercase__       ) != add_prefix_space:
                a_     :							List[Any]     						=  getattr(lowercase__						,						pre_tok_state.pop("""type"""       )       )
                a_     :							Optional[Any]     						=  add_prefix_space
                a_     :							List[str]     						=  pre_tok_class(**lowercase__       )

           a_     :							List[Any]     						=  add_prefix_space

           a_     :							Any     						=  """post_processor"""
           a_     :							Dict     						=  getattr(self.backend_tokenizer						,						lowercase__						,						lowercase__       )
           if tokenizer_component_instance:
                a_     :							Optional[Any]     						=  json.loads(tokenizer_component_instance.__getstate__()       )

                # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
                if "sep" in state:
                     a_     :							Dict     						=  tuple(state["""sep"""]       )
                if "cls" in state:
                     a_     :							List[str]     						=  tuple(state["""cls"""]       )

                a_     :							str     						=  False

                if state.get("""add_prefix_space"""						,						lowercase__       ) != add_prefix_space:
                     a_     :							Any     						=  add_prefix_space
                     a_     :							List[str]     						=  True

                if state.get("""trim_offsets"""						,						lowercase__       ) != trim_offsets:
                     a_     :							Optional[int]     						=  trim_offsets
                     a_     :							Tuple     						=  True

                if changes_to_apply:
                     a_     :							str     						=  getattr(lowercase__						,						state.pop("""type"""       )       )
                     a_     :							Union[str, Any]     						=  component_class(**lowercase__       )
                     setattr(self.backend_tokenizer						,						lowercase__						,						lowercase__       )






      @property
      def 							lowercase_						( self   :  List[str]       ):


           '''simple docstring'''

           if self._mask_token is None:
                if self.verbose:
                     logger.error("""Using mask_token, but it is not set yet."""       )
                return None
           return str(self._mask_token       )






      @mask_token.setter
      def 							lowercase_						( self   :  Union[str, Any]						,						lowercase__   :  Tuple       ):


           '''simple docstring'''

           a_     :							List[Any]     						=  AddedToken(lowercase__						,						lstrip=lowercase__						,						rstrip=lowercase__       ) if isinstance(lowercase__						,						lowercase__       ) else value
           a_     :							Union[str, Any]     						=  value






      def 							lowercase_						( self   :  Union[str, Any]						,						*lowercase__   :  Union[str, Any]						,						**lowercase__   :  Union[str, Any]       ):


           '''simple docstring'''

           a_     :							Any     						=  kwargs.get("""is_split_into_words"""						,						lowercase__       )
           assert self.add_prefix_space or not is_split_into_words, (
               F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
               "to use it with pretokenized inputs."
           )

           return super()._batch_encode_plus(*lowercase__						,						**lowercase__       )






      def 							lowercase_						( self   :  Dict						,						*lowercase__   :  int						,						**lowercase__   :  Dict       ):


           '''simple docstring'''

           a_     :							Any     						=  kwargs.get("""is_split_into_words"""						,						lowercase__       )

           assert self.add_prefix_space or not is_split_into_words, (
               F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
               "to use it with pretokenized inputs."
           )

           return super()._encode_plus(*lowercase__						,						**lowercase__       )






      def 							lowercase_						( self   :  Dict						,						lowercase__   :  str						,						lowercase__   :  Optional[str] = None       ):


           '''simple docstring'''

           a_     :							str     						=  self._tokenizer.model.save(lowercase__						,						name=lowercase__       )
           return tuple(lowercase__       )






      def 							lowercase_						( self   :  Dict						,						lowercase__   :  List[Any]						,						lowercase__   :  int=None       ):


           '''simple docstring'''

           a_     :							int     						=  [self.bos_token_id] + token_ids_a + [self.eos_token_id]
           if token_ids_a is None:
                return output

           return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]






      def 							lowercase_						( self   :  List[str]						,						lowercase__   :  List[int]						,						lowercase__   :  Optional[List[int]] = None       ):


           '''simple docstring'''

           a_     :							Optional[Any]     						=  [self.sep_token_id]
           a_     :							Optional[int]     						=  [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]
