



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     _UpperCAmelCase			(       unittest.TestCase     ):




					def __init__(				self ,						a__ ,						a__=7 ,						a__=3 ,						a__=3_0 ,						a__=4_0_0 ,						a__=True ,						a__=None ,						a__=True ,						a__=[0.5, 0.5, 0.5] ,						a__=[0.5, 0.5, 0.5] ,						a__=True ,						a__=1 / 2_5_5 ,						a__=True ,						):
									# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
									A__		 =					size if size is not None else {'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3}
									A__		 =					parent
									A__		 =					batch_size
									A__		 =					num_channels
									A__		 =					min_resolution
									A__		 =					max_resolution
									A__		 =					do_resize
									A__		 =					size
									A__		 =					do_normalize
									A__		 =					image_mean
									A__		 =					image_std
									A__		 =					do_rescale
									A__		 =					rescale_factor
									A__		 =					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 ,						a__ ,						a__=False):
									if not batched:
													A__		 =					image_inputs[0]
													if isinstance(a__ ,						Image.Image):
																	A__ ,	A__		 =					image.size
													else:
																	A__ ,	A__		 =					image.shape[1], image.shape[2]
													if w < h:
																	A__		 =					int(self.size['''shortest_edge'''] * h / w)
																	A__		 =					self.size['''shortest_edge''']
													elif w > h:
																	A__		 =					self.size['''shortest_edge''']
																	A__		 =					int(self.size['''shortest_edge'''] * w / h)
													else:
																	A__		 =					self.size['''shortest_edge''']
																	A__		 =					self.size['''shortest_edge''']

									else:
													A__		 =					[]
													for image in image_inputs:
																	A__ ,	A__		 =					self.get_expected_values([image])
																	expected_values.append((expected_height, expected_width))
													A__		 =					max(a__ ,						key=lambda a__: item[0])[0]
													A__		 =					max(a__ ,						key=lambda a__: item[1])[1]

									return expected_height, expected_width







@require_torch
@require_vision
class     _UpperCAmelCase			(       A__			,      unittest.TestCase     ):
					UpperCamelCase__		    =				YolosImageProcessor if is_vision_available() else None




					def 		snake_case_		(				self):
									A__		 =					YolosImageProcessingTester(self)




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




					def 		snake_case_		(				self):
									A__		 =					self.image_processing_class(**self.image_processor_dict)
									self.assertTrue(hasattr(a__ ,						'''image_mean'''))
									self.assertTrue(hasattr(a__ ,						'''image_std'''))
									self.assertTrue(hasattr(a__ ,						'''do_normalize'''))
									self.assertTrue(hasattr(a__ ,						'''do_resize'''))
									self.assertTrue(hasattr(a__ ,						'''size'''))




					def 		snake_case_		(				self):
									A__		 =					self.image_processing_class.from_dict(self.image_processor_dict)
									self.assertEqual(image_processor.size ,						{'''shortest_edge''': 1_8, '''longest_edge''': 1_3_3_3})
									self.assertEqual(image_processor.do_pad ,						a__)

									A__		 =					self.image_processing_class.from_dict(
									    self.image_processor_dict ,						size=4_2 ,						max_size=8_4 ,						pad_and_return_pixel_mask=a__)
									self.assertEqual(image_processor.size ,						{'''shortest_edge''': 4_2, '''longest_edge''': 8_4})
									self.assertEqual(image_processor.do_pad ,						a__)




					def 		snake_case_		(				self):
									pass




					def 		snake_case_		(				self):
									# Initialize image_processing
									A__		 =					self.image_processing_class(**self.image_processor_dict)
									# create random PIL images
									A__		 =					prepare_image_inputs(self.image_processor_tester ,						equal_resolution=a__)
									for image in image_inputs:
													self.assertIsInstance(a__ ,						Image.Image)

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

									A__ ,	A__		 =					self.image_processor_tester.get_expected_values(a__)

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

									# Test batched
									A__ ,	A__		 =					self.image_processor_tester.get_expected_values(a__ ,						batched=a__)

									A__		 =					image_processing(a__ ,						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
									A__		 =					self.image_processing_class(**self.image_processor_dict)
									# create random numpy tensors
									A__		 =					prepare_image_inputs(self.image_processor_tester ,						equal_resolution=a__ ,						numpify=a__)
									for image in image_inputs:
													self.assertIsInstance(a__ ,						np.ndarray)

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

									A__ ,	A__		 =					self.image_processor_tester.get_expected_values(a__)

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

									# Test batched
									A__		 =					image_processing(a__ ,						return_tensors='''pt''').pixel_values

									A__ ,	A__		 =					self.image_processor_tester.get_expected_values(a__ ,						batched=a__)

									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
									A__		 =					self.image_processing_class(**self.image_processor_dict)
									# create random PyTorch tensors
									A__		 =					prepare_image_inputs(self.image_processor_tester ,						equal_resolution=a__ ,						torchify=a__)
									for image in image_inputs:
													self.assertIsInstance(a__ ,						torch.Tensor)

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

									A__ ,	A__		 =					self.image_processor_tester.get_expected_values(a__)

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

									# Test batched
									A__		 =					image_processing(a__ ,						return_tensors='''pt''').pixel_values

									A__ ,	A__		 =					self.image_processor_tester.get_expected_values(a__ ,						batched=a__)

									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
									A__		 =					self.image_processing_class(**self.image_processor_dict)
									A__		 =					self.image_processing_class(do_resize=a__ ,						do_normalize=a__ ,						do_rescale=a__)
									# create random PyTorch tensors
									A__		 =					prepare_image_inputs(self.image_processor_tester ,						equal_resolution=a__ ,						torchify=a__)
									for image in image_inputs:
													self.assertIsInstance(a__ ,						torch.Tensor)

									# Test whether the method "pad" and calling the image processor return the same tensors
									A__		 =					image_processing_a.pad(a__ ,						return_tensors='''pt''')
									A__		 =					image_processing_a(a__ ,						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
									A__		 =					Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''')
									with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt''' ,						'''r''') as f:
													A__		 =					json.loads(f.read())

									A__		 =					{'''image_id''': 3_9_7_6_9, '''annotations''': target}

									# encode them
									A__		 =					YolosImageProcessor.from_pretrained('''hustvl/yolos-small''')
									A__		 =					image_processing(images=a__ ,						annotations=a__ ,						return_tensors='''pt''')

									# verify pixel values
									A__		 =					torch.Size([1, 3, 8_0_0, 1_0_6_6])
									self.assertEqual(encoding['''pixel_values'''].shape ,						a__)

									A__		 =					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] ,						a__ ,						atol=1e-4))

									# verify area
									A__		 =					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'''] ,						a__))
									# verify boxes
									A__		 =					torch.Size([6, 4])
									self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape ,						a__)
									A__		 =					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] ,						a__ ,						atol=1e-3))
									# verify image_id
									A__		 =					torch.tensor([3_9_7_6_9])
									self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] ,						a__))
									# verify is_crowd
									A__		 =					torch.tensor([0, 0, 0, 0, 0, 0])
									self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] ,						a__))
									# verify class_labels
									A__		 =					torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7])
									self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] ,						a__))
									# verify orig_size
									A__		 =					torch.tensor([4_8_0, 6_4_0])
									self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] ,						a__))
									# verify size
									A__		 =					torch.tensor([8_0_0, 1_0_6_6])
									self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] ,						a__))




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

									A__		 =					{'''file_name''': '''000000039769.png''', '''image_id''': 3_9_7_6_9, '''segments_info''': target}

									A__		 =					pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic''')

									# encode them
									A__		 =					YolosImageProcessor(format='''coco_panoptic''')
									A__		 =					image_processing(images=a__ ,						annotations=a__ ,						masks_path=a__ ,						return_tensors='''pt''')

									# verify pixel values
									A__		 =					torch.Size([1, 3, 8_0_0, 1_0_6_6])
									self.assertEqual(encoding['''pixel_values'''].shape ,						a__)

									A__		 =					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] ,						a__ ,						atol=1e-4))

									# verify area
									A__		 =					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'''] ,						a__))
									# verify boxes
									A__		 =					torch.Size([6, 4])
									self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape ,						a__)
									A__		 =					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] ,						a__ ,						atol=1e-3))
									# verify image_id
									A__		 =					torch.tensor([3_9_7_6_9])
									self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] ,						a__))
									# verify is_crowd
									A__		 =					torch.tensor([0, 0, 0, 0, 0, 0])
									self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] ,						a__))
									# verify class_labels
									A__		 =					torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3])
									self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] ,						a__))
									# verify masks
									A__		 =					8_2_2_8_7_3
									self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item() ,						a__)
									# verify orig_size
									A__		 =					torch.tensor([4_8_0, 6_4_0])
									self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] ,						a__))
									# verify size
									A__		 =					torch.tensor([8_0_0, 1_0_6_6])
									self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] ,						a__))





import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

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


_lowercase						  =		logging.get_logger(__name__)

_lowercase						  =		"▁"

_lowercase						  =		{"vocab_file": "vocab.txt", "sentencepiece_model_ckpt": "sentencepiece.bpe.model"}

_lowercase						  =		{
    "sentencepiece_model_file": "sentencepiece.bpe.model",
    "vocab_file": "vocab.txt",
}

_lowercase						  =		{
    "vocab_file": {
        "ernie-m-base": "https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt",
        "ernie-m-large": "https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt",
    },
    "sentencepiece_model_file": {
        "ernie-m-base": "https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model",
        "ernie-m-large": "https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model",
    },
}

_lowercase						  =		{
    "ernie-m-base": 514,
    "ernie-m-large": 514,
}

_lowercase						  =		{
    "ernie-m-base": {"do_lower_case": False},
    "ernie-m-large": {"do_lower_case": False},
}



class     _UpperCAmelCase			(       A__     ):
     UpperCamelCase__		    =				["input_ids"]

     UpperCamelCase__		    =				VOCAB_FILES_NAMES
     UpperCamelCase__		    =				PRETRAINED_INIT_CONFIGURATION
     UpperCamelCase__		    =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCamelCase__		    =				PRETRAINED_VOCAB_FILES_MAP
     UpperCamelCase__		    =				RESOURCE_FILES_NAMES




     def __init__(				self ,						a__ ,						a__=None ,						a__=False ,						a__="utf8" ,						a__="[UNK]" ,						a__="[SEP]" ,						a__="[PAD]" ,						a__="[CLS]" ,						a__="[MASK]" ,						a__ = None ,						**a__ ,						):
         # Mask token behave like a normal word, i.e. include the space before it and
         # is included in the raw text, there should be a match in a non-normalized sentence.

         A__		 =					{} if sp_model_kwargs is None else sp_model_kwargs
         super().__init__(
             do_lower_case=a__ ,						unk_token=a__ ,						sep_token=a__ ,						pad_token=a__ ,						cls_token=a__ ,						mask_token=a__ ,						vocab_file=a__ ,						encoding=a__ ,						sp_model_kwargs=self.sp_model_kwargs ,						**a__ ,						)
         A__		 =					do_lower_case
         A__		 =					sentencepiece_model_ckpt
         A__		 =					spm.SentencePieceProcessor(**self.sp_model_kwargs)
         self.sp_model.Load(a__)

         # to mimic paddlenlp.transformers.ernie_m.tokenizer.ErnieMTokenizer functioning
         if vocab_file is not None:
             A__		 =					self.load_vocab(filepath=a__)
         else:
             A__		 =					{self.sp_model.id_to_piece(a__): id for id in range(self.sp_model.get_piece_size())}
         A__		 =					{v: k for k, v in self.vocab.items()}




     def 		snake_case_		(				self ,						a__):
         if text is None:
             return None

         A__		 =					self.tokenize(a__)
         A__ ,	A__		 =					'''''', []

         for i, ch in enumerate(a__):
             if ch in self.SP_CHAR_MAPPING:
                 A__		 =					self.SP_CHAR_MAPPING.get(a__)
             else:
                 A__		 =					unicodedata.normalize('''NFKC''' ,						a__)
             if self.is_whitespace(a__):
                 continue
             normalized_text += ch
             char_mapping.extend([i] * len(a__))

         A__ ,	A__ ,	A__		 =					normalized_text, [], 0

         if self.do_lower_case:
             A__		 =					text.lower()

         for token in split_tokens:
             if token[:1] == "▁":
                 A__		 =					token[1:]
             A__		 =					text[offset:].index(a__) + offset
             A__		 =					start + len(a__)

             token_mapping.append((char_mapping[start], char_mapping[end - 1] + 1))
             A__		 =					end
         return token_mapping




     @property
     def 		snake_case_		(				self):
         return len(self.vocab)




     def 		snake_case_		(				self):
         return dict(self.vocab ,						**self.added_tokens_encoder)




     def __getstate__(				self):
         A__		 =					self.__dict__.copy()
         A__		 =					None
         return state




     def __setstate__(				self ,						a__):
         A__		 =					d

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

         A__		 =					spm.SentencePieceProcessor(**self.sp_model_kwargs)
         self.sp_model.Load(self.sentencepiece_model_ckpt)




     def 		snake_case_		(				self ,						a__):
         return "".join((self.SP_CHAR_MAPPING.get(a__ ,						a__) for c in text))




     def 		snake_case_		(				self ,						a__ ,						a__=False ,						a__=6_4 ,						a__=0.1):

         if self.sp_model_kwargs.get('''enable_sampling''') is True:
             A__		 =					True
         if self.sp_model_kwargs.get('''alpha''') is not None:
             A__		 =					self.sp_model_kwargs.get('''alpha''')
         if self.sp_model_kwargs.get('''nbest_size''') is not None:
             A__		 =					self.sp_model_kwargs.get('''nbest_size''')

         if not enable_sampling:
             A__		 =					self.sp_model.EncodeAsPieces(a__)
         else:
             A__		 =					self.sp_model.SampleEncodeAsPieces(a__ ,						a__ ,						a__)
         A__		 =					[]
         for pi, piece in enumerate(a__):
             if piece == SPIECE_UNDERLINE:
                 if not pieces[pi + 1].startswith(a__) and pi != 0:
                     new_pieces.append(a__)
                     continue
                 else:
                     continue
             A__		 =					0
             for i, chunk in enumerate(a__):
                 if chunk == SPIECE_UNDERLINE:
                     continue
                 if self.is_ch_char(a__) or self.is_punct(a__):
                     if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
                         new_pieces.append(piece[lst_i:i])
                     new_pieces.append(a__)
                     A__		 =					i + 1
                 elif chunk.isdigit() and i > 0 and not piece[i - 1].isdigit():
                     if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
                         new_pieces.append(piece[lst_i:i])
                     A__		 =					i
                 elif not chunk.isdigit() and i > 0 and piece[i - 1].isdigit():
                     if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
                         new_pieces.append(piece[lst_i:i])
                     A__		 =					i
             if len(a__) > lst_i:
                 new_pieces.append(piece[lst_i:])
         return new_pieces




     def 		snake_case_		(				self ,						a__):
         A__		 =					''''''.join(a__).replace(a__ ,						''' ''').strip()
         return out_string




     def 		snake_case_		(				self ,						a__):
         A__		 =					self.convert_ids_to_tokens(a__)
         A__		 =					''''''.join(a__).replace(a__ ,						''' ''').strip()
         return out_string




     def 		snake_case_		(				self ,						a__):
         return self.vocab.get(a__ ,						self.vocab.get(self.unk_token))




     def 		snake_case_		(				self ,						a__):
         return self.reverse_vocab.get(a__ ,						self.unk_token)




     def 		snake_case_		(				self ,						a__ ,						a__=None):
         if token_ids_a is None:
             return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
         A__		 =					[self.cls_token_id]
         A__		 =					[self.sep_token_id]
         return _cls + token_ids_a + _sep + _sep + token_ids_a + _sep




     def 		snake_case_		(				self ,						a__ ,						a__=None):
         if offset_mapping_a is None:
             return [(0, 0)] + offset_mapping_a + [(0, 0)]

         return [(0, 0)] + offset_mapping_a + [(0, 0), (0, 0)] + offset_mapping_a + [(0, 0)]




     def 		snake_case_		(				self ,						a__ ,						a__=None ,						a__=False):

         if already_has_special_tokens:
             if token_ids_a is not None:
                 raise ValueError(
                     '''You should not supply a second sequence if the provided sequence of '''
                     '''ids is already formatted with special tokens for the model.''')
             return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

         if token_ids_a is not None:
             return [1] + ([0] * len(a__)) + [1, 1] + ([0] * len(a__)) + [1]
         return [1] + ([0] * len(a__)) + [1]




     def 		snake_case_		(				self ,						a__ ,						a__ = None):
         # called when `add_special_tokens` is True, so align with `build_inputs_with_special_tokens` method
         if token_ids_a is None:
             # [CLS] X [SEP]
             return (len(a__) + 2) * [0]

         # [CLS] A [SEP] [SEP] B [SEP]
         return [0] * (len(a__) + 1) + [1] * (len(a__) + 3)




     def 		snake_case_		(				self ,						a__):
         if "\u4e00" <= char <= "\u9fff":
             return True
         return False




     def 		snake_case_		(				self ,						a__):
         if ("a" <= char <= "z") or ("A" <= char <= "Z"):
             return True
         return False




     def 		snake_case_		(				self ,						a__):
         if char in ",;:.?!~，；：。？！《》【】":
             return True
         return False




     def 		snake_case_		(				self ,						a__):
         if char == " " or char == "\t" or char == "\n" or char == "\r":
             return True
         if len(a__) == 1:
             A__		 =					unicodedata.category(a__)
             if cat == "Zs":
                 return True
         return False




     def 		snake_case_		(				self ,						a__):
         A__		 =					{}
         with io.open(a__ ,						'''r''' ,						encoding='''utf-8''') as f:
             for index, line in enumerate(a__):
                 A__		 =					line.rstrip('''\n''')
                 A__		 =					int(a__)

         return token_to_idx




     def 		snake_case_		(				self ,						a__ ,						a__ = None):
         A__		 =					0
         if os.path.isdir(a__):
             A__		 =					os.path.join(
                 a__ ,						(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''])
         else:
             A__		 =					(filename_prefix + '''-''' if filename_prefix else '''''') + save_directory
         with open(a__ ,						'''w''' ,						encoding='''utf-8''') as writer:
             for token, token_index in sorted(self.vocab.items() ,						key=lambda a__: kv[1]):
                 if index != token_index:
                     logger.warning(
                         F"Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."
                         ''' Please check that the vocabulary is not corrupted!''')
                     A__		 =					token_index
                 writer.write(token + '''\n''')
                 index += 1

         A__		 =					os.path.join(a__ ,						'''sentencepiece.bpe.model''')
         with open(a__ ,						'''wb''') as fi:
             A__		 =					self.sp_model.serialized_model_proto()
             fi.write(a__)

         return (vocab_file,)
