




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					):

    '''simple docstring'''






    def __init__(      self				:     Union[str, Any] ,			UpperCAmelCase__				:     int ,			UpperCAmelCase__				:     str=7 ,			UpperCAmelCase__				:     Optional[int]=3 ,			UpperCAmelCase__				:     Optional[Any]=30 ,			UpperCAmelCase__				:     Tuple=400 ,			UpperCAmelCase__				:     str=True ,			UpperCAmelCase__				:     Dict=None ,			UpperCAmelCase__				:     Dict=True ,			UpperCAmelCase__				:     List[str]=[0.5, 0.5, 0.5] ,			UpperCAmelCase__				:     Union[str, Any]=[0.5, 0.5, 0.5] ,			UpperCAmelCase__				:     Tuple=True ,			UpperCAmelCase__				:     List[str]=1 / 255 ,			UpperCAmelCase__				:     str=True ,			)						->List[Any]:





           '''simple docstring'''

           A__							=   size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1_333}
           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 	SCREAMING_SNAKE_CASE							(      self				:     str)						->Dict:





           '''simple docstring'''

           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 ,			UpperCAmelCase__				:     int ,			UpperCAmelCase__				:     Tuple=False)						->Dict:





           '''simple docstring'''

           if not batched:
                  A__							=   image_inputs[0]
                  if isinstance(UpperCAmelCase__ ,			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(UpperCAmelCase__ ,			key=lambda UpperCAmelCase__: item[0])[0]
                  A__							=   max(UpperCAmelCase__ ,			key=lambda UpperCAmelCase__: item[1])[1]

           return expected_height, expected_width


@require_torch
@require_vision
class     UpperCamelCase_   (			UpperCAmelCase__		, unittest.TestCase					):

    '''simple docstring'''






    UpperCAmelCase__							  =				DeformableDetrImageProcessor if is_vision_available() else None


    def 	SCREAMING_SNAKE_CASE							(      self				:     Union[str, Any])						->Optional[Any]:





           '''simple docstring'''

           A__							=   DeformableDetrImageProcessingTester(self)


    @property
    def 	SCREAMING_SNAKE_CASE							(      self				:     Union[str, Any])						->Dict:





           '''simple docstring'''

           return self.image_processor_tester.prepare_image_processor_dict()


    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[Any])						->Optional[int]:





           '''simple docstring'''

           A__							=   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__ ,			'''do_rescale'''))
           self.assertTrue(hasattr(UpperCAmelCase__ ,			'''do_pad'''))
           self.assertTrue(hasattr(UpperCAmelCase__ ,			'''size'''))


    def 	SCREAMING_SNAKE_CASE							(      self				:     str)						->Any:





           '''simple docstring'''

           A__							=   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 ,			UpperCAmelCase__)

           A__							=   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 	SCREAMING_SNAKE_CASE							(      self				:     List[Any])						->List[str]:





           '''simple docstring'''

           pass


    def 	SCREAMING_SNAKE_CASE							(      self				:     Any)						->List[str]:





           '''simple docstring'''

           A__							=   self.image_processing_class(**self.image_processor_dict)
           # create random PIL images
           A__							=   prepare_image_inputs(self.image_processor_tester ,			equal_resolution=UpperCAmelCase__)
           for image in image_inputs:
                  self.assertIsInstance(UpperCAmelCase__ ,			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(UpperCAmelCase__)

           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(UpperCAmelCase__ ,			batched=UpperCAmelCase__)

           A__							=   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 	SCREAMING_SNAKE_CASE							(      self				:     str)						->Optional[int]:





           '''simple docstring'''

           A__							=   self.image_processing_class(**self.image_processor_dict)
           # create random numpy tensors
           A__							=   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
           A__							=   image_processing(image_inputs[0] ,			return_tensors='''pt''').pixel_values

           A__			,							A__							=   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
           A__							=   image_processing(UpperCAmelCase__ ,			return_tensors='''pt''').pixel_values

           A__			,							A__							=   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 	SCREAMING_SNAKE_CASE							(      self				:     Any)						->Tuple:





           '''simple docstring'''

           A__							=   self.image_processing_class(**self.image_processor_dict)
           # create random PyTorch tensors
           A__							=   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
           A__							=   image_processing(image_inputs[0] ,			return_tensors='''pt''').pixel_values

           A__			,							A__							=   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
           A__							=   image_processing(UpperCAmelCase__ ,			return_tensors='''pt''').pixel_values

           A__			,							A__							=   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,
               ) ,			)


    @slow
    def 	SCREAMING_SNAKE_CASE							(      self				:     List[Any])						->List[Any]:





           '''simple docstring'''

           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''': 39_769, '''annotations''': target}

           # encode them
           A__							=   DeformableDetrImageProcessor()
           A__							=   image_processing(images=UpperCAmelCase__ ,			annotations=UpperCAmelCase__ ,			return_tensors='''pt''')

           # verify pixel values
           A__							=   torch.Size([1, 3, 800, 1_066])
           self.assertEqual(encoding['''pixel_values'''].shape ,			UpperCAmelCase__)

           A__							=   torch.tensor([0.2796, 0.3138, 0.3481])
           self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] ,			UpperCAmelCase__ ,			atol=1e-4))

           # verify area
           A__							=   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
           A__							=   torch.Size([6, 4])
           self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape ,			UpperCAmelCase__)
           A__							=   torch.tensor([0.5503, 0.2765, 0.0604, 0.2215])
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] ,			UpperCAmelCase__ ,			atol=1e-3))
           # verify image_id
           A__							=   torch.tensor([39_769])
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] ,			UpperCAmelCase__))
           # verify is_crowd
           A__							=   torch.tensor([0, 0, 0, 0, 0, 0])
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] ,			UpperCAmelCase__))
           # verify class_labels
           A__							=   torch.tensor([75, 75, 63, 65, 17, 17])
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] ,			UpperCAmelCase__))
           # verify orig_size
           A__							=   torch.tensor([480, 640])
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] ,			UpperCAmelCase__))
           # verify size
           A__							=   torch.tensor([800, 1_066])
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size'''] ,			UpperCAmelCase__))





    @slow
    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[int])						->Optional[Any]:





           '''simple docstring'''

           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''': 39_769, '''segments_info''': target}

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

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

           # verify pixel values
           A__							=   torch.Size([1, 3, 800, 1_066])
           self.assertEqual(encoding['''pixel_values'''].shape ,			UpperCAmelCase__)

           A__							=   torch.tensor([0.2796, 0.3138, 0.3481])
           self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3] ,			UpperCAmelCase__ ,			atol=1e-4))

           # verify area
           A__							=   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
           A__							=   torch.Size([6, 4])
           self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape ,			UpperCAmelCase__)
           A__							=   torch.tensor([0.2625, 0.5437, 0.4688, 0.8625])
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0] ,			UpperCAmelCase__ ,			atol=1e-3))
           # verify image_id
           A__							=   torch.tensor([39_769])
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id'''] ,			UpperCAmelCase__))
           # verify is_crowd
           A__							=   torch.tensor([0, 0, 0, 0, 0, 0])
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd'''] ,			UpperCAmelCase__))
           # verify class_labels
           A__							=   torch.tensor([17, 17, 63, 75, 75, 93])
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels'''] ,			UpperCAmelCase__))
           # verify masks
           A__							=   822_873
           self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item() ,			UpperCAmelCase__)
           # verify orig_size
           A__							=   torch.tensor([480, 640])
           self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size'''] ,			UpperCAmelCase__))
           # verify size
           A__							=   torch.tensor([800, 1_066])
           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


_lowerCamelCase     :		Union[str, Any]								=       """▁"""

_lowerCamelCase     :		Optional[Any]								=       {"""vocab_file""": """spiece.model"""}

_lowerCamelCase     :		str								=       {
    """vocab_file""": {"""google/pegasus-xsum""": """https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"""}
}

_lowerCamelCase     :		List[str]								=       {
    """google/pegasus-xsum""": 512,
}


_lowerCamelCase     :		int								=       logging.get_logger(__name__)


class     UpperCamelCase_   (			UpperCAmelCase__					):

    '''simple docstring'''






    UpperCAmelCase__							  =				VOCAB_FILES_NAMES

    UpperCAmelCase__							  =				VOCAB_FILES_NAMES
    UpperCAmelCase__							  =				PRETRAINED_VOCAB_FILES_MAP
    UpperCAmelCase__							  =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    UpperCAmelCase__							  =				['''input_ids''', '''attention_mask''']


    def __init__(      self				:     Optional[int] ,			UpperCAmelCase__				:     Tuple ,			UpperCAmelCase__				:     str="<pad>" ,			UpperCAmelCase__				:     Optional[Any]="</s>" ,			UpperCAmelCase__				:     Any="<unk>" ,			UpperCAmelCase__				:     Union[str, Any]="<mask_2>" ,			UpperCAmelCase__				:     List[str]="<mask_1>" ,			UpperCAmelCase__				:     Dict=None ,			UpperCAmelCase__				:     Optional[Any]=103 ,			UpperCAmelCase__				:     Optional[Dict[str, Any]] = None ,			**UpperCAmelCase__				:     Dict ,			)						->None:





           '''simple docstring'''

           A__							=   offset
           if additional_special_tokens is not None:
                  if not isinstance(UpperCAmelCase__ ,			UpperCAmelCase__):
                         raise TypeError(
                             f"""additional_special_tokens should be of type {type(UpperCAmelCase__)}, but is"""
                             f""" {type(UpperCAmelCase__)}""")

                  A__							=   (
                      ([mask_token_sent] + additional_special_tokens)
                      if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
                      else additional_special_tokens
                  )
                  # fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
                  additional_special_tokens_extended += [
                      f"""<unk_{i}>""" for i in range(len(UpperCAmelCase__) ,			self.offset - 1)
                  ]

                  if len(set(UpperCAmelCase__)) != len(UpperCAmelCase__):
                         raise ValueError(
                             '''Please make sure that the provided additional_special_tokens do not contain an incorrectly'''
                             f""" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.""")
                  A__							=   additional_special_tokens_extended
           else:
                  A__							=   [mask_token_sent] if mask_token_sent is not None else []
                  additional_special_tokens += [f"""<unk_{i}>""" for i in range(2 ,			self.offset)]

           A__							=   {} if sp_model_kwargs is None else sp_model_kwargs

           super().__init__(
               eos_token=UpperCAmelCase__ ,			unk_token=UpperCAmelCase__ ,			mask_token=UpperCAmelCase__ ,			pad_token=UpperCAmelCase__ ,			mask_token_sent=UpperCAmelCase__ ,			offset=UpperCAmelCase__ ,			additional_special_tokens=UpperCAmelCase__ ,			sp_model_kwargs=self.sp_model_kwargs ,			**UpperCAmelCase__ ,			)
           A__							=   mask_token_sent
           A__							=   vocab_file
           A__							=   spm.SentencePieceProcessor(**self.sp_model_kwargs)
           self.sp_model.Load(UpperCAmelCase__)

           # add special tokens to encoder dict
           A__							=   {
               0: self.pad_token,
               1: self.eos_token,
           }

           if self.mask_token_sent is not None:
                  self.encoder.update(
                      {
                          2: self.mask_token_sent,
                          3: self.mask_token,
                      })

           if self.offset > 0:
                  # entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
                  # mask_token_sent is already added to list -> so start at 1
                  self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1 ,			self.offset - 1)})

           A__							=   {v: k for k, v in self.encoder.items()}


    @property
    def 	SCREAMING_SNAKE_CASE							(      self				:     Union[str, Any])						->int:





           '''simple docstring'''

           return len(self.sp_model) + self.offset


    def 	SCREAMING_SNAKE_CASE							(      self				:     Optional[Any])						->Dict[str, int]:





           '''simple docstring'''

           A__							=   {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				:     Any)						->Union[str, Any]:





           '''simple docstring'''

           A__							=   self.__dict__.copy()
           A__							=   None
           return state


    def __setstate__(      self				:     int ,			UpperCAmelCase__				:     Optional[int])						->Optional[int]:





           '''simple docstring'''

           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.vocab_file)


    def 	SCREAMING_SNAKE_CASE							(      self				:     str ,			UpperCAmelCase__				:     str)						->List[str]:





           '''simple docstring'''

           return self.sp_model.encode(UpperCAmelCase__ ,			out_type=UpperCAmelCase__)


    def 	SCREAMING_SNAKE_CASE							(      self				:     Any ,			UpperCAmelCase__				:     str)						->int:





           '''simple docstring'''

           if token in self.decoder:
                  return self.decoder[token]
           elif token in self.added_tokens_decoder:
                  return self.added_tokens_decoder[token]
           A__							=   self.sp_model.piece_to_id(UpperCAmelCase__)
           return sp_id + self.offset


    def 	SCREAMING_SNAKE_CASE							(      self				:     List[Any] ,			UpperCAmelCase__				:     int)						->str:





           '''simple docstring'''

           if index in self.encoder:
                  return self.encoder[index]
           elif index in self.added_tokens_encoder:
                  return self.added_tokens_encoder[index]
           else:
                  A__							=   self.sp_model.IdToPiece(index - self.offset)
           return token


    def 	SCREAMING_SNAKE_CASE							(      self				:     List[str] ,			UpperCAmelCase__				:     int)						->Optional[int]:





           '''simple docstring'''

           A__							=   []
           A__							=   ''''''
           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
                         A__							=   []
                  else:
                         current_sub_tokens.append(UpperCAmelCase__)
           out_string += self.sp_model.decode(UpperCAmelCase__)
           return out_string.strip()


    def 	SCREAMING_SNAKE_CASE							(      self				:     Union[str, Any] ,			UpperCAmelCase__				:     Optional[int]=False)						->Union[str, Any]:





           '''simple docstring'''

           return 1


    def 	SCREAMING_SNAKE_CASE							(      self				:     int ,			UpperCAmelCase__				:     Union[str, Any])						->Optional[Any]:





           '''simple docstring'''

           A__							=   set(self.all_special_ids)  # call it once instead of inside list comp
           all_special_ids.remove(self.unk_token_id)  # <unk> is only sometimes special

           return [1 if x in all_special_ids else 0 for x in seq]


    def 	SCREAMING_SNAKE_CASE							(      self				:     int ,			UpperCAmelCase__				:     List ,			UpperCAmelCase__				:     Optional[List] = None ,			UpperCAmelCase__				:     bool = False)						->List[int]:





           '''simple docstring'''

           if already_has_special_tokens:
                  return self._special_token_mask(UpperCAmelCase__)
           elif token_ids_a is None:
                  return self._special_token_mask(UpperCAmelCase__) + [1]
           else:
                  return self._special_token_mask(token_ids_a + token_ids_a) + [1]


    def 	SCREAMING_SNAKE_CASE							(      self				:     Tuple ,			UpperCAmelCase__				:     List[str] ,			UpperCAmelCase__				:     List[str]=None)						->List[int]:





           '''simple docstring'''

           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 	SCREAMING_SNAKE_CASE							(      self				:     str ,			UpperCAmelCase__				:     str ,			UpperCAmelCase__				:     Optional[str] = None)						->Tuple[str]:





           '''simple docstring'''

           if not os.path.isdir(UpperCAmelCase__):
                  logger.error(f"""Vocabulary path ({save_directory}) should be a directory""")
                  return
           A__							=   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:
                         A__							=   self.sp_model.serialized_model_proto()
                         fi.write(UpperCAmelCase__)

           return (out_vocab_file,)


