
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 					lowercase__(							unittest.TestCase				):



	"""simple docstring"""




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

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

					return expected_height, expected_width

@require_torch
@require_vision
class 					lowercase__(							UpperCAmelCase					,		unittest.TestCase				):



	"""simple docstring"""




	a     :Optional[int]       	=      YolosImageProcessor if is_vision_available() else None
	def 		_lowercase    (			self    :     List[Any]			)			->	Dict:
					lowercase_	    =   YolosImageProcessingTester(self			)
	@property
	def 		_lowercase    (			self    :     List[str]			)			->	int:
					return self.image_processor_tester.prepare_image_processor_dict()
	def 		_lowercase    (			self    :     Any			)			->	List[Any]:
					lowercase_	    =   self.image_processing_class(**self.image_processor_dict			)
					self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_			,				'''image_mean'''			)			)
					self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_			,				'''image_std'''			)			)
					self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_			,				'''do_normalize'''			)			)
					self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_			,				'''do_resize'''			)			)
					self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_			,				'''size'''			)			)
	def 		_lowercase    (			self    :     str			)			->	Any:
					lowercase_	    =   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			,				SCREAMING_SNAKE_CASE_			)

					lowercase_	    =   self.image_processing_class.from_dict(
					    self.image_processor_dict			,				size=4_2			,				max_size=8_4			,				pad_and_return_pixel_mask=SCREAMING_SNAKE_CASE_			)
					self.assertEqual(image_processor.size			,				{'''shortest_edge''': 4_2, '''longest_edge''': 8_4}			)
					self.assertEqual(image_processor.do_pad			,				SCREAMING_SNAKE_CASE_			)
	def 		_lowercase    (			self    :     List[str]			)			->	Tuple:
					pass
	def 		_lowercase    (			self    :     Optional[Any]			)			->	Any:
					# Initialize image_processing
					lowercase_	    =   self.image_processing_class(**self.image_processor_dict			)
					# create random PIL images
					lowercase_	    =   prepare_image_inputs(self.image_processor_tester			,				equal_resolution=SCREAMING_SNAKE_CASE_			)
					for image in image_inputs:
									self.assertIsInstance(SCREAMING_SNAKE_CASE_			,				Image.Image			)

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

					lowercase_    ,      lowercase_	    =   self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_			)

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

					# Test batched
					lowercase_    ,      lowercase_	    =   self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_			,				batched=SCREAMING_SNAKE_CASE_			)

					lowercase_	    =   image_processing(SCREAMING_SNAKE_CASE_			,				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 		_lowercase    (			self    :     List[Any]			)			->	Tuple:
					# Initialize image_processing
					lowercase_	    =   self.image_processing_class(**self.image_processor_dict			)
					# create random numpy tensors
					lowercase_	    =   prepare_image_inputs(self.image_processor_tester			,				equal_resolution=SCREAMING_SNAKE_CASE_			,				numpify=SCREAMING_SNAKE_CASE_			)
					for image in image_inputs:
									self.assertIsInstance(SCREAMING_SNAKE_CASE_			,				np.ndarray			)

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

					lowercase_    ,      lowercase_	    =   self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_			)

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

					# Test batched
					lowercase_	    =   image_processing(SCREAMING_SNAKE_CASE_			,				return_tensors='''pt'''			).pixel_values

					lowercase_    ,      lowercase_	    =   self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_			,				batched=SCREAMING_SNAKE_CASE_			)

					self.assertEqual(
					    encoded_images.shape			,				(
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        expected_height,
					        expected_width,
					    )			,				)
	def 		_lowercase    (			self    :     List[Any]			)			->	str:
					# Initialize image_processing
					lowercase_	    =   self.image_processing_class(**self.image_processor_dict			)
					# create random PyTorch tensors
					lowercase_	    =   prepare_image_inputs(self.image_processor_tester			,				equal_resolution=SCREAMING_SNAKE_CASE_			,				torchify=SCREAMING_SNAKE_CASE_			)
					for image in image_inputs:
									self.assertIsInstance(SCREAMING_SNAKE_CASE_			,				torch.Tensor			)

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

					lowercase_    ,      lowercase_	    =   self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_			)

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

					# Test batched
					lowercase_	    =   image_processing(SCREAMING_SNAKE_CASE_			,				return_tensors='''pt'''			).pixel_values

					lowercase_    ,      lowercase_	    =   self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_			,				batched=SCREAMING_SNAKE_CASE_			)

					self.assertEqual(
					    encoded_images.shape			,				(
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_channels,
					        expected_height,
					        expected_width,
					    )			,				)
	def 		_lowercase    (			self    :     Optional[Any]			)			->	Tuple:
					# Initialize image_processings
					lowercase_	    =   self.image_processing_class(**self.image_processor_dict			)
					lowercase_	    =   self.image_processing_class(do_resize=SCREAMING_SNAKE_CASE_			,				do_normalize=SCREAMING_SNAKE_CASE_			,				do_rescale=SCREAMING_SNAKE_CASE_			)
					# create random PyTorch tensors
					lowercase_	    =   prepare_image_inputs(self.image_processor_tester			,				equal_resolution=SCREAMING_SNAKE_CASE_			,				torchify=SCREAMING_SNAKE_CASE_			)
					for image in image_inputs:
									self.assertIsInstance(SCREAMING_SNAKE_CASE_			,				torch.Tensor			)

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

					self.assertTrue(
					    torch.allclose(encoded_images_with_method['''pixel_values''']			,				encoded_images['''pixel_values''']			,				atol=1e-4			)			)
	@slow
	def 		_lowercase    (			self    :     List[Any]			)			->	Dict:
					# prepare image and target
					lowercase_	    =   Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''			)
					with open('''./tests/fixtures/tests_samples/COCO/coco_annotations.txt'''			,				'''r'''			) as f:
									lowercase_	    =   json.loads(f.read()			)

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

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

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

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

					# verify area
					lowercase_	    =   torch.tensor([58_87.96_00, 1_12_50.20_61, 48_93_53.84_38, 83_71_22.75_00, 14_79_67.51_56, 16_57_32.34_38]			)
					self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']			,				SCREAMING_SNAKE_CASE_			)			)
					# verify boxes
					lowercase_	    =   torch.Size([6, 4]			)
					self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape			,				SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   torch.tensor([0.55_03, 0.27_65, 0.06_04, 0.22_15]			)
					self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]			,				SCREAMING_SNAKE_CASE_			,				atol=1e-3			)			)
					# verify image_id
					lowercase_	    =   torch.tensor([3_9_7_6_9]			)
					self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']			,				SCREAMING_SNAKE_CASE_			)			)
					# verify is_crowd
					lowercase_	    =   torch.tensor([0, 0, 0, 0, 0, 0]			)
					self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']			,				SCREAMING_SNAKE_CASE_			)			)
					# verify class_labels
					lowercase_	    =   torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7]			)
					self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']			,				SCREAMING_SNAKE_CASE_			)			)
					# verify orig_size
					lowercase_	    =   torch.tensor([4_8_0, 6_4_0]			)
					self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']			,				SCREAMING_SNAKE_CASE_			)			)
					# verify size
					lowercase_	    =   torch.tensor([8_0_0, 1_0_6_6]			)
					self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']			,				SCREAMING_SNAKE_CASE_			)			)




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

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

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

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

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

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

					# verify area
					lowercase_	    =   torch.tensor([14_79_79.68_75, 16_55_27.04_69, 48_46_38.59_38, 1_12_92.93_75, 58_79.65_62, 76_34.11_47]			)
					self.assertTrue(torch.allclose(encoding['''labels'''][0]['''area''']			,				SCREAMING_SNAKE_CASE_			)			)
					# verify boxes
					lowercase_	    =   torch.Size([6, 4]			)
					self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape			,				SCREAMING_SNAKE_CASE_			)
					lowercase_	    =   torch.tensor([0.26_25, 0.54_37, 0.46_88, 0.86_25]			)
					self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]			,				SCREAMING_SNAKE_CASE_			,				atol=1e-3			)			)
					# verify image_id
					lowercase_	    =   torch.tensor([3_9_7_6_9]			)
					self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']			,				SCREAMING_SNAKE_CASE_			)			)
					# verify is_crowd
					lowercase_	    =   torch.tensor([0, 0, 0, 0, 0, 0]			)
					self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']			,				SCREAMING_SNAKE_CASE_			)			)
					# verify class_labels
					lowercase_	    =   torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3]			)
					self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']			,				SCREAMING_SNAKE_CASE_			)			)
					# verify masks
					lowercase_	    =   8_2_2_8_7_3
					self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()			,				SCREAMING_SNAKE_CASE_			)
					# verify orig_size
					lowercase_	    =   torch.tensor([4_8_0, 6_4_0]			)
					self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']			,				SCREAMING_SNAKE_CASE_			)			)
					# verify size
					lowercase_	    =   torch.tensor([8_0_0, 1_0_6_6]			)
					self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']			,				SCREAMING_SNAKE_CASE_			)			)






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

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
						from .tokenization_barthez import BarthezTokenizer
else:
						__a         =   None

__a         =   logging.get_logger(__name__)

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

__a         =   {
    'vocab_file': {
        'moussaKam/mbarthez': 'https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model',
        'moussaKam/barthez': 'https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model',
        'moussaKam/barthez-orangesum-title': (
            'https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model'
        ),
    },
    'tokenizer_file': {
        'moussaKam/mbarthez': 'https://huggingface.co/moussaKam/mbarthez/resolve/main/tokenizer.json',
        'moussaKam/barthez': 'https://huggingface.co/moussaKam/barthez/resolve/main/tokenizer.json',
        'moussaKam/barthez-orangesum-title': (
            'https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/tokenizer.json'
        ),
    },
}

__a         =   {
    'moussaKam/mbarthez': 1_0_2_4,
    'moussaKam/barthez': 1_0_2_4,
    'moussaKam/barthez-orangesum-title': 1_0_2_4,
}

__a         =   '▁'
class 					lowercase__(							UpperCAmelCase				):



	"""simple docstring"""




	a     :int       	=      VOCAB_FILES_NAMES
	a     :Any       	=      PRETRAINED_VOCAB_FILES_MAP
	a     :Optional[int]       	=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	a     :Dict       	=      ['input_ids', 'attention_mask']
	a     :Optional[Any]       	=      BarthezTokenizer
	def __init__(			self    :     Dict			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]=None			,				SCREAMING_SNAKE_CASE_    :     Dict=None			,				SCREAMING_SNAKE_CASE_    :     Dict="<s>"			,				SCREAMING_SNAKE_CASE_    :     Optional[Any]="</s>"			,				SCREAMING_SNAKE_CASE_    :     int="</s>"			,				SCREAMING_SNAKE_CASE_    :     Optional[int]="<s>"			,				SCREAMING_SNAKE_CASE_    :     Union[str, Any]="<unk>"			,				SCREAMING_SNAKE_CASE_    :     List[str]="<pad>"			,				SCREAMING_SNAKE_CASE_    :     Any="<mask>"			,				**SCREAMING_SNAKE_CASE_    :     Union[str, Any]			,				)			->	Any:
					# Mask token behave like a normal word, i.e. include the space before it
					lowercase_	    =   AddedToken(SCREAMING_SNAKE_CASE_			,				lstrip=SCREAMING_SNAKE_CASE_			,				rstrip=SCREAMING_SNAKE_CASE_			) if isinstance(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			) else mask_token

					super().__init__(
					    SCREAMING_SNAKE_CASE_			,				tokenizer_file=SCREAMING_SNAKE_CASE_			,				bos_token=SCREAMING_SNAKE_CASE_			,				eos_token=SCREAMING_SNAKE_CASE_			,				unk_token=SCREAMING_SNAKE_CASE_			,				sep_token=SCREAMING_SNAKE_CASE_			,				cls_token=SCREAMING_SNAKE_CASE_			,				pad_token=SCREAMING_SNAKE_CASE_			,				mask_token=SCREAMING_SNAKE_CASE_			,				**SCREAMING_SNAKE_CASE_			,				)

					lowercase_	    =   vocab_file
					lowercase_	    =   False if not self.vocab_file else True
	def 		_lowercase    (			self    :     Optional[Any]			,				SCREAMING_SNAKE_CASE_    :     List[int]			,				SCREAMING_SNAKE_CASE_    :     Optional[List[int]] = None			)			->	List[int]:

					if token_ids_a is None:
									return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
					lowercase_	    =   [self.cls_token_id]
					lowercase_	    =   [self.sep_token_id]
					return cls + token_ids_a + sep + sep + token_ids_a + sep
	def 		_lowercase    (			self    :     int			,				SCREAMING_SNAKE_CASE_    :     List[int]			,				SCREAMING_SNAKE_CASE_    :     Optional[List[int]] = None			)			->	List[int]:
					lowercase_	    =   [self.sep_token_id]
					lowercase_	    =   [self.cls_token_id]

					if token_ids_a is None:
									return len(cls + token_ids_a + sep			) * [0]
					return len(cls + token_ids_a + sep + sep + token_ids_a + sep			) * [0]




	def 		_lowercase    (			self    :     Optional[Any]			,				SCREAMING_SNAKE_CASE_    :     str			,				SCREAMING_SNAKE_CASE_    :     Optional[str] = None			)			->	Tuple[str]:
					if not self.can_save_slow_tokenizer:
									raise ValueError(
									    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
									    '''tokenizer.'''			)

					if not os.path.isdir(SCREAMING_SNAKE_CASE_			):
									logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''			)
									return
					lowercase_	    =   os.path.join(
					    SCREAMING_SNAKE_CASE_			,				(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']			)

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

					return (out_vocab_file,)




