



import inspect
import unittest

import numpy as np

from transformers import BeitConfig
from transformers.testing_utils import require_flax, require_vision, slow
from transformers.utils import cached_property, is_flax_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor


if is_flax_available():
				import jax

				from transformers import FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling, FlaxBeitModel

if is_vision_available():
				from PIL import Image

				from transformers import BeitImageProcessor

class 				snake_case__(unittest.TestCase      ):

					"""simple docstring"""






					def __init__(							self :     List[Any]				,       SCREAMING_SNAKE_CASE :     Tuple				,       SCREAMING_SNAKE_CASE :     Optional[int]=100				,       SCREAMING_SNAKE_CASE :     Tuple=13				,       SCREAMING_SNAKE_CASE :     str=30				,       SCREAMING_SNAKE_CASE :     Union[str, Any]=2				,       SCREAMING_SNAKE_CASE :     Tuple=3				,       SCREAMING_SNAKE_CASE :     List[Any]=True				,       SCREAMING_SNAKE_CASE :     int=True				,       SCREAMING_SNAKE_CASE :     Dict=32				,       SCREAMING_SNAKE_CASE :     str=5				,       SCREAMING_SNAKE_CASE :     Any=4				,       SCREAMING_SNAKE_CASE :     str=37				,       SCREAMING_SNAKE_CASE :     Any="gelu"				,       SCREAMING_SNAKE_CASE :     Tuple=0.1				,       SCREAMING_SNAKE_CASE :     Optional[Any]=0.1				,       SCREAMING_SNAKE_CASE :     Union[str, Any]=10				,       SCREAMING_SNAKE_CASE :     Dict=0.02				,       SCREAMING_SNAKE_CASE :     Any=3				,       ):
									lowercase__					:   Optional[int]													=					parent
									lowercase__					:   Optional[int]													=					vocab_size
									lowercase__					:   Dict													=					batch_size
									lowercase__					:   List[Any]													=					image_size
									lowercase__					:   List[Any]													=					patch_size
									lowercase__					:   Tuple													=					num_channels
									lowercase__					:   Any													=					is_training
									lowercase__					:   str													=					use_labels
									lowercase__					:   List[Any]													=					hidden_size
									lowercase__					:   Optional[int]													=					num_hidden_layers
									lowercase__					:   Dict													=					num_attention_heads
									lowercase__					:   Optional[int]													=					intermediate_size
									lowercase__					:   int													=					hidden_act
									lowercase__					:   str													=					hidden_dropout_prob
									lowercase__					:   Union[str, Any]													=					attention_probs_dropout_prob
									lowercase__					:   int													=					type_sequence_label_size
									lowercase__					:   Optional[int]													=					initializer_range

									# in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
									lowercase__					:   str													=					(image_size // patch_size) ** 2
									lowercase__					:   List[str]													=					num_patches + 1







					def 			snake_case					(							self :     Tuple					):
									lowercase__					:   List[Any]													=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

									lowercase__					:   Union[str, Any]													=					None
									if self.use_labels:
													lowercase__					:   List[Any]													=					ids_tensor([self.batch_size]				,       self.type_sequence_label_size					)

									lowercase__					:   int													=					BeitConfig(
									    vocab_size=self.vocab_size				,       image_size=self.image_size				,       patch_size=self.patch_size				,       num_channels=self.num_channels				,       hidden_size=self.hidden_size				,       num_hidden_layers=self.num_hidden_layers				,       num_attention_heads=self.num_attention_heads				,       intermediate_size=self.intermediate_size				,       hidden_act=self.hidden_act				,       hidden_dropout_prob=self.hidden_dropout_prob				,       attention_probs_dropout_prob=self.attention_probs_dropout_prob				,       is_decoder=SCREAMING_SNAKE_CASE				,       initializer_range=self.initializer_range				,       )

									return config, pixel_values, labels







					def 			snake_case					(							self :     Optional[Any]				,       SCREAMING_SNAKE_CASE :     List[str]				,       SCREAMING_SNAKE_CASE :     Dict				,       SCREAMING_SNAKE_CASE :     List[Any]					):
									lowercase__					:   Optional[Any]													=					FlaxBeitModel(config=SCREAMING_SNAKE_CASE					)
									lowercase__					:   Optional[int]													=					model(SCREAMING_SNAKE_CASE					)
									self.parent.assertEqual(result.last_hidden_state.shape				,       (self.batch_size, self.seq_length, self.hidden_size)					)







					def 			snake_case					(							self :     Union[str, Any]				,       SCREAMING_SNAKE_CASE :     int				,       SCREAMING_SNAKE_CASE :     Tuple				,       SCREAMING_SNAKE_CASE :     List[str]					):
									lowercase__					:   int													=					FlaxBeitForMaskedImageModeling(config=SCREAMING_SNAKE_CASE					)
									lowercase__					:   Union[str, Any]													=					model(SCREAMING_SNAKE_CASE					)
									self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.seq_length - 1, self.vocab_size)					)







					def 			snake_case					(							self :     Optional[int]				,       SCREAMING_SNAKE_CASE :     str				,       SCREAMING_SNAKE_CASE :     List[Any]				,       SCREAMING_SNAKE_CASE :     Any					):
									lowercase__					:   Tuple													=					self.type_sequence_label_size
									lowercase__					:   Optional[int]													=					FlaxBeitForImageClassification(config=SCREAMING_SNAKE_CASE					)
									lowercase__					:   str													=					model(SCREAMING_SNAKE_CASE					)
									self.parent.assertEqual(result.logits.shape				,       (self.batch_size, self.type_sequence_label_size)					)

									# test greyscale images
									lowercase__					:   int													=					1
									lowercase__					:   List[str]													=					FlaxBeitForImageClassification(SCREAMING_SNAKE_CASE					)

									lowercase__					:   int													=					floats_tensor([self.batch_size, 1, self.image_size, self.image_size]					)
									lowercase__					:   List[str]													=					model(SCREAMING_SNAKE_CASE					)







					def 			snake_case					(							self :     Any					):
									lowercase__					:   Optional[Any]													=					self.prepare_config_and_inputs()
									(
									    (
									    lowercase__
									)							,							(
									    lowercase__
									)							,							(
									    lowercase__
									)							,							
									)					:   str													=					config_and_inputs
									lowercase__					:   Dict													=					{"pixel_values": pixel_values}
									return config, inputs_dict






@require_flax
class 				snake_case__(_UpperCamelCase    ,      unittest.TestCase      ):

					"""simple docstring"""






					lowercase_								=		(
					    (FlaxBeitModel, FlaxBeitForImageClassification, FlaxBeitForMaskedImageModeling) if is_flax_available() else ()
					)







					def 			snake_case					(							self :     Any					):
									lowercase__					:   List[Any]													=					FlaxBeitModelTester(self					)
									lowercase__					:   str													=					ConfigTester(self				,       config_class=SCREAMING_SNAKE_CASE				,       has_text_modality=SCREAMING_SNAKE_CASE				,       hidden_size=37					)







					def 			snake_case					(							self :     int					):
									self.config_tester.run_common_tests()







					def 			snake_case					(							self :     int					):
									lowercase__							,							lowercase__					:   Tuple													=					self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
													lowercase__					:   List[str]													=					model_class(SCREAMING_SNAKE_CASE					)
													lowercase__					:   str													=					inspect.signature(model.__call__					)
													# signature.parameters is an OrderedDict => so arg_names order is deterministic
													lowercase__					:   Optional[int]													=					[*signature.parameters.keys()]

													lowercase__					:   str													=					["pixel_values"]
													self.assertListEqual(arg_names[:1]				,       SCREAMING_SNAKE_CASE					)







					def 			snake_case					(							self :     List[Any]					):
									lowercase__							,							lowercase__					:   Optional[Any]													=					self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
													with self.subTest(model_class.__name__					):
																	lowercase__					:   Optional[int]													=					self._prepare_for_class(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE					)
																	lowercase__					:   List[str]													=					model_class(SCREAMING_SNAKE_CASE					)

																	@jax.jit
																	def model_jitted(SCREAMING_SNAKE_CASE :     Optional[Any]				,       **SCREAMING_SNAKE_CASE :     List[Any]					):
																					return model(pixel_values=SCREAMING_SNAKE_CASE				,       **SCREAMING_SNAKE_CASE					)

																	with self.subTest("JIT Enabled"					):
																					lowercase__					:   Union[str, Any]													=					model_jitted(**SCREAMING_SNAKE_CASE					).to_tuple()

																	with self.subTest("JIT Disabled"					):
																					with jax.disable_jit():
																									lowercase__					:   Optional[int]													=					model_jitted(**SCREAMING_SNAKE_CASE					).to_tuple()

																	self.assertEqual(len(SCREAMING_SNAKE_CASE					)				,       len(SCREAMING_SNAKE_CASE					)					)
																	for jitted_output, output in zip(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE					):
																					self.assertEqual(jitted_output.shape				,       output.shape					)







					def 			snake_case					(							self :     Optional[int]					):
									lowercase__					:   Union[str, Any]													=					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE					)







					def 			snake_case					(							self :     int					):
									lowercase__					:   List[Any]													=					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_masked_lm(*SCREAMING_SNAKE_CASE					)







					def 			snake_case					(							self :     Optional[Any]					):
									lowercase__					:   int													=					self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE					)







					@slow
					def 			snake_case					(							self :     Optional[int]					):
									for model_class_name in self.all_model_classes:
													lowercase__					:   Any													=					model_class_name.from_pretrained("microsoft/beit-base-patch16-224"					)
													lowercase__					:   Optional[int]													=					model(np.ones((1, 3, 224, 224)					)					)
													self.assertIsNotNone(SCREAMING_SNAKE_CASE					)






def  __lowerCamelCase (       ):

				"""simple docstring"""


				lowercase__					:   Dict													=					Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"						)
				return image

@require_vision
@require_flax
class 				snake_case__(unittest.TestCase      ):

					"""simple docstring"""






					@cached_property
					def 			snake_case					(							self :     int					):
									return BeitImageProcessor.from_pretrained("microsoft/beit-base-patch16-224"					) if is_vision_available() else None







					@slow
					def 			snake_case					(							self :     Union[str, Any]					):
									lowercase__					:   Tuple													=					FlaxBeitForMaskedImageModeling.from_pretrained("microsoft/beit-base-patch16-224-pt22k"					)

									lowercase__					:   int													=					self.default_image_processor
									lowercase__					:   Union[str, Any]													=					prepare_img()
									lowercase__					:   str													=					image_processor(images=SCREAMING_SNAKE_CASE				,       return_tensors="np"					).pixel_values

									# prepare bool_masked_pos
									lowercase__					:   Optional[Any]													=					np.ones((1, 196)				,       dtype=SCREAMING_SNAKE_CASE					)

									# forward pass
									lowercase__					:   Any													=					model(pixel_values=SCREAMING_SNAKE_CASE				,       bool_masked_pos=SCREAMING_SNAKE_CASE					)
									lowercase__					:   Tuple													=					outputs.logits

									# verify the logits
									lowercase__					:   List[str]													=					(1, 196, 8_192)
									self.assertEqual(logits.shape				,       SCREAMING_SNAKE_CASE					)

									lowercase__					:   Dict													=					np.array(
									    [[-3.2_437, 0.5_072, -13.9_174], [-3.2_456, 0.4_948, -13.9_401], [-3.2_033, 0.5_121, -13.8_550]]					)

									self.assertTrue(np.allclose(logits[bool_masked_pos][:3, :3]				,       SCREAMING_SNAKE_CASE				,       atol=1E-2					)					)







					@slow
					def 			snake_case					(							self :     Any					):
									lowercase__					:   Union[str, Any]													=					FlaxBeitForImageClassification.from_pretrained("microsoft/beit-base-patch16-224"					)

									lowercase__					:   Tuple													=					self.default_image_processor
									lowercase__					:   List[Any]													=					prepare_img()
									lowercase__					:   Optional[Any]													=					image_processor(images=SCREAMING_SNAKE_CASE				,       return_tensors="np"					)

									# forward pass
									lowercase__					:   str													=					model(**SCREAMING_SNAKE_CASE					)
									lowercase__					:   Dict													=					outputs.logits

									# verify the logits
									lowercase__					:   List[str]													=					(1, 1_000)
									self.assertEqual(logits.shape				,       SCREAMING_SNAKE_CASE					)

									lowercase__					:   Tuple													=					np.array([-1.2_385, -1.0_987, -1.0_108]					)

									self.assertTrue(np.allclose(logits[0, :3]				,       SCREAMING_SNAKE_CASE				,       atol=1E-4					)					)

									lowercase__					:   str													=					281
									self.assertEqual(logits.argmax(-1					).item()				,       SCREAMING_SNAKE_CASE					)







					@slow
					def 			snake_case					(							self :     str					):
									lowercase__					:   List[Any]													=					FlaxBeitForImageClassification.from_pretrained("microsoft/beit-large-patch16-224-pt22k-ft22k"					)

									lowercase__					:   Dict													=					self.default_image_processor
									lowercase__					:   Dict													=					prepare_img()
									lowercase__					:   List[Any]													=					image_processor(images=SCREAMING_SNAKE_CASE				,       return_tensors="np"					)

									# forward pass
									lowercase__					:   Union[str, Any]													=					model(**SCREAMING_SNAKE_CASE					)
									lowercase__					:   Optional[Any]													=					outputs.logits

									# verify the logits
									lowercase__					:   int													=					(1, 21_841)
									self.assertEqual(logits.shape				,       SCREAMING_SNAKE_CASE					)

									lowercase__					:   str													=					np.array([1.6_881, -0.2_787, 0.5_901]					)

									self.assertTrue(np.allclose(logits[0, :3]				,       SCREAMING_SNAKE_CASE				,       atol=1E-4					)					)

									lowercase__					:   Union[str, Any]													=					2_396
									self.assertEqual(logits.argmax(-1					).item()				,       SCREAMING_SNAKE_CASE					)









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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase__					  =  logging.get_logger(__name__)

lowerCAmelCase__					  =  '''▁'''

lowerCAmelCase__					  =  {'''vocab_file''': '''sentencepiece.bpe.model''', '''monolingual_vocab_file''': '''dict.txt'''}

lowerCAmelCase__					  =  {
    '''vocab_file''': {
        '''vinai/bartpho-syllable''': '''https://huggingface.co/vinai/bartpho-syllable/resolve/main/sentencepiece.bpe.model''',
    },
    '''monolingual_vocab_file''': {
        '''vinai/bartpho-syllable''': '''https://huggingface.co/vinai/bartpho-syllable/resolve/main/dict.txt''',
    },
}

lowerCAmelCase__					  =  {'''vinai/bartpho-syllable''': 1_0_2_4}

class 				snake_case__(_UpperCamelCase      ):

					"""simple docstring"""






					lowercase_								=		VOCAB_FILES_NAMES
					lowercase_								=		PRETRAINED_VOCAB_FILES_MAP
					lowercase_								=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					lowercase_								=		["""input_ids""", """attention_mask"""]







					def __init__(							self :     Optional[int]				,       SCREAMING_SNAKE_CASE :     List[str]				,       SCREAMING_SNAKE_CASE :     Optional[Any]				,       SCREAMING_SNAKE_CASE :     Optional[int]="<s>"				,       SCREAMING_SNAKE_CASE :     Optional[int]="</s>"				,       SCREAMING_SNAKE_CASE :     str="</s>"				,       SCREAMING_SNAKE_CASE :     List[str]="<s>"				,       SCREAMING_SNAKE_CASE :     Union[str, Any]="<unk>"				,       SCREAMING_SNAKE_CASE :     Tuple="<pad>"				,       SCREAMING_SNAKE_CASE :     List[str]="<mask>"				,       SCREAMING_SNAKE_CASE :     Optional[Dict[str, Any]] = None				,       **SCREAMING_SNAKE_CASE :     int				,       ):
									# Mask token behave like a normal word, i.e. include the space before it
									lowercase__					:   Dict													=					AddedToken(SCREAMING_SNAKE_CASE				,       lstrip=SCREAMING_SNAKE_CASE				,       rstrip=SCREAMING_SNAKE_CASE					) if isinstance(SCREAMING_SNAKE_CASE				,       SCREAMING_SNAKE_CASE					) else mask_token

									lowercase__					:   int													=					{} if sp_model_kwargs is None else sp_model_kwargs

									super().__init__(
									    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				,       sp_model_kwargs=self.sp_model_kwargs				,       **SCREAMING_SNAKE_CASE				,       )

									lowercase__					:   Dict													=					vocab_file
									lowercase__					:   Union[str, Any]													=					monolingual_vocab_file
									lowercase__					:   Union[str, Any]													=					spm.SentencePieceProcessor(**self.sp_model_kwargs					)
									self.sp_model.Load(str(SCREAMING_SNAKE_CASE					)					)

									# Load the reduced vocab

									# Keep order of special tokens for backward compatibility
									lowercase__					:   Any													=					{}
									lowercase__					:   int													=					0
									for token in [bos_token, pad_token, eos_token, unk_token, sep_token, cls_token]:
													if str(SCREAMING_SNAKE_CASE					) not in self.fairseq_tokens_to_ids:
																	lowercase__					:   Dict													=					cnt
																	cnt += 1
									with open(SCREAMING_SNAKE_CASE				,       "r"				,       encoding="utf-8"					) as f:
													for line in f.readlines():
																	lowercase__					:   int													=					line.strip().split()[0]
																	lowercase__					:   List[str]													=					len(self.fairseq_tokens_to_ids					)
									if str(SCREAMING_SNAKE_CASE					) not in self.fairseq_tokens_to_ids:
													lowercase__					:   Optional[Any]													=					len(self.fairseq_tokens_to_ids					)

									lowercase__					:   List[str]													=					{v: k for k, v in self.fairseq_tokens_to_ids.items()}







					def __getstate__(							self :     int					):
									lowercase__					:   Dict													=					self.__dict__.copy()
									lowercase__					:   Union[str, Any]													=					None
									lowercase__					:   Dict													=					self.sp_model.serialized_model_proto()
									return state







					def __setstate__(							self :     Union[str, Any]				,       SCREAMING_SNAKE_CASE :     Optional[Any]					):
									lowercase__					:   Union[str, Any]													=					d

									# for backward compatibility
									if not hasattr(self				,       "sp_model_kwargs"					):
													lowercase__					:   Dict													=					{}

									lowercase__					:   List[str]													=					spm.SentencePieceProcessor(**self.sp_model_kwargs					)
									self.sp_model.LoadFromSerializedProto(self.sp_model_proto					)







					def 			snake_case					(							self :     Optional[int]				,       SCREAMING_SNAKE_CASE :     List[int]				,       SCREAMING_SNAKE_CASE :     Optional[List[int]] = None					):

									if token_ids_a is None:
													return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
									lowercase__					:   List[Any]													=					[self.cls_token_id]
									lowercase__					:   str													=					[self.sep_token_id]
									return cls + token_ids_a + sep + sep + token_ids_a + sep







					def 			snake_case					(							self :     Dict				,       SCREAMING_SNAKE_CASE :     List[int]				,       SCREAMING_SNAKE_CASE :     Optional[List[int]] = None				,       SCREAMING_SNAKE_CASE :     bool = False					):

									if already_has_special_tokens:
													return super().get_special_tokens_mask(
													    token_ids_a=SCREAMING_SNAKE_CASE				,       token_ids_a=SCREAMING_SNAKE_CASE				,       already_has_special_tokens=SCREAMING_SNAKE_CASE					)

									if token_ids_a is None:
													return [1] + ([0] * len(SCREAMING_SNAKE_CASE					)) + [1]
									return [1] + ([0] * len(SCREAMING_SNAKE_CASE					)) + [1, 1] + ([0] * len(SCREAMING_SNAKE_CASE					)) + [1]







					def 			snake_case					(							self :     str				,       SCREAMING_SNAKE_CASE :     List[int]				,       SCREAMING_SNAKE_CASE :     Optional[List[int]] = None					):
									lowercase__					:   Tuple													=					[self.sep_token_id]
									lowercase__					:   str													=					[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]







					@property
					def 			snake_case					(							self :     Optional[int]					):
									return len(self.fairseq_ids_to_tokens					)







					def 			snake_case					(							self :     List[Any]					):
									lowercase__					:   Any													=					{self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE					): i for i in range(self.vocab_size					)}
									vocab.update(self.added_tokens_encoder					)
									return vocab







					def 			snake_case					(							self :     Union[str, Any]				,       SCREAMING_SNAKE_CASE :     str					):
									return self.sp_model.encode(SCREAMING_SNAKE_CASE				,       out_type=SCREAMING_SNAKE_CASE					)







					def 			snake_case					(							self :     List[str]				,       SCREAMING_SNAKE_CASE :     int					):
									if token in self.fairseq_tokens_to_ids:
													return self.fairseq_tokens_to_ids[token]
									else:
													return self.unk_token_id







					def 			snake_case					(							self :     List[str]				,       SCREAMING_SNAKE_CASE :     Any					):
									return self.fairseq_ids_to_tokens[index]







					def 			snake_case					(							self :     List[Any]				,       SCREAMING_SNAKE_CASE :     Union[str, Any]					):
									lowercase__					:   str													=					"".join(SCREAMING_SNAKE_CASE					).replace(SCREAMING_SNAKE_CASE				,       " "					).strip()
									return out_string







					def 			snake_case					(							self :     Optional[int]				,       SCREAMING_SNAKE_CASE :     str				,       SCREAMING_SNAKE_CASE :     Optional[str] = None					):
									if not os.path.isdir(SCREAMING_SNAKE_CASE					):
													logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""					)
													return
									lowercase__					:   str													=					os.path.join(
									    SCREAMING_SNAKE_CASE				,       (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]					)
									lowercase__					:   List[str]													=					os.path.join(
									    SCREAMING_SNAKE_CASE				,       (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["monolingual_vocab_file"]				,       )

									if os.path.abspath(self.vocab_file					) != os.path.abspath(SCREAMING_SNAKE_CASE					) and os.path.isfile(self.vocab_file					):
													copyfile(self.vocab_file				,       SCREAMING_SNAKE_CASE					)
									elif not os.path.isfile(self.vocab_file					):
													with open(SCREAMING_SNAKE_CASE				,       "wb"					) as fi:
																	lowercase__					:   str													=					self.sp_model.serialized_model_proto()
																	fi.write(SCREAMING_SNAKE_CASE					)

									if os.path.abspath(self.monolingual_vocab_file					) != os.path.abspath(
									    SCREAMING_SNAKE_CASE					) and os.path.isfile(self.monolingual_vocab_file					):
													copyfile(self.monolingual_vocab_file				,       SCREAMING_SNAKE_CASE					)
									elif not os.path.isfile(self.monolingual_vocab_file					):
													with open(SCREAMING_SNAKE_CASE				,       "w"				,       encoding="utf-8"					) as fp:
																	for token in self.fairseq_tokens_to_ids:
																					if token not in self.all_special_tokens:
																									fp.write(f"""{str(SCREAMING_SNAKE_CASE					)} \n"""					)

									return out_vocab_file, out_monolingual_vocab_file




