

import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler







class 						lowercase     (					unittest.TestCase				):


		"""simple docstring"""




		a__       							=	(ScoreSdeVeScheduler,)
		a__       							=	()


		@property
		def 		A__       (					self):
			_UpperCamelCase :		Dict								=    4
			_UpperCamelCase :		List[str]								=    3
			_UpperCamelCase :		Tuple								=    8
			_UpperCamelCase :		Optional[int]								=    8

			_UpperCamelCase :		Union[str, Any]								=    torch.rand((batch_size, num_channels, height, width))

			return sample


		@property
		def 		A__       (					self):
			_UpperCamelCase :		Union[str, Any]								=    4
			_UpperCamelCase :		Optional[Any]								=    3
			_UpperCamelCase :		Any								=    8
			_UpperCamelCase :		Tuple								=    8

			_UpperCamelCase :		List[Any]								=    batch_size * num_channels * height * width
			_UpperCamelCase :		str								=    torch.arange(__snake_case)
			_UpperCamelCase :		List[Any]								=    sample.reshape(__snake_case						,							__snake_case						,							__snake_case						,							__snake_case)
			_UpperCamelCase :		List[Any]								=    sample / num_elems
			_UpperCamelCase :		Optional[Any]								=    sample.permute(3						,							0						,							1						,							2)

			return sample


		def 		A__       (					self):
			def model(__snake_case						,							__snake_case						,							*__snake_case):
				return sample * t / (t + 1)

			return model


		def 		A__       (					self						,							**__snake_case):
			_UpperCamelCase :		int								=    {
			    'num_train_timesteps': 20_00,
			    'snr': 0.1_5,
			    'sigma_min': 0.0_1,
			    'sigma_max': 13_48,
			    'sampling_eps': 1e-5,
			}

			config.update(**__snake_case)
			return config


		def 		A__       (					self						,							__snake_case=0						,							**__snake_case):
			_UpperCamelCase :		int								=    dict(self.forward_default_kwargs)

			for scheduler_class in self.scheduler_classes:
				_UpperCamelCase :		List[str]								=    self.dummy_sample
				_UpperCamelCase :		int								=    0.1 * sample

				_UpperCamelCase :		str								=    self.get_scheduler_config(**__snake_case)
				_UpperCamelCase :		List[str]								=    scheduler_class(**__snake_case)

				with tempfile.TemporaryDirectory() as tmpdirname:
					scheduler.save_config(__snake_case)
					_UpperCamelCase :		List[Any]								=    scheduler_class.from_pretrained(__snake_case)

				_UpperCamelCase :		int								=    scheduler.step_pred(
				    __snake_case						,							__snake_case						,							__snake_case						,							generator=torch.manual_seed(0)						,							**__snake_case).prev_sample
				_UpperCamelCase :		int								=    new_scheduler.step_pred(
				    __snake_case						,							__snake_case						,							__snake_case						,							generator=torch.manual_seed(0)						,							**__snake_case).prev_sample

				assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"

				_UpperCamelCase :		List[str]								=    scheduler.step_correct(__snake_case						,							__snake_case						,							generator=torch.manual_seed(0)						,							**__snake_case).prev_sample
				_UpperCamelCase :		Union[str, Any]								=    new_scheduler.step_correct(
				    __snake_case						,							__snake_case						,							generator=torch.manual_seed(0)						,							**__snake_case).prev_sample

				assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler correction are not identical"


		def 		A__       (					self						,							__snake_case=0						,							**__snake_case):
			_UpperCamelCase :		Union[str, Any]								=    dict(self.forward_default_kwargs)
			kwargs.update(__snake_case)

			for scheduler_class in self.scheduler_classes:
				_UpperCamelCase :		Optional[Any]								=    self.dummy_sample
				_UpperCamelCase :		Optional[Any]								=    0.1 * sample

				_UpperCamelCase :		Any								=    self.get_scheduler_config()
				_UpperCamelCase :		Union[str, Any]								=    scheduler_class(**__snake_case)

				with tempfile.TemporaryDirectory() as tmpdirname:
					scheduler.save_config(__snake_case)
					_UpperCamelCase :		Tuple								=    scheduler_class.from_pretrained(__snake_case)

				_UpperCamelCase :		Union[str, Any]								=    scheduler.step_pred(
				    __snake_case						,							__snake_case						,							__snake_case						,							generator=torch.manual_seed(0)						,							**__snake_case).prev_sample
				_UpperCamelCase :		Optional[Any]								=    new_scheduler.step_pred(
				    __snake_case						,							__snake_case						,							__snake_case						,							generator=torch.manual_seed(0)						,							**__snake_case).prev_sample

				assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler outputs are not identical"

				_UpperCamelCase :		Tuple								=    scheduler.step_correct(__snake_case						,							__snake_case						,							generator=torch.manual_seed(0)						,							**__snake_case).prev_sample
				_UpperCamelCase :		Any								=    new_scheduler.step_correct(
				    __snake_case						,							__snake_case						,							generator=torch.manual_seed(0)						,							**__snake_case).prev_sample

				assert torch.sum(torch.abs(output - new_output)) < 1e-5, "Scheduler correction are not identical"


		def 		A__       (					self):
			for timesteps in [10, 1_00, 10_00]:
				self.check_over_configs(num_train_timesteps=__snake_case)


		def 		A__       (					self):
			for sigma_min, sigma_max in zip([0.0_0_0_1, 0.0_0_1, 0.0_1]						,							[1, 1_00, 10_00]):
				self.check_over_configs(sigma_min=__snake_case						,							sigma_max=__snake_case)


		def 		A__       (					self):
			for t in [0.1, 0.5, 0.7_5]:
				self.check_over_forward(time_step=__snake_case)


		def 		A__       (					self):
			_UpperCamelCase :		Tuple								=    dict(self.forward_default_kwargs)

			_UpperCamelCase :		List[Any]								=    self.scheduler_classes[0]
			_UpperCamelCase :		Tuple								=    self.get_scheduler_config()
			_UpperCamelCase :		List[Any]								=    scheduler_class(**__snake_case)

			_UpperCamelCase :		List[Any]								=    3

			_UpperCamelCase :		Any								=    self.dummy_model()
			_UpperCamelCase :		Dict								=    self.dummy_sample_deter

			scheduler.set_sigmas(__snake_case)
			scheduler.set_timesteps(__snake_case)
			_UpperCamelCase :		List[str]								=    torch.manual_seed(0)

			for i, t in enumerate(scheduler.timesteps):
				_UpperCamelCase :		Optional[int]								=    scheduler.sigmas[i]

				for _ in range(scheduler.config.correct_steps):
					with torch.no_grad():
						_UpperCamelCase :		str								=    model(__snake_case						,							__snake_case)
					_UpperCamelCase :		Optional[Any]								=    scheduler.step_correct(__snake_case						,							__snake_case						,							generator=__snake_case						,							**__snake_case).prev_sample

				with torch.no_grad():
					_UpperCamelCase :		Union[str, Any]								=    model(__snake_case						,							__snake_case)

				_UpperCamelCase :		Any								=    scheduler.step_pred(__snake_case						,							__snake_case						,							__snake_case						,							generator=__snake_case						,							**__snake_case)
				_UpperCamelCase						,		_UpperCamelCase :		Optional[int]								=    output.prev_sample, output.prev_sample_mean

			_UpperCamelCase :		int								=    torch.sum(torch.abs(__snake_case))
			_UpperCamelCase :		List[Any]								=    torch.mean(torch.abs(__snake_case))

			assert np.isclose(result_sum.item()						,							1_4_3_7_2_7_5_8_5_2_8.0)
			assert np.isclose(result_mean.item()						,							1_8_7_1_4_5_3_0.0)






		def 		A__       (					self):
			_UpperCamelCase :		List[str]								=    dict(self.forward_default_kwargs)

			_UpperCamelCase :		List[str]								=    kwargs.pop('num_inference_steps'						,							__snake_case)

			for scheduler_class in self.scheduler_classes:
				_UpperCamelCase :		List[Any]								=    self.get_scheduler_config()
				_UpperCamelCase :		Optional[Any]								=    scheduler_class(**__snake_case)

				_UpperCamelCase :		Optional[Any]								=    self.dummy_sample
				_UpperCamelCase :		int								=    0.1 * sample

				if num_inference_steps is not None and hasattr(__snake_case						,							'set_timesteps'):
					scheduler.set_timesteps(__snake_case)
				elif num_inference_steps is not None and not hasattr(__snake_case						,							'set_timesteps'):
					_UpperCamelCase :		Union[str, Any]								=    num_inference_steps

				_UpperCamelCase :		Any								=    scheduler.step_pred(__snake_case						,							0						,							__snake_case						,							generator=torch.manual_seed(0)						,							**__snake_case).prev_sample
				_UpperCamelCase :		Optional[int]								=    scheduler.step_pred(__snake_case						,							1						,							__snake_case						,							generator=torch.manual_seed(0)						,							**__snake_case).prev_sample

				self.assertEqual(output_a.shape						,							sample.shape)
				self.assertEqual(output_a.shape						,							output_a.shape)









"""simple docstring"""







import os
import pickle
import tempfile
import unittest
from typing import Callable, Optional

import numpy as np

from transformers import (
    BatchEncoding,
    BertTokenizer,
    BertTokenizerFast,
    PreTrainedTokenizer,
    PreTrainedTokenizerFast,
    TensorType,
    TokenSpan,
    is_tokenizers_available,
)
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import CaptureStderr, require_flax, require_tf, require_tokenizers, require_torch, slow


if is_tokenizers_available():
		from tokenizers import Tokenizer
		from tokenizers.models import WordPiece





class   UpperCamelCase	(     unittest.TestCase       ):




	"""simple docstring"""






	def    lowerCamelCase__					(     self				,UpperCAmelCase_   ):
			_lowercase				: Optional[int]     =   list(tokenizer_class.max_model_input_sizes.keys()   )
			for model_name in sa_models[:1]:
					_lowercase				: Any     =   tokenizer_class.from_pretrained(UpperCAmelCase_   )
					self.assertIsNotNone(UpperCAmelCase_   )
					self.assertIsInstance(UpperCAmelCase_				,UpperCAmelCase_   )
					self.assertIsInstance(UpperCAmelCase_				,UpperCAmelCase_   )

					for special_tok in tokenizer.all_special_tokens:
							self.assertIsInstance(UpperCAmelCase_				,UpperCAmelCase_   )
							_lowercase				: Any     =   tokenizer.convert_tokens_to_ids(UpperCAmelCase_   )
							self.assertIsInstance(UpperCAmelCase_				,UpperCAmelCase_   )






	def    lowerCamelCase__					(     self				,UpperCAmelCase_				,UpperCAmelCase_ = None   ):
			_lowercase				: Any     =   pickle.dumps(UpperCAmelCase_   )
			self.assertIsNotNone(UpperCAmelCase_   )

			_lowercase				: Optional[int]     =   pickle.loads(UpperCAmelCase_   )

			# Ensure is_fast is correctly restored
			self.assertEqual(be_restored.is_fast				,be_original.is_fast   )

			# Ensure encodings are potentially correctly restored
			if be_original.is_fast:
					self.assertIsNotNone(be_restored.encodings   )
			else:
					self.assertIsNone(be_restored.encodings   )

			# Ensure the keys are the same
			for original_v, restored_v in zip(be_original.values()				,be_restored.values()   ):
					if equal_op:
							self.assertTrue(equal_op(UpperCAmelCase_				,UpperCAmelCase_   )   )
					else:
							self.assertEqual(UpperCAmelCase_				,UpperCAmelCase_   )






	@slow
	def    lowerCamelCase__					(     self   ):
			self.check_tokenizer_from_pretrained(UpperCAmelCase_   )






	def    lowerCamelCase__					(     self   ):
			self.assertEqual(TensorType("""tf"""   )				,TensorType.TENSORFLOW   )
			self.assertEqual(TensorType("""pt"""   )				,TensorType.PYTORCH   )
			self.assertEqual(TensorType("""np"""   )				,TensorType.NUMPY   )






	@require_tokenizers
	def    lowerCamelCase__					(     self   ):
			import numpy as np

			_lowercase				: Optional[Any]     =   BertTokenizer.from_pretrained("""bert-base-cased"""   )
			_lowercase				: Dict     =   BertTokenizerFast.from_pretrained("""bert-base-cased"""   )

			# Python no tensor
			with self.subTest("""BatchEncoding (Python, return_tensors=None)"""   ):
					self.assert_dump_and_restore(tokenizer_p("""Small example to encode"""   )   )

			with self.subTest("""BatchEncoding (Python, return_tensors=NUMPY)"""   ):
					self.assert_dump_and_restore(
					    tokenizer_p("""Small example to encode"""				,return_tensors=TensorType.NUMPY   )				,np.array_equal   )

			with self.subTest("""BatchEncoding (Rust, return_tensors=None)"""   ):
					self.assert_dump_and_restore(tokenizer_r("""Small example to encode"""   )   )

			with self.subTest("""BatchEncoding (Rust, return_tensors=NUMPY)"""   ):
					self.assert_dump_and_restore(
					    tokenizer_r("""Small example to encode"""				,return_tensors=TensorType.NUMPY   )				,np.array_equal   )






	@require_tf
	@require_tokenizers
	def    lowerCamelCase__					(     self   ):
			import tensorflow as tf

			def tf_array_equals(UpperCAmelCase_				,UpperCAmelCase_   ):
					return tf.reduce_all(tf.equal(UpperCAmelCase_				,UpperCAmelCase_   )   )

			_lowercase				: Optional[int]     =   BertTokenizer.from_pretrained("""bert-base-cased"""   )
			_lowercase				: Any     =   BertTokenizerFast.from_pretrained("""bert-base-cased"""   )

			with self.subTest("""BatchEncoding (Python, return_tensors=TENSORFLOW)"""   ):
					self.assert_dump_and_restore(
					    tokenizer_p("""Small example to encode"""				,return_tensors=TensorType.TENSORFLOW   )				,UpperCAmelCase_   )

			with self.subTest("""BatchEncoding (Rust, return_tensors=TENSORFLOW)"""   ):
					self.assert_dump_and_restore(
					    tokenizer_r("""Small example to encode"""				,return_tensors=TensorType.TENSORFLOW   )				,UpperCAmelCase_   )






	@require_torch
	@require_tokenizers
	def    lowerCamelCase__					(     self   ):
			import torch

			_lowercase				: Union[str, Any]     =   BertTokenizer.from_pretrained("""bert-base-cased"""   )
			_lowercase				: int     =   BertTokenizerFast.from_pretrained("""bert-base-cased"""   )

			with self.subTest("""BatchEncoding (Python, return_tensors=PYTORCH)"""   ):
					self.assert_dump_and_restore(
					    tokenizer_p("""Small example to encode"""				,return_tensors=TensorType.PYTORCH   )				,torch.equal   )

			with self.subTest("""BatchEncoding (Rust, return_tensors=PYTORCH)"""   ):
					self.assert_dump_and_restore(
					    tokenizer_r("""Small example to encode"""				,return_tensors=TensorType.PYTORCH   )				,torch.equal   )






	@require_tokenizers
	def    lowerCamelCase__					(     self   ):
			_lowercase				: int     =   BertTokenizer.from_pretrained("""bert-base-cased"""   )
			_lowercase				: Tuple     =   BertTokenizerFast.from_pretrained("""bert-base-cased"""   )

			with self.subTest("""Python Tokenizer"""   ):
					self.assertFalse(tokenizer_p("""Small example to_encode"""   ).is_fast   )

			with self.subTest("""Rust Tokenizer"""   ):
					self.assertTrue(tokenizer_r("""Small example to_encode"""   ).is_fast   )






	@require_tokenizers
	def    lowerCamelCase__					(     self   ):
			_lowercase				: Optional[Any]     =   BertTokenizerFast.from_pretrained("""bert-base-cased"""   )
			_lowercase				: Optional[int]     =   tokenizer_r(["""Test""", """\xad""", """test"""]				,is_split_into_words=UpperCAmelCase_   )

			self.assertEqual(encoded.word_to_tokens(0   )				,TokenSpan(start=1				,end=2   )   )
			self.assertEqual(encoded.word_to_tokens(1   )				,UpperCAmelCase_   )
			self.assertEqual(encoded.word_to_tokens(2   )				,TokenSpan(start=2				,end=3   )   )






	def    lowerCamelCase__					(     self   ):
			_lowercase				: Tuple     =   BatchEncoding({"""inputs""": [[1, 2, 3], [4, 5, 6]], """labels""": [0, 1]}   )
			_lowercase				: Any     =   batch.convert_to_tensors(tensor_type="""np"""   )
			self.assertEqual(tensor_batch["""inputs"""].shape				,(2, 3)   )
			self.assertEqual(tensor_batch["""labels"""].shape				,(2,)   )
			# test converting the converted
			with CaptureStderr() as cs:
					_lowercase				: Tuple     =   batch.convert_to_tensors(tensor_type="""np"""   )
			self.assertFalse(len(cs.err   )				,msg=f"""should have no warning, but got {cs.err}"""   )

			_lowercase				: List[str]     =   BatchEncoding({"""inputs""": [1, 2, 3], """labels""": 0}   )
			_lowercase				: Any     =   batch.convert_to_tensors(tensor_type="""np"""				,prepend_batch_axis=UpperCAmelCase_   )
			self.assertEqual(tensor_batch["""inputs"""].shape				,(1, 3)   )
			self.assertEqual(tensor_batch["""labels"""].shape				,(1,)   )






	@require_torch
	def    lowerCamelCase__					(     self   ):
			_lowercase				: str     =   BatchEncoding({"""inputs""": [[1, 2, 3], [4, 5, 6]], """labels""": [0, 1]}   )
			_lowercase				: Any     =   batch.convert_to_tensors(tensor_type="""pt"""   )
			self.assertEqual(tensor_batch["""inputs"""].shape				,(2, 3)   )
			self.assertEqual(tensor_batch["""labels"""].shape				,(2,)   )
			# test converting the converted
			with CaptureStderr() as cs:
					_lowercase				: str     =   batch.convert_to_tensors(tensor_type="""pt"""   )
			self.assertFalse(len(cs.err   )				,msg=f"""should have no warning, but got {cs.err}"""   )

			_lowercase				: Dict     =   BatchEncoding({"""inputs""": [1, 2, 3], """labels""": 0}   )
			_lowercase				: List[str]     =   batch.convert_to_tensors(tensor_type="""pt"""				,prepend_batch_axis=UpperCAmelCase_   )
			self.assertEqual(tensor_batch["""inputs"""].shape				,(1, 3)   )
			self.assertEqual(tensor_batch["""labels"""].shape				,(1,)   )






	@require_tf
	def    lowerCamelCase__					(     self   ):
			_lowercase				: int     =   BatchEncoding({"""inputs""": [[1, 2, 3], [4, 5, 6]], """labels""": [0, 1]}   )
			_lowercase				: List[str]     =   batch.convert_to_tensors(tensor_type="""tf"""   )
			self.assertEqual(tensor_batch["""inputs"""].shape				,(2, 3)   )
			self.assertEqual(tensor_batch["""labels"""].shape				,(2,)   )
			# test converting the converted
			with CaptureStderr() as cs:
					_lowercase				: Tuple     =   batch.convert_to_tensors(tensor_type="""tf"""   )
			self.assertFalse(len(cs.err   )				,msg=f"""should have no warning, but got {cs.err}"""   )

			_lowercase				: Tuple     =   BatchEncoding({"""inputs""": [1, 2, 3], """labels""": 0}   )
			_lowercase				: List[str]     =   batch.convert_to_tensors(tensor_type="""tf"""				,prepend_batch_axis=UpperCAmelCase_   )
			self.assertEqual(tensor_batch["""inputs"""].shape				,(1, 3)   )
			self.assertEqual(tensor_batch["""labels"""].shape				,(1,)   )






	@require_flax
	def    lowerCamelCase__					(     self   ):
			_lowercase				: Any     =   BatchEncoding({"""inputs""": [[1, 2, 3], [4, 5, 6]], """labels""": [0, 1]}   )
			_lowercase				: Tuple     =   batch.convert_to_tensors(tensor_type="""jax"""   )
			self.assertEqual(tensor_batch["""inputs"""].shape				,(2, 3)   )
			self.assertEqual(tensor_batch["""labels"""].shape				,(2,)   )
			# test converting the converted
			with CaptureStderr() as cs:
					_lowercase				: str     =   batch.convert_to_tensors(tensor_type="""jax"""   )
			self.assertFalse(len(cs.err   )				,msg=f"""should have no warning, but got {cs.err}"""   )

			_lowercase				: Union[str, Any]     =   BatchEncoding({"""inputs""": [1, 2, 3], """labels""": 0}   )
			_lowercase				: List[Any]     =   batch.convert_to_tensors(tensor_type="""jax"""				,prepend_batch_axis=UpperCAmelCase_   )
			self.assertEqual(tensor_batch["""inputs"""].shape				,(1, 3)   )
			self.assertEqual(tensor_batch["""labels"""].shape				,(1,)   )






	def    lowerCamelCase__					(     self   ):
			_lowercase				: Optional[int]     =   [{"""input_ids""": np.array([0, 1, 2]   )}, {"""input_ids""": np.array([0, 1, 2, 3]   )}]
			_lowercase				: Optional[int]     =   BertTokenizer.from_pretrained("""bert-base-cased"""   )

			_lowercase				: List[str]     =   tokenizer.pad(UpperCAmelCase_				,padding=UpperCAmelCase_   )
			self.assertTrue(isinstance(batch["""input_ids"""]				,np.ndarray   )   )
			self.assertEqual(batch["""input_ids"""].tolist()				,[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]   )
			_lowercase				: Optional[int]     =   tokenizer.pad(UpperCAmelCase_				,padding=UpperCAmelCase_				,return_tensors="""np"""   )
			self.assertTrue(isinstance(batch["""input_ids"""]				,np.ndarray   )   )
			self.assertEqual(batch["""input_ids"""].tolist()				,[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]   )






	@require_torch
	def    lowerCamelCase__					(     self   ):
			import torch

			_lowercase				: List[Any]     =   [{"""input_ids""": torch.tensor([0, 1, 2]   )}, {"""input_ids""": torch.tensor([0, 1, 2, 3]   )}]
			_lowercase				: Tuple     =   BertTokenizer.from_pretrained("""bert-base-cased"""   )

			_lowercase				: List[Any]     =   tokenizer.pad(UpperCAmelCase_				,padding=UpperCAmelCase_   )
			self.assertTrue(isinstance(batch["""input_ids"""]				,torch.Tensor   )   )
			self.assertEqual(batch["""input_ids"""].tolist()				,[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]   )
			_lowercase				: List[Any]     =   tokenizer.pad(UpperCAmelCase_				,padding=UpperCAmelCase_				,return_tensors="""pt"""   )
			self.assertTrue(isinstance(batch["""input_ids"""]				,torch.Tensor   )   )
			self.assertEqual(batch["""input_ids"""].tolist()				,[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]   )






	@require_tf
	def    lowerCamelCase__					(     self   ):
			import tensorflow as tf

			_lowercase				: List[Any]     =   [{"""input_ids""": tf.constant([0, 1, 2]   )}, {"""input_ids""": tf.constant([0, 1, 2, 3]   )}]
			_lowercase				: Dict     =   BertTokenizer.from_pretrained("""bert-base-cased"""   )

			_lowercase				: Optional[int]     =   tokenizer.pad(UpperCAmelCase_				,padding=UpperCAmelCase_   )
			self.assertTrue(isinstance(batch["""input_ids"""]				,tf.Tensor   )   )
			self.assertEqual(batch["""input_ids"""].numpy().tolist()				,[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]   )
			_lowercase				: str     =   tokenizer.pad(UpperCAmelCase_				,padding=UpperCAmelCase_				,return_tensors="""tf"""   )
			self.assertTrue(isinstance(batch["""input_ids"""]				,tf.Tensor   )   )
			self.assertEqual(batch["""input_ids"""].numpy().tolist()				,[[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]   )






	@require_tokenizers
	def    lowerCamelCase__					(     self   ):
			_lowercase				: Tuple     =   Tokenizer(WordPiece(unk_token="""[UNK]"""   )   )
			PreTrainedTokenizerFast(tokenizer_object=UpperCAmelCase_   )






	@require_tokenizers
	def    lowerCamelCase__					(     self   ):
			_lowercase				: List[Any]     =   Tokenizer(WordPiece(unk_token="""[UNK]"""   )   )
			with tempfile.TemporaryDirectory() as tmpdirname:
					bert_tokenizer.save(os.path.join(UpperCAmelCase_				,"""tokenizer.json"""   )   )
					PreTrainedTokenizerFast(tokenizer_file=os.path.join(UpperCAmelCase_				,"""tokenizer.json"""   )   )



