
"""simple docstring"""






from typing import Any

import numpy as np



class  lowercase				:






   def __init__(self     :  List[str]			,SCREAMING_SNAKE_CASE_     :  Dict			,SCREAMING_SNAKE_CASE_     :  List[Any] )					->  List[str]:





    """simple docstring"""


    if np.any(tableau[:, -1]			,where=tableau[:, -1] < 0 ):
     raise ValueError('''RHS must be > 0''' )

    lowerCAmelCase				       =     tableau
    lowerCAmelCase       ,					lowerCAmelCase				       =     tableau.shape

    # Number of decision variables x1, x2, x3...
    lowerCAmelCase				       =     n_vars

    # Number of artificial variables to be minimised
    lowerCAmelCase				       =     len(np.where(tableau[self.n_vars : -1] == -1 )[0] )

    # 2 if there are >= or == constraints (nonstandard), 1 otherwise (std)
    lowerCAmelCase				       =     (self.n_art_vars > 0) + 1

    # Number of slack variables added to make inequalities into equalities
    lowerCAmelCase				       =     self.n_rows - self.n_stages

    # Objectives for each stage
    lowerCAmelCase				       =     ['''max''']

    # In two stage simplex, first minimise then maximise
    if self.n_art_vars:
     self.objectives.append('''min''' )

    lowerCAmelCase				       =     ['''''']

    # Index of current pivot row and column
    lowerCAmelCase				       =     None
    lowerCAmelCase				       =     None

    # Does objective row only contain (non)-negative values?
    lowerCAmelCase				       =     False






   @staticmethod
   def 				UpperCAmelCase       (*SCREAMING_SNAKE_CASE_     :  Any )					->  List[str]:





    """simple docstring"""


    if len(__SCREAMING_SNAKE_CASE ) != 3:
     raise ValueError('''Must provide n_vars, n_slack, and n_art_vars''' )

    if not all(x >= 0 and isinstance(__SCREAMING_SNAKE_CASE			,__SCREAMING_SNAKE_CASE ) for x in args ):
     raise ValueError('''All arguments must be non-negative integers''' )

    # decision | slack | artificial
    lowerCAmelCase				       =     ['''x''', '''s''', '''a''']
    lowerCAmelCase				       =     []
    for i in range(3 ):
     for j in range(args[i] ):
      titles.append(string_starts[i] + str(j + 1 ) )
    titles.append('''RHS''' )
    return titles






   def 				UpperCAmelCase       (self     :  List[str]			,SCREAMING_SNAKE_CASE_     :  List[str] )					->  Any:





    """simple docstring"""


    lowerCAmelCase				       =     self.objectives[-1]

    # Find entries of highest magnitude in objective rows
    lowerCAmelCase				       =     (objective == '''min''') - (objective == '''max''')
    lowerCAmelCase				       =     np.argmax(sign * tableau[0, : self.n_vars] )

    # Choice is only valid if below 0 for maximise, and above for minimise
    if sign * self.tableau[0, col_idx] <= 0:
     lowerCAmelCase				       =     True
     return 0, 0

    # Pivot row is chosen as having the lowest quotient when elements of
    # the pivot column divide the right-hand side

    # Slice excluding the objective rows
    lowerCAmelCase				       =     slice(self.n_stages			,self.n_rows )

    # RHS
    lowerCAmelCase				       =     tableau[s, -1]

    # Elements of pivot column within slice
    lowerCAmelCase				       =     tableau[s, col_idx]

    # Array filled with nans
    lowerCAmelCase				       =     np.full(self.n_rows - self.n_stages			,np.nan )

    # If element in pivot column is greater than zeron_stages, return
    # quotient or nan otherwise
    lowerCAmelCase				       =     np.divide(__SCREAMING_SNAKE_CASE			,__SCREAMING_SNAKE_CASE			,out=__SCREAMING_SNAKE_CASE			,where=divisor > 0 )

    # Arg of minimum quotient excluding the nan values. n_stages is added
    # to compensate for earlier exclusion of objective columns
    lowerCAmelCase				       =     np.nanargmin(__SCREAMING_SNAKE_CASE ) + self.n_stages
    return row_idx, col_idx






   def 				UpperCAmelCase       (self     :  Optional[int]			,SCREAMING_SNAKE_CASE_     :  Any			,SCREAMING_SNAKE_CASE_     :  List[Any]			,SCREAMING_SNAKE_CASE_     :  Dict )					->  Optional[Any]:





    """simple docstring"""


    lowerCAmelCase				       =     tableau[row_idx].copy()

    lowerCAmelCase				       =     piv_row[col_idx]

    # Entry becomes 1
    piv_row *= 1 / piv_val

    # Variable in pivot column becomes basic, ie the only non-zero entry
    for idx, coeff in enumerate(tableau[:, col_idx] ):
     tableau[idx] += -coeff * piv_row
    lowerCAmelCase				       =     piv_row
    return tableau






   def 				UpperCAmelCase       (self     :  Optional[Any]			,SCREAMING_SNAKE_CASE_     :  Union[str, Any] )					->  List[str]:





    """simple docstring"""


    self.objectives.pop()

    if not self.objectives:
     return tableau

    # Slice containing ids for artificial columns
    lowerCAmelCase				       =     slice(-self.n_art_vars - 1			,-1 )

    # Delete the artificial variable columns
    lowerCAmelCase				       =     np.delete(__SCREAMING_SNAKE_CASE			,__SCREAMING_SNAKE_CASE			,axis=1 )

    # Delete the objective row of the first stage
    lowerCAmelCase				       =     np.delete(__SCREAMING_SNAKE_CASE			,0			,axis=0 )

    lowerCAmelCase				       =     1
    self.n_rows -= 1
    lowerCAmelCase				       =     0
    lowerCAmelCase				       =     False
    return tableau






   def 				UpperCAmelCase       (self     :  Any )					->  Tuple:





    """simple docstring"""


    for _ in range(100 ):
     # Completion of each stage removes an objective. If both stages
     # are complete, then no objectives are left
     if not self.objectives:
      lowerCAmelCase				       =     self.generate_col_titles(
          self.n_vars			,self.n_slack			,self.n_art_vars )

      # Find the values of each variable at optimal solution
      return self.interpret_tableau(self.tableau			,self.col_titles )

     lowerCAmelCase       ,					lowerCAmelCase				       =     self.find_pivot(self.tableau )

     # If there are no more negative values in objective row
     if self.stop_iter:
      # Delete artificial variable columns and rows. Update attributes
      lowerCAmelCase				       =     self.change_stage(self.tableau )
     else:
      lowerCAmelCase				       =     self.pivot(self.tableau			,__SCREAMING_SNAKE_CASE			,__SCREAMING_SNAKE_CASE )
    return {}






   def 				UpperCAmelCase       (self     :  List[Any]			,SCREAMING_SNAKE_CASE_     :  Tuple			,SCREAMING_SNAKE_CASE_     :  int )					->  Tuple:





    """simple docstring"""


    lowerCAmelCase				       =     {'''P''': abs(tableau[0, -1] )}

    for i in range(self.n_vars ):
     # Gives ids of nonzero entries in the ith column
     lowerCAmelCase				       =     np.nonzero(tableau[:, i] )
     lowerCAmelCase				       =     len(nonzero[0] )

     # First entry in the nonzero ids
     lowerCAmelCase				       =     nonzero[0][0]
     lowerCAmelCase				       =     tableau[nonzero_rowidx, i]

     # If there is only one nonzero value in column, which is one
     if n_nonzero == nonzero_val == 1:
      lowerCAmelCase				       =     tableau[nonzero_rowidx, -1]
      lowerCAmelCase				       =     rhs_val

        # Check for basic variables
    for title in col_titles:
     # Don't add RHS or slack variables to output dict
     if title[0] not in "R-s-a":
      output_dict.setdefault(__SCREAMING_SNAKE_CASE			,0 )
    return output_dict


if __name__ == "__main__":
     import doctest

     doctest.testmod()




"""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  lowercase				(   unittest.TestCase		):






			def 				UpperCAmelCase       (self     :  str			,SCREAMING_SNAKE_CASE_     :  Dict )					->  int:





				"""simple docstring"""


				lowerCAmelCase				       =     list(tokenizer_class.max_model_input_sizes.keys() )
				for model_name in sa_models[:1]:
					lowerCAmelCase				       =     tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE_ )
					self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
					self.assertIsInstance(SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_ )
					self.assertIsInstance(SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_ )

					for special_tok in tokenizer.all_special_tokens:
						self.assertIsInstance(SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_ )
						lowerCAmelCase				       =     tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_ )
						self.assertIsInstance(SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_ )






			def 				UpperCAmelCase       (self     :  str			,SCREAMING_SNAKE_CASE_     :  BatchEncoding			,SCREAMING_SNAKE_CASE_     :  Optional[Callable] = None )					->  Dict:





				"""simple docstring"""


				lowerCAmelCase				       =     pickle.dumps(SCREAMING_SNAKE_CASE_ )
				self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )

				lowerCAmelCase				       =     pickle.loads(SCREAMING_SNAKE_CASE_ )

				# 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(SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_ ) )
					else:
						self.assertEqual(SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_ )






			@slow
			def 				UpperCAmelCase       (self     :  List[str] )					->  int:





				"""simple docstring"""


				self.check_tokenizer_from_pretrained(SCREAMING_SNAKE_CASE_ )






			def 				UpperCAmelCase       (self     :  str )					->  Dict:





				"""simple docstring"""


				self.assertEqual(TensorType('''tf''' )			,TensorType.TENSORFLOW )
				self.assertEqual(TensorType('''pt''' )			,TensorType.PYTORCH )
				self.assertEqual(TensorType('''np''' )			,TensorType.NUMPY )






			@require_tokenizers
			def 				UpperCAmelCase       (self     :  int )					->  Tuple:





				"""simple docstring"""


				import numpy as np

				lowerCAmelCase				       =     BertTokenizer.from_pretrained('''bert-base-cased''' )
				lowerCAmelCase				       =     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 				UpperCAmelCase       (self     :  str )					->  int:





				"""simple docstring"""


				import tensorflow as tf

				def tf_array_equals(SCREAMING_SNAKE_CASE_     :  Optional[Any]			,SCREAMING_SNAKE_CASE_     :  List[Any] ):
					return tf.reduce_all(tf.equal(SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_ ) )

				lowerCAmelCase				       =     BertTokenizer.from_pretrained('''bert-base-cased''' )
				lowerCAmelCase				       =     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 )			,SCREAMING_SNAKE_CASE_ )

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






			@require_torch
			@require_tokenizers
			def 				UpperCAmelCase       (self     :  Optional[Any] )					->  Union[str, Any]:





				"""simple docstring"""


				import torch

				lowerCAmelCase				       =     BertTokenizer.from_pretrained('''bert-base-cased''' )
				lowerCAmelCase				       =     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 				UpperCAmelCase       (self     :  str )					->  Tuple:





				"""simple docstring"""


				lowerCAmelCase				       =     BertTokenizer.from_pretrained('''bert-base-cased''' )
				lowerCAmelCase				       =     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 				UpperCAmelCase       (self     :  Any )					->  Dict:





				"""simple docstring"""


				lowerCAmelCase				       =     BertTokenizerFast.from_pretrained('''bert-base-cased''' )
				lowerCAmelCase				       =     tokenizer_r(['''Test''', '''\xad''', '''test''']			,is_split_into_words=SCREAMING_SNAKE_CASE_ )

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






			def 				UpperCAmelCase       (self     :  List[Any] )					->  int:





				"""simple docstring"""


				lowerCAmelCase				       =     BatchEncoding({'''inputs''': [[1, 2, 3], [4, 5, 6]], '''labels''': [0, 1]} )
				lowerCAmelCase				       =     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:
					lowerCAmelCase				       =     batch.convert_to_tensors(tensor_type='''np''' )
				self.assertFalse(len(cs.err )			,msg=F"""should have no warning, but got {cs.err}""" )

				lowerCAmelCase				       =     BatchEncoding({'''inputs''': [1, 2, 3], '''labels''': 0} )
				lowerCAmelCase				       =     batch.convert_to_tensors(tensor_type='''np'''			,prepend_batch_axis=SCREAMING_SNAKE_CASE_ )
				self.assertEqual(tensor_batch['''inputs'''].shape			,(1, 3) )
				self.assertEqual(tensor_batch['''labels'''].shape			,(1,) )






			@require_torch
			def 				UpperCAmelCase       (self     :  Union[str, Any] )					->  Optional[int]:





				"""simple docstring"""


				lowerCAmelCase				       =     BatchEncoding({'''inputs''': [[1, 2, 3], [4, 5, 6]], '''labels''': [0, 1]} )
				lowerCAmelCase				       =     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:
					lowerCAmelCase				       =     batch.convert_to_tensors(tensor_type='''pt''' )
				self.assertFalse(len(cs.err )			,msg=F"""should have no warning, but got {cs.err}""" )

				lowerCAmelCase				       =     BatchEncoding({'''inputs''': [1, 2, 3], '''labels''': 0} )
				lowerCAmelCase				       =     batch.convert_to_tensors(tensor_type='''pt'''			,prepend_batch_axis=SCREAMING_SNAKE_CASE_ )
				self.assertEqual(tensor_batch['''inputs'''].shape			,(1, 3) )
				self.assertEqual(tensor_batch['''labels'''].shape			,(1,) )






			@require_tf
			def 				UpperCAmelCase       (self     :  List[Any] )					->  List[Any]:





				"""simple docstring"""


				lowerCAmelCase				       =     BatchEncoding({'''inputs''': [[1, 2, 3], [4, 5, 6]], '''labels''': [0, 1]} )
				lowerCAmelCase				       =     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:
					lowerCAmelCase				       =     batch.convert_to_tensors(tensor_type='''tf''' )
				self.assertFalse(len(cs.err )			,msg=F"""should have no warning, but got {cs.err}""" )

				lowerCAmelCase				       =     BatchEncoding({'''inputs''': [1, 2, 3], '''labels''': 0} )
				lowerCAmelCase				       =     batch.convert_to_tensors(tensor_type='''tf'''			,prepend_batch_axis=SCREAMING_SNAKE_CASE_ )
				self.assertEqual(tensor_batch['''inputs'''].shape			,(1, 3) )
				self.assertEqual(tensor_batch['''labels'''].shape			,(1,) )






			@require_flax
			def 				UpperCAmelCase       (self     :  Union[str, Any] )					->  Optional[int]:





				"""simple docstring"""


				lowerCAmelCase				       =     BatchEncoding({'''inputs''': [[1, 2, 3], [4, 5, 6]], '''labels''': [0, 1]} )
				lowerCAmelCase				       =     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:
					lowerCAmelCase				       =     batch.convert_to_tensors(tensor_type='''jax''' )
				self.assertFalse(len(cs.err )			,msg=F"""should have no warning, but got {cs.err}""" )

				lowerCAmelCase				       =     BatchEncoding({'''inputs''': [1, 2, 3], '''labels''': 0} )
				lowerCAmelCase				       =     batch.convert_to_tensors(tensor_type='''jax'''			,prepend_batch_axis=SCREAMING_SNAKE_CASE_ )
				self.assertEqual(tensor_batch['''inputs'''].shape			,(1, 3) )
				self.assertEqual(tensor_batch['''labels'''].shape			,(1,) )






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





				"""simple docstring"""


				lowerCAmelCase				       =     [{'''input_ids''': np.array([0, 1, 2] )}, {'''input_ids''': np.array([0, 1, 2, 3] )}]
				lowerCAmelCase				       =     BertTokenizer.from_pretrained('''bert-base-cased''' )

				lowerCAmelCase				       =     tokenizer.pad(SCREAMING_SNAKE_CASE_			,padding=SCREAMING_SNAKE_CASE_ )
				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]] )
				lowerCAmelCase				       =     tokenizer.pad(SCREAMING_SNAKE_CASE_			,padding=SCREAMING_SNAKE_CASE_			,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 				UpperCAmelCase       (self     :  str )					->  Tuple:





				"""simple docstring"""


				import torch

				lowerCAmelCase				       =     [{'''input_ids''': torch.tensor([0, 1, 2] )}, {'''input_ids''': torch.tensor([0, 1, 2, 3] )}]
				lowerCAmelCase				       =     BertTokenizer.from_pretrained('''bert-base-cased''' )

				lowerCAmelCase				       =     tokenizer.pad(SCREAMING_SNAKE_CASE_			,padding=SCREAMING_SNAKE_CASE_ )
				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]] )
				lowerCAmelCase				       =     tokenizer.pad(SCREAMING_SNAKE_CASE_			,padding=SCREAMING_SNAKE_CASE_			,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 				UpperCAmelCase       (self     :  Tuple )					->  Any:





				"""simple docstring"""


				import tensorflow as tf

				lowerCAmelCase				       =     [{'''input_ids''': tf.constant([0, 1, 2] )}, {'''input_ids''': tf.constant([0, 1, 2, 3] )}]
				lowerCAmelCase				       =     BertTokenizer.from_pretrained('''bert-base-cased''' )

				lowerCAmelCase				       =     tokenizer.pad(SCREAMING_SNAKE_CASE_			,padding=SCREAMING_SNAKE_CASE_ )
				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]] )
				lowerCAmelCase				       =     tokenizer.pad(SCREAMING_SNAKE_CASE_			,padding=SCREAMING_SNAKE_CASE_			,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 				UpperCAmelCase       (self     :  Union[str, Any] )					->  List[str]:





				"""simple docstring"""


				lowerCAmelCase				       =     Tokenizer(WordPiece(unk_token='''[UNK]''' ) )
				PreTrainedTokenizerFast(tokenizer_object=SCREAMING_SNAKE_CASE_ )






			@require_tokenizers
			def 				UpperCAmelCase       (self     :  Tuple )					->  Optional[Any]:





				"""simple docstring"""


				lowerCAmelCase				       =     Tokenizer(WordPiece(unk_token='''[UNK]''' ) )
				with tempfile.TemporaryDirectory() as tmpdirname:
					bert_tokenizer.save(os.path.join(SCREAMING_SNAKE_CASE_			,'''tokenizer.json''' ) )
					PreTrainedTokenizerFast(tokenizer_file=os.path.join(SCREAMING_SNAKE_CASE_			,'''tokenizer.json''' ) )


