






import os
import unittest

from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin
class    _UpperCAmelCase      ( A__	,unittest.TestCase			):

				"""simple docstring"""




				lowercase__       							= TransfoXLTokenizer
				lowercase__       							= False
				lowercase__       							= False




				def        lowercase__     (  self		:					Dict						):

								'''simple docstring'''
								super().setUp()

								lowercase__     				=    [
								    '''<unk>''',
								    '''[CLS]''',
								    '''[SEP]''',
								    '''want''',
								    '''unwanted''',
								    '''wa''',
								    '''un''',
								    '''running''',
								    ''',''',
								    '''low''',
								    '''l''',
								]
								lowercase__     				=    os.path.join(self.tmpdirname,						VOCAB_FILES_NAMES['''vocab_file''']						)
								with open(self.vocab_file,						'''w''',						encoding='''utf-8'''						) as vocab_writer:
												vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]						)						)




				def        lowercase__     (  self		:					List[Any],						**lowerCamelCase		:					Any						):

								'''simple docstring'''
								lowercase__     				=    True
								return TransfoXLTokenizer.from_pretrained(self.tmpdirname,						**lowerCamelCase						)




				def        lowercase__     (  self		:					Any,						lowerCamelCase		:					Union[str, Any]						):

								'''simple docstring'''
								lowercase__     				=    '''<unk> UNwanted , running'''
								lowercase__     				=    '''<unk> unwanted, running'''
								return input_text, output_text




				def        lowercase__     (  self		:					Optional[int]						):

								'''simple docstring'''
								lowercase__     				=    TransfoXLTokenizer(vocab_file=self.vocab_file,						lower_case=lowerCamelCase						)

								lowercase__     				=    tokenizer.tokenize('''<unk> UNwanted , running'''						)
								self.assertListEqual(lowerCamelCase,						['''<unk>''', '''unwanted''', ''',''', '''running''']						)

								self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase						),						[0, 4, 8, 7]						)




				def        lowercase__     (  self		:					List[str]						):

								'''simple docstring'''
								lowercase__     				=    TransfoXLTokenizer(lower_case=lowerCamelCase						)

								self.assertListEqual(
								    tokenizer.tokenize(''' \tHeLLo ! how  \n Are yoU ?  '''						),						['''hello''', '''!''', '''how''', '''are''', '''you''', '''?''']						)




				def        lowercase__     (  self		:					Optional[int]						):

								'''simple docstring'''
								lowercase__     				=    TransfoXLTokenizer(lower_case=lowerCamelCase						)

								self.assertListEqual(
								    tokenizer.tokenize(''' \tHeLLo ! how  \n Are yoU ?  '''						),						['''HeLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?''']						)




				def        lowercase__     (  self		:					Any						):

								'''simple docstring'''
								lowercase__     				=    TransfoXLTokenizer(lower_case=lowerCamelCase						)
								lowercase__     				=    '''Hello (bracket) and side-scrolled [and] Henry\'s $5,000 with 3.34 m. What\'s up!?'''
								lowercase__     				=    [
								    '''Hello''',
								    '''(''',
								    '''bracket''',
								    ''')''',
								    '''and''',
								    '''side''',
								    '''@-@''',
								    '''scrolled''',
								    '''[''',
								    '''and''',
								    ''']''',
								    '''Henry''',
								    '''\'s''',
								    '''$''',
								    '''5''',
								    '''@,@''',
								    '''000''',
								    '''with''',
								    '''3''',
								    '''@.@''',
								    '''34''',
								    '''m''',
								    '''.''',
								    '''What''',
								    '''\'s''',
								    '''up''',
								    '''!''',
								    '''?''',
								]

								self.assertListEqual(tokenizer.tokenize(lowerCamelCase						),						lowerCamelCase						)

								self.assertEqual(tokenizer.convert_tokens_to_string(lowerCamelCase						),						lowerCamelCase						)




				def        lowercase__     (  self		:					List[Any]						):

								'''simple docstring'''
								lowercase__     				=    self.get_tokenizer()
								lowercase__     				=    len(lowerCamelCase						)

								tokenizer.add_tokens(['''new1''', '''new2''']						)
								tokenizer.move_added_token('''new1''',						1						)

								# Check that moved token is not copied (duplicate)
								self.assertEqual(len(lowerCamelCase						),						original_len + 2						)
								# Check that token is moved to specified id
								self.assertEqual(tokenizer.encode('''new1'''						),						[1]						)
								self.assertEqual(tokenizer.decode([1]						),						'''new1'''						)












import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


A__			:    Dict         =				50_00_00

A__     ,     A__			:    str         =				os.path.split(__file__)
A__			:    Optional[Any]         =				os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))






@get_duration
def 						a    (		lowerCamelCase_	,     **lowerCamelCase_       ):






				'''simple docstring'''
				lowercase__     				=    dataset.map(**lowerCamelCase_       )






@get_duration
def 						a    (		lowerCamelCase_	,     **lowerCamelCase_       ):






				'''simple docstring'''
				lowercase__     				=    dataset.filter(**lowerCamelCase_       )






def 						a    (		):






				'''simple docstring'''
				lowercase__     				=    {'''num examples''': SPEED_TEST_N_EXAMPLES}
				with tempfile.TemporaryDirectory() as tmp_dir:
								lowercase__     				=    datasets.Features({'''text''': datasets.Value('''string'''       ), '''numbers''': datasets.Value('''float32'''       )}       )
								lowercase__     				=    generate_example_dataset(
								    os.path.join(lowerCamelCase_	,     '''dataset.arrow'''       )	,     lowerCamelCase_	,     num_examples=lowerCamelCase_       )

								lowercase__     				=    transformers.AutoTokenizer.from_pretrained('''bert-base-cased'''	,     use_fast=lowerCamelCase_       )

								def tokenize(lowerCamelCase_       ):
												return tokenizer(examples['''text''']       )

								lowercase__     				=    map(lowerCamelCase_       )

								lowercase__     				=    map(lowerCamelCase_	,     batched=lowerCamelCase_       )

								lowercase__     				=    map(lowerCamelCase_	,     function=lambda lowerCamelCase_       : None	,     batched=lowerCamelCase_       )

								with dataset.formatted_as(type='''numpy'''       ):
												lowercase__     				=    map(lowerCamelCase_	,     function=lambda lowerCamelCase_       : None	,     batched=lowerCamelCase_       )

								with dataset.formatted_as(type='''pandas'''       ):
												lowercase__     				=    map(lowerCamelCase_	,     function=lambda lowerCamelCase_       : None	,     batched=lowerCamelCase_       )

								with dataset.formatted_as(type='''torch'''	,     columns='''numbers'''       ):
												lowercase__     				=    map(lowerCamelCase_	,     function=lambda lowerCamelCase_       : None	,     batched=lowerCamelCase_       )

								with dataset.formatted_as(type='''tensorflow'''	,     columns='''numbers'''       ):
												lowercase__     				=    map(lowerCamelCase_	,     function=lambda lowerCamelCase_       : None	,     batched=lowerCamelCase_       )

								lowercase__     				=    map(lowerCamelCase_	,     function=lowerCamelCase_	,     batched=lowerCamelCase_       )

								lowercase__     				=    filter(lowerCamelCase_       )

								# Activate later when tokenizer support batched inputs
								# with dataset.formatted_as(type='numpy'):
								#     times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)

				with open(lowerCamelCase_	,     '''wb'''       ) as f:
								f.write(json.dumps(lowerCamelCase_       ).encode('''utf-8'''       )       )


if __name__ == "__main__":  # useful to run the profiler
							benchmark_map_filter()




