




import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


__lowerCamelCase   :						Optional[int]				=			50_00_00

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







@get_duration
def 		lowercase__   (		__A:	datasets.Dataset							,**__A:	Optional[int]				):





     '''simple docstring'''






     __magic_name__							:	List[str]    							=							dataset.map(**__A				)







@get_duration
def 		lowercase__   (		__A:	datasets.Dataset							,**__A:	List[Any]				):





     '''simple docstring'''






     __magic_name__							:	str    							=							dataset.filter(**__A				)







def 		lowercase__   (		):





     '''simple docstring'''






     __magic_name__							:	str    							=							{'''num examples''': SPEED_TEST_N_EXAMPLES}
     with tempfile.TemporaryDirectory() as tmp_dir:
          __magic_name__							:	Optional[int]    							=							datasets.Features({'''text''': datasets.Value('''string'''				), '''numbers''': datasets.Value('''float32'''				)}				)
          __magic_name__							:	Tuple    							=							generate_example_dataset(
              os.path.join(__A							,'''dataset.arrow'''				)							,__A							,num_examples=__A				)

          __magic_name__							:	List[Any]    							=							transformers.AutoTokenizer.from_pretrained('''bert-base-cased'''							,use_fast=__A				)

          def tokenize(__A:	Optional[int]				):
               return tokenizer(examples['''text''']				)

          __magic_name__							:	Optional[int]    							=							map(__A				)

          __magic_name__							:	Dict    							=							map(__A							,batched=__A				)

          __magic_name__							:	List[str]    							=							map(__A							,function=lambda __A				: None							,batched=__A				)

          with dataset.formatted_as(type='''numpy'''				):
               __magic_name__							:	Optional[int]    							=							map(__A							,function=lambda __A				: None							,batched=__A				)

          with dataset.formatted_as(type='''pandas'''				):
               __magic_name__							:	Any    							=							map(__A							,function=lambda __A				: None							,batched=__A				)

          with dataset.formatted_as(type='''torch'''							,columns='''numbers'''				):
               __magic_name__							:	Optional[Any]    							=							map(__A							,function=lambda __A				: None							,batched=__A				)

          with dataset.formatted_as(type='''tensorflow'''							,columns='''numbers'''				):
               __magic_name__							:	Optional[int]    							=							map(__A							,function=lambda __A				: None							,batched=__A				)

          __magic_name__							:	Dict    							=							map(__A							,function=__A							,batched=__A				)

          __magic_name__							:	List[Any]    							=							filter(__A				)

          # 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(__A							,'''wb'''				) as f:
          f.write(json.dumps(__A				).encode('''utf-8'''				)				)


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









import json
import os
import unittest

from transformers.models.blenderbot_small.tokenization_blenderbot_small import (
    VOCAB_FILES_NAMES,
    BlenderbotSmallTokenizer,
)

from ...test_tokenization_common import TokenizerTesterMixin


class    lowerCamelCase    (		_lowerCamelCase				,unittest.TestCase						):






			'''simple docstring'''





			UpperCamelCase__								=BlenderbotSmallTokenizer
			UpperCamelCase__								=False

			def 							UpperCAmelCase__       ( self			:   Union[str, Any]  )						->   str:
								super().setUp()

								__magic_name__							:	Union[str, Any]    							=							['''__start__''', '''adapt''', '''act''', '''ap@@''', '''te''', '''__end__''', '''__unk__''']
								__magic_name__							:	Tuple    							=							dict(zip(lowerCamelCase_       ,						range(len(lowerCamelCase_  )  )  )  )

								__magic_name__							:	Tuple    							=							['''#version: 0.2''', '''a p''', '''t e</w>''', '''ap t</w>''', '''a d''', '''ad apt</w>''', '''a c''', '''ac t</w>''', '''''']
								__magic_name__							:	List[Any]    							=							{'''unk_token''': '''__unk__''', '''bos_token''': '''__start__''', '''eos_token''': '''__end__'''}

								__magic_name__							:	str    							=							os.path.join(self.tmpdirname       ,						VOCAB_FILES_NAMES['''vocab_file''']  )
								__magic_name__							:	List[str]    							=							os.path.join(self.tmpdirname       ,						VOCAB_FILES_NAMES['''merges_file''']  )
								with open(self.vocab_file       ,						'''w'''       ,						encoding='''utf-8'''  ) as fp:
													fp.write(json.dumps(lowerCamelCase_  ) + '''\n'''  )
								with open(self.merges_file       ,						'''w'''       ,						encoding='''utf-8'''  ) as fp:
													fp.write('''\n'''.join(lowerCamelCase_  )  )

			def 							UpperCAmelCase__       ( self			:   List[str]       ,						**lowerCamelCase_			:   Optional[Any]  )						->   List[Any]:
								kwargs.update(self.special_tokens_map  )
								return BlenderbotSmallTokenizer.from_pretrained(self.tmpdirname       ,						**lowerCamelCase_  )

			def 							UpperCAmelCase__       ( self			:   Dict       ,						lowerCamelCase_			:   Union[str, Any]  )						->   Optional[int]:
								__magic_name__							:	Union[str, Any]    							=							'''adapt act apte'''
								__magic_name__							:	Dict    							=							'''adapt act apte'''
								return input_text, output_text

			def 							UpperCAmelCase__       ( self			:   Union[str, Any]  )						->   Any:
								__magic_name__							:	int    							=							BlenderbotSmallTokenizer(self.vocab_file       ,						self.merges_file       ,						**self.special_tokens_map  )
								__magic_name__							:	str    							=							'''adapt act apte'''
								__magic_name__							:	Any    							=							['''adapt''', '''act''', '''ap@@''', '''te''']
								__magic_name__							:	List[str]    							=							tokenizer.tokenize(lowerCamelCase_  )
								self.assertListEqual(lowerCamelCase_       ,						lowerCamelCase_  )

								__magic_name__							:	Union[str, Any]    							=							[tokenizer.bos_token] + tokens + [tokenizer.eos_token]

								__magic_name__							:	List[Any]    							=							[0, 1, 2, 3, 4, 5]
								self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase_  )       ,						lowerCamelCase_  )

			def 							UpperCAmelCase__       ( self			:   int  )						->   int:
								__magic_name__							:	Any    							=							BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''  )
								assert tok('''sam'''  ).input_ids == [1384]
								__magic_name__							:	Dict    							=							'''I am a small frog.'''
								__magic_name__							:	Tuple    							=							tok([src_text]       ,						padding=lowerCamelCase_       ,						truncation=lowerCamelCase_  )['''input_ids''']
								__magic_name__							:	Tuple    							=							tok.batch_decode(lowerCamelCase_       ,						skip_special_tokens=lowerCamelCase_       ,						clean_up_tokenization_spaces=lowerCamelCase_  )[0]
								assert src_text != decoded  # I wish it did!
								assert decoded == "i am a small frog ."


			def 							UpperCAmelCase__       ( self			:   Tuple  )						->   Dict:
								__magic_name__							:	Tuple    							=							BlenderbotSmallTokenizer.from_pretrained('''facebook/blenderbot-90M'''  )
								__magic_name__							:	Any    							=							'''I am a small frog .'''
								__magic_name__							:	List[str]    							=							'''.'''
								__magic_name__							:	Tuple    							=							tok(lowerCamelCase_  )['''input_ids''']
								__magic_name__							:	Optional[Any]    							=							tok(lowerCamelCase_  )['''input_ids''']

								assert encoded[-1] == encoded_dot[0]



