



'''simple docstring'''





import re
from pathlib import Path
from unittest import TestCase

import pytest






@pytest.mark.integration
class __A     (    A		):



 '''simple docstring'''







 def    a__    (self					,  A			)       ->	Union[str, Any]:


      """simple docstring"""



      with open(A					,  encoding='''utf-8'''			) as input_file:
           _a           =    re.compile(R'''(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)'''			)
           _a           =    input_file.read()
           _a           =    regexp.search(A			)

      return match



 def    a__    (self					,  A			)       ->	Optional[Any]:


      """simple docstring"""



      with open(A					,  encoding='''utf-8'''			) as input_file:
           _a           =    re.compile(R'''#[^\r\n]*print\(|\"[^\r\n]*print\(|\"\"\".*?print\(.*?\"\"\"|(print\()'''					,  re.DOTALL			)
           _a           =    input_file.read()
           # use `re.finditer` to handle the case where the ignored groups would be matched first by `re.search`
           _a           =    regexp.finditer(A			)

      _a           =    [match for match in matches if match is not None and match.group(1			) is not None]
      return matches[0] if matches else None



 def    a__    (self			)       ->	Optional[Any]:


      """simple docstring"""



      _a           =    Path('''./datasets'''			)
      _a           =    list(dataset_paths.absolute().glob('''**/*.py'''			)			)

      for dataset in dataset_files:
           if self._no_encoding_on_file_open(str(A			)			):
                raise AssertionError(f'''open(...) must use utf-8 encoding in {dataset}'''			)



 def    a__    (self			)       ->	Optional[Any]:


      """simple docstring"""



      _a           =    Path('''./datasets'''			)
      _a           =    list(dataset_paths.absolute().glob('''**/*.py'''			)			)

      for dataset in dataset_files:
           if self._no_print_statements(str(A			)			):
                raise AssertionError(f'''print statement found in {dataset}. Use datasets.logger/logging instead.'''			)











import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


lowerCamelCase__       		=  50_0000

lowerCamelCase__		,      lowerCamelCase__       		=  os.path.split(__file__)
lowerCamelCase__       		=  os.path.join(RESULTS_BASEPATH, """results""", RESULTS_FILENAME.replace(""".py""", """.json"""))






@get_duration
def 	UpperCamelCase	(							snake_case__  : datasets.Dataset						,**snake_case__  : Any	):




     '''simple docstring'''






     __snake_case      :List[str] 							=    dataset.map(**snake_case__	)






@get_duration
def 	UpperCamelCase	(							snake_case__  : datasets.Dataset						,**snake_case__  : Dict	):




     '''simple docstring'''






     __snake_case      :int 							=    dataset.filter(**snake_case__	)






def 	UpperCamelCase	(							):




     '''simple docstring'''






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

          __snake_case      :str 							=    transformers.AutoTokenizer.from_pretrained("""bert-base-cased"""						,use_fast=snake_case__	)

          def tokenize(snake_case__  : Optional[int]	):
               return tokenizer(examples["""text"""]	)

          __snake_case      :Optional[Any] 							=    map(snake_case__	)

          __snake_case      :Optional[Any] 							=    map(snake_case__						,batched=snake_case__	)

          __snake_case      :Optional[Any] 							=    map(snake_case__						,function=lambda snake_case__	: None						,batched=snake_case__	)

          with dataset.formatted_as(type="""numpy"""	):
               __snake_case      :int 							=    map(snake_case__						,function=lambda snake_case__	: None						,batched=snake_case__	)

          with dataset.formatted_as(type="""pandas"""	):
               __snake_case      :List[Any] 							=    map(snake_case__						,function=lambda snake_case__	: None						,batched=snake_case__	)

          with dataset.formatted_as(type="""torch"""						,columns="""numbers"""	):
               __snake_case      :str 							=    map(snake_case__						,function=lambda snake_case__	: None						,batched=snake_case__	)

          with dataset.formatted_as(type="""tensorflow"""						,columns="""numbers"""	):
               __snake_case      :Optional[int] 							=    map(snake_case__						,function=lambda snake_case__	: None						,batched=snake_case__	)

          __snake_case      :Dict 							=    map(snake_case__						,function=snake_case__						,batched=snake_case__	)

          __snake_case      :Tuple 							=    filter(snake_case__	)

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


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


