




'''simple docstring'''






import json
import os
import tempfile

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features import ArrayaD
from utils import generate_examples, get_duration


lowercase						:   str      						=							(30, 487)
lowercase						:   Union[str, Any]      						=							(36, 1024)
lowercase						:   Optional[Any]      						=							(100, 100)
lowercase						:   Union[str, Any]      						=							100

lowercase						:   Any      						=							datasets.Features(
    {'''text''': ArrayaD(SHAPE_TEST_1, dtype='''float32'''), '''image''': ArrayaD(SHAPE_TEST_2, dtype='''float32''')}
)

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




@get_duration
def 		a_(			lowerCamelCase ,					lowerCamelCase ,					lowerCamelCase   )					->Dict:






       '''simple docstring'''




       with ArrowWriter(features=lowerCamelCase ,					path=os.path.join(lowerCamelCase ,					'''beta.arrow'''   )   ) as writer:
              for key, record in dummy_data:
                     lowercase	:		Tuple    =   my_features.encode_example(lowerCamelCase   )
                     writer.write(lowerCamelCase   )
              lowercase,  lowercase	:		int    =   writer.finalize()




@get_duration
def 		a_(			lowerCamelCase ,					lowerCamelCase   )					->int:






       '''simple docstring'''




       lowercase	:		List[str]    =   datasets.Dataset.from_file(
           filename=os.path.join(lowerCamelCase ,					'''beta.arrow'''   ) ,					info=datasets.DatasetInfo(features=lowerCamelCase   )   )
       for _ in dataset:
              pass




@get_duration
def 		a_(			lowerCamelCase ,					lowerCamelCase   )					->str:






       '''simple docstring'''




       lowercase	:		List[Any]    =   datasets.Dataset.from_file(
           filename=os.path.join(lowerCamelCase ,					'''beta.arrow'''   ) ,					info=datasets.DatasetInfo(features=lowerCamelCase   )   )
       dataset.set_format('''numpy'''   )
       for _ in dataset:
              pass




@get_duration
def 		a_(			lowerCamelCase ,					lowerCamelCase   )					->List[str]:






       '''simple docstring'''




       lowercase	:		int    =   10
       lowercase	:		List[Any]    =   datasets.Dataset.from_file(
           filename=os.path.join(lowerCamelCase ,					'''beta.arrow'''   ) ,					info=datasets.DatasetInfo(features=lowerCamelCase   )   )
       for i in range(0 ,					len(lowerCamelCase   ) ,					lowerCamelCase   ):
              lowercase	:		Any    =   dataset[i : i + batch_size]




@get_duration
def 		a_(			lowerCamelCase ,					lowerCamelCase   )					->Optional[int]:






       '''simple docstring'''




       lowercase	:		int    =   10
       lowercase	:		int    =   datasets.Dataset.from_file(
           filename=os.path.join(lowerCamelCase ,					'''beta.arrow'''   ) ,					info=datasets.DatasetInfo(features=lowerCamelCase   )   )
       dataset.set_format('''numpy'''   )
       for i in range(0 ,					len(lowerCamelCase   ) ,					lowerCamelCase   ):
              lowercase	:		Tuple    =   dataset[i : i + batch_size]




@get_duration
def 		a_(			lowerCamelCase ,					lowerCamelCase   )					->Tuple:






       '''simple docstring'''




       lowercase	:		str    =   datasets.Dataset.from_file(
           filename=os.path.join(lowerCamelCase ,					'''beta.arrow'''   ) ,					info=datasets.DatasetInfo(features=lowerCamelCase   )   )
       for col in feats:
              lowercase	:		int    =   dataset[col]




@get_duration
def 		a_(			lowerCamelCase ,					lowerCamelCase   )					->List[Any]:






       '''simple docstring'''




       lowercase	:		int    =   datasets.Dataset.from_file(
           filename=os.path.join(lowerCamelCase ,					'''beta.arrow'''   ) ,					info=datasets.DatasetInfo(features=lowerCamelCase   )   )
       dataset.set_format('''numpy'''   )
       for col in feats:
              lowercase	:		Optional[Any]    =   dataset[col]




def 		a_(			)					->Optional[Any]:






       '''simple docstring'''




       lowercase	:		Optional[int]    =   {}
       lowercase	:		str    =   (
           read_unformated,
           read_formatted_as_numpy,
           read_batch_unformated,
           read_batch_formatted_as_numpy,
           read_col_unformated,
           read_col_formatted_as_numpy,
       )
       with tempfile.TemporaryDirectory() as tmp_dir:
              lowercase	:		Optional[Any]    =   datasets.Features({'''image''': ArrayaD(lowerCamelCase ,					dtype='''float32'''   )}   )
              lowercase	:		Tuple    =   generate_examples(features=lowerCamelCase ,					num_examples=lowerCamelCase   )
              lowercase	:		List[str]    =   write(lowerCamelCase ,					lowerCamelCase ,					lowerCamelCase   )
              for read_func in read_functions:
                     lowercase	:		str    =   read_func(lowerCamelCase ,					lowerCamelCase   )

       with tempfile.TemporaryDirectory() as tmp_dir:
              # don't use fixed length for fair comparison
              # feats = datasets.Features(
              #     {"image": datasets.Sequence(datasets.Sequence(datasets.Value("float32"), SPEED_TEST_SHAPE[1]), SPEED_TEST_SHAPE[0])}
              # )
              lowercase	:		Union[str, Any]    =   datasets.Features({'''image''': datasets.Sequence(datasets.Sequence(datasets.Value('''float32'''   )   )   )}   )
              lowercase	:		Optional[int]    =   generate_examples(
                  features=lowerCamelCase ,					num_examples=lowerCamelCase ,					seq_shapes={'''image''': SPEED_TEST_SHAPE}   )
              lowercase	:		Optional[int]    =   write(lowerCamelCase ,					lowerCamelCase ,					lowerCamelCase   )
              for read_func in read_functions:
                     lowercase	:		List[Any]    =   read_func(lowerCamelCase ,					lowerCamelCase   )

       with tempfile.TemporaryDirectory() as tmp_dir:
              # don't use fixed length for fair comparison
              # feats = datasets.Features(
              #     {"image": datasets.Sequence(datasets.Value("float32"), SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1])}
              # )
              lowercase	:		Optional[Any]    =   datasets.Features({'''image''': datasets.Sequence(datasets.Value('''float32'''   )   )}   )
              lowercase	:		str    =   generate_examples(
                  features=lowerCamelCase ,					num_examples=lowerCamelCase ,					seq_shapes={'''image''': [SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1]]} ,					)
              lowercase	:		Optional[int]    =   write(lowerCamelCase ,					lowerCamelCase ,					lowerCamelCase   )
              for read_func in read_functions:
                     lowercase	:		Optional[int]    =   read_func(lowerCamelCase ,					lowerCamelCase   )

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


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










'''simple docstring'''






def 		a_(			lowerCamelCase ,					lowerCamelCase   )					->int:






       '''simple docstring'''




       if len(lowerCamelCase   ) < len(lowerCamelCase   ):
              return levenshtein_distance(lowerCamelCase ,					lowerCamelCase   )

       if len(lowerCamelCase   ) == 0:
              return len(lowerCamelCase   )

       lowercase	:		List[Any]    =   list(range(len(lowerCamelCase   ) + 1   )   )

       for i, ca in enumerate(lowerCamelCase   ):
              lowercase	:		Any    =   [i + 1]

              for j, ca in enumerate(lowerCamelCase   ):
                     # Calculate insertions, deletions and substitutions
                     lowercase	:		List[Any]    =   previous_row[j + 1] + 1
                     lowercase	:		str    =   current_row[j] + 1
                     lowercase	:		List[Any]    =   previous_row[j] + (ca != ca)

                     # Get the minimum to append to the current row
                     current_row.append(min(lowerCamelCase ,					lowerCamelCase ,					lowerCamelCase   )   )

              # Store the previous row
              lowercase	:		List[str]    =   current_row

       # Returns the last element (distance)
       return previous_row[-1]


if __name__ == "__main__":
      lowercase						:   Dict      						=							input('''Enter the first word:\n''').strip()
      lowercase						:   Tuple      						=							input('''Enter the second word:\n''').strip()

      lowercase						:   Tuple      						=							levenshtein_distance(first_word, second_word)
      print(f'Levenshtein distance between {first_word} and {second_word} is {result}')




