

'''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


_lowerCAmelCase    			=						(30, 487)
_lowerCAmelCase    			=						(36, 1024)
_lowerCAmelCase    			=						(100, 100)
_lowerCAmelCase    			=						100

_lowerCAmelCase    			=						datasets.Features(
    {"text": ArrayaD(SHAPE_TEST_1, dtype="float32"), "image": ArrayaD(SHAPE_TEST_2, dtype="float32")}
)

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




@get_duration
def 						UpperCamelCase       ( a		,						a		,						a )      ->	Optional[Any]:






     '''simple docstring'''
     with ArrowWriter(features=a		,						path=os.path.join(a		,						'''beta.arrow''' ) ) as writer:
          for key, record in dummy_data:
               __magic_name__						   =				my_features.encode_example(a )
               writer.write(a )
          __magic_name__   ,  __magic_name__						   =				writer.finalize()




@get_duration
def 						UpperCamelCase       ( a		,						a )      ->	List[str]:






     '''simple docstring'''
     __magic_name__						   =				datasets.Dataset.from_file(
         filename=os.path.join(a		,						'''beta.arrow''' )		,						info=datasets.DatasetInfo(features=a ) )
     for _ in dataset:
          pass




@get_duration
def 						UpperCamelCase       ( a		,						a )      ->	Any:






     '''simple docstring'''
     __magic_name__						   =				datasets.Dataset.from_file(
         filename=os.path.join(a		,						'''beta.arrow''' )		,						info=datasets.DatasetInfo(features=a ) )
     dataset.set_format('''numpy''' )
     for _ in dataset:
          pass




@get_duration
def 						UpperCamelCase       ( a		,						a )      ->	List[str]:






     '''simple docstring'''
     __magic_name__						   =				10
     __magic_name__						   =				datasets.Dataset.from_file(
         filename=os.path.join(a		,						'''beta.arrow''' )		,						info=datasets.DatasetInfo(features=a ) )
     for i in range(0		,						len(a )		,						a ):
          __magic_name__						   =				dataset[i : i + batch_size]




@get_duration
def 						UpperCamelCase       ( a		,						a )      ->	List[str]:






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




@get_duration
def 						UpperCamelCase       ( a		,						a )      ->	Union[str, Any]:






     '''simple docstring'''
     __magic_name__						   =				datasets.Dataset.from_file(
         filename=os.path.join(a		,						'''beta.arrow''' )		,						info=datasets.DatasetInfo(features=a ) )
     for col in feats:
          __magic_name__						   =				dataset[col]




@get_duration
def 						UpperCamelCase       ( a		,						a )      ->	Optional[int]:






     '''simple docstring'''
     __magic_name__						   =				datasets.Dataset.from_file(
         filename=os.path.join(a		,						'''beta.arrow''' )		,						info=datasets.DatasetInfo(features=a ) )
     dataset.set_format('''numpy''' )
     for col in feats:
          __magic_name__						   =				dataset[col]




def 						UpperCamelCase       ( )      ->	Optional[Any]:






     '''simple docstring'''
     __magic_name__						   =				{}
     __magic_name__						   =				(
         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:
          __magic_name__						   =				datasets.Features({'''image''': ArrayaD(a		,						dtype='''float32''' )} )
          __magic_name__						   =				generate_examples(features=a		,						num_examples=a )
          __magic_name__						   =				write(a		,						a		,						a )
          for read_func in read_functions:
               __magic_name__						   =				read_func(a		,						a )

     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])}
          # )
          __magic_name__						   =				datasets.Features({'''image''': datasets.Sequence(datasets.Sequence(datasets.Value('''float32''' ) ) )} )
          __magic_name__						   =				generate_examples(
              features=a		,						num_examples=a		,						seq_shapes={'''image''': SPEED_TEST_SHAPE} )
          __magic_name__						   =				write(a		,						a		,						a )
          for read_func in read_functions:
               __magic_name__						   =				read_func(a		,						a )

     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])}
          # )
          __magic_name__						   =				datasets.Features({'''image''': datasets.Sequence(datasets.Value('''float32''' ) )} )
          __magic_name__						   =				generate_examples(
              features=a		,						num_examples=a		,						seq_shapes={'''image''': [SPEED_TEST_SHAPE[0] * SPEED_TEST_SHAPE[1]]}		,						)
          __magic_name__						   =				write(a		,						a		,						a )
          for read_func in read_functions:
               __magic_name__						   =				read_func(a		,						a )

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


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













'''simple docstring'''







def    _lowerCAmelCase						(						lowercase	)					->					int:
			__lowerCAmelCase											=				len(lowercase	)
			__lowerCAmelCase											=				[0] * length
			__lowerCAmelCase											=				[[False for i in range(lowercase	)] for j in range(lowercase	)]
			for i, c in enumerate(lowercase	):
						__lowerCAmelCase											=				i
						for j in range(i + 1	):
									if c == string[j] and (i - j < 2 or is_palindromic[j + 1][i - 1]):
												__lowerCAmelCase											=				True
												__lowerCAmelCase											=				min(lowercase					,	0 if j == 0 else (cut[j - 1] + 1)	)
						__lowerCAmelCase											=				mincut
			return cut[length - 1]


if __name__ == "__main__":
	_a					: int      =    input("""Enter the string: """).strip()
	_a					: Dict      =    find_minimum_partitions(s)
	print(f'Minimum number of partitions required for the \'{s}\' is {ans}')

