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    :      Dict	 =						(30, 4_87)
_lowerCamelCase    :      Dict	 =						(36, 10_24)
_lowerCamelCase    :      Tuple	 =						(1_00, 1_00)
_lowerCamelCase    :      List[Any]	 =						1_00

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

_lowerCamelCase ,  _lowerCamelCase    :      Tuple	 =						os.path.split(__file__)
_lowerCamelCase    :      str	 =						os.path.join(RESULTS_BASEPATH, '''results''', RESULTS_FILENAME.replace('''.py''', '''.json'''))





@get_duration
def 		__lowerCamelCase							(UpperCAmelCase__   :							List[Any]					,       UpperCAmelCase__   :							Dict					,       UpperCAmelCase__   :							Dict     ):
					with ArrowWriter(features=snake_case__					,       path=os.path.join(snake_case__					,       "beta.arrow"     )     ) as writer:
										for key, record in dummy_data:
															SCREAMING_SNAKE_CASE				=				my_features.encode_example(snake_case__     )
															writer.write(snake_case__     )
										SCREAMING_SNAKE_CASE	,      SCREAMING_SNAKE_CASE				=				writer.finalize()





@get_duration
def 		__lowerCamelCase							(UpperCAmelCase__   :							List[Any]					,       UpperCAmelCase__   :							str     ):
					SCREAMING_SNAKE_CASE				=				datasets.Dataset.from_file(
					    filename=os.path.join(snake_case__					,       "beta.arrow"     )					,       info=datasets.DatasetInfo(features=snake_case__     )     )
					for _ in dataset:
										pass





@get_duration
def 		__lowerCamelCase							(UpperCAmelCase__   :							Tuple					,       UpperCAmelCase__   :							Union[str, Any]     ):
					SCREAMING_SNAKE_CASE				=				datasets.Dataset.from_file(
					    filename=os.path.join(snake_case__					,       "beta.arrow"     )					,       info=datasets.DatasetInfo(features=snake_case__     )     )
					dataset.set_format("numpy"     )
					for _ in dataset:
										pass





@get_duration
def 		__lowerCamelCase							(UpperCAmelCase__   :							List[Any]					,       UpperCAmelCase__   :							List[Any]     ):
					SCREAMING_SNAKE_CASE				=				1_0
					SCREAMING_SNAKE_CASE				=				datasets.Dataset.from_file(
					    filename=os.path.join(snake_case__					,       "beta.arrow"     )					,       info=datasets.DatasetInfo(features=snake_case__     )     )
					for i in range(0					,       len(snake_case__     )					,       snake_case__     ):
										SCREAMING_SNAKE_CASE				=				dataset[i : i + batch_size]





@get_duration
def 		__lowerCamelCase							(UpperCAmelCase__   :							List[Any]					,       UpperCAmelCase__   :							Dict     ):
					SCREAMING_SNAKE_CASE				=				1_0
					SCREAMING_SNAKE_CASE				=				datasets.Dataset.from_file(
					    filename=os.path.join(snake_case__					,       "beta.arrow"     )					,       info=datasets.DatasetInfo(features=snake_case__     )     )
					dataset.set_format("numpy"     )
					for i in range(0					,       len(snake_case__     )					,       snake_case__     ):
										SCREAMING_SNAKE_CASE				=				dataset[i : i + batch_size]





@get_duration
def 		__lowerCamelCase							(UpperCAmelCase__   :							Tuple					,       UpperCAmelCase__   :							List[str]     ):
					SCREAMING_SNAKE_CASE				=				datasets.Dataset.from_file(
					    filename=os.path.join(snake_case__					,       "beta.arrow"     )					,       info=datasets.DatasetInfo(features=snake_case__     )     )
					for col in feats:
										SCREAMING_SNAKE_CASE				=				dataset[col]





@get_duration
def 		__lowerCamelCase							(UpperCAmelCase__   :							List[Any]					,       UpperCAmelCase__   :							Tuple     ):
					SCREAMING_SNAKE_CASE				=				datasets.Dataset.from_file(
					    filename=os.path.join(snake_case__					,       "beta.arrow"     )					,       info=datasets.DatasetInfo(features=snake_case__     )     )
					dataset.set_format("numpy"     )
					for col in feats:
										SCREAMING_SNAKE_CASE				=				dataset[col]





def 		__lowerCamelCase							():
					SCREAMING_SNAKE_CASE				=				{}
					SCREAMING_SNAKE_CASE				=				(
					    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:
										SCREAMING_SNAKE_CASE				=				datasets.Features({"image": ArrayaD(snake_case__					,       dtype="float32"     )}     )
										SCREAMING_SNAKE_CASE				=				generate_examples(features=snake_case__					,       num_examples=snake_case__     )
										SCREAMING_SNAKE_CASE				=				write(snake_case__					,       snake_case__					,       snake_case__     )
										for read_func in read_functions:
															SCREAMING_SNAKE_CASE				=				read_func(snake_case__					,       snake_case__     )

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

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

					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_array_xd()







def 						UpperCamelCase_(			snake_case__:  list   )       -> list:
 UpperCAmelCase__							=				len(snake_case__   )

 # Set the gap value to be decreased by a factor of 1.3
 # after each iteration
 UpperCAmelCase__							=				1.3

 # Continue sorting until the gap is 1
 while gap > 1:
  # Decrease the gap value
  UpperCAmelCase__							=				int(gap / shrink   )

  # Sort the elements using insertion sort
  for i in range(snake_case__  ,    len(snake_case__   )   ):
   UpperCAmelCase__							=				collection[i]
   UpperCAmelCase__							=				i
   while j >= gap and collection[j - gap] > temp:
    UpperCAmelCase__							=				collection[j - gap]
    j -= gap
   UpperCAmelCase__							=				temp

 return collection


if __name__ == "__main__":
 import doctest

 doctest.testmod()



