import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


__UpperCamelCase		:       int									=     50_0000

__UpperCamelCase						,			__UpperCamelCase		:       Tuple									=     os.path.split(__file__)
__UpperCamelCase		:       Union[str, Any]									=     os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))
@get_duration
def       A	(    _lowercase		,  **_lowercase						):
						SCREAMING_SNAKE_CASE							:    Dict							=							dataset.map(**_lowercase						)
@get_duration
def       A	(    _lowercase		,  **_lowercase						):
						SCREAMING_SNAKE_CASE							:    Dict							=							dataset.filter(**_lowercase						)
def       A	(    ):
						SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							{'''num examples''': SPEED_TEST_N_EXAMPLES}
						with tempfile.TemporaryDirectory() as tmp_dir:
												SCREAMING_SNAKE_CASE							:    Optional[int]							=							datasets.Features({'''text''': datasets.Value('''string'''						), '''numbers''': datasets.Value('''float32'''						)}						)
												SCREAMING_SNAKE_CASE							:    Tuple							=							generate_example_dataset(
												    os.path.join(_lowercase		,  '''dataset.arrow'''						)		,  _lowercase		,  num_examples=_lowercase						)

												SCREAMING_SNAKE_CASE							:    Optional[int]							=							transformers.AutoTokenizer.from_pretrained('''bert-base-cased'''		,  use_fast=_lowercase						)

												def tokenize(_lowercase						):
																		return tokenizer(examples['''text''']						)

												SCREAMING_SNAKE_CASE							:    List[Any]							=							map(_lowercase						)

												SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							map(_lowercase		,  batched=_lowercase						)

												SCREAMING_SNAKE_CASE							:    int							=							map(_lowercase		,  function=lambda _lowercase						: None		,  batched=_lowercase						)

												with dataset.formatted_as(type='''numpy'''						):
																		SCREAMING_SNAKE_CASE							:    Optional[Any]							=							map(_lowercase		,  function=lambda _lowercase						: None		,  batched=_lowercase						)

												with dataset.formatted_as(type='''pandas'''						):
																		SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							map(_lowercase		,  function=lambda _lowercase						: None		,  batched=_lowercase						)

												with dataset.formatted_as(type='''torch'''		,  columns='''numbers'''						):
																		SCREAMING_SNAKE_CASE							:    Optional[int]							=							map(_lowercase		,  function=lambda _lowercase						: None		,  batched=_lowercase						)

												with dataset.formatted_as(type='''tensorflow'''		,  columns='''numbers'''						):
																		SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							map(_lowercase		,  function=lambda _lowercase						: None		,  batched=_lowercase						)

												SCREAMING_SNAKE_CASE							:    List[str]							=							map(_lowercase		,  function=_lowercase		,  batched=_lowercase						)

												SCREAMING_SNAKE_CASE							:    List[str]							=							filter(_lowercase						)

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


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






def       A	(    _lowercase		,  _lowercase						):
						SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							len(_lowercase						)
						SCREAMING_SNAKE_CASE							:    Any							=							len(_lowercase						)
						SCREAMING_SNAKE_CASE							:    Optional[int]							=							[[False for _ in range(m + 1						)] for _ in range(n + 1						)]
						SCREAMING_SNAKE_CASE							:    Union[str, Any]							=							True
						for i in range(_lowercase						):
												for j in range(m + 1						):
																		if dp[i][j]:
																								if j < m and a[i].upper() == b[j]:
																														SCREAMING_SNAKE_CASE							:    List[str]							=							True
																								if a[i].islower():
																														SCREAMING_SNAKE_CASE							:    Dict							=							True
						return dp[n][m]


if __name__ == "__main__":
					import doctest

					doctest.testmod()





