






import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


lowercase  							=					500000

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





@get_duration
def 						__UpperCAmelCase			(     a_							,     **a_):
							snake_case_				=							dataset.map(**lowercase__)





@get_duration
def 						__UpperCAmelCase			(     a_							,     **a_):
							snake_case_				=							dataset.filter(**lowercase__)





def 						__UpperCAmelCase			(     ):
							snake_case_				=							{'''num examples''': SPEED_TEST_N_EXAMPLES}
							with tempfile.TemporaryDirectory() as tmp_dir:
														snake_case_				=							datasets.Features({'text': datasets.Value('string'), 'numbers': datasets.Value('float32')})
														snake_case_				=							generate_example_dataset(
														    os.path.join(lowercase__							,     'dataset.arrow')							,     lowercase__							,     num_examples=lowercase__)

														snake_case_				=							transformers.AutoTokenizer.from_pretrained('bert-base-cased'							,     use_fast=lowercase__)

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

														snake_case_				=							map(lowercase__)

														snake_case_				=							map(lowercase__							,     batched=lowercase__)

														snake_case_				=							map(lowercase__							,     function=lambda a_: None							,     batched=lowercase__)

														with dataset.formatted_as(type='numpy'):
																					snake_case_				=							map(lowercase__							,     function=lambda a_: None							,     batched=lowercase__)

														with dataset.formatted_as(type='pandas'):
																					snake_case_				=							map(lowercase__							,     function=lambda a_: None							,     batched=lowercase__)

														with dataset.formatted_as(type='torch'							,     columns='numbers'):
																					snake_case_				=							map(lowercase__							,     function=lambda a_: None							,     batched=lowercase__)

														with dataset.formatted_as(type='tensorflow'							,     columns='numbers'):
																					snake_case_				=							map(lowercase__							,     function=lambda a_: None							,     batched=lowercase__)

														snake_case_				=							map(lowercase__							,     function=lowercase__							,     batched=lowercase__)

														snake_case_				=							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()









import unittest

from transformers.testing_utils import require_bsa
from transformers.utils import is_bsa_available

from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin


if is_bsa_available():
						from transformers import MarkupLMFeatureExtractor
class        _lowercase      (			unittest.TestCase       ):






				'''simple docstring'''



				def __init__(       self			:Optional[Any]			,			lowerCAmelCase__			:Optional[Any]	)		->			str:
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		parent



				def 		__magic_name__(       self			:List[Any]	)		->			Tuple:
										return {}




def        _UpperCamelCase							(      ):
						__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		'''<HTML>

    <HEAD>
    <TITLE>sample document</TITLE>
    </HEAD>

    <BODY BGCOLOR="FFFFFF">
    <HR>
    <a href="http://google.com">Goog</a>
    <H1>This is one header</H1>
    <H2>This is a another Header</H2>
    <P>Travel from
        <P>
        <B>SFO to JFK</B>
        <BR>
        <B><I>on May 2, 2015 at 2:00 pm. For details go to confirm.com </I></B>
        <HR>
        <div style="color:#0000FF">
            <h3>Traveler <b> name </b> is
            <p> John Doe </p>
        </div>'''

						__SCREAMING_SNAKE_CASE						:     str					=		'''
    <!DOCTYPE html>
    <html>
    <body>

    <h1>My First Heading</h1>
    <p>My first paragraph.</p>

    </body>
    </html>
    '''

						return [html_string_a, html_string_a]
@require_bsa
class        _lowercase      (			A__						,			unittest.TestCase       ):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__				:			Optional[int]						=		MarkupLMFeatureExtractor if is_bsa_available() else None


				def 		__magic_name__(       self			:int	)		->			Optional[Any]:
										__SCREAMING_SNAKE_CASE						:     Optional[Any]					=		MarkupLMFeatureExtractionTester(self	)


				@property
				def 		__magic_name__(       self			:Any	)		->			Optional[Any]:
										return self.feature_extract_tester.prepare_feat_extract_dict()



				def 		__magic_name__(       self			:Optional[int]	)		->			Any:
										# Initialize feature_extractor
										__SCREAMING_SNAKE_CASE						:     int					=		self.feature_extraction_class()

										# Test not batched input
										__SCREAMING_SNAKE_CASE						:     Tuple					=		get_html_strings()[0]
										__SCREAMING_SNAKE_CASE						:     Dict					=		feature_extractor(lowerCAmelCase__	)

										# fmt: off
										__SCREAMING_SNAKE_CASE						:     str					=		[['''sample document''', '''Goog''', '''This is one header''', '''This is a another Header''', '''Travel from''', '''SFO to JFK''', '''on May 2, 2015 at 2:00 pm. For details go to confirm.com''', '''Traveler''', '''name''', '''is''', '''John Doe''']]
										__SCREAMING_SNAKE_CASE						:     List[str]					=		[['''/html/head/title''', '''/html/body/a''', '''/html/body/h1''', '''/html/body/h2''', '''/html/body/p''', '''/html/body/p/p/b[1]''', '''/html/body/p/p/b[2]/i''', '''/html/body/p/p/div/h3''', '''/html/body/p/p/div/h3/b''', '''/html/body/p/p/div/h3''', '''/html/body/p/p/div/h3/p''']]
										# fmt: on

										self.assertEqual(encoding.nodes			,			lowerCAmelCase__	)
										self.assertEqual(encoding.xpaths			,			lowerCAmelCase__	)

										# Test batched
										__SCREAMING_SNAKE_CASE						:     Tuple					=		get_html_strings()
										__SCREAMING_SNAKE_CASE						:     Dict					=		feature_extractor(lowerCAmelCase__	)

										# fmt: off
										__SCREAMING_SNAKE_CASE						:     int					=		expected_nodes + [['''My First Heading''', '''My first paragraph.''']]
										__SCREAMING_SNAKE_CASE						:     str					=		expected_xpaths + [['''/html/body/h1''', '''/html/body/p''']]

										self.assertEqual(len(encoding.nodes	)			,			2	)
										self.assertEqual(len(encoding.xpaths	)			,			2	)

										self.assertEqual(encoding.nodes			,			lowerCAmelCase__	)
										self.assertEqual(encoding.xpaths			,			lowerCAmelCase__	)
