

import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


_lowerCAmelCase  :      Any			 =50_00_00

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



@get_duration
def 			_A	(      SCREAMING_SNAKE_CASE	,**SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	Tuple									= dataset.map(**SCREAMING_SNAKE_CASE      )



@get_duration
def 			_A	(      SCREAMING_SNAKE_CASE	,**SCREAMING_SNAKE_CASE      ):
	UpperCAmelCase__:	List[str]									= dataset.filter(**SCREAMING_SNAKE_CASE      )



def 			_A	(      ):
	UpperCAmelCase__:	Union[str, Any]									= {"num examples": SPEED_TEST_N_EXAMPLES}
	with tempfile.TemporaryDirectory() as tmp_dir:
		UpperCAmelCase__:	List[str]									= datasets.Features({"text": datasets.Value("string"      ), "numbers": datasets.Value("float32"      )}      )
		UpperCAmelCase__:	Optional[Any]									= generate_example_dataset(
		    os.path.join(SCREAMING_SNAKE_CASE	,"dataset.arrow"      )	,SCREAMING_SNAKE_CASE	,num_examples=SCREAMING_SNAKE_CASE      )

		UpperCAmelCase__:	Dict									= transformers.AutoTokenizer.from_pretrained("bert-base-cased"	,use_fast=SCREAMING_SNAKE_CASE      )

		def tokenize(SCREAMING_SNAKE_CASE      ):
			return tokenizer(examples["text"]      )

		UpperCAmelCase__:	str									= map(SCREAMING_SNAKE_CASE      )

		UpperCAmelCase__:	Any									= map(SCREAMING_SNAKE_CASE	,batched=SCREAMING_SNAKE_CASE      )

		UpperCAmelCase__:	Tuple									= map(SCREAMING_SNAKE_CASE	,function=lambda SCREAMING_SNAKE_CASE      : None	,batched=SCREAMING_SNAKE_CASE      )

		with dataset.formatted_as(type="numpy"      ):
			UpperCAmelCase__:	List[str]									= map(SCREAMING_SNAKE_CASE	,function=lambda SCREAMING_SNAKE_CASE      : None	,batched=SCREAMING_SNAKE_CASE      )

		with dataset.formatted_as(type="pandas"      ):
			UpperCAmelCase__:	Dict									= map(SCREAMING_SNAKE_CASE	,function=lambda SCREAMING_SNAKE_CASE      : None	,batched=SCREAMING_SNAKE_CASE      )

		with dataset.formatted_as(type="torch"	,columns="numbers"      ):
			UpperCAmelCase__:	List[str]									= map(SCREAMING_SNAKE_CASE	,function=lambda SCREAMING_SNAKE_CASE      : None	,batched=SCREAMING_SNAKE_CASE      )

		with dataset.formatted_as(type="tensorflow"	,columns="numbers"      ):
			UpperCAmelCase__:	str									= map(SCREAMING_SNAKE_CASE	,function=lambda SCREAMING_SNAKE_CASE      : None	,batched=SCREAMING_SNAKE_CASE      )

		UpperCAmelCase__:	Optional[Any]									= map(SCREAMING_SNAKE_CASE	,function=SCREAMING_SNAKE_CASE	,batched=SCREAMING_SNAKE_CASE      )

		UpperCAmelCase__:	Dict									= filter(SCREAMING_SNAKE_CASE      )

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


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


import unittest

from transformers import DonutProcessor


_lowerCAmelCase  :      str			 ="""naver-clova-ix/donut-base"""

class __UpperCamelCase     (				unittest.TestCase		):






					'''simple docstring'''



					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	Any									= DonutProcessor.from_pretrained(lowerCamelCase__						)





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	Any									= {
						    "name": "John Doe",
						    "age": "99",
						    "city": "Atlanta",
						    "state": "GA",
						    "zip": "30301",
						    "phone": "123-4567",
						    "nicknames": [{"nickname": "Johnny"}, {"nickname": "JD"}],
						}

						UpperCAmelCase__:	Optional[Any]									= (
						    "<s_name>John Doe</s_name><s_age>99</s_age><s_city>Atlanta</s_city>"
						    "<s_state>GA</s_state><s_zip>30301</s_zip><s_phone>123-4567</s_phone>"
						    "<s_nicknames><s_nickname>Johnny</s_nickname>"
						    "<sep/><s_nickname>JD</s_nickname></s_nicknames>"
						)
						UpperCAmelCase__:	str									= self.processor.tokenajson(lowerCamelCase__						)

						self.assertDictEqual(lowerCamelCase__				,      lowerCamelCase__						)