


'''simple docstring'''



import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
      from PIL import Image

      from transformers import AlignProcessor, EfficientNetImageProcessor





@require_vision
class    UpperCAmelCase_  (unittest.TestCase  ):





    """simple docstring"""






    def       lowercase_				(							self	)				->     int:
         __lowerCamelCase   :	Tuple								=		tempfile.mkdtemp()

         __lowerCamelCase   :	Tuple								=		[
             """[UNK]""",
             """[CLS]""",
             """[SEP]""",
             """[PAD]""",
             """[MASK]""",
             """want""",
             """##want""",
             """##ed""",
             """wa""",
             """un""",
             """runn""",
             """##ing""",
             """,""",
             """low""",
             """lowest""",
         ]
         __lowerCamelCase   :	Any								=		os.path.join(self.tmpdirname			,     VOCAB_FILES_NAMES['vocab_file']	)
         with open(self.vocab_file			,     'w'			,     encoding='utf-8'	) as vocab_writer:
              vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]	)	)

         __lowerCamelCase   :	Any								=		{
             """do_resize""": True,
             """size""": 20,
             """do_center_crop""": True,
             """crop_size""": 18,
             """do_normalize""": True,
             """image_mean""": [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],
             """image_std""": [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],
         }
         __lowerCamelCase   :	int								=		os.path.join(self.tmpdirname			,     SCREAMING_SNAKE_CASE_	)
         with open(self.image_processor_file			,     'w'			,     encoding='utf-8'	) as fp:
              json.dump(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)







    def       lowercase_				(							self			,     **SCREAMING_SNAKE_CASE_	)				->     int:
         return BertTokenizer.from_pretrained(self.tmpdirname			,     **SCREAMING_SNAKE_CASE_	)







    def       lowercase_				(							self			,     **SCREAMING_SNAKE_CASE_	)				->     List[Any]:
         return BertTokenizerFast.from_pretrained(self.tmpdirname			,     **SCREAMING_SNAKE_CASE_	)







    def       lowercase_				(							self			,     **SCREAMING_SNAKE_CASE_	)				->     Dict:
         return EfficientNetImageProcessor.from_pretrained(self.tmpdirname			,     **SCREAMING_SNAKE_CASE_	)







    def       lowercase_				(							self	)				->     Tuple:
         shutil.rmtree(self.tmpdirname	)







    def       lowercase_				(							self	)				->     Tuple:
         __lowerCamelCase   :	Any								=		[np.random.randint(2_55			,     size=(3, 30, 4_00)			,     dtype=np.uinta	)]
         __lowerCamelCase   :	Any								=		[Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE_			,     0			,     -1	)	) for x in image_inputs]
         return image_inputs







    def       lowercase_				(							self	)				->     Dict:
         __lowerCamelCase   :	Dict								=		self.get_tokenizer()
         __lowerCamelCase   :	List[Any]								=		self.get_rust_tokenizer()
         __lowerCamelCase   :	Tuple								=		self.get_image_processor()

         __lowerCamelCase   :	List[Any]								=		AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE_			,     image_processor=SCREAMING_SNAKE_CASE_	)
         processor_slow.save_pretrained(self.tmpdirname	)
         __lowerCamelCase   :	List[str]								=		AlignProcessor.from_pretrained(self.tmpdirname			,     use_fast=SCREAMING_SNAKE_CASE_	)

         __lowerCamelCase   :	List[str]								=		AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE_			,     image_processor=SCREAMING_SNAKE_CASE_	)
         processor_fast.save_pretrained(self.tmpdirname	)
         __lowerCamelCase   :	int								=		AlignProcessor.from_pretrained(self.tmpdirname	)

         self.assertEqual(processor_slow.tokenizer.get_vocab()			,     tokenizer_slow.get_vocab()	)
         self.assertEqual(processor_fast.tokenizer.get_vocab()			,     tokenizer_fast.get_vocab()	)
         self.assertEqual(tokenizer_slow.get_vocab()			,     tokenizer_fast.get_vocab()	)
         self.assertIsInstance(processor_slow.tokenizer			,     SCREAMING_SNAKE_CASE_	)
         self.assertIsInstance(processor_fast.tokenizer			,     SCREAMING_SNAKE_CASE_	)

         self.assertEqual(processor_slow.image_processor.to_json_string()			,     image_processor.to_json_string()	)
         self.assertEqual(processor_fast.image_processor.to_json_string()			,     image_processor.to_json_string()	)
         self.assertIsInstance(processor_slow.image_processor			,     SCREAMING_SNAKE_CASE_	)
         self.assertIsInstance(processor_fast.image_processor			,     SCREAMING_SNAKE_CASE_	)







    def       lowercase_				(							self	)				->     List[str]:
         __lowerCamelCase   :	Any								=		AlignProcessor(tokenizer=self.get_tokenizer()			,     image_processor=self.get_image_processor()	)
         processor.save_pretrained(self.tmpdirname	)

         __lowerCamelCase   :	Optional[Any]								=		self.get_tokenizer(bos_token='(BOS)'			,     eos_token='(EOS)'	)
         __lowerCamelCase   :	List[str]								=		self.get_image_processor(do_normalize=SCREAMING_SNAKE_CASE_			,     padding_value=1.0	)

         __lowerCamelCase   :	List[Any]								=		AlignProcessor.from_pretrained(
             self.tmpdirname			,     bos_token='(BOS)'			,     eos_token='(EOS)'			,     do_normalize=SCREAMING_SNAKE_CASE_			,     padding_value=1.0	)

         self.assertEqual(processor.tokenizer.get_vocab()			,     tokenizer_add_kwargs.get_vocab()	)
         self.assertIsInstance(processor.tokenizer			,     SCREAMING_SNAKE_CASE_	)

         self.assertEqual(processor.image_processor.to_json_string()			,     image_processor_add_kwargs.to_json_string()	)
         self.assertIsInstance(processor.image_processor			,     SCREAMING_SNAKE_CASE_	)







    def       lowercase_				(							self	)				->     List[Any]:
         __lowerCamelCase   :	int								=		self.get_image_processor()
         __lowerCamelCase   :	Union[str, Any]								=		self.get_tokenizer()

         __lowerCamelCase   :	Optional[Any]								=		AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE_			,     image_processor=SCREAMING_SNAKE_CASE_	)

         __lowerCamelCase   :	str								=		self.prepare_image_inputs()

         __lowerCamelCase   :	Tuple								=		image_processor(SCREAMING_SNAKE_CASE_			,     return_tensors='np'	)
         __lowerCamelCase   :	Dict								=		processor(images=SCREAMING_SNAKE_CASE_			,     return_tensors='np'	)

         for key in input_image_proc.keys():
              self.assertAlmostEqual(input_image_proc[key].sum()			,     input_processor[key].sum()			,     delta=1E-2	)







    def       lowercase_				(							self	)				->     str:
         __lowerCamelCase   :	int								=		self.get_image_processor()
         __lowerCamelCase   :	Any								=		self.get_tokenizer()

         __lowerCamelCase   :	Union[str, Any]								=		AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE_			,     image_processor=SCREAMING_SNAKE_CASE_	)

         __lowerCamelCase   :	List[str]								=		"""lower newer"""

         __lowerCamelCase   :	List[str]								=		processor(text=SCREAMING_SNAKE_CASE_	)

         __lowerCamelCase   :	List[str]								=		tokenizer(SCREAMING_SNAKE_CASE_			,     padding='max_length'			,     max_length=64	)

         for key in encoded_tok.keys():
              self.assertListEqual(encoded_tok[key]			,     encoded_processor[key]	)







    def       lowercase_				(							self	)				->     Union[str, Any]:
         __lowerCamelCase   :	List[Any]								=		self.get_image_processor()
         __lowerCamelCase   :	Dict								=		self.get_tokenizer()

         __lowerCamelCase   :	str								=		AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE_			,     image_processor=SCREAMING_SNAKE_CASE_	)

         __lowerCamelCase   :	int								=		"""lower newer"""
         __lowerCamelCase   :	Optional[int]								=		self.prepare_image_inputs()

         __lowerCamelCase   :	Dict								=		processor(text=SCREAMING_SNAKE_CASE_			,     images=SCREAMING_SNAKE_CASE_	)

         self.assertListEqual(list(inputs.keys()	)			,     ['input_ids', 'token_type_ids', 'attention_mask', 'pixel_values']	)

         # test if it raises when no input is passed
         with pytest.raises(SCREAMING_SNAKE_CASE_	):
              processor()







    def       lowercase_				(							self	)				->     Optional[Any]:
         __lowerCamelCase   :	List[Any]								=		self.get_image_processor()
         __lowerCamelCase   :	Optional[int]								=		self.get_tokenizer()

         __lowerCamelCase   :	Dict								=		AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE_			,     image_processor=SCREAMING_SNAKE_CASE_	)

         __lowerCamelCase   :	Optional[Any]								=		[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

         __lowerCamelCase   :	List[Any]								=		processor.batch_decode(SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	List[str]								=		tokenizer.batch_decode(SCREAMING_SNAKE_CASE_	)

         self.assertListEqual(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)







    def       lowercase_				(							self	)				->     Optional[int]:
         __lowerCamelCase   :	Tuple								=		self.get_image_processor()
         __lowerCamelCase   :	int								=		self.get_tokenizer()

         __lowerCamelCase   :	Optional[int]								=		AlignProcessor(tokenizer=SCREAMING_SNAKE_CASE_			,     image_processor=SCREAMING_SNAKE_CASE_	)

         __lowerCamelCase   :	Dict								=		"""lower newer"""
         __lowerCamelCase   :	int								=		self.prepare_image_inputs()

         __lowerCamelCase   :	int								=		processor(text=SCREAMING_SNAKE_CASE_			,     images=SCREAMING_SNAKE_CASE_	)

         self.assertListEqual(list(inputs.keys()	)			,     processor.model_input_names	)








import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


_lowerCamelCase					   =5_0_0_0_0_0

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





@get_duration
def     _a					(						lowerCamelCase,	**lowerCamelCase							):
 lowerCamelCase		:	Optional[Any]							=      dataset.map(**lowerCamelCase							)





@get_duration
def     _a					(						lowerCamelCase,	**lowerCamelCase							):
 lowerCamelCase		:	Optional[Any]							=      dataset.filter(**lowerCamelCase							)





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

  lowerCamelCase		:	Tuple							=      transformers.AutoTokenizer.from_pretrained("""bert-base-cased""",	use_fast=lowerCamelCase							)

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

  lowerCamelCase		:	List[str]							=      map(lowerCamelCase							)

  lowerCamelCase		:	int							=      map(lowerCamelCase,	batched=lowerCamelCase							)

  lowerCamelCase		:	int							=      map(lowerCamelCase,	function=lambda lowerCamelCase							: None,	batched=lowerCamelCase							)

  with dataset.formatted_as(type="""numpy"""							):
   lowerCamelCase		:	Optional[int]							=      map(lowerCamelCase,	function=lambda lowerCamelCase							: None,	batched=lowerCamelCase							)

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

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

  with dataset.formatted_as(type="""tensorflow""",	columns="""numbers"""							):
   lowerCamelCase		:	Optional[int]							=      map(lowerCamelCase,	function=lambda lowerCamelCase							: None,	batched=lowerCamelCase							)

  lowerCamelCase		:	int							=      map(lowerCamelCase,	function=lowerCamelCase,	batched=lowerCamelCase							)

  lowerCamelCase		:	Union[str, Any]							=      filter(lowerCamelCase							)

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


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




