
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 							A__		(	unittest.TestCase):
      def 						UpperCamelCase__							(      self      ):
       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""": 2_0,
           """do_center_crop""": True,
           """crop_size""": 1_8,
           """do_normalize""": True,
           """image_mean""": [0.48_145_466, 0.4_578_275, 0.40_821_073],
           """image_std""": [0.26_862_954, 0.26_130_258, 0.27_577_711],
       }
       lowerCamelCase		:	int							=      os.path.join(self.tmpdirname    ,  __magic_name__      )
       with open(self.image_processor_file    ,  """w"""    ,  encoding="""utf-8"""      ) as fp:
        json.dump(__magic_name__    ,  __magic_name__      )
      def 						UpperCamelCase__							(      self    ,  **__magic_name__      ):
       return BertTokenizer.from_pretrained(self.tmpdirname    ,  **__magic_name__      )
      def 						UpperCamelCase__							(      self    ,  **__magic_name__      ):
       return BertTokenizerFast.from_pretrained(self.tmpdirname    ,  **__magic_name__      )
      def 						UpperCamelCase__							(      self    ,  **__magic_name__      ):
       return EfficientNetImageProcessor.from_pretrained(self.tmpdirname    ,  **__magic_name__      )
      def 						UpperCamelCase__							(      self      ):
       shutil.rmtree(self.tmpdirname      )
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	Any							=      [np.random.randint(2_5_5    ,  size=(3, 3_0, 4_0_0)    ,  dtype=np.uinta      )]
       lowerCamelCase		:	Any							=      [Image.fromarray(np.moveaxis(__magic_name__    ,  0    ,  -1      )      ) for x in image_inputs]
       return image_inputs
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	Dict							=      self.get_tokenizer()
       lowerCamelCase		:	List[Any]							=      self.get_rust_tokenizer()
       lowerCamelCase		:	Tuple							=      self.get_image_processor()

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

       lowerCamelCase		:	List[str]							=      AlignProcessor(tokenizer=__magic_name__    ,  image_processor=__magic_name__      )
       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    ,  __magic_name__      )
       self.assertIsInstance(processor_fast.tokenizer    ,  __magic_name__      )

       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    ,  __magic_name__      )
       self.assertIsInstance(processor_fast.image_processor    ,  __magic_name__      )
      def 						UpperCamelCase__							(      self      ):
       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=__magic_name__    ,  padding_value=1.0      )

       lowerCamelCase		:	List[Any]							=      AlignProcessor.from_pretrained(
           self.tmpdirname    ,  bos_token="""(BOS)"""    ,  eos_token="""(EOS)"""    ,  do_normalize=__magic_name__    ,  padding_value=1.0      )

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

       self.assertEqual(processor.image_processor.to_json_string()    ,  image_processor_add_kwargs.to_json_string()      )
       self.assertIsInstance(processor.image_processor    ,  __magic_name__      )
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	int							=      self.get_image_processor()
       lowerCamelCase		:	Union[str, Any]							=      self.get_tokenizer()

       lowerCamelCase		:	Optional[Any]							=      AlignProcessor(tokenizer=__magic_name__    ,  image_processor=__magic_name__      )

       lowerCamelCase		:	str							=      self.prepare_image_inputs()

       lowerCamelCase		:	Tuple							=      image_processor(__magic_name__    ,  return_tensors="""np"""      )
       lowerCamelCase		:	Dict							=      processor(images=__magic_name__    ,  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 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	int							=      self.get_image_processor()
       lowerCamelCase		:	Any							=      self.get_tokenizer()

       lowerCamelCase		:	Union[str, Any]							=      AlignProcessor(tokenizer=__magic_name__    ,  image_processor=__magic_name__      )

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

       lowerCamelCase		:	List[str]							=      processor(text=__magic_name__      )

       lowerCamelCase		:	List[str]							=      tokenizer(__magic_name__    ,  padding="""max_length"""    ,  max_length=6_4      )

       for key in encoded_tok.keys():
        self.assertListEqual(encoded_tok[key]    ,  encoded_processor[key]      )
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	List[Any]							=      self.get_image_processor()
       lowerCamelCase		:	Dict							=      self.get_tokenizer()

       lowerCamelCase		:	str							=      AlignProcessor(tokenizer=__magic_name__    ,  image_processor=__magic_name__      )

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

       lowerCamelCase		:	Dict							=      processor(text=__magic_name__    ,  images=__magic_name__      )

       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(__magic_name__      ):
        processor()
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	List[Any]							=      self.get_image_processor()
       lowerCamelCase		:	Optional[int]							=      self.get_tokenizer()

       lowerCamelCase		:	Dict							=      AlignProcessor(tokenizer=__magic_name__    ,  image_processor=__magic_name__      )

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

       lowerCamelCase		:	List[Any]							=      processor.batch_decode(__magic_name__      )
       lowerCamelCase		:	List[str]							=      tokenizer.batch_decode(__magic_name__      )

       self.assertListEqual(__magic_name__    ,  __magic_name__      )






      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	Tuple							=      self.get_image_processor()
       lowerCamelCase		:	int							=      self.get_tokenizer()

       lowerCamelCase		:	Optional[int]							=      AlignProcessor(tokenizer=__magic_name__    ,  image_processor=__magic_name__      )

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

       lowerCamelCase		:	int							=      processor(text=__magic_name__    ,  images=__magic_name__      )

       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()




