





"""simple docstring"""





import json
import os
import random
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 FlavaImageProcessor, FlavaProcessor
  from transformers.models.flava.image_processing_flava import (
      FLAVA_CODEBOOK_MEAN,
      FLAVA_CODEBOOK_STD,
      FLAVA_IMAGE_MEAN,
      FLAVA_IMAGE_STD,
  )


@require_vision
class       __lowerCamelCase	(   unittest.TestCase    ):







   def 							A__						(self				):




      '''simple docstring'''




      _lowerCAmelCase					     =		tempfile.mkdtemp()

      # fmt: off
      _lowerCAmelCase					     =		["""[UNK]""", """[CLS]""", """[SEP]""", """[PAD]""", """[MASK]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing""", """,""", """low""", """lowest"""]
      # fmt: on
      _lowerCAmelCase					     =		os.path.join(self.tmpdirname       ,	VOCAB_FILES_NAMES["""vocab_file"""]				)

      with open(self.vocab_file       ,	"""w"""       ,	encoding="""utf-8"""				) as fp:
         fp.write("""""".join([x + """\n""" for x in vocab_tokens]				)				)

      _lowerCAmelCase					     =		{
          """image_mean""": FLAVA_IMAGE_MEAN,
          """image_std""": FLAVA_IMAGE_STD,
          """do_normalize""": True,
          """do_resize""": True,
          """size""": 224,
          """do_center_crop""": True,
          """crop_size""": 224,
          """input_size_patches""": 14,
          """total_mask_patches""": 75,
          """mask_group_max_patches""": None,
          """mask_group_min_patches""": 16,
          """mask_group_min_aspect_ratio""": 0.3,
          """mask_group_max_aspect_ratio""": None,
          """codebook_do_resize""": True,
          """codebook_size""": 112,
          """codebook_do_center_crop""": True,
          """codebook_crop_size""": 112,
          """codebook_do_map_pixels""": True,
          """codebook_do_normalize""": True,
          """codebook_image_mean""": FLAVA_CODEBOOK_MEAN,
          """codebook_image_std""": FLAVA_CODEBOOK_STD,
      }

      _lowerCAmelCase					     =		os.path.join(self.tmpdirname       ,	lowerCamelCase				)
      with open(self.image_processor_file       ,	"""w"""       ,	encoding="""utf-8"""				) as fp:
         json.dump(lowerCamelCase       ,	lowerCamelCase				)







   def 							A__						(self       ,	**lowerCamelCase				):




      '''simple docstring'''




      return BertTokenizer.from_pretrained(self.tmpdirname       ,	**lowerCamelCase				)







   def 							A__						(self       ,	**lowerCamelCase				):




      '''simple docstring'''




      return BertTokenizerFast.from_pretrained(self.tmpdirname       ,	**lowerCamelCase				)







   def 							A__						(self       ,	**lowerCamelCase				):




      '''simple docstring'''




      return FlavaImageProcessor.from_pretrained(self.tmpdirname       ,	**lowerCamelCase				)







   def 							A__						(self				):




      '''simple docstring'''




      shutil.rmtree(self.tmpdirname				)







   def 							A__						(self				):




      '''simple docstring'''




      _lowerCAmelCase					     =		[np.random.randint(255       ,	size=(3, 30, 400)       ,	dtype=np.uinta				)]

      _lowerCAmelCase					     =		[Image.fromarray(np.moveaxis(lowerCamelCase       ,	0       ,	-1				)				) for x in image_inputs]

      return image_inputs







   def 							A__						(self				):




      '''simple docstring'''




      _lowerCAmelCase					     =		self.get_tokenizer()
      _lowerCAmelCase					     =		self.get_rust_tokenizer()
      _lowerCAmelCase					     =		self.get_image_processor()

      _lowerCAmelCase					     =		FlavaProcessor(tokenizer=lowerCamelCase       ,	image_processor=lowerCamelCase				)
      processor_slow.save_pretrained(self.tmpdirname				)
      _lowerCAmelCase					     =		FlavaProcessor.from_pretrained(self.tmpdirname       ,	use_fast=lowerCamelCase				)

      _lowerCAmelCase					     =		FlavaProcessor(tokenizer=lowerCamelCase       ,	image_processor=lowerCamelCase				)
      processor_fast.save_pretrained(self.tmpdirname				)
      _lowerCAmelCase					     =		FlavaProcessor.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       ,	lowerCamelCase				)
      self.assertIsInstance(processor_fast.tokenizer       ,	lowerCamelCase				)

      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       ,	lowerCamelCase				)
      self.assertIsInstance(processor_fast.image_processor       ,	lowerCamelCase				)







   def 							A__						(self				):




      '''simple docstring'''




      _lowerCAmelCase					     =		FlavaProcessor(tokenizer=self.get_tokenizer()       ,	image_processor=self.get_image_processor()				)
      processor.save_pretrained(self.tmpdirname				)

      _lowerCAmelCase					     =		self.get_tokenizer(bos_token="""(BOS)"""       ,	eos_token="""(EOS)"""				)
      _lowerCAmelCase					     =		self.get_image_processor(do_normalize=lowerCamelCase       ,	padding_value=1.0				)

      _lowerCAmelCase					     =		FlavaProcessor.from_pretrained(
          self.tmpdirname       ,	bos_token="""(BOS)"""       ,	eos_token="""(EOS)"""       ,	do_normalize=lowerCamelCase       ,	padding_value=1.0				)

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

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







   def 							A__						(self				):




      '''simple docstring'''




      _lowerCAmelCase					     =		self.get_image_processor()
      _lowerCAmelCase					     =		self.get_tokenizer()

      _lowerCAmelCase					     =		FlavaProcessor(tokenizer=lowerCamelCase       ,	image_processor=lowerCamelCase				)

      _lowerCAmelCase					     =		self.prepare_image_inputs()

      _lowerCAmelCase					     =		image_processor(lowerCamelCase       ,	return_tensors="""np"""				)
      _lowerCAmelCase					     =		processor(images=lowerCamelCase       ,	return_tensors="""np"""				)

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

      # With rest of the args
      random.seed(1_234				)
      _lowerCAmelCase					     =		image_processor(
          lowerCamelCase       ,	return_image_mask=lowerCamelCase       ,	return_codebook_pixels=lowerCamelCase       ,	return_tensors="""np"""				)
      random.seed(1_234				)
      _lowerCAmelCase					     =		processor(
          images=lowerCamelCase       ,	return_image_mask=lowerCamelCase       ,	return_codebook_pixels=lowerCamelCase       ,	return_tensors="""np"""				)

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







   def 							A__						(self				):




      '''simple docstring'''




      _lowerCAmelCase					     =		self.get_image_processor()
      _lowerCAmelCase					     =		self.get_tokenizer()

      _lowerCAmelCase					     =		FlavaProcessor(tokenizer=lowerCamelCase       ,	image_processor=lowerCamelCase				)

      _lowerCAmelCase					     =		"""lower newer"""

      _lowerCAmelCase					     =		processor(text=lowerCamelCase				)

      _lowerCAmelCase					     =		tokenizer(lowerCamelCase				)

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







   def 							A__						(self				):




      '''simple docstring'''




      _lowerCAmelCase					     =		self.get_image_processor()
      _lowerCAmelCase					     =		self.get_tokenizer()

      _lowerCAmelCase					     =		FlavaProcessor(tokenizer=lowerCamelCase       ,	image_processor=lowerCamelCase				)

      _lowerCAmelCase					     =		"""lower newer"""
      _lowerCAmelCase					     =		self.prepare_image_inputs()

      _lowerCAmelCase					     =		processor(text=lowerCamelCase       ,	images=lowerCamelCase				)

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

      # add extra args
      _lowerCAmelCase					     =		processor(text=lowerCamelCase       ,	images=lowerCamelCase       ,	return_codebook_pixels=lowerCamelCase       ,	return_image_mask=lowerCamelCase				)

      self.assertListEqual(
          list(inputs.keys()				)       ,	[
              """input_ids""",
              """token_type_ids""",
              """attention_mask""",
              """pixel_values""",
              """codebook_pixel_values""",
              """bool_masked_pos""",
          ]       ,	)

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







   def 							A__						(self				):




      '''simple docstring'''




      _lowerCAmelCase					     =		self.get_image_processor()
      _lowerCAmelCase					     =		self.get_tokenizer()

      _lowerCAmelCase					     =		FlavaProcessor(tokenizer=lowerCamelCase       ,	image_processor=lowerCamelCase				)

      _lowerCAmelCase					     =		[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

      _lowerCAmelCase					     =		processor.batch_decode(lowerCamelCase				)
      _lowerCAmelCase					     =		tokenizer.batch_decode(lowerCamelCase				)

      self.assertListEqual(lowerCamelCase       ,	lowerCamelCase				)







   def 							A__						(self				):




      '''simple docstring'''




      _lowerCAmelCase					     =		self.get_image_processor()
      _lowerCAmelCase					     =		self.get_tokenizer()

      _lowerCAmelCase					     =		FlavaProcessor(tokenizer=lowerCamelCase       ,	image_processor=lowerCamelCase				)

      _lowerCAmelCase					     =		"""lower newer"""
      _lowerCAmelCase					     =		self.prepare_image_inputs()

      _lowerCAmelCase					     =		processor(text=lowerCamelCase       ,	images=lowerCamelCase				)

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




"""simple docstring"""





from itertools import combinations_with_replacement






def 	__UpperCAmelCase					(       snake_case_  :  int = 100   )							->	int:






   """simple docstring"""


   _lowerCAmelCase					     =		[*list(range(1   ,	21   )   ), 25]
   _lowerCAmelCase					     =		[2 * x for x in range(1   ,	21   )] + [50]
   _lowerCAmelCase					     =		[3 * x for x in range(1   ,	21   )]
   _lowerCAmelCase					     =		singles + doubles + triples + [0]

   _lowerCAmelCase					     =		0
   _lowerCAmelCase					     =		42
   _lowerCAmelCase					     =		42
   _lowerCAmelCase					     =		42
   _lowerCAmelCase					     =		42

   for double in doubles:
      for throwa, throwa in combinations_with_replacement(snake_case_   ,	2   ):
         _lowerCAmelCase					     =		double + throwa + throwa
         if checkout_total < limit:
            num_checkouts += 1

   return num_checkouts


if __name__ == "__main__":
  print(F'{solution() = }')