
"""simple docstring"""

from __future__ import annotations

_A            = 'Alexander Joslin'
def 		UpperCAmelCase					(		a_			):





		'''simple docstring'''


		if number <= 0:
				lowerCamelCase     :			Tuple		=    F"""n must be greater than 0. Got n = {number}"""
				raise ValueError(__lowerCamelCase			)

		lowerCamelCase     :			Any		=    [number + 1] * (number + 1)

		# starting position
		lowerCamelCase     :			Union[str, Any]		=    0
		for i in range(1,						__lowerCamelCase			):
				lowerCamelCase     :			List[Any]		=    min(table[i + 1],						table[i] + 1			)
				# check if out of bounds
				if i * 2 <= number:
						lowerCamelCase     :			Tuple		=    min(table[i * 2],						table[i] + 1			)
				# check if out of bounds
				if i * 3 <= number:
						lowerCamelCase     :			Any		=    min(table[i * 3],						table[i] + 1			)
		return table[number]


if __name__ == "__main__":
					import doctest

					doctest.testmod()













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			):






       '''simple docstring'''


       def 			UpperCamelCase_	(					self							)	->   str:
            _SCREAMING_SNAKE_CASE				:		str  			=							tempfile.mkdtemp()

            # fmt: off
            _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "want", "##want", "##ed", "wa", "un", "runn", "##ing", ",", "low", "lowest"]
            # fmt: on
            _SCREAMING_SNAKE_CASE				:		int  			=							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]							)							)

            _SCREAMING_SNAKE_CASE				:		Any  			=							{
                "image_mean": FLAVA_IMAGE_MEAN,
                "image_std": FLAVA_IMAGE_STD,
                "do_normalize": True,
                "do_resize": True,
                "size": 2_2_4,
                "do_center_crop": True,
                "crop_size": 2_2_4,
                "input_size_patches": 1_4,
                "total_mask_patches": 7_5,
                "mask_group_max_patches": None,
                "mask_group_min_patches": 1_6,
                "mask_group_min_aspect_ratio": 0.3,
                "mask_group_max_aspect_ratio": None,
                "codebook_do_resize": True,
                "codebook_size": 1_1_2,
                "codebook_do_center_crop": True,
                "codebook_crop_size": 1_1_2,
                "codebook_do_map_pixels": True,
                "codebook_do_normalize": True,
                "codebook_image_mean": FLAVA_CODEBOOK_MEAN,
                "codebook_image_std": FLAVA_CODEBOOK_STD,
            }

            _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							os.path.join(self.tmpdirname			,  __lowerCamelCase							)
            with open(self.image_processor_file			,  "w"			,  encoding="utf-8"							) as fp:
                 json.dump(__lowerCamelCase			,  __lowerCamelCase							)




       def 			UpperCamelCase_	(					self			,  **__lowerCamelCase							)	->   List[str]:
            return BertTokenizer.from_pretrained(self.tmpdirname			,  **__lowerCamelCase							)




       def 			UpperCamelCase_	(					self			,  **__lowerCamelCase							)	->   Optional[Any]:
            return BertTokenizerFast.from_pretrained(self.tmpdirname			,  **__lowerCamelCase							)




       def 			UpperCamelCase_	(					self			,  **__lowerCamelCase							)	->   Tuple:
            return FlavaImageProcessor.from_pretrained(self.tmpdirname			,  **__lowerCamelCase							)




       def 			UpperCamelCase_	(					self							)	->   int:
            shutil.rmtree(self.tmpdirname							)




       def 			UpperCamelCase_	(					self							)	->   Optional[int]:
            _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							[np.random.randint(2_5_5			,  size=(3, 3_0, 4_0_0)			,  dtype=np.uinta							)]

            _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							[Image.fromarray(np.moveaxis(__lowerCamelCase			,  0			,  -1							)							) for x in image_inputs]

            return image_inputs




       def 			UpperCamelCase_	(					self							)	->   Any:
            _SCREAMING_SNAKE_CASE				:		Dict  			=							self.get_tokenizer()
            _SCREAMING_SNAKE_CASE				:		Any  			=							self.get_rust_tokenizer()
            _SCREAMING_SNAKE_CASE				:		Dict  			=							self.get_image_processor()

            _SCREAMING_SNAKE_CASE				:		List[Any]  			=							FlavaProcessor(tokenizer=__lowerCamelCase			,  image_processor=__lowerCamelCase							)
            processor_slow.save_pretrained(self.tmpdirname							)
            _SCREAMING_SNAKE_CASE				:		Tuple  			=							FlavaProcessor.from_pretrained(self.tmpdirname			,  use_fast=__lowerCamelCase							)

            _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							FlavaProcessor(tokenizer=__lowerCamelCase			,  image_processor=__lowerCamelCase							)
            processor_fast.save_pretrained(self.tmpdirname							)
            _SCREAMING_SNAKE_CASE				:		int  			=							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 			UpperCamelCase_	(					self							)	->   Optional[int]:
            _SCREAMING_SNAKE_CASE				:		Dict  			=							FlavaProcessor(tokenizer=self.get_tokenizer()			,  image_processor=self.get_image_processor()							)
            processor.save_pretrained(self.tmpdirname							)

            _SCREAMING_SNAKE_CASE				:		str  			=							self.get_tokenizer(bos_token="(BOS)"			,  eos_token="(EOS)"							)
            _SCREAMING_SNAKE_CASE				:		List[str]  			=							self.get_image_processor(do_normalize=__lowerCamelCase			,  padding_value=1.0							)

            _SCREAMING_SNAKE_CASE				:		Dict  			=							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 			UpperCamelCase_	(					self							)	->   Any:
            _SCREAMING_SNAKE_CASE				:		str  			=							self.get_image_processor()
            _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							self.get_tokenizer()

            _SCREAMING_SNAKE_CASE				:		Any  			=							FlavaProcessor(tokenizer=__lowerCamelCase			,  image_processor=__lowerCamelCase							)

            _SCREAMING_SNAKE_CASE				:		int  			=							self.prepare_image_inputs()

            _SCREAMING_SNAKE_CASE				:		List[str]  			=							image_processor(__lowerCamelCase			,  return_tensors="np"							)
            _SCREAMING_SNAKE_CASE				:		str  			=							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_2_3_4							)
            _SCREAMING_SNAKE_CASE				:		List[Any]  			=							image_processor(
                __lowerCamelCase			,  return_image_mask=__lowerCamelCase			,  return_codebook_pixels=__lowerCamelCase			,  return_tensors="np"							)
            random.seed(1_2_3_4							)
            _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							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 			UpperCamelCase_	(					self							)	->   int:
            _SCREAMING_SNAKE_CASE				:		Any  			=							self.get_image_processor()
            _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							self.get_tokenizer()

            _SCREAMING_SNAKE_CASE				:		int  			=							FlavaProcessor(tokenizer=__lowerCamelCase			,  image_processor=__lowerCamelCase							)

            _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							"lower newer"

            _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							processor(text=__lowerCamelCase							)

            _SCREAMING_SNAKE_CASE				:		List[str]  			=							tokenizer(__lowerCamelCase							)

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




       def 			UpperCamelCase_	(					self							)	->   Tuple:
            _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							self.get_image_processor()
            _SCREAMING_SNAKE_CASE				:		Optional[Any]  			=							self.get_tokenizer()

            _SCREAMING_SNAKE_CASE				:		List[Any]  			=							FlavaProcessor(tokenizer=__lowerCamelCase			,  image_processor=__lowerCamelCase							)

            _SCREAMING_SNAKE_CASE				:		Any  			=							"lower newer"
            _SCREAMING_SNAKE_CASE				:		Dict  			=							self.prepare_image_inputs()

            _SCREAMING_SNAKE_CASE				:		List[Any]  			=							processor(text=__lowerCamelCase			,  images=__lowerCamelCase							)

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

            # add extra args
            _SCREAMING_SNAKE_CASE				:		Tuple  			=							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 			UpperCamelCase_	(					self							)	->   Dict:
            _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							self.get_image_processor()
            _SCREAMING_SNAKE_CASE				:		str  			=							self.get_tokenizer()

            _SCREAMING_SNAKE_CASE				:		int  			=							FlavaProcessor(tokenizer=__lowerCamelCase			,  image_processor=__lowerCamelCase							)

            _SCREAMING_SNAKE_CASE				:		Optional[int]  			=							[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

            _SCREAMING_SNAKE_CASE				:		Dict  			=							processor.batch_decode(__lowerCamelCase							)
            _SCREAMING_SNAKE_CASE				:		Any  			=							tokenizer.batch_decode(__lowerCamelCase							)

            self.assertListEqual(__lowerCamelCase			,  __lowerCamelCase							)







       def 			UpperCamelCase_	(					self							)	->   Any:
            _SCREAMING_SNAKE_CASE				:		int  			=							self.get_image_processor()
            _SCREAMING_SNAKE_CASE				:		Union[str, Any]  			=							self.get_tokenizer()

            _SCREAMING_SNAKE_CASE				:		Dict  			=							FlavaProcessor(tokenizer=__lowerCamelCase			,  image_processor=__lowerCamelCase							)

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

            _SCREAMING_SNAKE_CASE				:		List[Any]  			=							processor(text=__lowerCamelCase			,  images=__lowerCamelCase							)

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