






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        a__      (	unittest.TestCase		):






		def 					_lowerCamelCase					(      self						)				->				List[str]:
				__A    =							tempfile.mkdtemp()

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

				__A    =							{
				    "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,
				}

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






		def 					_lowerCamelCase					(      self							, **lowercase__						)				->				int:
				return BertTokenizer.from_pretrained(self.tmpdirname							, **_A						)






		def 					_lowerCamelCase					(      self							, **lowercase__						)				->				Optional[Any]:
				return BertTokenizerFast.from_pretrained(self.tmpdirname							, **_A						)






		def 					_lowerCamelCase					(      self							, **lowercase__						)				->				Any:
				return FlavaImageProcessor.from_pretrained(self.tmpdirname							, **_A						)






		def 					_lowerCamelCase					(      self						)				->				List[str]:
				shutil.rmtree(self.tmpdirname						)






		def 					_lowerCamelCase					(      self						)				->				Dict:
				__A    =							[np.random.randint(255							, size=(3, 30, 400)							, dtype=np.uinta						)]

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

				return image_inputs






		def 					_lowerCamelCase					(      self						)				->				Union[str, Any]:
				__A    =							self.get_tokenizer()
				__A    =							self.get_rust_tokenizer()
				__A    =							self.get_image_processor()

				__A    =							FlavaProcessor(tokenizer=_A							, image_processor=_A						)
				processor_slow.save_pretrained(self.tmpdirname						)
				__A    =							FlavaProcessor.from_pretrained(self.tmpdirname							, use_fast=_A						)

				__A    =							FlavaProcessor(tokenizer=_A							, image_processor=_A						)
				processor_fast.save_pretrained(self.tmpdirname						)
				__A    =							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							, _A						)
				self.assertIsInstance(processor_fast.tokenizer							, _A						)

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






		def 					_lowerCamelCase					(      self						)				->				Optional[Any]:
				__A    =							FlavaProcessor(tokenizer=self.get_tokenizer()							, image_processor=self.get_image_processor()						)
				processor.save_pretrained(self.tmpdirname						)

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

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

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

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






		def 					_lowerCamelCase					(      self						)				->				int:
				__A    =							self.get_image_processor()
				__A    =							self.get_tokenizer()

				__A    =							FlavaProcessor(tokenizer=_A							, image_processor=_A						)

				__A    =							self.prepare_image_inputs()

				__A    =							image_processor(_A							, return_tensors="np"						)
				__A    =							processor(images=_A							, 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(1234						)
				__A    =							image_processor(
				    _A							, return_image_mask=_A							, return_codebook_pixels=_A							, return_tensors="np"						)
				random.seed(1234						)
				__A    =							processor(
				    images=_A							, return_image_mask=_A							, return_codebook_pixels=_A							, 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 					_lowerCamelCase					(      self						)				->				Any:
				__A    =							self.get_image_processor()
				__A    =							self.get_tokenizer()

				__A    =							FlavaProcessor(tokenizer=_A							, image_processor=_A						)

				__A    =							"lower newer"

				__A    =							processor(text=_A						)

				__A    =							tokenizer(_A						)

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






		def 					_lowerCamelCase					(      self						)				->				Dict:
				__A    =							self.get_image_processor()
				__A    =							self.get_tokenizer()

				__A    =							FlavaProcessor(tokenizer=_A							, image_processor=_A						)

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

				__A    =							processor(text=_A							, images=_A						)

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

				# add extra args
				__A    =							processor(text=_A							, images=_A							, return_codebook_pixels=_A							, return_image_mask=_A						)

				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(_A						):
						processor()






		def 					_lowerCamelCase					(      self						)				->				Union[str, Any]:
				__A    =							self.get_image_processor()
				__A    =							self.get_tokenizer()

				__A    =							FlavaProcessor(tokenizer=_A							, image_processor=_A						)

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

				__A    =							processor.batch_decode(_A						)
				__A    =							tokenizer.batch_decode(_A						)

				self.assertListEqual(_A							, _A						)






		def 					_lowerCamelCase					(      self						)				->				Any:
				__A    =							self.get_image_processor()
				__A    =							self.get_tokenizer()

				__A    =							FlavaProcessor(tokenizer=_A							, image_processor=_A						)

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

				__A    =							processor(text=_A							, images=_A						)

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



_A       =					{
    "0": 1,
    "1": 1,
    "2": 2,
    "3": 6,
    "4": 24,
    "5": 1_20,
    "6": 7_20,
    "7": 50_40,
    "8": 4_03_20,
    "9": 36_28_80,
}

_A       =					{1_45: 1_45}

_A       =					{
    1_45: 0,
    1_69: 3,
    3_63_01: 3,
    14_54: 3,
    8_71: 2,
    4_53_61: 2,
    8_72: 2,
}



def 		lowercase_     (  A__						)				->				int:
    """simple docstring"""

    if n in CACHE_SUM_DIGIT_FACTORIALS:
        return CACHE_SUM_DIGIT_FACTORIALS[n]
    snake_case						=   sum(DIGIT_FACTORIALS[let] for let in str(A__						)						)
    snake_case						=   ret
    return ret



def 		lowercase_     (  A__					,						A__ = None						)				->				int:
    """simple docstring"""

    snake_case						=   previous or set()
    if n in CHAIN_LENGTH_CACHE:
        return CHAIN_LENGTH_CACHE[n]
    snake_case						=   sum_digit_factorials(A__						)
    if next_number in previous:
        snake_case						=   0
        return 0
    else:
        previous.add(A__						)
        snake_case						=   1 + chain_length(A__					,						A__						)
        snake_case						=   ret
        return ret



def 		lowercase_     (  A__ = 60					,						A__ = 100_0000						)				->				int:
    """simple docstring"""

    return sum(1 for i in range(1					,						A__						) if chain_length(A__						) == num_terms						)


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

