
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 						UpperCAmelCase(self :       str  )		->		List[str]:
         snake_case						=   tempfile.mkdtemp()

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

         snake_case						=   {
             "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,
         }

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





     def 						UpperCAmelCase(self :       Any							,       **_A :       Optional[Any]  )		->		int:
         return BertTokenizer.from_pretrained(self.tmpdirname							,       **_A  )





     def 						UpperCAmelCase(self :       Union[str, Any]							,       **_A :       List[Any]  )		->		Optional[Any]:
         return BertTokenizerFast.from_pretrained(self.tmpdirname							,       **_A  )





     def 						UpperCAmelCase(self :       List[str]							,       **_A :       Optional[Any]  )		->		Any:
         return FlavaImageProcessor.from_pretrained(self.tmpdirname							,       **_A  )





     def 						UpperCAmelCase(self :       Optional[Any]  )		->		List[str]:
         shutil.rmtree(self.tmpdirname  )





     def 						UpperCAmelCase(self :       Tuple  )		->		Dict:
         snake_case						=   [np.random.randint(2_5_5							,       size=(3, 3_0, 4_0_0)							,       dtype=np.uinta  )]

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

         return image_inputs





     def 						UpperCAmelCase(self :       Tuple  )		->		Union[str, Any]:
         snake_case						=   self.get_tokenizer()
         snake_case						=   self.get_rust_tokenizer()
         snake_case						=   self.get_image_processor()

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

         snake_case						=   FlavaProcessor(tokenizer=_A							,       image_processor=_A  )
         processor_fast.save_pretrained(self.tmpdirname  )
         snake_case						=   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 						UpperCAmelCase(self :       Optional[int]  )		->		Optional[Any]:
         snake_case						=   FlavaProcessor(tokenizer=self.get_tokenizer()							,       image_processor=self.get_image_processor()  )
         processor.save_pretrained(self.tmpdirname  )

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

         snake_case						=   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 						UpperCAmelCase(self :       Optional[Any]  )		->		int:
         snake_case						=   self.get_image_processor()
         snake_case						=   self.get_tokenizer()

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

         snake_case						=   self.prepare_image_inputs()

         snake_case						=   image_processor(_A							,       return_tensors="np"  )
         snake_case						=   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(1_2_3_4  )
         snake_case						=   image_processor(
             _A							,       return_image_mask=_A							,       return_codebook_pixels=_A							,       return_tensors="np"  )
         random.seed(1_2_3_4  )
         snake_case						=   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 						UpperCAmelCase(self :       List[Any]  )		->		Any:
         snake_case						=   self.get_image_processor()
         snake_case						=   self.get_tokenizer()

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

         snake_case						=   "lower newer"

         snake_case						=   processor(text=_A  )

         snake_case						=   tokenizer(_A  )

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





     def 						UpperCAmelCase(self :       Tuple  )		->		Dict:
         snake_case						=   self.get_image_processor()
         snake_case						=   self.get_tokenizer()

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

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

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

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

         # add extra args
         snake_case						=   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 						UpperCAmelCase(self :       List[str]  )		->		Union[str, Any]:
         snake_case						=   self.get_image_processor()
         snake_case						=   self.get_tokenizer()

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

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

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

         self.assertListEqual(_A							,       _A  )





     def 						UpperCAmelCase(self :       str  )		->		Any:
         snake_case						=   self.get_image_processor()
         snake_case						=   self.get_tokenizer()

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

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

         snake_case						=   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() = }")

