


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    ):




      '''simple docstring'''


      def      _snake_case	(				self:       List[str]				):
       __lowerCamelCase		:							str							=						tempfile.mkdtemp()

       # fmt: off
       __lowerCamelCase		:							List[Any]							=						["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "want", "##want", "##ed", "wa", "un", "runn", "##ing", ",", "low", "lowest"]
       # fmt: on
       __lowerCamelCase		:							List[Any]							=						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		:							str							=						{
           "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		:							str							=						os.path.join(self.tmpdirname						,      snake_case__				)
       with open(self.image_processor_file						,      'w'						,      encoding='utf-8'				) as fp:
        json.dump(snake_case__						,      snake_case__				)




      def      _snake_case	(				self:       Union[str, Any]						,      **a:       List[str]				):
       return BertTokenizer.from_pretrained(self.tmpdirname						,      **snake_case__				)




      def      _snake_case	(				self:       List[str]						,      **a:       Union[str, Any]				):
       return BertTokenizerFast.from_pretrained(self.tmpdirname						,      **snake_case__				)




      def      _snake_case	(				self:       Optional[int]						,      **a:       Optional[Any]				):
       return FlavaImageProcessor.from_pretrained(self.tmpdirname						,      **snake_case__				)




      def      _snake_case	(				self:       List[str]				):
       shutil.rmtree(self.tmpdirname				)




      def      _snake_case	(				self:       Optional[int]				):
       __lowerCamelCase		:							Optional[Any]							=						[np.random.randint(255						,      size=(3, 30, 400)						,      dtype=np.uinta				)]

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

       return image_inputs




      def      _snake_case	(				self:       int				):
       __lowerCamelCase		:							Tuple							=						self.get_tokenizer()
       __lowerCamelCase		:							List[Any]							=						self.get_rust_tokenizer()
       __lowerCamelCase		:							Dict							=						self.get_image_processor()

       __lowerCamelCase		:							List[str]							=						FlavaProcessor(tokenizer=snake_case__						,      image_processor=snake_case__				)
       processor_slow.save_pretrained(self.tmpdirname				)
       __lowerCamelCase		:							Dict							=						FlavaProcessor.from_pretrained(self.tmpdirname						,      use_fast=snake_case__				)

       __lowerCamelCase		:							Optional[Any]							=						FlavaProcessor(tokenizer=snake_case__						,      image_processor=snake_case__				)
       processor_fast.save_pretrained(self.tmpdirname				)
       __lowerCamelCase		:							Dict							=						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						,      snake_case__				)
       self.assertIsInstance(processor_fast.tokenizer						,      snake_case__				)

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




      def      _snake_case	(				self:       Union[str, Any]				):
       __lowerCamelCase		:							Dict							=						FlavaProcessor(tokenizer=self.get_tokenizer()						,      image_processor=self.get_image_processor()				)
       processor.save_pretrained(self.tmpdirname				)

       __lowerCamelCase		:							List[str]							=						self.get_tokenizer(bos_token='(BOS)'						,      eos_token='(EOS)'				)
       __lowerCamelCase		:							Optional[Any]							=						self.get_image_processor(do_normalize=snake_case__						,      padding_value=1.0				)

       __lowerCamelCase		:							Any							=						FlavaProcessor.from_pretrained(
           self.tmpdirname						,      bos_token='(BOS)'						,      eos_token='(EOS)'						,      do_normalize=snake_case__						,      padding_value=1.0				)

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

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




      def      _snake_case	(				self:       List[Any]				):
       __lowerCamelCase		:							str							=						self.get_image_processor()
       __lowerCamelCase		:							int							=						self.get_tokenizer()

       __lowerCamelCase		:							List[str]							=						FlavaProcessor(tokenizer=snake_case__						,      image_processor=snake_case__				)

       __lowerCamelCase		:							Dict							=						self.prepare_image_inputs()

       __lowerCamelCase		:							Optional[int]							=						image_processor(snake_case__						,      return_tensors='np'				)
       __lowerCamelCase		:							Any							=						processor(images=snake_case__						,      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				)
       __lowerCamelCase		:							Dict							=						image_processor(
           snake_case__						,      return_image_mask=snake_case__						,      return_codebook_pixels=snake_case__						,      return_tensors='np'				)
       random.seed(1234				)
       __lowerCamelCase		:							List[str]							=						processor(
           images=snake_case__						,      return_image_mask=snake_case__						,      return_codebook_pixels=snake_case__						,      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      _snake_case	(				self:       List[Any]				):
       __lowerCamelCase		:							Tuple							=						self.get_image_processor()
       __lowerCamelCase		:							Dict							=						self.get_tokenizer()

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

       __lowerCamelCase		:							int							=						"lower newer"

       __lowerCamelCase		:							List[str]							=						processor(text=snake_case__				)

       __lowerCamelCase		:							Optional[int]							=						tokenizer(snake_case__				)

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




      def      _snake_case	(				self:       List[Any]				):
       __lowerCamelCase		:							Tuple							=						self.get_image_processor()
       __lowerCamelCase		:							int							=						self.get_tokenizer()

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

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

       __lowerCamelCase		:							Tuple							=						processor(text=snake_case__						,      images=snake_case__				)

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

       # add extra args
       __lowerCamelCase		:							Dict							=						processor(text=snake_case__						,      images=snake_case__						,      return_codebook_pixels=snake_case__						,      return_image_mask=snake_case__				)

       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(snake_case__				):
        processor()




      def      _snake_case	(				self:       int				):
       __lowerCamelCase		:							Any							=						self.get_image_processor()
       __lowerCamelCase		:							Any							=						self.get_tokenizer()

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

       __lowerCamelCase		:							List[str]							=						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

       __lowerCamelCase		:							Union[str, Any]							=						processor.batch_decode(snake_case__				)
       __lowerCamelCase		:							Dict							=						tokenizer.batch_decode(snake_case__				)

       self.assertListEqual(snake_case__						,      snake_case__				)




      def      _snake_case	(				self:       Any				):
       __lowerCamelCase		:							List[str]							=						self.get_image_processor()
       __lowerCamelCase		:							Dict							=						self.get_tokenizer()

       __lowerCamelCase		:							List[str]							=						FlavaProcessor(tokenizer=snake_case__						,      image_processor=snake_case__				)

       __lowerCamelCase		:							Union[str, Any]							=						"lower newer"
       __lowerCamelCase		:							Optional[Any]							=						self.prepare_image_inputs()

       __lowerCamelCase		:							Any							=						processor(text=snake_case__						,      images=snake_case__				)

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









"""simple docstring"""



def      SCREAMING_SNAKE_CASE__    (   snake_case    :      list       , snake_case    :      int       , snake_case    :      int							)->						None:






     '''simple docstring'''



     if right <= left:
          return
     UpperCAmelCase__		:  str				=				left
     UpperCAmelCase__		:  List[str]				=				right
     UpperCAmelCase__		:  List[Any]				=				sorting[left]
     while i <= b:
          if sorting[i] < pivot:
               UpperCAmelCase__				,							UpperCAmelCase__		:  List[str]				=				sorting[i], sorting[a]
               a += 1
               i += 1
          elif sorting[i] > pivot:
               UpperCAmelCase__				,							UpperCAmelCase__		:  Optional[int]				=				sorting[i], sorting[b]
               b -= 1
          else:
               i += 1
     quick_sort_apartition(snake_case       , snake_case       , a - 1							)
     quick_sort_apartition(snake_case       , b + 1       , snake_case							)


def      SCREAMING_SNAKE_CASE__    (   snake_case    :      list       , snake_case    :      int       , snake_case    :      int							)->						None:






     '''simple docstring'''



     if left < right:
          UpperCAmelCase__		:  int				=				lomuto_partition(snake_case       , snake_case       , snake_case							)
          quick_sort_lomuto_partition(snake_case       , snake_case       , pivot_index - 1							)
          quick_sort_lomuto_partition(snake_case       , pivot_index + 1       , snake_case							)


def      SCREAMING_SNAKE_CASE__    (   snake_case    :      list       , snake_case    :      int       , snake_case    :      int							)->						int:






     '''simple docstring'''



     UpperCAmelCase__		:  Union[str, Any]				=				sorting[right]
     UpperCAmelCase__		:  Optional[Any]				=				left
     for i in range(snake_case       , snake_case							):
          if sorting[i] < pivot:
               UpperCAmelCase__				,							UpperCAmelCase__		:  List[Any]				=				sorting[i], sorting[store_index]
               store_index += 1
     UpperCAmelCase__				,							UpperCAmelCase__		:  List[Any]				=				sorting[store_index], sorting[right]
     return store_index


def      SCREAMING_SNAKE_CASE__    (   snake_case    :      list							)->						list:






     '''simple docstring'''



     if len(snake_case							) <= 1:
          return sorting
     return (
         three_way_radix_quicksort([i for i in sorting if i < sorting[0]]							)
         + [i for i in sorting if i == sorting[0]]
         + three_way_radix_quicksort([i for i in sorting if i > sorting[0]]							)
     )


if __name__ == "__main__":
 import doctest

 doctest.testmod(verbose=True)

 _lowerCAmelCase     :     Optional[int]            =	input("""Enter numbers separated by a comma:\n""").strip()
 _lowerCAmelCase     :     str            =	[int(item) for item in user_input.split(""",""")]
 quick_sort_apartition(unsorted, 0, len(unsorted) - 1)
 print(unsorted)


