



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  snake_case__						(	unittest.TestCase					):






     """simple docstring"""


     def        lowercase_       (	self  :  List[str] )       ->Tuple:
            snake_case__							:     Dict            =     tempfile.mkdtemp()

            # fmt: off
            snake_case__							:     Dict            =     ['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing', ',', 'low', 'lowest']
            # fmt: on
            snake_case__							:     str            =     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__							:     int            =     {
                '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__							:     Dict            =     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        lowercase_       (	self  :  int,			**_snake_case  :  Dict )       ->Dict:
            return BertTokenizer.from_pretrained(self.tmpdirname,			**_snake_case )






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






     def        lowercase_       (	self  :  int,			**_snake_case  :  Tuple )       ->List[str]:
            return FlavaImageProcessor.from_pretrained(self.tmpdirname,			**_snake_case )






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






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

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

            return image_inputs






     def        lowercase_       (	self  :  Tuple )       ->Tuple:
            snake_case__							:     Optional[Any]            =     self.get_tokenizer()
            snake_case__							:     List[str]            =     self.get_rust_tokenizer()
            snake_case__							:     Dict            =     self.get_image_processor()

            snake_case__							:     int            =     FlavaProcessor(tokenizer=_snake_case,			image_processor=_snake_case )
            processor_slow.save_pretrained(self.tmpdirname )
            snake_case__							:     Any            =     FlavaProcessor.from_pretrained(self.tmpdirname,			use_fast=_snake_case )

            snake_case__							:     Any            =     FlavaProcessor(tokenizer=_snake_case,			image_processor=_snake_case )
            processor_fast.save_pretrained(self.tmpdirname )
            snake_case__							:     Union[str, Any]            =     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        lowercase_       (	self  :  Union[str, Any] )       ->Optional[int]:
            snake_case__							:     str            =     FlavaProcessor(tokenizer=self.get_tokenizer(),			image_processor=self.get_image_processor() )
            processor.save_pretrained(self.tmpdirname )

            snake_case__							:     str            =     self.get_tokenizer(bos_token='(BOS)',			eos_token='(EOS)' )
            snake_case__							:     str            =     self.get_image_processor(do_normalize=_snake_case,			padding_value=1.0 )

            snake_case__							:     Optional[int]            =     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        lowercase_       (	self  :  str )       ->Optional[int]:
            snake_case__							:     Tuple            =     self.get_image_processor()
            snake_case__							:     List[str]            =     self.get_tokenizer()

            snake_case__							:     Union[str, Any]            =     FlavaProcessor(tokenizer=_snake_case,			image_processor=_snake_case )

            snake_case__							:     List[Any]            =     self.prepare_image_inputs()

            snake_case__							:     Optional[int]            =     image_processor(_snake_case,			return_tensors='np' )
            snake_case__							:     List[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(1_2_3_4 )
            snake_case__							:     Optional[Any]            =     image_processor(
                _snake_case,			return_image_mask=_snake_case,			return_codebook_pixels=_snake_case,			return_tensors='np' )
            random.seed(1_2_3_4 )
            snake_case__							:     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        lowercase_       (	self  :  List[Any] )       ->List[str]:
            snake_case__							:     Union[str, Any]            =     self.get_image_processor()
            snake_case__							:     int            =     self.get_tokenizer()

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

            snake_case__							:     str            =     'lower newer'

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

            snake_case__							:     Any            =     tokenizer(_snake_case )

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






     def        lowercase_       (	self  :  Union[str, Any] )       ->Optional[Any]:
            snake_case__							:     int            =     self.get_image_processor()
            snake_case__							:     Optional[int]            =     self.get_tokenizer()

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

            snake_case__							:     str            =     'lower newer'
            snake_case__							:     Optional[int]            =     self.prepare_image_inputs()

            snake_case__							:     Optional[Any]            =     processor(text=_snake_case,			images=_snake_case )

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

            # add extra args
            snake_case__							:     Any            =     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        lowercase_       (	self  :  List[str] )       ->Tuple:
            snake_case__							:     List[str]            =     self.get_image_processor()
            snake_case__							:     Tuple            =     self.get_tokenizer()

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

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

            snake_case__							:     List[str]            =     processor.batch_decode(_snake_case )
            snake_case__							:     int            =     tokenizer.batch_decode(_snake_case )

            self.assertListEqual(_snake_case,			_snake_case )






     def        lowercase_       (	self  :  Optional[int] )       ->List[str]:
            snake_case__							:     Dict            =     self.get_image_processor()
            snake_case__							:     Any            =     self.get_tokenizer()

            snake_case__							:     Optional[int]            =     FlavaProcessor(tokenizer=_snake_case,			image_processor=_snake_case )

            snake_case__							:     str            =     'lower newer'
            snake_case__							:     str            =     self.prepare_image_inputs()

            snake_case__							:     Optional[int]            =     processor(text=_snake_case,			images=_snake_case )

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











def 	lowercase_							(A			:			list				):
       for i, _ in enumerate(A				):
              if (i % 2 == 1) == (nums[i - 1] > nums[i]):
                     snake_case__     ,    snake_case__							:     List[str]            =     nums[i], nums[i - 1]

       return nums


if __name__ == "__main__":
 print("Enter the array elements:")
 a_       :Tuple				  =				list(map(int, input().split()))
 print("The unsorted array is:")
 print(array)
 print("Array after Wiggle sort:")
 print(wiggle_sort(array))






