


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       ):






        '''simple docstring'''






        snake_case :			int							=   tempfile.mkdtemp()

        # fmt: off
        snake_case :			List[Any]							=   ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''want''', '''##want''', '''##ed''', '''wa''', '''un''', '''runn''', '''##ing''', ''',''', '''low''', '''lowest''']
        # fmt: on
        snake_case :			List[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 :			Any							=   {
            '''image_mean''': FLAVA_IMAGE_MEAN,
            '''image_std''': FLAVA_IMAGE_STD,
            '''do_normalize''': True,
            '''do_resize''': True,
            '''size''': 2_24,
            '''do_center_crop''': True,
            '''crop_size''': 2_24,
            '''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''': 1_12,
            '''codebook_do_center_crop''': True,
            '''codebook_crop_size''': 1_12,
            '''codebook_do_map_pixels''': True,
            '''codebook_do_normalize''': True,
            '''codebook_image_mean''': FLAVA_CODEBOOK_MEAN,
            '''codebook_image_std''': FLAVA_CODEBOOK_STD,
        }

        snake_case :			Optional[Any]							=   os.path.join(self.tmpdirname						,    SCREAMING_SNAKE_CASE_       )
        with open(self.image_processor_file						,    '''w'''						,    encoding='''utf-8'''       ) as fp:
         json.dump(SCREAMING_SNAKE_CASE_						,    SCREAMING_SNAKE_CASE_       )

       def 					UpperCAmelCase					(      self						,    **a__       ):






        '''simple docstring'''






        return BertTokenizer.from_pretrained(self.tmpdirname						,    **SCREAMING_SNAKE_CASE_       )

       def 					UpperCAmelCase					(      self						,    **a__       ):






        '''simple docstring'''






        return BertTokenizerFast.from_pretrained(self.tmpdirname						,    **SCREAMING_SNAKE_CASE_       )

       def 					UpperCAmelCase					(      self						,    **a__       ):






        '''simple docstring'''






        return FlavaImageProcessor.from_pretrained(self.tmpdirname						,    **SCREAMING_SNAKE_CASE_       )

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        shutil.rmtree(self.tmpdirname       )

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			List[str]							=   [np.random.randint(2_55						,    size=(3, 30, 4_00)						,    dtype=np.uinta       )]

        snake_case :			Optional[Any]							=   [Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE_						,    0						,    -1       )       ) for x in image_inputs]

        return image_inputs

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			List[Any]							=   self.get_tokenizer()
        snake_case :			List[Any]							=   self.get_rust_tokenizer()
        snake_case :			int							=   self.get_image_processor()

        snake_case :			List[str]							=   FlavaProcessor(tokenizer=SCREAMING_SNAKE_CASE_						,    image_processor=SCREAMING_SNAKE_CASE_       )
        processor_slow.save_pretrained(self.tmpdirname       )
        snake_case :			List[str]							=   FlavaProcessor.from_pretrained(self.tmpdirname						,    use_fast=SCREAMING_SNAKE_CASE_       )

        snake_case :			Optional[Any]							=   FlavaProcessor(tokenizer=SCREAMING_SNAKE_CASE_						,    image_processor=SCREAMING_SNAKE_CASE_       )
        processor_fast.save_pretrained(self.tmpdirname       )
        snake_case :			Optional[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						,    SCREAMING_SNAKE_CASE_       )
        self.assertIsInstance(processor_fast.tokenizer						,    SCREAMING_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						,    SCREAMING_SNAKE_CASE_       )
        self.assertIsInstance(processor_fast.image_processor						,    SCREAMING_SNAKE_CASE_       )

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			List[Any]							=   FlavaProcessor(tokenizer=self.get_tokenizer()						,    image_processor=self.get_image_processor()       )
        processor.save_pretrained(self.tmpdirname       )

        snake_case :			Any							=   self.get_tokenizer(bos_token='''(BOS)'''						,    eos_token='''(EOS)'''       )
        snake_case :			Union[str, Any]							=   self.get_image_processor(do_normalize=SCREAMING_SNAKE_CASE_						,    padding_value=1.0       )

        snake_case :			List[str]							=   FlavaProcessor.from_pretrained(
            self.tmpdirname						,    bos_token='''(BOS)'''						,    eos_token='''(EOS)'''						,    do_normalize=SCREAMING_SNAKE_CASE_						,    padding_value=1.0       )

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

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

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			Tuple							=   self.get_image_processor()
        snake_case :			Optional[Any]							=   self.get_tokenizer()

        snake_case :			List[str]							=   FlavaProcessor(tokenizer=SCREAMING_SNAKE_CASE_						,    image_processor=SCREAMING_SNAKE_CASE_       )

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

        snake_case :			Optional[Any]							=   image_processor(SCREAMING_SNAKE_CASE_						,    return_tensors='''np'''       )
        snake_case :			str							=   processor(images=SCREAMING_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(12_34       )
        snake_case :			Optional[Any]							=   image_processor(
            SCREAMING_SNAKE_CASE_						,    return_image_mask=SCREAMING_SNAKE_CASE_						,    return_codebook_pixels=SCREAMING_SNAKE_CASE_						,    return_tensors='''np'''       )
        random.seed(12_34       )
        snake_case :			Union[str, Any]							=   processor(
            images=SCREAMING_SNAKE_CASE_						,    return_image_mask=SCREAMING_SNAKE_CASE_						,    return_codebook_pixels=SCREAMING_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 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			Optional[Any]							=   self.get_image_processor()
        snake_case :			Dict							=   self.get_tokenizer()

        snake_case :			Dict							=   FlavaProcessor(tokenizer=SCREAMING_SNAKE_CASE_						,    image_processor=SCREAMING_SNAKE_CASE_       )

        snake_case :			Union[str, Any]							=   '''lower newer'''

        snake_case :			Optional[Any]							=   processor(text=SCREAMING_SNAKE_CASE_       )

        snake_case :			List[str]							=   tokenizer(SCREAMING_SNAKE_CASE_       )

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

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






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

        snake_case :			List[Any]							=   FlavaProcessor(tokenizer=SCREAMING_SNAKE_CASE_						,    image_processor=SCREAMING_SNAKE_CASE_       )

        snake_case :			Optional[int]							=   '''lower newer'''
        snake_case :			Tuple							=   self.prepare_image_inputs()

        snake_case :			Optional[int]							=   processor(text=SCREAMING_SNAKE_CASE_						,    images=SCREAMING_SNAKE_CASE_       )

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

        # add extra args
        snake_case :			List[str]							=   processor(text=SCREAMING_SNAKE_CASE_						,    images=SCREAMING_SNAKE_CASE_						,    return_codebook_pixels=SCREAMING_SNAKE_CASE_						,    return_image_mask=SCREAMING_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(SCREAMING_SNAKE_CASE_       ):
         processor()

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			Optional[Any]							=   self.get_image_processor()
        snake_case :			Any							=   self.get_tokenizer()

        snake_case :			str							=   FlavaProcessor(tokenizer=SCREAMING_SNAKE_CASE_						,    image_processor=SCREAMING_SNAKE_CASE_       )

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

        snake_case :			str							=   processor.batch_decode(SCREAMING_SNAKE_CASE_       )
        snake_case :			Any							=   tokenizer.batch_decode(SCREAMING_SNAKE_CASE_       )

        self.assertListEqual(SCREAMING_SNAKE_CASE_						,    SCREAMING_SNAKE_CASE_       )



       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			Optional[Any]							=   self.get_image_processor()
        snake_case :			Tuple							=   self.get_tokenizer()

        snake_case :			List[str]							=   FlavaProcessor(tokenizer=SCREAMING_SNAKE_CASE_						,    image_processor=SCREAMING_SNAKE_CASE_       )

        snake_case :			Optional[int]							=   '''lower newer'''
        snake_case :			Optional[Any]							=   self.prepare_image_inputs()

        snake_case :			Dict							=   processor(text=SCREAMING_SNAKE_CASE_						,    images=SCREAMING_SNAKE_CASE_       )

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








from __future__ import annotations







class  _lowerCamelCase			:

     def __init__(  self							,       SCREAMING_SNAKE_CASE_ = 0    ):
            __snake_case						       =   value
            __snake_case						       =   None
            __snake_case						       =   None





def        __lowercase(							__snake_case				:		Node | None    ,__snake_case				:		Node | None					)       ->   Node | None:
       if treea is None:
              return treea
       if treea is None:
              return treea

       __snake_case						       =   treea.value + treea.value
       __snake_case						       =   merge_two_binary_trees(treea.left    ,treea.left					)
       __snake_case						       =   merge_two_binary_trees(treea.right    ,treea.right					)
       return treea
def        __lowercase(							__snake_case				:		Node | None					)       ->   None:
       if root:
              print(root.value					)
              print_preorder(root.left					)
              print_preorder(root.right					)


if __name__ == "__main__":
 lowerCamelCase_				:							int       =    Node(1)
 lowerCamelCase_				:							Tuple       =    Node(2)
 lowerCamelCase_				:							Tuple       =    Node(3)
 lowerCamelCase_				:							int       =    Node(4)

 lowerCamelCase_				:							Tuple       =    Node(2)
 lowerCamelCase_				:							List[Any]       =    Node(4)
 lowerCamelCase_				:							List[Any]       =    Node(6)
 lowerCamelCase_				:							Optional[Any]       =    Node(9)
 lowerCamelCase_				:							List[str]       =    Node(5)

 print("Tree1 is: ")
 print_preorder(treea)
 print("Tree2 is: ")
 print_preorder(treea)
 lowerCamelCase_				:							int       =    merge_two_binary_trees(treea, treea)
 print("Merged Tree is: ")
 print_preorder(merged_tree)
