




'''simple docstring'''





import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
  from PIL import Image

  from transformers import AutoProcessor, BertTokenizer, CLIPImageProcessor, GitProcessor, PreTrainedTokenizerFast




@require_vision
class      UpperCamelCase       (	unittest.TestCase       ):



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							Optional[Any]							       =      tempfile.mkdtemp()

      _a				:							Any							       =      CLIPImageProcessor()
      _a				:							Any							       =      BertTokenizer.from_pretrained(
          """hf-internal-testing/tiny-random-BertModel"""		,     model_input_names=["""input_ids""", """attention_mask"""] )

      _a				:							Optional[int]							       =      GitProcessor(__lowerCAmelCase		,     __lowerCAmelCase )

      processor.save_pretrained(self.tmpdirname )



  def     a_							(			self		,     **__lowerCAmelCase ):







      '''simple docstring'''


      return AutoProcessor.from_pretrained(self.tmpdirname		,     **__lowerCAmelCase ).tokenizer



  def     a_							(			self		,     **__lowerCAmelCase ):







      '''simple docstring'''


      return AutoProcessor.from_pretrained(self.tmpdirname		,     **__lowerCAmelCase ).image_processor



  def     a_							(			self ):







      '''simple docstring'''


      shutil.rmtree(self.tmpdirname )



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							Dict							       =      [np.random.randint(255		,     size=(3, 30, 400)		,     dtype=np.uinta )]

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

      return image_inputs



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							Any							       =      GitProcessor(tokenizer=self.get_tokenizer()		,     image_processor=self.get_image_processor() )
      processor.save_pretrained(self.tmpdirname )

      _a				:							Optional[int]							       =      self.get_tokenizer(bos_token="""(BOS)"""		,     eos_token="""(EOS)""" )
      _a				:							Dict							       =      self.get_image_processor(do_normalize=__lowerCAmelCase		,     padding_value=1.0 )

      _a				:							Dict							       =      GitProcessor.from_pretrained(
          self.tmpdirname		,     bos_token="""(BOS)"""		,     eos_token="""(EOS)"""		,     do_normalize=__lowerCAmelCase		,     padding_value=1.0 )

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

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



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							Any							       =      self.get_image_processor()
      _a				:							Tuple							       =      self.get_tokenizer()

      _a				:							int							       =      GitProcessor(tokenizer=__lowerCAmelCase		,     image_processor=__lowerCAmelCase )

      _a				:							Optional[Any]							       =      self.prepare_image_inputs()

      _a				:							List[str]							       =      image_processor(__lowerCAmelCase		,     return_tensors="""np""" )
      _a				:							Any							       =      processor(images=__lowerCAmelCase		,     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     a_							(			self ):







      '''simple docstring'''


      _a				:							Optional[int]							       =      self.get_image_processor()
      _a				:							str							       =      self.get_tokenizer()

      _a				:							List[Any]							       =      GitProcessor(tokenizer=__lowerCAmelCase		,     image_processor=__lowerCAmelCase )

      _a				:							Optional[Any]							       =      """lower newer"""

      _a				:							str							       =      processor(text=__lowerCAmelCase )

      _a				:							List[str]							       =      tokenizer(__lowerCAmelCase		,     return_token_type_ids=__lowerCAmelCase )

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



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							Optional[Any]							       =      self.get_image_processor()
      _a				:							Union[str, Any]							       =      self.get_tokenizer()

      _a				:							List[Any]							       =      GitProcessor(tokenizer=__lowerCAmelCase		,     image_processor=__lowerCAmelCase )

      _a				:							Optional[int]							       =      """lower newer"""
      _a				:							List[Any]							       =      self.prepare_image_inputs()

      _a				:							Any							       =      processor(text=__lowerCAmelCase		,     images=__lowerCAmelCase )

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

      # test if it raises when no input is passed
      with pytest.raises(__lowerCAmelCase ):
          processor()



  def     a_							(			self ):







      '''simple docstring'''


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

      _a				:							Dict							       =      GitProcessor(tokenizer=__lowerCAmelCase		,     image_processor=__lowerCAmelCase )

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

      _a				:							Union[str, Any]							       =      processor.batch_decode(__lowerCAmelCase )
      _a				:							Optional[Any]							       =      tokenizer.batch_decode(__lowerCAmelCase )

      self.assertListEqual(__lowerCAmelCase		,     __lowerCAmelCase )



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							Optional[Any]							       =      self.get_image_processor()
      _a				:							Union[str, Any]							       =      self.get_tokenizer()

      _a				:							str							       =      GitProcessor(tokenizer=__lowerCAmelCase		,     image_processor=__lowerCAmelCase )

      _a				:							Any							       =      """lower newer"""
      _a				:							int							       =      self.prepare_image_inputs()

      _a				:							int							       =      processor(text=__lowerCAmelCase		,     images=__lowerCAmelCase )

      # For now the processor supports only ['input_ids', 'attention_mask', 'pixel_values']
      self.assertListEqual(list(inputs.keys() )		,     ["""input_ids""", """attention_mask""", """pixel_values"""] )







"""simple docstring"""




from __future__ import annotations

from collections import defaultdict
from dataclasses import dataclass


@dataclass
class 	a__		:
   _lowerCamelCase       				=					42
   _lowerCamelCase       				=					None
   _lowerCamelCase       				=					None




def 	lowercase__  (      )  ->      TreeNode:






      '''simple docstring'''


      return TreeNode(3						,   TreeNode(9					)						,   TreeNode(20						,   TreeNode(15					)						,   TreeNode(7					)					)					)


def 	lowercase__  (      _UpperCAmelCase					)  ->      list[int]:






      '''simple docstring'''


      def depth_first_search(
          _UpperCAmelCase						,   _UpperCAmelCase						,   _UpperCAmelCase					) -> None:
            if not root:
                  return

            if depth == len(_UpperCAmelCase					):
                  right_view.append(root.val					)

            depth_first_search(root.right						,   depth + 1						,   _UpperCAmelCase					)
            depth_first_search(root.left						,   depth + 1						,   _UpperCAmelCase					)

      lowercase	:   list											= []
      if not root:
            return right_view

      depth_first_search(_UpperCAmelCase						,   0						,   _UpperCAmelCase					)
      return right_view


def 	lowercase__  (      _UpperCAmelCase					)  ->      list[int]:






      '''simple docstring'''


      def depth_first_search(
          _UpperCAmelCase						,   _UpperCAmelCase						,   _UpperCAmelCase					) -> None:
            if not root:
                  return

            if depth == len(_UpperCAmelCase					):
                  left_view.append(root.val					)

            depth_first_search(root.left						,   depth + 1						,   _UpperCAmelCase					)
            depth_first_search(root.right						,   depth + 1						,   _UpperCAmelCase					)

      lowercase	:   list											= []
      if not root:
            return left_view

      depth_first_search(_UpperCAmelCase						,   0						,   _UpperCAmelCase					)
      return left_view


def 	lowercase__  (      _UpperCAmelCase					)  ->      list[int]:






      '''simple docstring'''


      def breadth_first_search(_UpperCAmelCase						,   _UpperCAmelCase					) -> None:
            lowercase	:   List[str]											= [(root, 0)]
            lowercase	:   List[Any]											= defaultdict(_UpperCAmelCase					)

            while queue:
                  lowercase	:   Union[str, Any]											= queue.pop(0					)
                  lowercase						,     lowercase	:   str											= first

                  lookup[hd].append(node.val					)

                  if node.left:
                        queue.append((node.left, hd - 1)					)
                  if node.right:
                        queue.append((node.right, hd + 1)					)

            for pair in sorted(lookup.items()						,   key=lambda _UpperCAmelCase					: each[0]					):
                  top_view.append(pair[1][0]					)

      lowercase	:   list											= []
      if not root:
            return top_view

      breadth_first_search(_UpperCAmelCase						,   _UpperCAmelCase					)
      return top_view


def 	lowercase__  (      _UpperCAmelCase					)  ->      list[int]:






      '''simple docstring'''


      from collections import defaultdict

      def breadth_first_search(_UpperCAmelCase						,   _UpperCAmelCase					) -> None:
            lowercase	:   Any											= [(root, 0)]
            lowercase	:   Dict											= defaultdict(_UpperCAmelCase					)

            while queue:
                  lowercase	:   str											= queue.pop(0					)
                  lowercase						,     lowercase	:   List[Any]											= first
                  lookup[hd].append(node.val					)

                  if node.left:
                        queue.append((node.left, hd - 1)					)
                  if node.right:
                        queue.append((node.right, hd + 1)					)

            for pair in sorted(lookup.items()						,   key=lambda _UpperCAmelCase					: each[0]					):
                  bottom_view.append(pair[1][-1]					)

      lowercase	:   list											= []
      if not root:
            return bottom_view

      breadth_first_search(_UpperCAmelCase						,   _UpperCAmelCase					)
      return bottom_view


if __name__ == "__main__":
     import doctest

     doctest.testmod()
