"""simple docstring"""




import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip 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 CLIPSegProcessor, ViTImageProcessor
@require_vision
class 		__A							(				unittest.TestCase	):






     def 					__A						(	self						):
       _lowerCAmelCase		: List[Any]						    =  tempfile.mkdtemp()

       # fmt: off
       _lowerCAmelCase		: Union[str, Any]						    =  ['l', 'o', 'w', 'e', 'r', 's', 't', 'i', 'd', 'n', 'lo', 'l</w>', 'w</w>', 'r</w>', 't</w>', 'low</w>', 'er</w>', 'lowest</w>', 'newer</w>', 'wider', '<unk>', '<|startoftext|>', '<|endoftext|>']
       # fmt: on
       _lowerCAmelCase		: Union[str, Any]						    =  dict(zip(UpperCamelCase__       ,  range(len(UpperCamelCase__						)						)						)						)
       _lowerCAmelCase		: Any						    =  ['#version: 0.2', 'l o', 'lo w</w>', 'e r</w>', '']
       _lowerCAmelCase		: int						    =  {'unk_token': '<unk>'}

       _lowerCAmelCase		: Tuple						    =  os.path.join(self.tmpdirname       ,  VOCAB_FILES_NAMES["""vocab_file"""]						)
       _lowerCAmelCase		: Optional[Any]						    =  os.path.join(self.tmpdirname       ,  VOCAB_FILES_NAMES["""merges_file"""]						)
       with open(self.vocab_file       ,  """w"""       ,  encoding="""utf-8"""						) as fp:
         fp.write(json.dumps(UpperCamelCase__						) + """\n"""						)
       with open(self.merges_file       ,  """w"""       ,  encoding="""utf-8"""						) as fp:
         fp.write("""\n""".join(UpperCamelCase__						)						)

       _lowerCAmelCase		: Dict						    =  {
           'do_resize': True,
           'size': 20,
           'do_center_crop': True,
           'crop_size': 18,
           'do_normalize': True,
           'image_mean': [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],
           'image_std': [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],
       }
       _lowerCAmelCase		: List[str]						    =  os.path.join(self.tmpdirname       ,  UpperCamelCase__						)
       with open(self.image_processor_file       ,  """w"""       ,  encoding="""utf-8"""						) as fp:
         json.dump(UpperCamelCase__       ,  UpperCamelCase__						)






     def 					__A						(	self       ,  **a__						):
       return CLIPTokenizer.from_pretrained(self.tmpdirname       ,  **UpperCamelCase__						)






     def 					__A						(	self       ,  **a__						):
       return CLIPTokenizerFast.from_pretrained(self.tmpdirname       ,  **UpperCamelCase__						)






     def 					__A						(	self       ,  **a__						):
       return ViTImageProcessor.from_pretrained(self.tmpdirname       ,  **UpperCamelCase__						)






     def 					__A						(	self						):
       shutil.rmtree(self.tmpdirname						)






     def 					__A						(	self						):
       _lowerCAmelCase		: Tuple						    =  [np.random.randint(255       ,  size=(3, 30, 400)       ,  dtype=np.uinta						)]

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

       return image_inputs






     def 					__A						(	self						):
       _lowerCAmelCase		: str						    =  self.get_tokenizer()
       _lowerCAmelCase		: str						    =  self.get_rust_tokenizer()
       _lowerCAmelCase		: Union[str, Any]						    =  self.get_image_processor()

       _lowerCAmelCase		: str						    =  CLIPSegProcessor(tokenizer=UpperCamelCase__       ,  image_processor=UpperCamelCase__						)
       processor_slow.save_pretrained(self.tmpdirname						)
       _lowerCAmelCase		: Tuple						    =  CLIPSegProcessor.from_pretrained(self.tmpdirname       ,  use_fast=UpperCamelCase__						)

       _lowerCAmelCase		: List[str]						    =  CLIPSegProcessor(tokenizer=UpperCamelCase__       ,  image_processor=UpperCamelCase__						)
       processor_fast.save_pretrained(self.tmpdirname						)
       _lowerCAmelCase		: List[Any]						    =  CLIPSegProcessor.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       ,  UpperCamelCase__						)
       self.assertIsInstance(processor_fast.tokenizer       ,  UpperCamelCase__						)

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






     def 					__A						(	self						):
       _lowerCAmelCase		: str						    =  CLIPSegProcessor(tokenizer=self.get_tokenizer()       ,  image_processor=self.get_image_processor()						)
       processor.save_pretrained(self.tmpdirname						)

       _lowerCAmelCase		: str						    =  self.get_tokenizer(bos_token="""(BOS)"""       ,  eos_token="""(EOS)"""						)
       _lowerCAmelCase		: List[Any]						    =  self.get_image_processor(do_normalize=UpperCamelCase__       ,  padding_value=1.0						)

       _lowerCAmelCase		: int						    =  CLIPSegProcessor.from_pretrained(
           self.tmpdirname       ,  bos_token="""(BOS)"""       ,  eos_token="""(EOS)"""       ,  do_normalize=UpperCamelCase__       ,  padding_value=1.0						)

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

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






     def 					__A						(	self						):
       _lowerCAmelCase		: Optional[Any]						    =  self.get_image_processor()
       _lowerCAmelCase		: Any						    =  self.get_tokenizer()

       _lowerCAmelCase		: int						    =  CLIPSegProcessor(tokenizer=UpperCamelCase__       ,  image_processor=UpperCamelCase__						)

       _lowerCAmelCase		: str						    =  self.prepare_image_inputs()

       _lowerCAmelCase		: Optional[int]						    =  image_processor(UpperCamelCase__       ,  return_tensors="""np"""						)
       _lowerCAmelCase		: Union[str, Any]						    =  processor(images=UpperCamelCase__       ,  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						):
       _lowerCAmelCase		: Optional[int]						    =  self.get_image_processor()
       _lowerCAmelCase		: Optional[int]						    =  self.get_tokenizer()

       _lowerCAmelCase		: List[str]						    =  CLIPSegProcessor(tokenizer=UpperCamelCase__       ,  image_processor=UpperCamelCase__						)

       _lowerCAmelCase		: Tuple						    =  'lower newer'

       _lowerCAmelCase		: Dict						    =  processor(text=UpperCamelCase__						)

       _lowerCAmelCase		: List[str]						    =  tokenizer(UpperCamelCase__						)

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






     def 					__A						(	self						):
       _lowerCAmelCase		: List[str]						    =  self.get_image_processor()
       _lowerCAmelCase		: Union[str, Any]						    =  self.get_tokenizer()

       _lowerCAmelCase		: str						    =  CLIPSegProcessor(tokenizer=UpperCamelCase__       ,  image_processor=UpperCamelCase__						)

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

       _lowerCAmelCase		: Optional[Any]						    =  processor(text=UpperCamelCase__       ,  images=UpperCamelCase__						)

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

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






     def 					__A						(	self						):
       _lowerCAmelCase		: Union[str, Any]						    =  self.get_image_processor()
       _lowerCAmelCase		: Optional[Any]						    =  self.get_tokenizer()

       _lowerCAmelCase		: Dict						    =  CLIPSegProcessor(tokenizer=UpperCamelCase__       ,  image_processor=UpperCamelCase__						)

       _lowerCAmelCase		: Optional[Any]						    =  self.prepare_image_inputs()
       _lowerCAmelCase		: List[Any]						    =  self.prepare_image_inputs()

       _lowerCAmelCase		: Optional[Any]						    =  processor(images=UpperCamelCase__       ,  visual_prompt=UpperCamelCase__						)

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

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






     def 					__A						(	self						):
       _lowerCAmelCase		: List[str]						    =  self.get_image_processor()
       _lowerCAmelCase		: Any						    =  self.get_tokenizer()

       _lowerCAmelCase		: List[Any]						    =  CLIPSegProcessor(tokenizer=UpperCamelCase__       ,  image_processor=UpperCamelCase__						)

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

       _lowerCAmelCase		: Union[str, Any]						    =  processor.batch_decode(UpperCamelCase__						)
       _lowerCAmelCase		: List[Any]						    =  tokenizer.batch_decode(UpperCamelCase__						)

       self.assertListEqual(UpperCamelCase__       ,  UpperCamelCase__						)





"""simple docstring"""




from __future__ import annotations

from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class 		__A							:
     _UpperCamelCase							:    int
     _UpperCamelCase							:    Node | None	      =	None
     _UpperCamelCase							:    Node | None	      =	None







def 							SCREAMING_SNAKE_CASE  (    ) ->   Node | None:
  _lowerCAmelCase		: Tuple						    =  Node(1			)
  _lowerCAmelCase		: int						    =  Node(2			)
  _lowerCAmelCase		: int						    =  Node(3			)
  _lowerCAmelCase		: Any						    =  Node(4			)
  _lowerCAmelCase		: Dict						    =  Node(5			)
  return tree





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Node | None			) ->   list[int]:
  return [root.data, *preorder(root.left			), *preorder(root.right			)] if root else []





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Node | None			) ->   list[int]:
  return postorder(root.left			) + postorder(root.right			) + [root.data] if root else []





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Node | None			) ->   list[int]:
  return [*inorder(root.left			), root.data, *inorder(root.right			)] if root else []





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Node | None			) ->   int:
  return (max(height(root.left			)    ,height(root.right			)			) + 1) if root else 0





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Node | None			) ->   Sequence[Node | None]:
  _lowerCAmelCase		: list[Any]						    =  []

  if root is None:
    return output

  _lowerCAmelCase		: Union[str, Any]						    =  deque([root]			)

  while process_queue:
    _lowerCAmelCase		: Optional[Any]						    =  process_queue.popleft()
    output.append(node.data			)

    if node.left:
      process_queue.append(node.left			)
    if node.right:
      process_queue.append(node.right			)
  return output





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Node | None    ,_lowerCamelCase		: int			) ->   Sequence[Node | None]:
  _lowerCAmelCase		: list[Any]						    =  []

  def populate_output(_lowerCamelCase		: Node | None    ,_lowerCamelCase		: int			) -> None:
    if not root:
      return
    if level == 1:
      output.append(root.data			)
    elif level > 1:
      populate_output(root.left    ,level - 1			)
      populate_output(root.right    ,level - 1			)

  populate_output(_lowerCamelCase    ,_lowerCamelCase			)
  return output





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Node | None    ,_lowerCamelCase		: int			) ->   Sequence[Node | None]:
  _lowerCAmelCase		: list[Any]						    =  []

  def populate_output(_lowerCamelCase		: Node | None    ,_lowerCamelCase		: int			) -> None:
    if root is None:
      return
    if level == 1:
      output.append(root.data			)
    elif level > 1:
      populate_output(root.right    ,level - 1			)
      populate_output(root.left    ,level - 1			)

  populate_output(_lowerCamelCase    ,_lowerCamelCase			)
  return output





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Node | None			) ->   Sequence[Node | None] | list[Any]:
  if root is None:
    return []

  _lowerCAmelCase		: list[Sequence[Node | None]]						    =  []

  _lowerCAmelCase		: Tuple						    =  0
  _lowerCAmelCase		: Dict						    =  height(_lowerCamelCase			)

  for h in range(1    ,height_tree + 1			):
    if not flag:
      output.append(get_nodes_from_left_to_right(_lowerCamelCase    ,_lowerCamelCase			)			)
      _lowerCAmelCase		: Any						    =  1
    else:
      output.append(get_nodes_from_right_to_left(_lowerCamelCase    ,_lowerCamelCase			)			)
      _lowerCAmelCase		: Optional[int]						    =  0

  return output





def 							SCREAMING_SNAKE_CASE  (    ) ->   None:  # Main function for testing.
  _lowerCAmelCase		: int						    =  make_tree()
  print(f"In-order Traversal: {inorder(_lowerCamelCase			)}"			)
  print(f"Pre-order Traversal: {preorder(_lowerCamelCase			)}"			)
  print(f"Post-order Traversal: {postorder(_lowerCamelCase			)}"    ,"""\n"""			)

  print(f"Height of Tree: {height(_lowerCamelCase			)}"    ,"""\n"""			)

  print("""Complete Level Order Traversal: """			)
  print(level_order(_lowerCamelCase			)    ,"""\n"""			)

  print("""Level-wise order Traversal: """			)

  for level in range(1    ,height(_lowerCamelCase			) + 1			):
    print(f"Level {level}:"    ,get_nodes_from_left_to_right(_lowerCamelCase    ,level=_lowerCamelCase			)			)

  print("""\nZigZag order Traversal: """			)
  print(zigzag(_lowerCamelCase			)			)


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     main()




