





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, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast







@require_vision
class   __lowerCAmelCase			(					unittest.TestCase  ):





   def 	_snake_case ( self      )				->  Tuple:
      """simple docstring"""





      a__	:						Tuple      		=  tempfile.mkdtemp()

      a__	:						Optional[int]      		=  BlipImageProcessor()
      a__	:						Any      		=  GPTaTokenizer.from_pretrained("hf-internal-testing/tiny-random-GPT2Model"      )

      a__	:						Tuple      		=  BlipaProcessor(snake_case					,			snake_case      )

      processor.save_pretrained(self.tmpdirname      )





   def 	_snake_case ( self					,			**snake_case      )				->  List[Any]:
      """simple docstring"""





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





   def 	_snake_case ( self					,			**snake_case      )				->  List[str]:
      """simple docstring"""





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





   def 	_snake_case ( self      )				->  Any:
      """simple docstring"""





      shutil.rmtree(self.tmpdirname      )





   def 	_snake_case ( self      )				->  Any:
      """simple docstring"""





      a__	:						Union[str, Any]      		=  [np.random.randint(255					,			size=(3, 30, 400)					,			dtype=np.uinta      )]

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

      return image_inputs





   def 	_snake_case ( self      )				->  List[str]:
      """simple docstring"""





      a__	:						Tuple      		=  BlipaProcessor(tokenizer=self.get_tokenizer()					,			image_processor=self.get_image_processor()      )
      processor.save_pretrained(self.tmpdirname      )

      a__	:						Tuple      		=  self.get_tokenizer(bos_token="(BOS)"					,			eos_token="(EOS)"      )
      a__	:						int      		=  self.get_image_processor(do_normalize=snake_case					,			padding_value=1.0      )

      a__	:						Optional[int]      		=  BlipaProcessor.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      )				->  Dict:
      """simple docstring"""





      a__	:						Dict      		=  self.get_image_processor()
      a__	:						Any      		=  self.get_tokenizer()

      a__	:						str      		=  BlipaProcessor(tokenizer=snake_case					,			image_processor=snake_case      )

      a__	:						str      		=  self.prepare_image_inputs()

      a__	:						Dict      		=  image_processor(snake_case					,			return_tensors="np"      )
      a__	:						Optional[int]      		=  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      )





   def 	_snake_case ( self      )				->  Optional[Any]:
      """simple docstring"""





      a__	:						Union[str, Any]      		=  self.get_image_processor()
      a__	:						int      		=  self.get_tokenizer()

      a__	:						Union[str, Any]      		=  BlipaProcessor(tokenizer=snake_case					,			image_processor=snake_case      )

      a__	:						Optional[int]      		=  "lower newer"

      a__	:						Dict      		=  processor(text=snake_case      )

      a__	:						str      		=  tokenizer(snake_case					,			return_token_type_ids=snake_case      )

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





   def 	_snake_case ( self      )				->  Any:
      """simple docstring"""





      a__	:						Union[str, Any]      		=  self.get_image_processor()
      a__	:						Dict      		=  self.get_tokenizer()

      a__	:						Union[str, Any]      		=  BlipaProcessor(tokenizer=snake_case					,			image_processor=snake_case      )

      a__	:						List[Any]      		=  "lower newer"
      a__	:						Any      		=  self.prepare_image_inputs()

      a__	:						Any      		=  processor(text=snake_case					,			images=snake_case      )

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

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





   def 	_snake_case ( self      )				->  int:
      """simple docstring"""





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

      a__	:						List[Any]      		=  BlipaProcessor(tokenizer=snake_case					,			image_processor=snake_case      )

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

      a__	:						List[Any]      		=  processor.batch_decode(snake_case      )
      a__	:						int      		=  tokenizer.batch_decode(snake_case      )

      self.assertListEqual(snake_case					,			snake_case      )





   def 	_snake_case ( self      )				->  List[str]:
      """simple docstring"""





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

      a__	:						Optional[Any]      		=  BlipaProcessor(tokenizer=snake_case					,			image_processor=snake_case      )

      a__	:						Tuple      		=  "lower newer"
      a__	:						Dict      		=  self.prepare_image_inputs()

      a__	:						str      		=  processor(text=snake_case					,			images=snake_case      )

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








from __future__ import annotations

from random import random







class   __lowerCAmelCase			:





   def __init__( self					,			snake_case = None      )				->  Any:
      """simple docstring"""





      a__	:						Optional[int]      		=  value
      a__	:						Tuple      		=  random()
      a__	:						Node | None      		=  None
      a__	:						Node | None      		=  None





   def __repr__( self      )				->  str:
      """simple docstring"""





      from pprint import pformat

      if self.left is None and self.right is None:
         return F"""'{self.value}: {self.prior:.5}'"""
      else:
         return pformat(
             {F"""{self.value}: {self.prior:.5}""": (self.left, self.right)}					,			indent=1      )





   def __str__( self      )				->  str:
      """simple docstring"""





      a__	:						List[Any]      		=  str(self.value      ) + " "
      a__	:						List[str]      		=  str(self.left or ""      )
      a__	:						Tuple      		=  str(self.right or ""      )
      return value + left + right







def       _A  (      lowerCamelCase							, lowerCamelCase   ):
   if root is None:  # None tree is split into 2 Nones
      return None, None
   elif root.value is None:
      return None, None
   else:
      if value < root.value:
         a__		,     a__	:						Dict      		=  split(root.left							, lowerCamelCase   )
         return left, root
      else:
         a__		,     a__	:						int      		=  split(root.right							, lowerCamelCase   )
         return root, right






def       _A  (      lowerCamelCase							, lowerCamelCase   ):
   if (not left) or (not right):  # If one node is None, return the other
      return left or right
   elif left.prior < right.prior:
      a__	:						List[Any]      		=  merge(left.right							, lowerCamelCase   )
      return left
   else:
      a__	:						int      		=  merge(lowerCamelCase							, right.left   )
      return right






def       _A  (      lowerCamelCase							, lowerCamelCase   ):
   a__	:						Any      		=  Node(lowerCamelCase   )
   a__		,     a__	:						List[str]      		=  split(lowerCamelCase							, lowerCamelCase   )
   return merge(merge(lowerCamelCase							, lowerCamelCase   )							, lowerCamelCase   )






def       _A  (      lowerCamelCase							, lowerCamelCase   ):
   a__		,     a__	:						Optional[int]      		=  split(lowerCamelCase							, value - 1   )
   a__		,     a__	:						Tuple      		=  split(lowerCamelCase							, lowerCamelCase   )
   return merge(lowerCamelCase							, lowerCamelCase   )






def       _A  (      lowerCamelCase   ):
   if not root:  # None
      return
   else:
      inorder(root.left   )
      print(root.value							, end=","   )
      inorder(root.right   )






def       _A  (      lowerCamelCase							, lowerCamelCase   ):
   for arg in args.split():
      if arg[0] == "+":
         a__	:						int      		=  insert(lowerCamelCase							, int(arg[1:]   )   )

      elif arg[0] == "-":
         a__	:						Union[str, Any]      		=  erase(lowerCamelCase							, int(arg[1:]   )   )

      else:
         print("Unknown command"   )

   return root






def       _A  (      ):
   a__	:						List[str]      		=  None
   print(
       "enter numbers to create a tree, + value to add value into treap, "
       "- value to erase all nodes with value. 'q' to quit. "   )

   a__	:						int      		=  input()
   while args != "q":
      a__	:						Union[str, Any]      		=  interact_treap(lowerCamelCase							, lowerCamelCase   )
      print(lowerCamelCase   )
      a__	:						Optional[Any]      		=  input()

   print("good by!"   )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()

