
class      __A:

  """simple docstring"""







  def __init__(self	,      SCREAMING_SNAKE_CASE_ = ""	,      SCREAMING_SNAKE_CASE_ = False						):
       # Mapping from the first character of the prefix of the node
       UpperCamelCase__				=    {}

       # A node will be a leaf if the tree contains its word
       UpperCamelCase__				=    is_leaf

       UpperCamelCase__				=    prefix







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=    0
       for q, w in zip(self.prefix	,      SCREAMING_SNAKE_CASE_						):
            if q != w:
                 break

            x += 1

       return self.prefix[:x], self.prefix[x:], word[x:]







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_						):
       for word in words:
            self.insert(SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_						):
       # Case 1: If the word is the prefix of the node
       # Solution: We set the current node as leaf
       if self.prefix == word:
            UpperCamelCase__				=    True

       # Case 2: The node has no edges that have a prefix to the word
       # Solution: We create an edge from the current node to a new one
       # containing the word
       elif word[0] not in self.nodes:
            UpperCamelCase__				=    RadixNode(prefix=SCREAMING_SNAKE_CASE_	,      is_leaf=SCREAMING_SNAKE_CASE_						)

       else:
            UpperCamelCase__				=    self.nodes[word[0]]
            UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__				=    incoming_node.match(
                SCREAMING_SNAKE_CASE_						)

            # Case 3: The node prefix is equal to the matching
            # Solution: We insert remaining word on the next node
            if remaining_prefix == "":
                 self.nodes[matching_string[0]].insert(SCREAMING_SNAKE_CASE_						)

            # Case 4: The word is greater equal to the matching
            # Solution: Create a node in between both nodes, change
            # prefixes and add the new node for the remaining word
            else:
                 UpperCamelCase__				=    remaining_prefix

                 UpperCamelCase__				=    self.nodes[matching_string[0]]
                 UpperCamelCase__				=    RadixNode(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)
                 UpperCamelCase__				=    aux_node

                 if remaining_word == "":
                      UpperCamelCase__				=    True
                 else:
                      self.nodes[matching_string[0]].insert(SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=    self.nodes.get(word[0]	,      SCREAMING_SNAKE_CASE_						)
       if not incoming_node:
            return False
       else:
            UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__				=    incoming_node.match(
                SCREAMING_SNAKE_CASE_						)
            # If there is remaining prefix, the word can't be on the tree
            if remaining_prefix != "":
                 return False
            # This applies when the word and the prefix are equal
            elif remaining_word == "":
                 return incoming_node.is_leaf
            # We have word remaining so we check the next node
            else:
                 return incoming_node.find(SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_						):
       UpperCamelCase__				=    self.nodes.get(word[0]	,      SCREAMING_SNAKE_CASE_						)
       if not incoming_node:
            return False
       else:
            UpperCamelCase__		,	UpperCamelCase__		,	UpperCamelCase__				=    incoming_node.match(
                SCREAMING_SNAKE_CASE_						)
            # If there is remaining prefix, the word can't be on the tree
            if remaining_prefix != "":
                 return False
            # We have word remaining so we check the next node
            elif remaining_word != "":
                 return incoming_node.delete(SCREAMING_SNAKE_CASE_						)
            else:
                 # If it is not a leaf, we don't have to delete
                 if not incoming_node.is_leaf:
                      return False
                 else:
                      # We delete the nodes if no edges go from it
                      if len(incoming_node.nodes						) == 0:
                           del self.nodes[word[0]]
                           # We merge the current node with its only child
                           if len(self.nodes						) == 1 and not self.is_leaf:
                                UpperCamelCase__				=    list(self.nodes.values()						)[0]
                                UpperCamelCase__				=    merging_node.is_leaf
                                self.prefix += merging_node.prefix
                                UpperCamelCase__				=    merging_node.nodes
                    # If there is more than 1 edge, we just mark it as non-leaf
                      elif len(incoming_node.nodes						) > 1:
                           UpperCamelCase__				=    False
                      # If there is 1 edge, we merge it with its child
                      else:
                           UpperCamelCase__				=    list(incoming_node.nodes.values()						)[0]
                           UpperCamelCase__				=    merging_node.is_leaf
                           incoming_node.prefix += merging_node.prefix
                           UpperCamelCase__				=    merging_node.nodes

                      return True







  def 	UpperCAmelCase_ (self	,      SCREAMING_SNAKE_CASE_ = 0						):
       if self.prefix != "":
            print("""-""" * height	,      self.prefix	,      """  (leaf)""" if self.is_leaf else """"""						)

       for value in self.nodes.values():
            value.print_tree(height + 1						)






def 						__magic_name__			(   ):



     '''simple docstring'''




     UpperCamelCase__				=    """banana bananas bandana band apple all beast""".split()
     UpperCamelCase__				=    RadixNode()
     root.insert_many(__a						)

     assert all(root.find(__a						) for word in words						)
     assert not root.find("""bandanas"""						)
     assert not root.find("""apps"""						)
     root.delete("""all"""						)
     assert not root.find("""all"""						)
     root.delete("""banana"""						)
     assert not root.find("""banana"""						)
     assert root.find("""bananas"""						)

     return True






def 						__magic_name__			(   ):



     '''simple docstring'''




     assert test_trie()






def 						__magic_name__			(   ):



     '''simple docstring'''




     UpperCamelCase__				=    RadixNode()
     UpperCamelCase__				=    """banana bananas bandanas bandana band apple all beast""".split()
     root.insert_many(__a						)

     print("""Words:"""	,		__a						)
     print("""Tree:"""						)
     root.print_tree()


if __name__ == "__main__":
    main()







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,
        BertTokenizerFast,
        BlipImageProcessor,
        GPTaTokenizer,
        InstructBlipProcessor,
        PreTrainedTokenizerFast,
    )




@require_vision
class      __A(    unittest.TestCase			):

  """simple docstring"""







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    tempfile.mkdtemp()

       UpperCamelCase__				=    BlipImageProcessor()
       UpperCamelCase__				=    GPTaTokenizer.from_pretrained("""hf-internal-testing/tiny-random-GPT2Model"""						)
       UpperCamelCase__				=    BertTokenizerFast.from_pretrained("""hf-internal-testing/tiny-random-bert"""						)

       UpperCamelCase__				=    InstructBlipProcessor(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)

       processor.save_pretrained(self.tmpdirname						)







  def 	UpperCAmelCase_ (self	,      **SCREAMING_SNAKE_CASE_						):
       return AutoProcessor.from_pretrained(self.tmpdirname	,      **SCREAMING_SNAKE_CASE_						).tokenizer







  def 	UpperCAmelCase_ (self	,      **SCREAMING_SNAKE_CASE_						):
       return AutoProcessor.from_pretrained(self.tmpdirname	,      **SCREAMING_SNAKE_CASE_						).image_processor







  def 	UpperCAmelCase_ (self	,      **SCREAMING_SNAKE_CASE_						):
       return AutoProcessor.from_pretrained(self.tmpdirname	,      **SCREAMING_SNAKE_CASE_						).qformer_tokenizer







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







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    [np.random.randint(2_55	,      size=(3, 30, 4_00)	,      dtype=np.uinta						)]

       UpperCamelCase__				=    [Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE_	,      0	,      -1						)						) for x in image_inputs]

       return image_inputs







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    InstructBlipProcessor(
           tokenizer=self.get_tokenizer()	,      image_processor=self.get_image_processor()	,      qformer_tokenizer=self.get_qformer_tokenizer()	,      )
       processor.save_pretrained(self.tmpdirname						)

       UpperCamelCase__				=    self.get_tokenizer(bos_token="""(BOS)"""	,      eos_token="""(EOS)"""						)
       UpperCamelCase__				=    self.get_image_processor(do_normalize=SCREAMING_SNAKE_CASE_	,      padding_value=1.0						)

       UpperCamelCase__				=    InstructBlipProcessor.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_						)
       self.assertIsInstance(processor.qformer_tokenizer	,      SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self.get_image_processor()
       UpperCamelCase__				=    self.get_tokenizer()
       UpperCamelCase__				=    self.get_qformer_tokenizer()

       UpperCamelCase__				=    InstructBlipProcessor(
           tokenizer=SCREAMING_SNAKE_CASE_	,      image_processor=SCREAMING_SNAKE_CASE_	,      qformer_tokenizer=SCREAMING_SNAKE_CASE_						)

       UpperCamelCase__				=    self.prepare_image_inputs()

       UpperCamelCase__				=    image_processor(SCREAMING_SNAKE_CASE_	,      return_tensors="""np"""						)
       UpperCamelCase__				=    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						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self.get_image_processor()
       UpperCamelCase__				=    self.get_tokenizer()
       UpperCamelCase__				=    self.get_qformer_tokenizer()

       UpperCamelCase__				=    InstructBlipProcessor(
           tokenizer=SCREAMING_SNAKE_CASE_	,      image_processor=SCREAMING_SNAKE_CASE_	,      qformer_tokenizer=SCREAMING_SNAKE_CASE_						)

       UpperCamelCase__				=    """lower newer"""

       UpperCamelCase__				=    processor(text=SCREAMING_SNAKE_CASE_						)

       UpperCamelCase__				=    tokenizer(SCREAMING_SNAKE_CASE_	,      return_token_type_ids=SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    qformer_tokenizer(SCREAMING_SNAKE_CASE_	,      return_token_type_ids=SCREAMING_SNAKE_CASE_						)

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

       for key in encoded_tokens_qformer.keys():
            self.assertListEqual(encoded_tokens_qformer[key]	,      encoded_processor["""qformer_""" + key]						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self.get_image_processor()
       UpperCamelCase__				=    self.get_tokenizer()
       UpperCamelCase__				=    self.get_qformer_tokenizer()

       UpperCamelCase__				=    InstructBlipProcessor(
           tokenizer=SCREAMING_SNAKE_CASE_	,      image_processor=SCREAMING_SNAKE_CASE_	,      qformer_tokenizer=SCREAMING_SNAKE_CASE_						)

       UpperCamelCase__				=    """lower newer"""
       UpperCamelCase__				=    self.prepare_image_inputs()

       UpperCamelCase__				=    processor(text=SCREAMING_SNAKE_CASE_	,      images=SCREAMING_SNAKE_CASE_						)

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

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







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self.get_image_processor()
       UpperCamelCase__				=    self.get_tokenizer()
       UpperCamelCase__				=    self.get_qformer_tokenizer()

       UpperCamelCase__				=    InstructBlipProcessor(
           tokenizer=SCREAMING_SNAKE_CASE_	,      image_processor=SCREAMING_SNAKE_CASE_	,      qformer_tokenizer=SCREAMING_SNAKE_CASE_						)

       UpperCamelCase__				=    [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

       UpperCamelCase__				=    processor.batch_decode(SCREAMING_SNAKE_CASE_						)
       UpperCamelCase__				=    tokenizer.batch_decode(SCREAMING_SNAKE_CASE_						)

       self.assertListEqual(SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self.get_image_processor()
       UpperCamelCase__				=    self.get_tokenizer()
       UpperCamelCase__				=    self.get_qformer_tokenizer()

       UpperCamelCase__				=    InstructBlipProcessor(
           tokenizer=SCREAMING_SNAKE_CASE_	,      image_processor=SCREAMING_SNAKE_CASE_	,      qformer_tokenizer=SCREAMING_SNAKE_CASE_						)

       UpperCamelCase__				=    """lower newer"""
       UpperCamelCase__				=    self.prepare_image_inputs()

       UpperCamelCase__				=    processor(text=SCREAMING_SNAKE_CASE_	,      images=SCREAMING_SNAKE_CASE_						)

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





