


"""simple docstring"""


class 				_SCREAMING_SNAKE_CASE				:
   """simple docstring"""



   def __init__(						self ,     lowerCamelCase__ = "" ,     lowerCamelCase__ = False						)      ->							None:
    # Mapping from the first character of the prefix of the node
    lowercase__       :       dict[str, RadixNode]				=						{}

    # A node will be a leaf if the tree contains its word
    lowercase__       :       Tuple				=						is_leaf

    lowercase__       :       List[Any]				=						prefix


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__						)      ->							tuple[str, str, str]:
    lowercase__       :       List[Any]				=						0
    for q, w in zip(self.prefix ,     lowerCamelCase__						):
     if q != w:
      break

     x += 1

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


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__						)      ->							None:
    for word in words:
     self.insert(lowerCamelCase__						)


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__						)      ->							None:
    # Case 1: If the word is the prefix of the node
    # Solution: We set the current node as leaf
    if self.prefix == word:
     lowercase__       :       Optional[int]				=						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:
     lowercase__       :       str				=						RadixNode(prefix=lowerCamelCase__ ,     is_leaf=lowerCamelCase__						)

    else:
     lowercase__       :       Any				=						self.nodes[word[0]]
     lowercase__	,       lowercase__	,       lowercase__       :       List[str]				=						incoming_node.match(
         lowerCamelCase__						)

     # 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(lowerCamelCase__						)

     # 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:
      lowercase__       :       List[Any]				=						remaining_prefix

      lowercase__       :       List[Any]				=						self.nodes[matching_string[0]]
      lowercase__       :       Tuple				=						RadixNode(lowerCamelCase__ ,     lowerCamelCase__						)
      lowercase__       :       Tuple				=						aux_node

      if remaining_word == "":
       lowercase__       :       List[str]				=						True
      else:
       self.nodes[matching_string[0]].insert(lowerCamelCase__						)


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__						)      ->							bool:
    lowercase__       :       int				=						self.nodes.get(word[0] ,     lowerCamelCase__						)
    if not incoming_node:
     return False
    else:
     lowercase__	,       lowercase__	,       lowercase__       :       Optional[int]				=						incoming_node.match(
         lowerCamelCase__						)
     # 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(lowerCamelCase__						)


   def 					UpperCAmelCase__(						self ,     lowerCamelCase__						)      ->							bool:
    lowercase__       :       Union[str, Any]				=						self.nodes.get(word[0] ,     lowerCamelCase__						)
    if not incoming_node:
     return False
    else:
     lowercase__	,       lowercase__	,       lowercase__       :       Union[str, Any]				=						incoming_node.match(
         lowerCamelCase__						)
     # 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(lowerCamelCase__						)
     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:
         lowercase__       :       List[str]				=						list(self.nodes.values()						)[0]
         lowercase__       :       List[Any]				=						merging_node.is_leaf
         self.prefix += merging_node.prefix
         lowercase__       :       str				=						merging_node.nodes
                    # If there is more than 1 edge, we just mark it as non-leaf
       elif len(incoming_node.nodes						) > 1:
        lowercase__       :       Union[str, Any]				=						False
       # If there is 1 edge, we merge it with its child
       else:
        lowercase__       :       List[str]				=						list(incoming_node.nodes.values()						)[0]
        lowercase__       :       Optional[int]				=						merging_node.is_leaf
        incoming_node.prefix += merging_node.prefix
        lowercase__       :       Optional[Any]				=						merging_node.nodes

       return True







   def 					UpperCAmelCase__(						self ,     lowerCamelCase__ = 0						)      ->							None:
    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    _lowerCamelCase   (     ):
 lowercase__       :       Union[str, Any]				=						"""banana bananas bandana band apple all beast""".split()
 lowercase__       :       List[str]				=						RadixNode()
 root.insert_many(lowerCamelCase__  )

 assert all(root.find(lowerCamelCase__  ) 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    _lowerCamelCase   (     ):
 assert test_trie()


def    _lowerCamelCase   (     ):
 lowercase__       :       Tuple				=						RadixNode()
 lowercase__       :       int				=						"""banana bananas bandanas bandana band apple all beast""".split()
 root.insert_many(lowerCamelCase__  )

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


if __name__ == "__main__":
   main()



"""simple docstring"""


import json
import os
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import ImageGPTImageProcessor

class 				_SCREAMING_SNAKE_CASE				(							unittest.TestCase  ):
   """simple docstring"""



   def __init__(						self ,     lowerCamelCase__ ,     lowerCamelCase__=7 ,     lowerCamelCase__=3 ,     lowerCamelCase__=18 ,     lowerCamelCase__=30 ,     lowerCamelCase__=400 ,     lowerCamelCase__=True ,     lowerCamelCase__=None ,     lowerCamelCase__=True ,     )      ->							int:
    lowercase__       :       int				=						size if size is not None else {"""height""": 18, """width""": 18}
    lowercase__       :       Optional[Any]				=						parent
    lowercase__       :       Dict				=						batch_size
    lowercase__       :       Union[str, Any]				=						num_channels
    lowercase__       :       Tuple				=						image_size
    lowercase__       :       str				=						min_resolution
    lowercase__       :       Optional[Any]				=						max_resolution
    lowercase__       :       Union[str, Any]				=						do_resize
    lowercase__       :       Dict				=						size
    lowercase__       :       Optional[Any]				=						do_normalize







   def 					UpperCAmelCase__(						self						)      ->							Any:
    return {
        # here we create 2 clusters for the sake of simplicity
        "clusters": np.asarray(
            [
                [0.8866_4436_3403_3203, 0.6618_8293_6954_4983, 0.3891_7464_0178_6804],
                [-0.6042_5591_4688_1104, -0.0_2295_0088_6052_8469, 0.5423_7973_6900_3296],
            ]						),
        "do_resize": self.do_resize,
        "size": self.size,
        "do_normalize": self.do_normalize,
    }

@require_torch
@require_vision
class 				_SCREAMING_SNAKE_CASE				(							__UpperCAmelCase     ,   unittest.TestCase  ):
   """simple docstring"""



   _a   :       List[Any]									=    ImageGPTImageProcessor if is_vision_available() else None


   def 					UpperCAmelCase__(						self						)      ->							Optional[int]:
    lowercase__       :       str				=						ImageGPTImageProcessingTester(self						)


   @property
   def 					UpperCAmelCase__(						self						)      ->							Optional[Any]:
    return self.image_processor_tester.prepare_image_processor_dict()


   def 					UpperCAmelCase__(						self						)      ->							int:
    lowercase__       :       List[Any]				=						self.image_processing_class(**self.image_processor_dict						)
    self.assertTrue(hasattr(lowerCamelCase__ ,     """clusters"""						)						)
    self.assertTrue(hasattr(lowerCamelCase__ ,     """do_resize"""						)						)
    self.assertTrue(hasattr(lowerCamelCase__ ,     """size"""						)						)
    self.assertTrue(hasattr(lowerCamelCase__ ,     """do_normalize"""						)						)


   def 					UpperCAmelCase__(						self						)      ->							Any:
    lowercase__       :       Optional[Any]				=						self.image_processing_class.from_dict(self.image_processor_dict						)
    self.assertEqual(image_processor.size ,     {"""height""": 18, """width""": 18}						)

    lowercase__       :       Union[str, Any]				=						self.image_processing_class.from_dict(self.image_processor_dict ,     size=42						)
    self.assertEqual(image_processor.size ,     {"""height""": 42, """width""": 42}						)


   def 					UpperCAmelCase__(						self						)      ->							List[str]:
    lowercase__       :       List[Any]				=						self.image_processing_class(**self.image_processor_dict						)
    lowercase__       :       Optional[Any]				=						json.loads(image_processor.to_json_string()						)
    for key, value in self.image_processor_dict.items():
     if key == "clusters":
      self.assertTrue(np.array_equal(lowerCamelCase__ ,     obj[key]						)						)
     else:
      self.assertEqual(obj[key] ,     lowerCamelCase__						)


   def 					UpperCAmelCase__(						self						)      ->							Dict:
    lowercase__       :       Optional[Any]				=						self.image_processing_class(**self.image_processor_dict						)

    with tempfile.TemporaryDirectory() as tmpdirname:
     lowercase__       :       Optional[int]				=						os.path.join(lowerCamelCase__ ,     """image_processor.json"""						)
     image_processor_first.to_json_file(lowerCamelCase__						)
     lowercase__       :       str				=						self.image_processing_class.from_json_file(lowerCamelCase__						).to_dict()

    lowercase__       :       Optional[int]				=						image_processor_first.to_dict()
    for key, value in image_processor_first.items():
     if key == "clusters":
      self.assertTrue(np.array_equal(lowerCamelCase__ ,     image_processor_second[key]						)						)
     else:
      self.assertEqual(image_processor_first[key] ,     lowerCamelCase__						)


   def 					UpperCAmelCase__(						self						)      ->							List[str]:
    lowercase__       :       List[Any]				=						self.image_processing_class(**self.image_processor_dict						)

    with tempfile.TemporaryDirectory() as tmpdirname:
     image_processor_first.save_pretrained(lowerCamelCase__						)
     lowercase__       :       Union[str, Any]				=						self.image_processing_class.from_pretrained(lowerCamelCase__						).to_dict()

    lowercase__       :       Dict				=						image_processor_first.to_dict()
    for key, value in image_processor_first.items():
     if key == "clusters":
      self.assertTrue(np.array_equal(lowerCamelCase__ ,     image_processor_second[key]						)						)
     else:
      self.assertEqual(image_processor_first[key] ,     lowerCamelCase__						)







   @unittest.skip("""ImageGPT requires clusters at initialization"""						)
   def 					UpperCAmelCase__(						self						)      ->							Dict:
    pass


def    _lowerCamelCase   (     ):
 lowercase__       :       Tuple				=						load_dataset("""hf-internal-testing/fixtures_image_utils"""       ,		split="""test"""  )

 lowercase__       :       Optional[int]				=						Image.open(dataset[4]["""file"""]  )
 lowercase__       :       Union[str, Any]				=						Image.open(dataset[5]["""file"""]  )

 lowercase__       :       Optional[int]				=						[imagea, imagea]

 return images






@require_vision
@require_torch
class 				_SCREAMING_SNAKE_CASE				(							unittest.TestCase  ):
   """simple docstring"""



   @slow
   def 					UpperCAmelCase__(						self						)      ->							str:
    lowercase__       :       Optional[int]				=						ImageGPTImageProcessor.from_pretrained("""openai/imagegpt-small"""						)

    lowercase__       :       int				=						prepare_images()

    # test non-batched
    lowercase__       :       int				=						image_processing(images[0] ,     return_tensors="""pt"""						)

    self.assertIsInstance(encoding.input_ids ,     torch.LongTensor						)
    self.assertEqual(encoding.input_ids.shape ,     (1, 1024)						)

    lowercase__       :       Any				=						[306, 191, 191]
    self.assertEqual(encoding.input_ids[0, :3].tolist() ,     lowerCamelCase__						)

    # test batched
    lowercase__       :       str				=						image_processing(lowerCamelCase__ ,     return_tensors="""pt"""						)

    self.assertIsInstance(encoding.input_ids ,     torch.LongTensor						)
    self.assertEqual(encoding.input_ids.shape ,     (2, 1024)						)

    lowercase__       :       Optional[int]				=						[303, 13, 13]
    self.assertEqual(encoding.input_ids[1, -3:].tolist() ,     lowerCamelCase__						)