


'''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     lowercase__  (						unittest.TestCase					):





							'''simple docstring'''


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






							def UpperCAmelCase_	(    self  ):
											return {
											    # here we create 2 clusters for the sake of simplicity
											    "clusters": np.asarray(
											        [
											            [0.8866443634033203, 0.6618829369544983, 0.3891746401786804],
											            [-0.6042559146881104, -0.02295008860528469, 0.5423797369003296],
											        ]  ),
											    "do_resize": self.do_resize,
											    "size": self.size,
											    "do_normalize": self.do_normalize,
											}





@require_torch
@require_vision
class     lowercase__  (						_snake_case     ,     unittest.TestCase					):





							'''simple docstring'''


							A_							:      int      	=  ImageGPTImageProcessor if is_vision_available() else None






							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       str     		=      ImageGPTImageProcessingTester(self  )






							@property
							def UpperCAmelCase_	(    self  ):
											return self.image_processor_tester.prepare_image_processor_dict()






							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      self.image_processing_class(**self.image_processor_dict  )
											self.assertTrue(hasattr(__snake_case    ,					"""clusters"""  )  )
											self.assertTrue(hasattr(__snake_case    ,					"""do_resize"""  )  )
											self.assertTrue(hasattr(__snake_case    ,					"""size"""  )  )
											self.assertTrue(hasattr(__snake_case    ,					"""do_normalize"""  )  )






							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       Dict     		=      self.image_processing_class.from_dict(self.image_processor_dict  )
											self.assertEqual(image_processor.size    ,					{"""height""": 18, """width""": 18}  )

											_SCREAMING_SNAKE_CASE :       str     		=      self.image_processing_class.from_dict(self.image_processor_dict    ,					size=42  )
											self.assertEqual(image_processor.size    ,					{"""height""": 42, """width""": 42}  )






							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       List[str]     		=      self.image_processing_class(**self.image_processor_dict  )
											_SCREAMING_SNAKE_CASE :       Dict     		=      json.loads(image_processor.to_json_string()  )
											for key, value in self.image_processor_dict.items():
															if key == "clusters":
																			self.assertTrue(np.array_equal(__snake_case    ,					obj[key]  )  )
															else:
																			self.assertEqual(obj[key]    ,					__snake_case  )






							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      self.image_processing_class(**self.image_processor_dict  )

											with tempfile.TemporaryDirectory() as tmpdirname:
															_SCREAMING_SNAKE_CASE :       Dict     		=      os.path.join(__snake_case    ,					"""image_processor.json"""  )
															image_processor_first.to_json_file(__snake_case  )
															_SCREAMING_SNAKE_CASE :       str     		=      self.image_processing_class.from_json_file(__snake_case  ).to_dict()

											_SCREAMING_SNAKE_CASE :       List[str]     		=      image_processor_first.to_dict()
											for key, value in image_processor_first.items():
															if key == "clusters":
																			self.assertTrue(np.array_equal(__snake_case    ,					image_processor_second[key]  )  )
															else:
																			self.assertEqual(image_processor_first[key]    ,					__snake_case  )






							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      self.image_processing_class(**self.image_processor_dict  )

											with tempfile.TemporaryDirectory() as tmpdirname:
															image_processor_first.save_pretrained(__snake_case  )
															_SCREAMING_SNAKE_CASE :       Tuple     		=      self.image_processing_class.from_pretrained(__snake_case  ).to_dict()

											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      image_processor_first.to_dict()
											for key, value in image_processor_first.items():
															if key == "clusters":
																			self.assertTrue(np.array_equal(__snake_case    ,					image_processor_second[key]  )  )
															else:
																			self.assertEqual(image_processor_first[key]    ,					__snake_case  )






							@unittest.skip("""ImageGPT requires clusters at initialization"""  )
							def UpperCAmelCase_	(    self  ):
											pass







def 			snake_case_   (  ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE :       List[str]     		=      load_dataset("""hf-internal-testing/fixtures_image_utils"""							,      split="""test"""     )

				_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      Image.open(dataset[4]["""file"""]     )
				_SCREAMING_SNAKE_CASE :       Dict     		=      Image.open(dataset[5]["""file"""]     )

				_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      [imagea, imagea]

				return images







@require_vision
@require_torch
class     lowercase__  (						unittest.TestCase					):





							'''simple docstring'''


							@slow
							def UpperCAmelCase_	(    self  ):
											_SCREAMING_SNAKE_CASE :       Tuple     		=      ImageGPTImageProcessor.from_pretrained("""openai/imagegpt-small"""  )

											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      prepare_images()

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

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

											_SCREAMING_SNAKE_CASE :       List[str]     		=      [306, 191, 191]
											self.assertEqual(encoding.input_ids[0, :3].tolist()    ,					__snake_case  )

											# test batched
											_SCREAMING_SNAKE_CASE :       List[str]     		=      image_processing(__snake_case    ,					return_tensors="""pt"""  )

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

											_SCREAMING_SNAKE_CASE :       Any     		=      [303, 13, 13]
											self.assertEqual(encoding.input_ids[1, -3:].tolist()    ,					__snake_case  )








'''simple docstring'''


class     lowercase__  :





							'''simple docstring'''


							def __init__(    self    ,					__snake_case = ""    ,					__snake_case = False  ):
											# Mapping from the first character of the prefix of the node
											_SCREAMING_SNAKE_CASE :       dict[str, RadixNode]     		=      {}

											# A node will be a leaf if the tree contains its word
											_SCREAMING_SNAKE_CASE :       List[Any]     		=      is_leaf

											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      prefix






							def UpperCAmelCase_	(    self    ,					__snake_case  ):
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      0
											for q, w in zip(self.prefix    ,					__snake_case  ):
															if q != w:
																			break

															x += 1

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






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






							def UpperCAmelCase_	(    self    ,					__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:
															_SCREAMING_SNAKE_CASE :       List[Any]     		=      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:
															_SCREAMING_SNAKE_CASE :       List[str]     		=      RadixNode(prefix=__snake_case    ,					is_leaf=__snake_case  )

											else:
															_SCREAMING_SNAKE_CASE :       int     		=      self.nodes[word[0]]
															_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE :       Optional[Any]     		=      incoming_node.match(
															    __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(__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:
																			_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      remaining_prefix

																			_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      self.nodes[matching_string[0]]
																			_SCREAMING_SNAKE_CASE :       List[Any]     		=      RadixNode(__snake_case    ,					__snake_case  )
																			_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      aux_node

																			if remaining_word == "":
																							_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      True
																			else:
																							self.nodes[matching_string[0]].insert(__snake_case  )






							def UpperCAmelCase_	(    self    ,					__snake_case  ):
											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      self.nodes.get(word[0]    ,					__snake_case  )
											if not incoming_node:
															return False
											else:
															_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE :       Tuple     		=      incoming_node.match(
															    __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(__snake_case  )






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

																							return True






							def UpperCAmelCase_	(    self    ,					__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 			snake_case_   (  ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      """banana bananas bandana band apple all beast""".split()
				_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      RadixNode()
				root.insert_many(SCREAMING_SNAKE_CASE__     )

				assert all(root.find(SCREAMING_SNAKE_CASE__     ) 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 			snake_case_   (  ):







				"""simple docstring"""





				assert test_trie()







def 			snake_case_   (  ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE :       Tuple     		=      RadixNode()
				_SCREAMING_SNAKE_CASE :       Optional[int]     		=      """banana bananas bandanas bandana band apple all beast""".split()
				root.insert_many(SCREAMING_SNAKE_CASE__     )

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


if __name__ == "__main__":
				main()




