"""simple docstring"""



class 						__UpperCAmelCase    :







			'''simple docstring'''





			def __init__(			self   ,      snake_case_ = ""   ,      snake_case_ = False							):







										'''simple docstring'''


										A__       :     dict[str, RadixNode]								=			{}

										# A node will be a leaf if the tree contains its word
										A__       :     Optional[int]								=			is_leaf

										A__       :     Union[str, Any]								=			prefix





			def 		lowerCamelCase					(			self   ,      snake_case_							):







										'''simple docstring'''


										A__       :     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 		lowerCamelCase					(			self   ,      snake_case_							):







										'''simple docstring'''


										for word in words:
																	self.insert(snake_case_							)





			def 		lowerCamelCase					(			self   ,      snake_case_							):







										'''simple docstring'''


										if self.prefix == word:
																	A__       :     Union[str, 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:
																	A__       :     Optional[Any]								=			RadixNode(prefix=snake_case_   ,      is_leaf=snake_case_							)

										else:
																	A__       :     List[Any]								=			self.nodes[word[0]]
																	A__				,							A__				,							A__       :     Tuple								=			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:
																								A__       :     int								=			remaining_prefix

																								A__       :     Tuple								=			self.nodes[matching_string[0]]
																								A__       :     Union[str, Any]								=			RadixNode(snake_case_   ,      snake_case_							)
																								A__       :     int								=			aux_node

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





			def 		lowerCamelCase					(			self   ,      snake_case_							):







										'''simple docstring'''


										A__       :     Optional[Any]								=			self.nodes.get(word[0]   ,      snake_case_							)
										if not incoming_node:
																	return False
										else:
																	A__				,							A__				,							A__       :     List[str]								=			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 		lowerCamelCase					(			self   ,      snake_case_							):







										'''simple docstring'''


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

																															return True





			def 		lowerCamelCase					(			self   ,      snake_case_ = 0							):







										'''simple docstring'''


										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  _A(		):
							A__       :     Union[str, Any]								=			"""banana bananas bandana band apple all beast""".split()
							A__       :     Tuple								=			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  _A(		):
							assert test_trie()

def  _A(		):
							A__       :     Dict								=			RadixNode()
							A__       :     Dict								=			"""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 sys
import tempfile
import unittest
from pathlib import Path

import transformers
from transformers import (
    CONFIG_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    CLIPConfig,
    CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER


sys.path.append(str(Path(__file__).parent.parent.parent.parent / "utils"))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_image_processing import CustomImageProcessor  # noqa E402
class 						__UpperCAmelCase    (unittest.TestCase     ):







			'''simple docstring'''





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     List[str]								=			0





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										A__       :     List[str]								=			AutoImageProcessor.from_pretrained("""openai/clip-vit-base-patch32"""							)
										self.assertIsInstance(snake_case_   ,      snake_case_							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										with tempfile.TemporaryDirectory() as tmpdirname:
																	A__       :     Optional[Any]								=			Path(snake_case_							) / """preprocessor_config.json"""
																	A__       :     Any								=			Path(snake_case_							) / """config.json"""
																	json.dump(
																	    {"""image_processor_type""": """CLIPImageProcessor""", """processor_class""": """CLIPProcessor"""}   ,      open(snake_case_   ,      """w"""							)   ,      )
																	json.dump({"""model_type""": """clip"""}   ,      open(snake_case_   ,      """w"""							)							)

																	A__       :     Any								=			AutoImageProcessor.from_pretrained(snake_case_							)
																	self.assertIsInstance(snake_case_   ,      snake_case_							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										with tempfile.TemporaryDirectory() as tmpdirname:
																	A__       :     str								=			Path(snake_case_							) / """preprocessor_config.json"""
																	A__       :     Union[str, Any]								=			Path(snake_case_							) / """config.json"""
																	json.dump(
																	    {"""feature_extractor_type""": """CLIPFeatureExtractor""", """processor_class""": """CLIPProcessor"""}   ,      open(snake_case_   ,      """w"""							)   ,      )
																	json.dump({"""model_type""": """clip"""}   ,      open(snake_case_   ,      """w"""							)							)

																	A__       :     Optional[int]								=			AutoImageProcessor.from_pretrained(snake_case_							)
																	self.assertIsInstance(snake_case_   ,      snake_case_							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										with tempfile.TemporaryDirectory() as tmpdirname:
																	A__       :     Union[str, Any]								=			CLIPConfig()

																	# Create a dummy config file with image_proceesor_type
																	A__       :     Dict								=			Path(snake_case_							) / """preprocessor_config.json"""
																	A__       :     Optional[int]								=			Path(snake_case_							) / """config.json"""
																	json.dump(
																	    {"""image_processor_type""": """CLIPImageProcessor""", """processor_class""": """CLIPProcessor"""}   ,      open(snake_case_   ,      """w"""							)   ,      )
																	json.dump({"""model_type""": """clip"""}   ,      open(snake_case_   ,      """w"""							)							)

																	# remove image_processor_type to make sure config.json alone is enough to load image processor locally
																	A__       :     List[str]								=			AutoImageProcessor.from_pretrained(snake_case_							).to_dict()

																	config_dict.pop("""image_processor_type"""							)
																	A__       :     str								=			CLIPImageProcessor(**snake_case_							)

																	# save in new folder
																	model_config.save_pretrained(snake_case_							)
																	config.save_pretrained(snake_case_							)

																	A__       :     Optional[int]								=			AutoImageProcessor.from_pretrained(snake_case_							)

																	# make sure private variable is not incorrectly saved
																	A__       :     Dict								=			json.loads(config.to_json_string()							)
																	self.assertTrue("""_processor_class""" not in dict_as_saved							)

										self.assertIsInstance(snake_case_   ,      snake_case_							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										with tempfile.TemporaryDirectory() as tmpdirname:
																	A__       :     List[Any]								=			Path(snake_case_							) / """preprocessor_config.json"""
																	json.dump(
																	    {"""image_processor_type""": """CLIPImageProcessor""", """processor_class""": """CLIPProcessor"""}   ,      open(snake_case_   ,      """w"""							)   ,      )

																	A__       :     List[str]								=			AutoImageProcessor.from_pretrained(snake_case_							)
																	self.assertIsInstance(snake_case_   ,      snake_case_							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										with self.assertRaisesRegex(
										    snake_case_   ,      """clip-base is not a local folder and is not a valid model identifier"""							):
																	A__       :     Optional[Any]								=			AutoImageProcessor.from_pretrained("""clip-base"""							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										with self.assertRaisesRegex(
										    snake_case_   ,      r"""aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"""							):
																	A__       :     Any								=			AutoImageProcessor.from_pretrained(snake_case_   ,      revision="""aaaaaa"""							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										with self.assertRaisesRegex(
										    snake_case_   ,      """hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json."""   ,      ):
																	A__       :     Optional[Any]								=			AutoImageProcessor.from_pretrained("""hf-internal-testing/config-no-model"""							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										with self.assertRaises(snake_case_							):
																	A__       :     str								=			AutoImageProcessor.from_pretrained("""hf-internal-testing/test_dynamic_image_processor"""							)
										# If remote code is disabled, we can't load this config.
										with self.assertRaises(snake_case_							):
																	A__       :     int								=			AutoImageProcessor.from_pretrained(
																	    """hf-internal-testing/test_dynamic_image_processor"""   ,      trust_remote_code=snake_case_							)

										A__       :     Optional[int]								=			AutoImageProcessor.from_pretrained(
										    """hf-internal-testing/test_dynamic_image_processor"""   ,      trust_remote_code=snake_case_							)
										self.assertEqual(image_processor.__class__.__name__   ,      """NewImageProcessor"""							)

										# Test image processor can be reloaded.
										with tempfile.TemporaryDirectory() as tmp_dir:
																	image_processor.save_pretrained(snake_case_							)
																	A__       :     Optional[Any]								=			AutoImageProcessor.from_pretrained(snake_case_   ,      trust_remote_code=snake_case_							)
										self.assertEqual(reloaded_image_processor.__class__.__name__   ,      """NewImageProcessor"""							)





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										try:
																	AutoConfig.register("""custom"""   ,      snake_case_							)
																	AutoImageProcessor.register(snake_case_   ,      snake_case_							)
																	# Trying to register something existing in the Transformers library will raise an error
																	with self.assertRaises(snake_case_							):
																								AutoImageProcessor.register(snake_case_   ,      snake_case_							)

																	with tempfile.TemporaryDirectory() as tmpdirname:
																								A__       :     Tuple								=			Path(snake_case_							) / """preprocessor_config.json"""
																								A__       :     List[str]								=			Path(snake_case_							) / """config.json"""
																								json.dump(
																								    {"""feature_extractor_type""": """CLIPFeatureExtractor""", """processor_class""": """CLIPProcessor"""}   ,      open(snake_case_   ,      """w"""							)   ,      )
																								json.dump({"""model_type""": """clip"""}   ,      open(snake_case_   ,      """w"""							)							)

																								A__       :     Optional[int]								=			CustomImageProcessor.from_pretrained(snake_case_							)

																	# Now that the config is registered, it can be used as any other config with the auto-API
																	with tempfile.TemporaryDirectory() as tmp_dir:
																								image_processor.save_pretrained(snake_case_							)
																								A__       :     int								=			AutoImageProcessor.from_pretrained(snake_case_							)
																								self.assertIsInstance(snake_case_   ,      snake_case_							)

										finally:
																	if "custom" in CONFIG_MAPPING._extra_content:
																								del CONFIG_MAPPING._extra_content["custom"]
																	if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
																								del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]





			def 		lowerCamelCase					(			self							):







										'''simple docstring'''


										class 						__UpperCAmelCase    (__A     ):







													'''simple docstring'''
													_UpperCamelCase  :						Union[str, Any]					     =				True

										try:
																	AutoConfig.register("""custom"""   ,      snake_case_							)
																	AutoImageProcessor.register(snake_case_   ,      snake_case_							)
																	# If remote code is not set, the default is to use local
																	A__       :     int								=			AutoImageProcessor.from_pretrained("""hf-internal-testing/test_dynamic_image_processor"""							)
																	self.assertEqual(image_processor.__class__.__name__   ,      """NewImageProcessor"""							)
																	self.assertTrue(image_processor.is_local							)

																	# If remote code is disabled, we load the local one.
																	A__       :     Any								=			AutoImageProcessor.from_pretrained(
																	    """hf-internal-testing/test_dynamic_image_processor"""   ,      trust_remote_code=snake_case_							)
																	self.assertEqual(image_processor.__class__.__name__   ,      """NewImageProcessor"""							)
																	self.assertTrue(image_processor.is_local							)

																	# If remote is enabled, we load from the Hub
																	A__       :     Optional[Any]								=			AutoImageProcessor.from_pretrained(
																	    """hf-internal-testing/test_dynamic_image_processor"""   ,      trust_remote_code=snake_case_							)
																	self.assertEqual(image_processor.__class__.__name__   ,      """NewImageProcessor"""							)
																	self.assertTrue(not hasattr(snake_case_   ,      """is_local"""							)							)

										finally:
																	if "custom" in CONFIG_MAPPING._extra_content:
																								del CONFIG_MAPPING._extra_content["custom"]
																	if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
																								del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]





