





"""simple docstring"""





class 							snake_case							:

						'''simple docstring'''



						def __init__(							self       :      Optional[Any]						,				__lowercase       :      str = ""						,				__lowercase       :      bool = False ):


								'''simple docstring'''




								__UpperCAmelCase    :						dict[str, RadixNode]       =						{}

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

								__UpperCAmelCase    :						Tuple       =						prefix

						def       A_					(							self       :      Any						,				__lowercase       :      str ):


								'''simple docstring'''




								__UpperCAmelCase    :						Tuple       =						0
								for q, w in zip(self.prefix						,				__lowercase ):
										if q != w:
												break

										x += 1

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

						def       A_					(							self       :      Any						,				__lowercase       :      list[str] ):


								'''simple docstring'''




								for word in words:
										self.insert(__lowercase )

						def       A_					(							self       :      int						,				__lowercase       :      str ):


								'''simple docstring'''




								if self.prefix == word:
										__UpperCAmelCase    :						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:
										__UpperCAmelCase    :						Optional[Any]       =						RadixNode(prefix=__lowercase						,				is_leaf=__lowercase )

								else:
										__UpperCAmelCase    :						Optional[int]       =						self.nodes[word[0]]
										__UpperCAmelCase    :						Optional[Any]       =						incoming_node.match(
										    __lowercase )

										# 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(__lowercase )

										# 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    :						List[str]       =						remaining_prefix

												__UpperCAmelCase    :						Any       =						self.nodes[matching_string[0]]
												__UpperCAmelCase    :						Dict       =						RadixNode(__lowercase						,				__lowercase )
												__UpperCAmelCase    :						Dict       =						aux_node

												if remaining_word == "":
														__UpperCAmelCase    :						Any       =						True
												else:
														self.nodes[matching_string[0]].insert(__lowercase )

						def       A_					(							self       :      int						,				__lowercase       :      str ):


								'''simple docstring'''




								__UpperCAmelCase    :						Any       =						self.nodes.get(word[0]						,				__lowercase )
								if not incoming_node:
										return False
								else:
										__UpperCAmelCase    :						List[Any]       =						incoming_node.match(
										    __lowercase )
										# 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(__lowercase )

						def       A_					(							self       :      int						,				__lowercase       :      str ):


								'''simple docstring'''




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

														return True







						def       A_					(							self       :      List[str]						,				__lowercase       :      int = 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       lowerCamelCase_       (   )						->bool:





		"""simple docstring"""


		__UpperCAmelCase    :						str       =						'''banana bananas bandana band apple all beast'''.split()
		__UpperCAmelCase    :						Union[str, Any]       =						RadixNode()
		root.insert_many(UpperCAmelCase_  )

		assert all(root.find(UpperCAmelCase_  ) 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_       (   )						->None:





		"""simple docstring"""


		assert test_trie()


def       lowerCamelCase_       (   )						->None:





		"""simple docstring"""


		__UpperCAmelCase    :						Union[str, Any]       =						RadixNode()
		__UpperCAmelCase    :						str       =						'''banana bananas bandanas bandana band apple all beast'''.split()
		root.insert_many(UpperCAmelCase_  )

		print('''Words:'''  ,			UpperCAmelCase_  )
		print('''Tree:'''  )
		root.print_tree()


if __name__ == "__main__":
		main()






"""simple docstring"""





import json
import os
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
  from PIL import Image

  from transformers import CLIPSegProcessor, ViTImageProcessor




@require_vision
class 							snake_case							(			unittest.TestCase ):

      '''simple docstring'''



      def       A_					(							self       :      List[Any] ):


        '''simple docstring'''




        __UpperCAmelCase    :						Any       =						tempfile.mkdtemp()

        # fmt: off
        __UpperCAmelCase    :						Optional[int]       =						['''l''', '''o''', '''w''', '''e''', '''r''', '''s''', '''t''', '''i''', '''d''', '''n''', '''lo''', '''l</w>''', '''w</w>''', '''r</w>''', '''t</w>''', '''low</w>''', '''er</w>''', '''lowest</w>''', '''newer</w>''', '''wider''', '''<unk>''', '''<|startoftext|>''', '''<|endoftext|>''']
        # fmt: on
        __UpperCAmelCase    :						List[str]       =						dict(zip(__lowercase						,				range(len(__lowercase ) ) ) )
        __UpperCAmelCase    :						Any       =						['''#version: 0.2''', '''l o''', '''lo w</w>''', '''e r</w>''', '''''']
        __UpperCAmelCase    :						Dict       =						{'''unk_token''': '''<unk>'''}

        __UpperCAmelCase    :						Optional[int]       =						os.path.join(self.tmpdirname						,				VOCAB_FILES_NAMES['''vocab_file'''] )
        __UpperCAmelCase    :						Any       =						os.path.join(self.tmpdirname						,				VOCAB_FILES_NAMES['''merges_file'''] )
        with open(self.vocab_file						,				'''w'''						,				encoding='''utf-8''' ) as fp:
          fp.write(json.dumps(__lowercase ) + '''\n''' )
        with open(self.merges_file						,				'''w'''						,				encoding='''utf-8''' ) as fp:
          fp.write('''\n'''.join(__lowercase ) )

        __UpperCAmelCase    :						List[str]       =						{
            '''do_resize''': True,
            '''size''': 20,
            '''do_center_crop''': True,
            '''crop_size''': 18,
            '''do_normalize''': True,
            '''image_mean''': [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3],
            '''image_std''': [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1],
        }
        __UpperCAmelCase    :						Any       =						os.path.join(self.tmpdirname						,				__lowercase )
        with open(self.image_processor_file						,				'''w'''						,				encoding='''utf-8''' ) as fp:
          json.dump(__lowercase						,				__lowercase )

      def       A_					(							self       :      Optional[Any]						,				**__lowercase       :      Tuple ):


        '''simple docstring'''




        return CLIPTokenizer.from_pretrained(self.tmpdirname						,				**__lowercase )

      def       A_					(							self       :      Any						,				**__lowercase       :      Optional[Any] ):


        '''simple docstring'''




        return CLIPTokenizerFast.from_pretrained(self.tmpdirname						,				**__lowercase )

      def       A_					(							self       :      Dict						,				**__lowercase       :      str ):


        '''simple docstring'''




        return ViTImageProcessor.from_pretrained(self.tmpdirname						,				**__lowercase )

      def       A_					(							self       :      List[str] ):


        '''simple docstring'''




        shutil.rmtree(self.tmpdirname )

      def       A_					(							self       :      Union[str, Any] ):


        '''simple docstring'''




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

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

        return image_inputs

      def       A_					(							self       :      Optional[Any] ):


        '''simple docstring'''




        __UpperCAmelCase    :						Optional[int]       =						self.get_tokenizer()
        __UpperCAmelCase    :						Dict       =						self.get_rust_tokenizer()
        __UpperCAmelCase    :						Optional[Any]       =						self.get_image_processor()

        __UpperCAmelCase    :						Any       =						CLIPSegProcessor(tokenizer=__lowercase						,				image_processor=__lowercase )
        processor_slow.save_pretrained(self.tmpdirname )
        __UpperCAmelCase    :						List[Any]       =						CLIPSegProcessor.from_pretrained(self.tmpdirname						,				use_fast=__lowercase )

        __UpperCAmelCase    :						Tuple       =						CLIPSegProcessor(tokenizer=__lowercase						,				image_processor=__lowercase )
        processor_fast.save_pretrained(self.tmpdirname )
        __UpperCAmelCase    :						str       =						CLIPSegProcessor.from_pretrained(self.tmpdirname )

        self.assertEqual(processor_slow.tokenizer.get_vocab()						,				tokenizer_slow.get_vocab() )
        self.assertEqual(processor_fast.tokenizer.get_vocab()						,				tokenizer_fast.get_vocab() )
        self.assertEqual(tokenizer_slow.get_vocab()						,				tokenizer_fast.get_vocab() )
        self.assertIsInstance(processor_slow.tokenizer						,				__lowercase )
        self.assertIsInstance(processor_fast.tokenizer						,				__lowercase )

        self.assertEqual(processor_slow.image_processor.to_json_string()						,				image_processor.to_json_string() )
        self.assertEqual(processor_fast.image_processor.to_json_string()						,				image_processor.to_json_string() )
        self.assertIsInstance(processor_slow.image_processor						,				__lowercase )
        self.assertIsInstance(processor_fast.image_processor						,				__lowercase )

      def       A_					(							self       :      Optional[int] ):


        '''simple docstring'''




        __UpperCAmelCase    :						Any       =						CLIPSegProcessor(tokenizer=self.get_tokenizer()						,				image_processor=self.get_image_processor() )
        processor.save_pretrained(self.tmpdirname )

        __UpperCAmelCase    :						Any       =						self.get_tokenizer(bos_token='''(BOS)'''						,				eos_token='''(EOS)''' )
        __UpperCAmelCase    :						Tuple       =						self.get_image_processor(do_normalize=__lowercase						,				padding_value=1.0 )

        __UpperCAmelCase    :						List[str]       =						CLIPSegProcessor.from_pretrained(
            self.tmpdirname						,				bos_token='''(BOS)'''						,				eos_token='''(EOS)'''						,				do_normalize=__lowercase						,				padding_value=1.0 )

        self.assertEqual(processor.tokenizer.get_vocab()						,				tokenizer_add_kwargs.get_vocab() )
        self.assertIsInstance(processor.tokenizer						,				__lowercase )

        self.assertEqual(processor.image_processor.to_json_string()						,				image_processor_add_kwargs.to_json_string() )
        self.assertIsInstance(processor.image_processor						,				__lowercase )

      def       A_					(							self       :      int ):


        '''simple docstring'''




        __UpperCAmelCase    :						int       =						self.get_image_processor()
        __UpperCAmelCase    :						List[str]       =						self.get_tokenizer()

        __UpperCAmelCase    :						Optional[Any]       =						CLIPSegProcessor(tokenizer=__lowercase						,				image_processor=__lowercase )

        __UpperCAmelCase    :						List[Any]       =						self.prepare_image_inputs()

        __UpperCAmelCase    :						Dict       =						image_processor(__lowercase						,				return_tensors='''np''' )
        __UpperCAmelCase    :						Optional[int]       =						processor(images=__lowercase						,				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       A_					(							self       :      Dict ):


        '''simple docstring'''




        __UpperCAmelCase    :						Tuple       =						self.get_image_processor()
        __UpperCAmelCase    :						Any       =						self.get_tokenizer()

        __UpperCAmelCase    :						Any       =						CLIPSegProcessor(tokenizer=__lowercase						,				image_processor=__lowercase )

        __UpperCAmelCase    :						int       =						'''lower newer'''

        __UpperCAmelCase    :						int       =						processor(text=__lowercase )

        __UpperCAmelCase    :						Any       =						tokenizer(__lowercase )

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

      def       A_					(							self       :      List[str] ):


        '''simple docstring'''




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

        __UpperCAmelCase    :						List[Any]       =						CLIPSegProcessor(tokenizer=__lowercase						,				image_processor=__lowercase )

        __UpperCAmelCase    :						str       =						'''lower newer'''
        __UpperCAmelCase    :						int       =						self.prepare_image_inputs()

        __UpperCAmelCase    :						str       =						processor(text=__lowercase						,				images=__lowercase )

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

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

      def       A_					(							self       :      List[str] ):


        '''simple docstring'''




        __UpperCAmelCase    :						Dict       =						self.get_image_processor()
        __UpperCAmelCase    :						str       =						self.get_tokenizer()

        __UpperCAmelCase    :						Any       =						CLIPSegProcessor(tokenizer=__lowercase						,				image_processor=__lowercase )

        __UpperCAmelCase    :						Union[str, Any]       =						self.prepare_image_inputs()
        __UpperCAmelCase    :						Optional[Any]       =						self.prepare_image_inputs()

        __UpperCAmelCase    :						Tuple       =						processor(images=__lowercase						,				visual_prompt=__lowercase )

        self.assertListEqual(list(inputs.keys() )						,				['''pixel_values''', '''conditional_pixel_values'''] )

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







      def       A_					(							self       :      str ):


        '''simple docstring'''




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

        __UpperCAmelCase    :						Any       =						CLIPSegProcessor(tokenizer=__lowercase						,				image_processor=__lowercase )

        __UpperCAmelCase    :						Dict       =						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

        __UpperCAmelCase    :						int       =						processor.batch_decode(__lowercase )
        __UpperCAmelCase    :						int       =						tokenizer.batch_decode(__lowercase )

        self.assertListEqual(__lowercase						,				__lowercase )