



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   __lowerCAmelCase  (		unittest.TestCase   ):
					"""simple docstring"""






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






								"""simple docstring"""


								A__	  =     tempfile.mkdtemp()

								# fmt: off
								A__	  =     ['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
								A__	  =     dict(zip(_snake_case					,       range(len(_snake_case       )       )       )       )
								A__	  =     ['#version: 0.2', 'l o', 'lo w</w>', 'e r</w>', '']
								A__	  =     {'unk_token': '<unk>'}

								A__	  =     os.path.join(self.tmpdirname					,       VOCAB_FILES_NAMES['vocab_file']       )
								A__	  =     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(_snake_case       ) + '\n'       )
								with open(self.merges_file					,       'w'					,       encoding='utf-8'       ) as fp:
											fp.write('\n'.join(_snake_case       )       )

								A__	  =     {
								    'do_resize': True,
								    'size': 20,
								    'do_center_crop': True,
								    'crop_size': 18,
								    'do_normalize': True,
								    'image_mean': [0.4814_5466, 0.457_8275, 0.4082_1073],
								    'image_std': [0.2686_2954, 0.2613_0258, 0.2757_7711],
								}
								A__	  =     os.path.join(self.tmpdirname					,       _snake_case       )
								with open(self.image_processor_file					,       'w'					,       encoding='utf-8'       ) as fp:
											json.dump(_snake_case					,       _snake_case       )



					def      _a				(  self       :							Union[str, Any]					,       **_snake_case       :							Optional[int]       ):






								"""simple docstring"""


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



					def      _a				(  self       :							Union[str, Any]					,       **_snake_case       :							Union[str, Any]       ):






								"""simple docstring"""


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



					def      _a				(  self       :							List[str]					,       **_snake_case       :							int       ):






								"""simple docstring"""


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



					def      _a				(  self       :							Dict       ):






								"""simple docstring"""


								shutil.rmtree(self.tmpdirname       )



					def      _a				(  self       :							List[Any]       ):






								"""simple docstring"""


								A__	  =     [np.random.randint(2_55					,       size=(3, 30, 4_00)					,       dtype=np.uinta       )]

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

								return image_inputs



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






								"""simple docstring"""


								A__	  =     self.get_tokenizer()
								A__	  =     self.get_rust_tokenizer()
								A__	  =     self.get_image_processor()

								A__	  =     CLIPSegProcessor(tokenizer=_snake_case					,       image_processor=_snake_case       )
								processor_slow.save_pretrained(self.tmpdirname       )
								A__	  =     CLIPSegProcessor.from_pretrained(self.tmpdirname					,       use_fast=_snake_case       )

								A__	  =     CLIPSegProcessor(tokenizer=_snake_case					,       image_processor=_snake_case       )
								processor_fast.save_pretrained(self.tmpdirname       )
								A__	  =     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					,       _snake_case       )
								self.assertIsInstance(processor_fast.tokenizer					,       _snake_case       )

								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					,       _snake_case       )
								self.assertIsInstance(processor_fast.image_processor					,       _snake_case       )



					def      _a				(  self       :							Dict       ):






								"""simple docstring"""


								A__	  =     CLIPSegProcessor(tokenizer=self.get_tokenizer()					,       image_processor=self.get_image_processor()       )
								processor.save_pretrained(self.tmpdirname       )

								A__	  =     self.get_tokenizer(bos_token='(BOS)'					,       eos_token='(EOS)'       )
								A__	  =     self.get_image_processor(do_normalize=_snake_case					,       padding_value=1.0       )

								A__	  =     CLIPSegProcessor.from_pretrained(
								    self.tmpdirname					,       bos_token='(BOS)'					,       eos_token='(EOS)'					,       do_normalize=_snake_case					,       padding_value=1.0       )

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

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



					def      _a				(  self       :							List[str]       ):






								"""simple docstring"""


								A__	  =     self.get_image_processor()
								A__	  =     self.get_tokenizer()

								A__	  =     CLIPSegProcessor(tokenizer=_snake_case					,       image_processor=_snake_case       )

								A__	  =     self.prepare_image_inputs()

								A__	  =     image_processor(_snake_case					,       return_tensors='np'       )
								A__	  =     processor(images=_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      _a				(  self       :							Tuple       ):






								"""simple docstring"""


								A__	  =     self.get_image_processor()
								A__	  =     self.get_tokenizer()

								A__	  =     CLIPSegProcessor(tokenizer=_snake_case					,       image_processor=_snake_case       )

								A__	  =     'lower newer'

								A__	  =     processor(text=_snake_case       )

								A__	  =     tokenizer(_snake_case       )

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



					def      _a				(  self       :							Optional[int]       ):






								"""simple docstring"""


								A__	  =     self.get_image_processor()
								A__	  =     self.get_tokenizer()

								A__	  =     CLIPSegProcessor(tokenizer=_snake_case					,       image_processor=_snake_case       )

								A__	  =     'lower newer'
								A__	  =     self.prepare_image_inputs()

								A__	  =     processor(text=_snake_case					,       images=_snake_case       )

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

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



					def      _a				(  self       :							Dict       ):






								"""simple docstring"""


								A__	  =     self.get_image_processor()
								A__	  =     self.get_tokenizer()

								A__	  =     CLIPSegProcessor(tokenizer=_snake_case					,       image_processor=_snake_case       )

								A__	  =     self.prepare_image_inputs()
								A__	  =     self.prepare_image_inputs()

								A__	  =     processor(images=_snake_case					,       visual_prompt=_snake_case       )

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

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



					def      _a				(  self       :							int       ):






								"""simple docstring"""


								A__	  =     self.get_image_processor()
								A__	  =     self.get_tokenizer()

								A__	  =     CLIPSegProcessor(tokenizer=_snake_case					,       image_processor=_snake_case       )

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

								A__	  =     processor.batch_decode(_snake_case       )
								A__	  =     tokenizer.batch_decode(_snake_case       )

								self.assertListEqual(_snake_case					,       _snake_case       )






"""simple docstring"""




from __future__ import annotations



def 							_lowerCamelCase			(			__a,				__a = None     ):
		SCREAMING_SNAKE_CASE_    			=			word_bank or []
		# create a table
		SCREAMING_SNAKE_CASE_    			=			len(__a     ) + 1

		SCREAMING_SNAKE_CASE_    			=			[]
		for _ in range(__a     ):
				table.append([]     )
		# seed value
		SCREAMING_SNAKE_CASE_    			=			[[]]  # because empty string has empty combination

		# iterate through the indices
		for i in range(__a     ):
				# condition
				if table[i] != []:
						for word in word_bank:
								# slice condition
								if target[i : i + len(__a     )] == word:
										SCREAMING_SNAKE_CASE_    			=			[
										    [word, *way] for way in table[i]
										]
										# adds the word to every combination the current position holds
										# now,push that combination to the table[i+len(word)]
										table[i + len(__a     )] += new_combinations

    # combinations are in reverse order so reverse for better output
		for combination in table[len(__a     )]:
				combination.reverse()

		return table[len(__a     )]


if __name__ == "__main__":
						print(all_construct('jwajalapa', ['jwa', 'j', 'w', 'a', 'la', 'lapa']))
						print(all_construct('rajamati', ['s', 'raj', 'amat', 'raja', 'ma', 'i', 't']))
						print(
						    all_construct(
						        'hexagonosaurus',
						        ['h', 'ex', 'hex', 'ag', 'ago', 'ru', 'auru', 'rus', 'go', 'no', 'o', 's'],
						    )
						)