'''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 OwlViTImageProcessor, OwlViTProcessor






@require_vision
class 				UpperCAmelCase (       unittest.TestCase ):




    '''simple docstring'''







    def       _UpperCamelCase    (       self )							-> Optional[int]:
         SCREAMING_SNAKE_CASE					:			Any    	=       tempfile.mkdtemp()

         # fmt: off
         SCREAMING_SNAKE_CASE					:			List[str]    	=       ['', '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
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       dict(zip(SCREAMING_SNAKE_CASE_    ,							range(len(SCREAMING_SNAKE_CASE_ ) ) ) )
         SCREAMING_SNAKE_CASE					:			str    	=       ['#version: 0.2', 'l o', 'lo w</w>', 'e r</w>', '']
         SCREAMING_SNAKE_CASE					:			Tuple    	=       {'unk_token': '<unk>'}

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

         SCREAMING_SNAKE_CASE					:			int    	=       {
             '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],
         }
         SCREAMING_SNAKE_CASE					:			Tuple    	=       os.path.join(self.tmpdirname    ,							SCREAMING_SNAKE_CASE_ )
         with open(self.image_processor_file    ,							'w'    ,							encoding='utf-8' ) as fp:
              json.dump(SCREAMING_SNAKE_CASE_    ,							SCREAMING_SNAKE_CASE_ )






    def       _UpperCamelCase    (       self    ,							**lowercase__ )							-> Any:
         return CLIPTokenizer.from_pretrained(self.tmpdirname    ,							pad_token='!'    ,							**SCREAMING_SNAKE_CASE_ )






    def       _UpperCamelCase    (       self    ,							**lowercase__ )							-> Tuple:
         return CLIPTokenizerFast.from_pretrained(self.tmpdirname    ,							pad_token='!'    ,							**SCREAMING_SNAKE_CASE_ )






    def       _UpperCamelCase    (       self    ,							**lowercase__ )							-> Optional[int]:
         return OwlViTImageProcessor.from_pretrained(self.tmpdirname    ,							**SCREAMING_SNAKE_CASE_ )






    def       _UpperCamelCase    (       self )							-> List[str]:
         shutil.rmtree(self.tmpdirname )






    def       _UpperCamelCase    (       self )							-> List[Any]:
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       [np.random.randint(255    ,							size=(3, 30, 400)    ,							dtype=np.uinta )]

         SCREAMING_SNAKE_CASE					:			str    	=       [Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE_    ,							0    ,							-1 ) ) for x in image_inputs]

         return image_inputs






    def       _UpperCamelCase    (       self )							-> str:
         SCREAMING_SNAKE_CASE					:			Any    	=       self.get_tokenizer()
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       self.get_rust_tokenizer()
         SCREAMING_SNAKE_CASE					:			int    	=       self.get_image_processor()

         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       OwlViTProcessor(tokenizer=SCREAMING_SNAKE_CASE_    ,							image_processor=SCREAMING_SNAKE_CASE_ )
         processor_slow.save_pretrained(self.tmpdirname )
         SCREAMING_SNAKE_CASE					:			Any    	=       OwlViTProcessor.from_pretrained(self.tmpdirname    ,							use_fast=SCREAMING_SNAKE_CASE_ )

         SCREAMING_SNAKE_CASE					:			str    	=       OwlViTProcessor(tokenizer=SCREAMING_SNAKE_CASE_    ,							image_processor=SCREAMING_SNAKE_CASE_ )
         processor_fast.save_pretrained(self.tmpdirname )
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       OwlViTProcessor.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    ,							SCREAMING_SNAKE_CASE_ )
         self.assertIsInstance(processor_fast.tokenizer    ,							SCREAMING_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    ,							SCREAMING_SNAKE_CASE_ )
         self.assertIsInstance(processor_fast.image_processor    ,							SCREAMING_SNAKE_CASE_ )






    def       _UpperCamelCase    (       self )							-> Any:
         SCREAMING_SNAKE_CASE					:			Any    	=       OwlViTProcessor(tokenizer=self.get_tokenizer()    ,							image_processor=self.get_image_processor() )
         processor.save_pretrained(self.tmpdirname )

         SCREAMING_SNAKE_CASE					:			str    	=       self.get_tokenizer(bos_token='(BOS)'    ,							eos_token='(EOS)' )
         SCREAMING_SNAKE_CASE					:			Tuple    	=       self.get_image_processor(do_normalize=SCREAMING_SNAKE_CASE_ )

         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       OwlViTProcessor.from_pretrained(
             self.tmpdirname    ,							bos_token='(BOS)'    ,							eos_token='(EOS)'    ,							do_normalize=SCREAMING_SNAKE_CASE_ )

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

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






    def       _UpperCamelCase    (       self )							-> str:
         SCREAMING_SNAKE_CASE					:			Any    	=       self.get_image_processor()
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       self.get_tokenizer()

         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       OwlViTProcessor(tokenizer=SCREAMING_SNAKE_CASE_    ,							image_processor=SCREAMING_SNAKE_CASE_ )

         SCREAMING_SNAKE_CASE					:			Dict    	=       self.prepare_image_inputs()

         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       image_processor(SCREAMING_SNAKE_CASE_    ,							return_tensors='np' )
         SCREAMING_SNAKE_CASE					:			List[str]    	=       processor(images=SCREAMING_SNAKE_CASE_    ,							return_tensors='np' )

         for key in input_image_proc.keys():
              self.assertAlmostEqual(input_image_proc[key].sum()    ,							input_processor[key].sum()    ,							delta=1E-2 )






    def       _UpperCamelCase    (       self )							-> Optional[Any]:
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       self.get_image_processor()
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       self.get_tokenizer()

         SCREAMING_SNAKE_CASE					:			Any    	=       OwlViTProcessor(tokenizer=SCREAMING_SNAKE_CASE_    ,							image_processor=SCREAMING_SNAKE_CASE_ )

         SCREAMING_SNAKE_CASE					:			str    	=       'lower newer'

         SCREAMING_SNAKE_CASE					:			int    	=       processor(text=SCREAMING_SNAKE_CASE_    ,							return_tensors='np' )

         SCREAMING_SNAKE_CASE					:			Dict    	=       tokenizer(SCREAMING_SNAKE_CASE_    ,							return_tensors='np' )

         for key in encoded_tok.keys():
              self.assertListEqual(encoded_tok[key][0].tolist()    ,							encoded_processor[key][0].tolist() )






    def       _UpperCamelCase    (       self )							-> Union[str, Any]:
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       self.get_image_processor()
         SCREAMING_SNAKE_CASE					:			Any    	=       self.get_tokenizer()

         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       OwlViTProcessor(tokenizer=SCREAMING_SNAKE_CASE_    ,							image_processor=SCREAMING_SNAKE_CASE_ )

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

         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       processor(text=SCREAMING_SNAKE_CASE_    ,							images=SCREAMING_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(SCREAMING_SNAKE_CASE_ ):
              processor()






    def       _UpperCamelCase    (       self )							-> Optional[int]:
         SCREAMING_SNAKE_CASE					:			str    	=       'google/owlvit-base-patch32'
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       OwlViTProcessor.from_pretrained(SCREAMING_SNAKE_CASE_ )

         SCREAMING_SNAKE_CASE					:			Any    	=       ['cat', 'nasa badge']
         SCREAMING_SNAKE_CASE					:			Any    	=       processor(text=SCREAMING_SNAKE_CASE_ )

         SCREAMING_SNAKE_CASE					:			int    	=       16
         self.assertListEqual(list(inputs.keys() )    ,							['input_ids', 'attention_mask'] )
         self.assertEqual(inputs['input_ids'].shape    ,							(2, seq_length) )

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






    def       _UpperCamelCase    (       self )							-> Dict:
         SCREAMING_SNAKE_CASE					:			Dict    	=       'google/owlvit-base-patch32'
         SCREAMING_SNAKE_CASE					:			str    	=       OwlViTProcessor.from_pretrained(SCREAMING_SNAKE_CASE_ )

         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       [['cat', 'nasa badge'], ['person']]
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       processor(text=SCREAMING_SNAKE_CASE_ )

         SCREAMING_SNAKE_CASE					:			List[Any]    	=       16
         SCREAMING_SNAKE_CASE					:			str    	=       len(SCREAMING_SNAKE_CASE_ )
         SCREAMING_SNAKE_CASE					:			Any    	=       max([len(SCREAMING_SNAKE_CASE_ ) for texts in input_texts] )

         self.assertListEqual(list(inputs.keys() )    ,							['input_ids', 'attention_mask'] )
         self.assertEqual(inputs['input_ids'].shape    ,							(batch_size * num_max_text_queries, seq_length) )

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






    def       _UpperCamelCase    (       self )							-> Tuple:
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       'google/owlvit-base-patch32'
         SCREAMING_SNAKE_CASE					:			int    	=       OwlViTProcessor.from_pretrained(SCREAMING_SNAKE_CASE_ )

         SCREAMING_SNAKE_CASE					:			Any    	=       ['cat', 'nasa badge']
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       processor(text=SCREAMING_SNAKE_CASE_ )

         SCREAMING_SNAKE_CASE					:			Any    	=       16
         SCREAMING_SNAKE_CASE					:			int    	=       inputs['input_ids']
         SCREAMING_SNAKE_CASE					:			str    	=       [
             [49_406, 2_368, 49_407, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
             [49_406, 6_841, 11_301, 49_407, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
         ]

         self.assertListEqual(list(inputs.keys() )    ,							['input_ids', 'attention_mask'] )
         self.assertEqual(inputs['input_ids'].shape    ,							(2, seq_length) )
         self.assertListEqual(list(input_ids[0] )    ,							predicted_ids[0] )
         self.assertListEqual(list(input_ids[1] )    ,							predicted_ids[1] )






    def       _UpperCamelCase    (       self )							-> str:
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       self.get_image_processor()
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       self.get_tokenizer()

         SCREAMING_SNAKE_CASE					:			Any    	=       OwlViTProcessor(tokenizer=SCREAMING_SNAKE_CASE_    ,							image_processor=SCREAMING_SNAKE_CASE_ )

         SCREAMING_SNAKE_CASE					:			List[str]    	=       self.prepare_image_inputs()
         SCREAMING_SNAKE_CASE					:			Any    	=       self.prepare_image_inputs()

         SCREAMING_SNAKE_CASE					:			List[Any]    	=       processor(images=SCREAMING_SNAKE_CASE_    ,							query_images=SCREAMING_SNAKE_CASE_ )

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

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






    def       _UpperCamelCase    (       self )							-> Optional[Any]:
         SCREAMING_SNAKE_CASE					:			int    	=       self.get_image_processor()
         SCREAMING_SNAKE_CASE					:			List[str]    	=       self.get_tokenizer()

         SCREAMING_SNAKE_CASE					:			List[str]    	=       OwlViTProcessor(tokenizer=SCREAMING_SNAKE_CASE_    ,							image_processor=SCREAMING_SNAKE_CASE_ )

         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

         SCREAMING_SNAKE_CASE					:			List[str]    	=       processor.batch_decode(SCREAMING_SNAKE_CASE_ )
         SCREAMING_SNAKE_CASE					:			Tuple    	=       tokenizer.batch_decode(SCREAMING_SNAKE_CASE_ )

         self.assertListEqual(SCREAMING_SNAKE_CASE_    ,							SCREAMING_SNAKE_CASE_ )

'''simple docstring'''





from __future__ import annotations


def     __lowerCAmelCase						(       a_       ,      a_ = None		)      ->	list[list[str]]:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			Optional[int]    	=       word_bank or []
     # create a table
     SCREAMING_SNAKE_CASE					:			int    	=       len(a_		) + 1

     SCREAMING_SNAKE_CASE					:			list[list[list[str]]]    	=       []
     for _ in range(a_		):
          table.append([]		)
     # seed value
     SCREAMING_SNAKE_CASE					:			List[Any]    	=       [[]]  # 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					:			list[list[str]]    	=       [
                             [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"""],
     )
 )



