





"""simple docstring"""






from __future__ import annotations




class 					__lowercase      :







					'''simple docstring'''




					def __init__(    self						,      _UpperCAmelCase						,      _UpperCAmelCase  ):
									__a							,    __a			:    List[Any]      							=	text, pattern
									__a							,    __a			:    Tuple      							=	len(_UpperCAmelCase  ), len(_UpperCAmelCase  )







					def 			_lowerCamelCase		(    self						,      _UpperCAmelCase  ):

									for i in range(self.patLen - 1						,      -1						,      -1  ):
													if char == self.pattern[i]:
																	return i
									return -1







					def 			_lowerCamelCase		(    self						,      _UpperCAmelCase  ):

									for i in range(self.patLen - 1						,      -1						,      -1  ):
													if self.pattern[i] != self.text[current_pos + i]:
																	return current_pos + i
									return -1







					def 			_lowerCamelCase		(    self  ):
									# searches pattern in text and returns index positions
									__a			:    Dict      							=	[]
									for i in range(self.textLen - self.patLen + 1  ):
													__a			:    List[str]      							=	self.mismatch_in_text(_UpperCAmelCase  )
													if mismatch_index == -1:
																	positions.append(_UpperCAmelCase  )
													else:
																	__a			:    Tuple      							=	self.match_in_pattern(self.text[mismatch_index]  )
																	__a			:    Optional[int]      							=	(
																	    mismatch_index - match_index
																	)  # shifting index lgtm [py/multiple-definition]
									return positions






A			      =	'''ABAABA'''
A			      =	'''AB'''
A			      =	BoyerMooreSearch(text, pattern)
A			      =	bms.bad_character_heuristic()

if len(positions) == 0:
		print('''No match found''')
else:
		print('''Pattern found in following positions: ''')
		print(positions)




"""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       UpperCAmelCase_    (							unittest.TestCase		):



      def 		snake_case_   (	self	:      Optional[Any]		):
           _UpperCAmelCase     :      Tuple														=       tempfile.mkdtemp()

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

           _UpperCAmelCase     :      List[Any]														=       os.path.join(self.tmpdirname	,      VOCAB_FILES_NAMES["vocab_file"]		)
           _UpperCAmelCase     :      int														=       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(A		) + "\n"		)
           with open(self.merges_file	,      "w"	,      encoding="utf-8"		) as fp:
                fp.write("\n".join(A		)		)

           _UpperCAmelCase     :      List[Any]														=       {
               "do_resize": True,
               "size": 2_0,
               "do_center_crop": True,
               "crop_size": 1_8,
               "do_normalize": True,
               "image_mean": [0.48_145_466, 0.4_578_275, 0.40_821_073],
               "image_std": [0.26_862_954, 0.26_130_258, 0.27_577_711],
           }
           _UpperCAmelCase     :      Tuple														=       os.path.join(self.tmpdirname	,      A		)
           with open(self.image_processor_file	,      "w"	,      encoding="utf-8"		) as fp:
                json.dump(A	,      A		)



      def 		snake_case_   (	self	:      int	,      **A	:      Any		):
           return CLIPTokenizer.from_pretrained(self.tmpdirname	,      **A		)



      def 		snake_case_   (	self	:      List[str]	,      **A	:      Tuple		):
           return CLIPTokenizerFast.from_pretrained(self.tmpdirname	,      **A		)



      def 		snake_case_   (	self	:      Any	,      **A	:      Optional[Any]		):
           return ViTImageProcessor.from_pretrained(self.tmpdirname	,      **A		)



      def 		snake_case_   (	self	:      Dict		):
           shutil.rmtree(self.tmpdirname		)



      def 		snake_case_   (	self	:      List[str]		):
           _UpperCAmelCase     :      int														=       [np.random.randint(2_5_5	,      size=(3, 3_0, 4_0_0)	,      dtype=np.uinta		)]

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

           return image_inputs



      def 		snake_case_   (	self	:      List[str]		):
           _UpperCAmelCase     :      str														=       self.get_tokenizer()
           _UpperCAmelCase     :      int														=       self.get_rust_tokenizer()
           _UpperCAmelCase     :      List[str]														=       self.get_image_processor()

           _UpperCAmelCase     :      Union[str, Any]														=       CLIPSegProcessor(tokenizer=A	,      image_processor=A		)
           processor_slow.save_pretrained(self.tmpdirname		)
           _UpperCAmelCase     :      Dict														=       CLIPSegProcessor.from_pretrained(self.tmpdirname	,      use_fast=A		)

           _UpperCAmelCase     :      Any														=       CLIPSegProcessor(tokenizer=A	,      image_processor=A		)
           processor_fast.save_pretrained(self.tmpdirname		)
           _UpperCAmelCase     :      int														=       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	,      A		)
           self.assertIsInstance(processor_fast.tokenizer	,      A		)

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



      def 		snake_case_   (	self	:      Optional[Any]		):
           _UpperCAmelCase     :      Union[str, Any]														=       CLIPSegProcessor(tokenizer=self.get_tokenizer()	,      image_processor=self.get_image_processor()		)
           processor.save_pretrained(self.tmpdirname		)

           _UpperCAmelCase     :      List[str]														=       self.get_tokenizer(bos_token="(BOS)"	,      eos_token="(EOS)"		)
           _UpperCAmelCase     :      int														=       self.get_image_processor(do_normalize=A	,      padding_value=1.0		)

           _UpperCAmelCase     :      Optional[Any]														=       CLIPSegProcessor.from_pretrained(
               self.tmpdirname	,      bos_token="(BOS)"	,      eos_token="(EOS)"	,      do_normalize=A	,      padding_value=1.0		)

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

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



      def 		snake_case_   (	self	:      str		):
           _UpperCAmelCase     :      Any														=       self.get_image_processor()
           _UpperCAmelCase     :      Optional[int]														=       self.get_tokenizer()

           _UpperCAmelCase     :      Dict														=       CLIPSegProcessor(tokenizer=A	,      image_processor=A		)

           _UpperCAmelCase     :      Dict														=       self.prepare_image_inputs()

           _UpperCAmelCase     :      int														=       image_processor(A	,      return_tensors="np"		)
           _UpperCAmelCase     :      int														=       processor(images=A	,      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 		snake_case_   (	self	:      Dict		):
           _UpperCAmelCase     :      List[Any]														=       self.get_image_processor()
           _UpperCAmelCase     :      Dict														=       self.get_tokenizer()

           _UpperCAmelCase     :      Optional[Any]														=       CLIPSegProcessor(tokenizer=A	,      image_processor=A		)

           _UpperCAmelCase     :      Tuple														=       "lower newer"

           _UpperCAmelCase     :      Any														=       processor(text=A		)

           _UpperCAmelCase     :      int														=       tokenizer(A		)

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



      def 		snake_case_   (	self	:      Optional[Any]		):
           _UpperCAmelCase     :      List[str]														=       self.get_image_processor()
           _UpperCAmelCase     :      Union[str, Any]														=       self.get_tokenizer()

           _UpperCAmelCase     :      Tuple														=       CLIPSegProcessor(tokenizer=A	,      image_processor=A		)

           _UpperCAmelCase     :      List[Any]														=       "lower newer"
           _UpperCAmelCase     :      List[Any]														=       self.prepare_image_inputs()

           _UpperCAmelCase     :      str														=       processor(text=A	,      images=A		)

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

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



      def 		snake_case_   (	self	:      Optional[Any]		):
           _UpperCAmelCase     :      Dict														=       self.get_image_processor()
           _UpperCAmelCase     :      Optional[int]														=       self.get_tokenizer()

           _UpperCAmelCase     :      str														=       CLIPSegProcessor(tokenizer=A	,      image_processor=A		)

           _UpperCAmelCase     :      List[Any]														=       self.prepare_image_inputs()
           _UpperCAmelCase     :      int														=       self.prepare_image_inputs()

           _UpperCAmelCase     :      Any														=       processor(images=A	,      visual_prompt=A		)

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

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



      def 		snake_case_   (	self	:      Optional[Any]		):
           _UpperCAmelCase     :      Dict														=       self.get_image_processor()
           _UpperCAmelCase     :      Optional[Any]														=       self.get_tokenizer()

           _UpperCAmelCase     :      Optional[Any]														=       CLIPSegProcessor(tokenizer=A	,      image_processor=A		)

           _UpperCAmelCase     :      str														=       [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

           _UpperCAmelCase     :      Union[str, Any]														=       processor.batch_decode(A		)
           _UpperCAmelCase     :      List[Any]														=       tokenizer.batch_decode(A		)

           self.assertListEqual(A	,      A		)
