


import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
	from PIL import Image

	from transformers import AutoProcessor, BlipaProcessor, BlipImageProcessor, GPTaTokenizer, PreTrainedTokenizerFast






@require_vision
class      UpperCamelCase(	unittest.TestCase ):


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Any    )      ->						int:




											'''simple docstring'''

											__snake_case				=		tempfile.mkdtemp()

											__snake_case				=		BlipImageProcessor()
											__snake_case				=		GPTaTokenizer.from_pretrained("hf-internal-testing/tiny-random-GPT2Model"    )

											__snake_case				=		BlipaProcessor(SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE    )

											processor.save_pretrained(self.tmpdirname    )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Dict  ,     **SCREAMING_SNAKE_CASE						:		Optional[int]    )      ->						Optional[int]:




											'''simple docstring'''

											return AutoProcessor.from_pretrained(self.tmpdirname  ,     **SCREAMING_SNAKE_CASE    ).tokenizer


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Union[str, Any]  ,     **SCREAMING_SNAKE_CASE						:		List[Any]    )      ->						List[str]:




											'''simple docstring'''

											return AutoProcessor.from_pretrained(self.tmpdirname  ,     **SCREAMING_SNAKE_CASE    ).image_processor


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Tuple    )      ->						List[str]:




											'''simple docstring'''

											shutil.rmtree(self.tmpdirname    )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Optional[int]    )      ->						int:




											'''simple docstring'''

											__snake_case				=		[np.random.randint(2_5_5  ,     size=(3, 3_0, 4_0_0)  ,     dtype=np.uinta    )]

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

											return image_inputs


					def 				SCREAMING_SNAKE_CASE_	(    self						:		List[Any]    )      ->						Dict:




											'''simple docstring'''

											__snake_case				=		BlipaProcessor(tokenizer=self.get_tokenizer()  ,     image_processor=self.get_image_processor()    )
											processor.save_pretrained(self.tmpdirname    )

											__snake_case				=		self.get_tokenizer(bos_token="(BOS)"  ,     eos_token="(EOS)"    )
											__snake_case				=		self.get_image_processor(do_normalize=SCREAMING_SNAKE_CASE  ,     padding_value=1.0    )

											__snake_case				=		BlipaProcessor.from_pretrained(
											    self.tmpdirname  ,     bos_token="(BOS)"  ,     eos_token="(EOS)"  ,     do_normalize=SCREAMING_SNAKE_CASE  ,     padding_value=1.0    )

											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 				SCREAMING_SNAKE_CASE_	(    self						:		Tuple    )      ->						Any:




											'''simple docstring'''

											__snake_case				=		self.get_image_processor()
											__snake_case				=		self.get_tokenizer()

											__snake_case				=		BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE  ,     image_processor=SCREAMING_SNAKE_CASE    )

											__snake_case				=		self.prepare_image_inputs()

											__snake_case				=		image_processor(SCREAMING_SNAKE_CASE  ,     return_tensors="np"    )
											__snake_case				=		processor(images=SCREAMING_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 				SCREAMING_SNAKE_CASE_	(    self						:		Optional[int]    )      ->						Dict:




											'''simple docstring'''

											__snake_case				=		self.get_image_processor()
											__snake_case				=		self.get_tokenizer()

											__snake_case				=		BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE  ,     image_processor=SCREAMING_SNAKE_CASE    )

											__snake_case				=		"lower newer"

											__snake_case				=		processor(text=SCREAMING_SNAKE_CASE    )

											__snake_case				=		tokenizer(SCREAMING_SNAKE_CASE  ,     return_token_type_ids=SCREAMING_SNAKE_CASE    )

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


					def 				SCREAMING_SNAKE_CASE_	(    self						:		str    )      ->						Optional[Any]:




											'''simple docstring'''

											__snake_case				=		self.get_image_processor()
											__snake_case				=		self.get_tokenizer()

											__snake_case				=		BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE  ,     image_processor=SCREAMING_SNAKE_CASE    )

											__snake_case				=		"lower newer"
											__snake_case				=		self.prepare_image_inputs()

											__snake_case				=		processor(text=SCREAMING_SNAKE_CASE  ,     images=SCREAMING_SNAKE_CASE    )

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

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


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Union[str, Any]    )      ->						Tuple:




											'''simple docstring'''

											__snake_case				=		self.get_image_processor()
											__snake_case				=		self.get_tokenizer()

											__snake_case				=		BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE  ,     image_processor=SCREAMING_SNAKE_CASE    )

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

											__snake_case				=		processor.batch_decode(SCREAMING_SNAKE_CASE    )
											__snake_case				=		tokenizer.batch_decode(SCREAMING_SNAKE_CASE    )

											self.assertListEqual(SCREAMING_SNAKE_CASE  ,     SCREAMING_SNAKE_CASE    )






					def 				SCREAMING_SNAKE_CASE_	(    self						:		Any    )      ->						int:




											'''simple docstring'''

											__snake_case				=		self.get_image_processor()
											__snake_case				=		self.get_tokenizer()

											__snake_case				=		BlipaProcessor(tokenizer=SCREAMING_SNAKE_CASE  ,     image_processor=SCREAMING_SNAKE_CASE    )

											__snake_case				=		"lower newer"
											__snake_case				=		self.prepare_image_inputs()

											__snake_case				=		processor(text=SCREAMING_SNAKE_CASE  ,     images=SCREAMING_SNAKE_CASE    )

											# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
											self.assertListEqual(list(inputs.keys()    )  ,     ["pixel_values", "input_ids", "attention_mask"]    )









from __future__ import annotations






class      UpperCamelCase:


					def __init__(    self						:		Optional[int]  ,     SCREAMING_SNAKE_CASE						:		str  ,     SCREAMING_SNAKE_CASE						:		str    )      ->						str:




											'''simple docstring'''

											__snake_case		,       __snake_case				=		text, pattern
											__snake_case		,       __snake_case				=		len(SCREAMING_SNAKE_CASE    ), len(SCREAMING_SNAKE_CASE    )


					def 				SCREAMING_SNAKE_CASE_	(    self						:		Optional[Any]  ,     SCREAMING_SNAKE_CASE						:		str    )      ->						int:




											'''simple docstring'''

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


					def 				SCREAMING_SNAKE_CASE_	(    self						:		int  ,     SCREAMING_SNAKE_CASE						:		int    )      ->						int:




											'''simple docstring'''

											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 				SCREAMING_SNAKE_CASE_	(    self						:		List[Any]    )      ->						list[int]:




											'''simple docstring'''

											__snake_case				=		[]
											for i in range(self.textLen - self.patLen + 1    ):
																	__snake_case				=		self.mismatch_in_text(SCREAMING_SNAKE_CASE    )
																	if mismatch_index == -1:
																							positions.append(SCREAMING_SNAKE_CASE    )
																	else:
																							__snake_case				=		self.match_in_pattern(self.text[mismatch_index]    )
																							__snake_case				=		(
																							    mismatch_index - match_index
																							)  # shifting index lgtm [py/multiple-definition]
											return positions




A				:       str      =  'ABAABA'
A				:       Optional[Any]      =  'AB'
A				:       Any      =  BoyerMooreSearch(text, pattern)
A				:       Tuple      =  bms.bad_character_heuristic()

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





