'''simple docstring'''

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,
							    BertTokenizerFast,
							    BlipImageProcessor,
							    GPTaTokenizer,
							    InstructBlipProcessor,
							    PreTrainedTokenizerFast,
							)
@require_vision
class   SCREAMING_SNAKE_CASE			(						unittest.TestCase):







		"""simple docstring"""






		def 		UpperCamelCase__       (   self				) ->     Union[str, Any]:
								_lowerCAmelCase										=tempfile.mkdtemp()

								_lowerCAmelCase										=BlipImageProcessor()
								_lowerCAmelCase										=GPTaTokenizer.from_pretrained('hf-internal-testing/tiny-random-GPT2Model'				)
								_lowerCAmelCase										=BertTokenizerFast.from_pretrained('hf-internal-testing/tiny-random-bert'				)

								_lowerCAmelCase										=InstructBlipProcessor(__A		,						__A		,						__A				)

								processor.save_pretrained(self.tmpdirname				)






		def 		UpperCamelCase__       (   self		,						**__A				) ->     str:
								return AutoProcessor.from_pretrained(self.tmpdirname		,						**__A				).tokenizer






		def 		UpperCamelCase__       (   self		,						**__A				) ->     Any:
								return AutoProcessor.from_pretrained(self.tmpdirname		,						**__A				).image_processor






		def 		UpperCamelCase__       (   self		,						**__A				) ->     Optional[int]:
								return AutoProcessor.from_pretrained(self.tmpdirname		,						**__A				).qformer_tokenizer






		def 		UpperCamelCase__       (   self				) ->     Union[str, Any]:
								shutil.rmtree(self.tmpdirname				)






		def 		UpperCamelCase__       (   self				) ->     List[str]:
								_lowerCAmelCase										=[np.random.randint(255		,						size=(3, 30, 400)		,						dtype=np.uinta				)]

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

								return image_inputs






		def 		UpperCamelCase__       (   self				) ->     Tuple:
								_lowerCAmelCase										=InstructBlipProcessor(
								    tokenizer=self.get_tokenizer()		,						image_processor=self.get_image_processor()		,						qformer_tokenizer=self.get_qformer_tokenizer()		,						)
								processor.save_pretrained(self.tmpdirname				)

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

								_lowerCAmelCase										=InstructBlipProcessor.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				)
								self.assertIsInstance(processor.qformer_tokenizer		,						__A				)






		def 		UpperCamelCase__       (   self				) ->     int:
								_lowerCAmelCase										=self.get_image_processor()
								_lowerCAmelCase										=self.get_tokenizer()
								_lowerCAmelCase										=self.get_qformer_tokenizer()

								_lowerCAmelCase										=InstructBlipProcessor(
								    tokenizer=__A		,						image_processor=__A		,						qformer_tokenizer=__A				)

								_lowerCAmelCase										=self.prepare_image_inputs()

								_lowerCAmelCase										=image_processor(__A		,						return_tensors='np'				)
								_lowerCAmelCase										=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 		UpperCamelCase__       (   self				) ->     Dict:
								_lowerCAmelCase										=self.get_image_processor()
								_lowerCAmelCase										=self.get_tokenizer()
								_lowerCAmelCase										=self.get_qformer_tokenizer()

								_lowerCAmelCase										=InstructBlipProcessor(
								    tokenizer=__A		,						image_processor=__A		,						qformer_tokenizer=__A				)

								_lowerCAmelCase										="lower newer"

								_lowerCAmelCase										=processor(text=__A				)

								_lowerCAmelCase										=tokenizer(__A		,						return_token_type_ids=__A				)
								_lowerCAmelCase										=qformer_tokenizer(__A		,						return_token_type_ids=__A				)

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

								for key in encoded_tokens_qformer.keys():
														self.assertListEqual(encoded_tokens_qformer[key]		,						encoded_processor['qformer_' + key]				)






		def 		UpperCamelCase__       (   self				) ->     int:
								_lowerCAmelCase										=self.get_image_processor()
								_lowerCAmelCase										=self.get_tokenizer()
								_lowerCAmelCase										=self.get_qformer_tokenizer()

								_lowerCAmelCase										=InstructBlipProcessor(
								    tokenizer=__A		,						image_processor=__A		,						qformer_tokenizer=__A				)

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

								_lowerCAmelCase										=processor(text=__A		,						images=__A				)

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

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






		def 		UpperCamelCase__       (   self				) ->     Optional[int]:
								_lowerCAmelCase										=self.get_image_processor()
								_lowerCAmelCase										=self.get_tokenizer()
								_lowerCAmelCase										=self.get_qformer_tokenizer()

								_lowerCAmelCase										=InstructBlipProcessor(
								    tokenizer=__A		,						image_processor=__A		,						qformer_tokenizer=__A				)

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

								_lowerCAmelCase										=processor.batch_decode(__A				)
								_lowerCAmelCase										=tokenizer.batch_decode(__A				)

								self.assertListEqual(__A		,						__A				)






		def 		UpperCamelCase__       (   self				) ->     List[Any]:
								_lowerCAmelCase										=self.get_image_processor()
								_lowerCAmelCase										=self.get_tokenizer()
								_lowerCAmelCase										=self.get_qformer_tokenizer()

								_lowerCAmelCase										=InstructBlipProcessor(
								    tokenizer=__A		,						image_processor=__A		,						qformer_tokenizer=__A				)

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

								_lowerCAmelCase										=processor(text=__A		,						images=__A				)

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






'''simple docstring'''

from __future__ import annotations



def 							UpperCamelCase__			(			a__      ):


						'''simple docstring'''






						_lowerCAmelCase										=len(a__      ) // 2

						# choose the middle 3 elements
						_lowerCAmelCase										=lst[m - 1 : m + 2]

						# if middle element is peak
						if three[1] > three[0] and three[1] > three[2]:
												return three[1]

						# if increasing, recurse on right
						elif three[0] < three[2]:
												if len(lst[:m]      ) == 2:
																		m -= 1
												return peak(lst[m:]      )

						# decreasing
						else:
												if len(lst[:m]      ) == 2:
																		m += 1
												return peak(lst[:m]      )


if __name__ == "__main__":
							import doctest

							doctest.testmod()





