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        UpperCamelCase						(				unittest.TestCase				):





						def        A_							(self						)				->				str:
												UpperCamelCase_		:							int  =		tempfile.mkdtemp()

												UpperCamelCase_		:							str  =		BlipImageProcessor()
												UpperCamelCase_		:							str  =		GPTaTokenizer.from_pretrained("""hf-internal-testing/tiny-random-GPT2Model"""						)
												UpperCamelCase_		:							Optional[Any]  =		BertTokenizerFast.from_pretrained("""hf-internal-testing/tiny-random-bert"""						)

												UpperCamelCase_		:							Union[str, Any]  =		InstructBlipProcessor(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE						)

												processor.save_pretrained(self.tmpdirname						)





						def        A_							(self			,     **__UpperCamelCase						)				->				Dict:
												return AutoProcessor.from_pretrained(self.tmpdirname			,     **__SCREAMING_SNAKE_CASE						).tokenizer





						def        A_							(self			,     **__UpperCamelCase						)				->				Tuple:
												return AutoProcessor.from_pretrained(self.tmpdirname			,     **__SCREAMING_SNAKE_CASE						).image_processor





						def        A_							(self			,     **__UpperCamelCase						)				->				List[str]:
												return AutoProcessor.from_pretrained(self.tmpdirname			,     **__SCREAMING_SNAKE_CASE						).qformer_tokenizer





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





						def        A_							(self						)				->				str:
												UpperCamelCase_		:							Tuple  =		[np.random.randint(255			,     size=(3, 30, 400)			,     dtype=np.uinta						)]

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

												return image_inputs





						def        A_							(self						)				->				Dict:
												UpperCamelCase_		:							List[str]  =		InstructBlipProcessor(
												    tokenizer=self.get_tokenizer()			,     image_processor=self.get_image_processor()			,     qformer_tokenizer=self.get_qformer_tokenizer()			,     )
												processor.save_pretrained(self.tmpdirname						)

												UpperCamelCase_		:							int  =		self.get_tokenizer(bos_token="""(BOS)"""			,     eos_token="""(EOS)"""						)
												UpperCamelCase_		:							Dict  =		self.get_image_processor(do_normalize=__SCREAMING_SNAKE_CASE			,     padding_value=1.0						)

												UpperCamelCase_		:							Optional[Any]  =		InstructBlipProcessor.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						)
												self.assertIsInstance(processor.qformer_tokenizer			,     __SCREAMING_SNAKE_CASE						)





						def        A_							(self						)				->				List[str]:
												UpperCamelCase_		:							List[Any]  =		self.get_image_processor()
												UpperCamelCase_		:							List[str]  =		self.get_tokenizer()
												UpperCamelCase_		:							Dict  =		self.get_qformer_tokenizer()

												UpperCamelCase_		:							Tuple  =		InstructBlipProcessor(
												    tokenizer=__SCREAMING_SNAKE_CASE			,     image_processor=__SCREAMING_SNAKE_CASE			,     qformer_tokenizer=__SCREAMING_SNAKE_CASE						)

												UpperCamelCase_		:							str  =		self.prepare_image_inputs()

												UpperCamelCase_		:							Tuple  =		image_processor(__SCREAMING_SNAKE_CASE			,     return_tensors="""np"""						)
												UpperCamelCase_		:							Union[str, Any]  =		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        A_							(self						)				->				Optional[Any]:
												UpperCamelCase_		:							List[str]  =		self.get_image_processor()
												UpperCamelCase_		:							Dict  =		self.get_tokenizer()
												UpperCamelCase_		:							List[Any]  =		self.get_qformer_tokenizer()

												UpperCamelCase_		:							Optional[int]  =		InstructBlipProcessor(
												    tokenizer=__SCREAMING_SNAKE_CASE			,     image_processor=__SCREAMING_SNAKE_CASE			,     qformer_tokenizer=__SCREAMING_SNAKE_CASE						)

												UpperCamelCase_		:							str  =		"""lower newer"""

												UpperCamelCase_		:							Tuple  =		processor(text=__SCREAMING_SNAKE_CASE						)

												UpperCamelCase_		:							Dict  =		tokenizer(__SCREAMING_SNAKE_CASE			,     return_token_type_ids=__SCREAMING_SNAKE_CASE						)
												UpperCamelCase_		:							Any  =		qformer_tokenizer(__SCREAMING_SNAKE_CASE			,     return_token_type_ids=__SCREAMING_SNAKE_CASE						)

												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        A_							(self						)				->				Optional[int]:
												UpperCamelCase_		:							List[Any]  =		self.get_image_processor()
												UpperCamelCase_		:							Any  =		self.get_tokenizer()
												UpperCamelCase_		:							List[str]  =		self.get_qformer_tokenizer()

												UpperCamelCase_		:							Any  =		InstructBlipProcessor(
												    tokenizer=__SCREAMING_SNAKE_CASE			,     image_processor=__SCREAMING_SNAKE_CASE			,     qformer_tokenizer=__SCREAMING_SNAKE_CASE						)

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

												UpperCamelCase_		:							Optional[Any]  =		processor(text=__SCREAMING_SNAKE_CASE			,     images=__SCREAMING_SNAKE_CASE						)

												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(__SCREAMING_SNAKE_CASE						):
																		processor()





						def        A_							(self						)				->				int:
												UpperCamelCase_		:							Optional[int]  =		self.get_image_processor()
												UpperCamelCase_		:							Any  =		self.get_tokenizer()
												UpperCamelCase_		:							str  =		self.get_qformer_tokenizer()

												UpperCamelCase_		:							List[Any]  =		InstructBlipProcessor(
												    tokenizer=__SCREAMING_SNAKE_CASE			,     image_processor=__SCREAMING_SNAKE_CASE			,     qformer_tokenizer=__SCREAMING_SNAKE_CASE						)

												UpperCamelCase_		:							List[Any]  =		[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

												UpperCamelCase_		:							List[Any]  =		processor.batch_decode(__SCREAMING_SNAKE_CASE						)
												UpperCamelCase_		:							List[str]  =		tokenizer.batch_decode(__SCREAMING_SNAKE_CASE						)

												self.assertListEqual(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE						)





						def        A_							(self						)				->				Union[str, Any]:
												UpperCamelCase_		:							Optional[Any]  =		self.get_image_processor()
												UpperCamelCase_		:							Optional[Any]  =		self.get_tokenizer()
												UpperCamelCase_		:							str  =		self.get_qformer_tokenizer()

												UpperCamelCase_		:							Any  =		InstructBlipProcessor(
												    tokenizer=__SCREAMING_SNAKE_CASE			,     image_processor=__SCREAMING_SNAKE_CASE			,     qformer_tokenizer=__SCREAMING_SNAKE_CASE						)

												UpperCamelCase_		:							Dict  =		"""lower newer"""
												UpperCamelCase_		:							Optional[int]  =		self.prepare_image_inputs()

												UpperCamelCase_		:							str  =		processor(text=__SCREAMING_SNAKE_CASE			,     images=__SCREAMING_SNAKE_CASE						)

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







from typing import Any



def 	__A		(      _A       ):



 """simple docstring"""


 if not input_list:
  return []
 __a							=	[input_list.count(_A       ) for value in input_list]
 __a							=	max(_A       )  # Gets the maximum count in the input list.
 # Gets values of modes
 return sorted({input_list[i] for i, value in enumerate(_A       ) if value == y}       )


if __name__ == "__main__":
  import doctest

  doctest.testmod()


