



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    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Any    =    tempfile.mkdtemp()

														_lowerCAmelCase     :							int    =    BlipImageProcessor()
														_lowerCAmelCase     :							Dict    =    GPTaTokenizer.from_pretrained("hf-internal-testing/tiny-random-GPT2Model")
														_lowerCAmelCase     :							Tuple    =    BertTokenizerFast.from_pretrained("hf-internal-testing/tiny-random-bert")

														_lowerCAmelCase     :							Optional[Any]    =    InstructBlipProcessor(__a,			__a,			__a)

														processor.save_pretrained(self.tmpdirname)







							def    snake_case__      (   self,			**__a):

														'''simple docstring'''

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







							def    snake_case__      (   self,			**__a):

														'''simple docstring'''

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







							def    snake_case__      (   self,			**__a):

														'''simple docstring'''

														return AutoProcessor.from_pretrained(self.tmpdirname,			**__a).qformer_tokenizer







							def    snake_case__      (   self):

														'''simple docstring'''

														shutil.rmtree(self.tmpdirname)







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Dict    =    [np.random.randint(255,			size=(3, 30, 400),			dtype=np.uinta)]

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

														return image_inputs







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							List[Any]    =    InstructBlipProcessor(
														    tokenizer=self.get_tokenizer(),			image_processor=self.get_image_processor(),			qformer_tokenizer=self.get_qformer_tokenizer(),			)
														processor.save_pretrained(self.tmpdirname)

														_lowerCAmelCase     :							str    =    self.get_tokenizer(bos_token="(BOS)",			eos_token="(EOS)")
														_lowerCAmelCase     :							int    =    self.get_image_processor(do_normalize=__a,			padding_value=1.0)

														_lowerCAmelCase     :							str    =    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    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Tuple    =    self.get_image_processor()
														_lowerCAmelCase     :							int    =    self.get_tokenizer()
														_lowerCAmelCase     :							int    =    self.get_qformer_tokenizer()

														_lowerCAmelCase     :							Dict    =    InstructBlipProcessor(
														    tokenizer=__a,			image_processor=__a,			qformer_tokenizer=__a)

														_lowerCAmelCase     :							int    =    self.prepare_image_inputs()

														_lowerCAmelCase     :							Union[str, Any]    =    image_processor(__a,			return_tensors="np")
														_lowerCAmelCase     :							Any    =    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):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[int]    =    self.get_image_processor()
														_lowerCAmelCase     :							Optional[Any]    =    self.get_tokenizer()
														_lowerCAmelCase     :							List[str]    =    self.get_qformer_tokenizer()

														_lowerCAmelCase     :							Union[str, Any]    =    InstructBlipProcessor(
														    tokenizer=__a,			image_processor=__a,			qformer_tokenizer=__a)

														_lowerCAmelCase     :							List[str]    =    "lower newer"

														_lowerCAmelCase     :							str    =    processor(text=__a)

														_lowerCAmelCase     :							Dict    =    tokenizer(__a,			return_token_type_ids=__a)
														_lowerCAmelCase     :							Union[str, Any]    =    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    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							List[str]    =    self.get_image_processor()
														_lowerCAmelCase     :							List[str]    =    self.get_tokenizer()
														_lowerCAmelCase     :							Optional[int]    =    self.get_qformer_tokenizer()

														_lowerCAmelCase     :							List[str]    =    InstructBlipProcessor(
														    tokenizer=__a,			image_processor=__a,			qformer_tokenizer=__a)

														_lowerCAmelCase     :							int    =    "lower newer"
														_lowerCAmelCase     :							Optional[Any]    =    self.prepare_image_inputs()

														_lowerCAmelCase     :							str    =    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    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Tuple    =    self.get_image_processor()
														_lowerCAmelCase     :							Union[str, Any]    =    self.get_tokenizer()
														_lowerCAmelCase     :							Dict    =    self.get_qformer_tokenizer()

														_lowerCAmelCase     :							Dict    =    InstructBlipProcessor(
														    tokenizer=__a,			image_processor=__a,			qformer_tokenizer=__a)

														_lowerCAmelCase     :							Union[str, Any]    =    [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

														_lowerCAmelCase     :							str    =    processor.batch_decode(__a)
														_lowerCAmelCase     :							List[str]    =    tokenizer.batch_decode(__a)

														self.assertListEqual(__a,			__a)







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Union[str, Any]    =    self.get_image_processor()
														_lowerCAmelCase     :							List[str]    =    self.get_tokenizer()
														_lowerCAmelCase     :							Optional[int]    =    self.get_qformer_tokenizer()

														_lowerCAmelCase     :							Dict    =    InstructBlipProcessor(
														    tokenizer=__a,			image_processor=__a,			qformer_tokenizer=__a)

														_lowerCAmelCase     :							Optional[Any]    =    "lower newer"
														_lowerCAmelCase     :							Tuple    =    self.prepare_image_inputs()

														_lowerCAmelCase     :							List[str]    =    processor(text=__a,			images=__a)

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





from PIL import Image






def 				A				(					_lowerCamelCase  ):





							'''simple docstring'''




							_lowerCAmelCase   ,    _lowerCAmelCase     :							int    =    image.size
							_lowerCAmelCase     :							Any    =    0
							_lowerCAmelCase     :							Tuple    =    image.load()
							for i in range(_lowerCamelCase  ):
														for j in range(_lowerCamelCase  ):
																					_lowerCAmelCase     :							Union[str, Any]    =    pixels[j, i]
																					mean += pixel
							mean //= width * height

							for j in range(_lowerCamelCase  ):
														for i in range(_lowerCamelCase  ):
																					_lowerCAmelCase     :							Optional[Any]    =    255 if pixels[i, j] > mean else 0
							return image


if __name__ == "__main__":
						_snake_case		=  mean_threshold(Image.open("path_to_image").convert("L"))
						image.save("output_image_path")
