





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       __A					(					unittest.TestCase				):

			'''simple docstring'''







			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				lowerCamelCase__              =	tempfile.mkdtemp()

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

				lowerCamelCase__              =	BlipaProcessor(__lowerCAmelCase ,						__lowerCAmelCase							)

				processor.save_pretrained(self.tmpdirname							)





			def 	__lowerCamelCase       (					self ,						**__lowerCAmelCase							):



				'''simple docstring'''







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





			def 	__lowerCamelCase       (					self ,						**__lowerCAmelCase							):



				'''simple docstring'''







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





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				shutil.rmtree(self.tmpdirname							)





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







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

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

				return image_inputs





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







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

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

				lowerCamelCase__              =	BlipaProcessor.from_pretrained(
				    self.tmpdirname ,						bos_token='''(BOS)''' ,						eos_token='''(EOS)''' ,						do_normalize=__lowerCAmelCase ,						padding_value=1.0							)

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

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





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				lowerCamelCase__              =	self.get_image_processor()
				lowerCamelCase__              =	self.get_tokenizer()

				lowerCamelCase__              =	BlipaProcessor(tokenizer=__lowerCAmelCase ,						image_processor=__lowerCAmelCase							)

				lowerCamelCase__              =	self.prepare_image_inputs()

				lowerCamelCase__              =	image_processor(__lowerCAmelCase ,						return_tensors='''np'''							)
				lowerCamelCase__              =	processor(images=__lowerCAmelCase ,						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 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				lowerCamelCase__              =	self.get_image_processor()
				lowerCamelCase__              =	self.get_tokenizer()

				lowerCamelCase__              =	BlipaProcessor(tokenizer=__lowerCAmelCase ,						image_processor=__lowerCAmelCase							)

				lowerCamelCase__              =	'''lower newer'''

				lowerCamelCase__              =	processor(text=__lowerCAmelCase							)

				lowerCamelCase__              =	tokenizer(__lowerCAmelCase ,						return_token_type_ids=__lowerCAmelCase							)

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





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				lowerCamelCase__              =	self.get_image_processor()
				lowerCamelCase__              =	self.get_tokenizer()

				lowerCamelCase__              =	BlipaProcessor(tokenizer=__lowerCAmelCase ,						image_processor=__lowerCAmelCase							)

				lowerCamelCase__              =	'''lower newer'''
				lowerCamelCase__              =	self.prepare_image_inputs()

				lowerCamelCase__              =	processor(text=__lowerCAmelCase ,						images=__lowerCAmelCase							)

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

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





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				lowerCamelCase__              =	self.get_image_processor()
				lowerCamelCase__              =	self.get_tokenizer()

				lowerCamelCase__              =	BlipaProcessor(tokenizer=__lowerCAmelCase ,						image_processor=__lowerCAmelCase							)

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

				lowerCamelCase__              =	processor.batch_decode(__lowerCAmelCase							)
				lowerCamelCase__              =	tokenizer.batch_decode(__lowerCAmelCase							)

				self.assertListEqual(__lowerCAmelCase ,						__lowerCAmelCase							)





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







				lowerCamelCase__              =	self.get_image_processor()
				lowerCamelCase__              =	self.get_tokenizer()

				lowerCamelCase__              =	BlipaProcessor(tokenizer=__lowerCAmelCase ,						image_processor=__lowerCAmelCase							)

				lowerCamelCase__              =	'''lower newer'''
				lowerCamelCase__              =	self.prepare_image_inputs()

				lowerCamelCase__              =	processor(text=__lowerCAmelCase ,						images=__lowerCAmelCase							)

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










_a						=   "\n# Transformers 설치 방법\n! pip install transformers datasets\n# 마지막 릴리스 대신 소스에서 설치하려면, 위 명령을 주석으로 바꾸고 아래 명령을 해제하세요.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"

_a						=   [{"type": "code", "content": INSTALL_CONTENT}]
_a						=   {
    "{processor_class}": "FakeProcessorClass",
    "{model_class}": "FakeModelClass",
    "{object_class}": "FakeObjectClass",
}



