





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       __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__              =	BertTokenizerFast.from_pretrained('''hf-internal-testing/tiny-random-bert'''							)

				lowerCamelCase__              =	InstructBlipProcessor(__lowerCAmelCase ,						__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 ,						**__lowerCAmelCase							):



				'''simple docstring'''







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





			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__              =	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=__lowerCAmelCase ,						padding_value=1.0							)

				lowerCamelCase__              =	InstructBlipProcessor.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							)
				self.assertIsInstance(processor.qformer_tokenizer ,						__lowerCAmelCase							)





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







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

				lowerCamelCase__              =	InstructBlipProcessor(
				    tokenizer=__lowerCAmelCase ,						image_processor=__lowerCAmelCase ,						qformer_tokenizer=__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__              =	self.get_qformer_tokenizer()

				lowerCamelCase__              =	InstructBlipProcessor(
				    tokenizer=__lowerCAmelCase ,						image_processor=__lowerCAmelCase ,						qformer_tokenizer=__lowerCAmelCase							)

				lowerCamelCase__              =	"lower newer"

				lowerCamelCase__              =	processor(text=__lowerCAmelCase							)

				lowerCamelCase__              =	tokenizer(__lowerCAmelCase ,						return_token_type_ids=__lowerCAmelCase							)
				lowerCamelCase__              =	qformer_tokenizer(__lowerCAmelCase ,						return_token_type_ids=__lowerCAmelCase							)

				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 	__lowerCamelCase       (					self							):



				'''simple docstring'''







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

				lowerCamelCase__              =	InstructBlipProcessor(
				    tokenizer=__lowerCAmelCase ,						image_processor=__lowerCAmelCase ,						qformer_tokenizer=__lowerCAmelCase							)

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

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

				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(__lowerCAmelCase							):
					processor()





			def 	__lowerCamelCase       (					self							):



				'''simple docstring'''







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

				lowerCamelCase__              =	InstructBlipProcessor(
				    tokenizer=__lowerCAmelCase ,						image_processor=__lowerCAmelCase ,						qformer_tokenizer=__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__              =	self.get_qformer_tokenizer()

				lowerCamelCase__              =	InstructBlipProcessor(
				    tokenizer=__lowerCAmelCase ,						image_processor=__lowerCAmelCase ,						qformer_tokenizer=__lowerCAmelCase							)

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

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

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










import os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


_a						=   namedtuple(
    "_TestCommandArgs",
    [
        "dataset",
        "name",
        "cache_dir",
        "data_dir",
        "all_configs",
        "save_infos",
        "ignore_verifications",
        "force_redownload",
        "clear_cache",
    ],
    defaults=[None, None, None, False, False, False, False, False],
)





def lowerCAmelCase__(__snake_case ,__snake_case							)       ->   List[str]:







	'''simple docstring'''







	return (abs(source - target							) / target) < 0.0_1





@pytest.mark.integration
def lowerCAmelCase__(__snake_case							)       ->   Tuple:







	'''simple docstring'''







	lowerCamelCase__              =	_TestCommandArgs(dataset=__snake_case ,all_configs=__snake_case ,save_infos=__snake_case							)
	lowerCamelCase__              =	TestCommand(*__snake_case							)
	test_command.run()
	lowerCamelCase__              =	os.path.join(__snake_case ,'''README.md'''							)
	assert os.path.exists(__snake_case							)
	lowerCamelCase__              =	DatasetInfosDict.from_directory(__snake_case							)
	lowerCamelCase__              =	DatasetInfosDict(
	    {
	        '''default''': DatasetInfo(
	            features=Features(
	                {
	                    '''tokens''': Sequence(Value('''string'''							)							),
	                    '''ner_tags''': Sequence(
	                        ClassLabel(names=['''O''', '''B-PER''', '''I-PER''', '''B-ORG''', '''I-ORG''', '''B-LOC''', '''I-LOC''']							)							),
	                    '''langs''': Sequence(Value('''string'''							)							),
	                    '''spans''': Sequence(Value('''string'''							)							),
	                }							) ,splits=[
	                {
	                    '''name''': '''train''',
	                    '''num_bytes''': 2351563,
	                    '''num_examples''': 10000,
	                },
	                {
	                    '''name''': '''validation''',
	                    '''num_bytes''': 238418,
	                    '''num_examples''': 1000,
	                },
	            ] ,download_size=3940680 ,dataset_size=2589981 ,)
	    }							)
	assert dataset_infos.keys() == expected_dataset_infos.keys()
	for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
		lowerCamelCase__       ,			lowerCamelCase__              =	getattr(dataset_infos['''default'''] ,__snake_case							), getattr(expected_dataset_infos['''default'''] ,__snake_case							)
		if key == "num_bytes":
			assert is_apercent_close(__snake_case ,__snake_case							)
		elif key == "splits":
			assert list(__snake_case							) == list(__snake_case							)
			for split in result:
				assert result[split].name == expected[split].name
				assert result[split].num_examples == expected[split].num_examples
				assert is_apercent_close(result[split].num_bytes ,expected[split].num_bytes							)
		else:
			result == expected



