






"""simple docstring"""

import warnings

from ...utils import logging
from .image_processing_flava import FlavaImageProcessor


lowercase__          = logging.get_logger(__name__)


class 		__lowerCamelCase				( A__			):





		'''simple docstring'''






		def __init__(   self							:	List[Any]						,			*a_							:	Optional[int]						,			**a_							:	str	):
				warnings.warn(
				    "The class FlavaFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
				    " use FlavaImageProcessor instead."						,			a_						,			)
				super().__init__(*a_						,			**a_	)








"""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, BertTokenizer, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast


@require_vision
class 		__lowerCamelCase				( unittest.TestCase			):





		'''simple docstring'''






		def    lowerCamelCase       (   self							:	Optional[int]	):
				lowerCAmelCase_   :							Optional[int]									=			tempfile.mkdtemp()

				lowerCAmelCase_   :							Optional[int]									=			BlipImageProcessor()
				lowerCAmelCase_   :							Dict									=			BertTokenizer.from_pretrained("hf-internal-testing/tiny-random-BertModel"	)

				lowerCAmelCase_   :							str									=			BlipProcessor(a_						,			a_	)

				processor.save_pretrained(self.tmpdirname	)





		def    lowerCamelCase       (   self							:	Optional[Any]						,			**a_							:	Union[str, Any]	):
				return AutoProcessor.from_pretrained(self.tmpdirname						,			**a_	).tokenizer





		def    lowerCamelCase       (   self							:	int						,			**a_							:	int	):
				return AutoProcessor.from_pretrained(self.tmpdirname						,			**a_	).image_processor





		def    lowerCamelCase       (   self							:	List[str]	):
				shutil.rmtree(self.tmpdirname	)





		def    lowerCamelCase       (   self							:	Union[str, Any]	):
				lowerCAmelCase_   :							Union[str, Any]									=			[np.random.randint(2_55						,			size=(3, 30, 4_00)						,			dtype=np.uinta	)]

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

				return image_inputs





		def    lowerCamelCase       (   self							:	Dict	):
				lowerCAmelCase_   :							Dict									=			BlipProcessor(tokenizer=self.get_tokenizer()						,			image_processor=self.get_image_processor()	)
				processor.save_pretrained(self.tmpdirname	)

				lowerCAmelCase_   :							Optional[Any]									=			self.get_tokenizer(bos_token="(BOS)"						,			eos_token="(EOS)"	)
				lowerCAmelCase_   :							Dict									=			self.get_image_processor(do_normalize=a_						,			padding_value=1.0	)

				lowerCAmelCase_   :							str									=			BlipProcessor.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_	)





		def    lowerCamelCase       (   self							:	Optional[int]	):
				lowerCAmelCase_   :							List[str]									=			self.get_image_processor()
				lowerCAmelCase_   :							Tuple									=			self.get_tokenizer()

				lowerCAmelCase_   :							str									=			BlipProcessor(tokenizer=a_						,			image_processor=a_	)

				lowerCAmelCase_   :							Any									=			self.prepare_image_inputs()

				lowerCAmelCase_   :							Any									=			image_processor(a_						,			return_tensors="np"	)
				lowerCAmelCase_   :							List[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    lowerCamelCase       (   self							:	str	):
				lowerCAmelCase_   :							Dict									=			self.get_image_processor()
				lowerCAmelCase_   :							int									=			self.get_tokenizer()

				lowerCAmelCase_   :							Tuple									=			BlipProcessor(tokenizer=a_						,			image_processor=a_	)

				lowerCAmelCase_   :							Dict									=			"lower newer"

				lowerCAmelCase_   :							List[str]									=			processor(text=a_	)

				lowerCAmelCase_   :							int									=			tokenizer(a_						,			return_token_type_ids=a_	)

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





		def    lowerCamelCase       (   self							:	Any	):
				lowerCAmelCase_   :							str									=			self.get_image_processor()
				lowerCAmelCase_   :							Dict									=			self.get_tokenizer()

				lowerCAmelCase_   :							Optional[int]									=			BlipProcessor(tokenizer=a_						,			image_processor=a_	)

				lowerCAmelCase_   :							Any									=			"lower newer"
				lowerCAmelCase_   :							str									=			self.prepare_image_inputs()

				lowerCAmelCase_   :							Any									=			processor(text=a_						,			images=a_	)

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

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





		def    lowerCamelCase       (   self							:	List[Any]	):
				lowerCAmelCase_   :							int									=			self.get_image_processor()
				lowerCAmelCase_   :							List[Any]									=			self.get_tokenizer()

				lowerCAmelCase_   :							str									=			BlipProcessor(tokenizer=a_						,			image_processor=a_	)

				lowerCAmelCase_   :							int									=			[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

				lowerCAmelCase_   :							List[str]									=			processor.batch_decode(a_	)
				lowerCAmelCase_   :							List[str]									=			tokenizer.batch_decode(a_	)

				self.assertListEqual(a_						,			a_	)





		def    lowerCamelCase       (   self							:	List[Any]	):
				lowerCAmelCase_   :							Optional[Any]									=			self.get_image_processor()
				lowerCAmelCase_   :							List[str]									=			self.get_tokenizer()

				lowerCAmelCase_   :							str									=			BlipProcessor(tokenizer=a_						,			image_processor=a_	)

				lowerCAmelCase_   :							List[str]									=			"lower newer"
				lowerCAmelCase_   :							Optional[int]									=			self.prepare_image_inputs()

				lowerCAmelCase_   :							Optional[int]									=			processor(text=a_						,			images=a_	)

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