


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      ):

							def 				lowerCamelCase_			(							self:     Tuple							)      ->       Dict:





									"""simple docstring"""







									lowercase__						=				tempfile.mkdtemp()

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

									lowercase__						=				BlipaProcessor(_lowerCAmelCase   ,						_lowerCAmelCase							)

									processor.save_pretrained(self.tmpdirname							)

							def 				lowerCamelCase_			(							self:     Tuple   ,						**UpperCamelCase_:     int							)      ->       str:





									"""simple docstring"""







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

							def 				lowerCamelCase_			(							self:     Union[str, Any]   ,						**UpperCamelCase_:     str							)      ->       Optional[int]:





									"""simple docstring"""







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

							def 				lowerCamelCase_			(							self:     Optional[Any]							)      ->       Optional[Any]:





									"""simple docstring"""







									shutil.rmtree(self.tmpdirname							)

							def 				lowerCamelCase_			(							self:     Optional[Any]							)      ->       List[Any]:





									"""simple docstring"""







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

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

									return image_inputs

							def 				lowerCamelCase_			(							self:     List[str]							)      ->       List[Any]:





									"""simple docstring"""







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

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

									lowercase__						=				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:     List[Any]							)      ->       Dict:





									"""simple docstring"""







									lowercase__						=				self.get_image_processor()
									lowercase__						=				self.get_tokenizer()

									lowercase__						=				BlipaProcessor(tokenizer=_lowerCAmelCase   ,						image_processor=_lowerCAmelCase							)

									lowercase__						=				self.prepare_image_inputs()

									lowercase__						=				image_processor(_lowerCAmelCase   ,						return_tensors='''np'''							)
									lowercase__						=				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:     Optional[int]							)      ->       Union[str, Any]:





									"""simple docstring"""







									lowercase__						=				self.get_image_processor()
									lowercase__						=				self.get_tokenizer()

									lowercase__						=				BlipaProcessor(tokenizer=_lowerCAmelCase   ,						image_processor=_lowerCAmelCase							)

									lowercase__						=				'''lower newer'''

									lowercase__						=				processor(text=_lowerCAmelCase							)

									lowercase__						=				tokenizer(_lowerCAmelCase   ,						return_token_type_ids=_lowerCAmelCase							)

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

							def 				lowerCamelCase_			(							self:     List[str]							)      ->       Dict:





									"""simple docstring"""







									lowercase__						=				self.get_image_processor()
									lowercase__						=				self.get_tokenizer()

									lowercase__						=				BlipaProcessor(tokenizer=_lowerCAmelCase   ,						image_processor=_lowerCAmelCase							)

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

									lowercase__						=				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:     Dict							)      ->       Optional[Any]:





									"""simple docstring"""







									lowercase__						=				self.get_image_processor()
									lowercase__						=				self.get_tokenizer()

									lowercase__						=				BlipaProcessor(tokenizer=_lowerCAmelCase   ,						image_processor=_lowerCAmelCase							)

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

									lowercase__						=				processor.batch_decode(_lowerCAmelCase							)
									lowercase__						=				tokenizer.batch_decode(_lowerCAmelCase							)

									self.assertListEqual(_lowerCAmelCase   ,						_lowerCAmelCase							)


							def 				lowerCamelCase_			(							self:     Any							)      ->       List[Any]:





									"""simple docstring"""







									lowercase__						=				self.get_image_processor()
									lowercase__						=				self.get_tokenizer()

									lowercase__						=				BlipaProcessor(tokenizer=_lowerCAmelCase   ,						image_processor=_lowerCAmelCase							)

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

									lowercase__						=				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''']							)





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase	=							{
    'configuration_luke': ['LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LukeConfig'],
    'tokenization_luke': ['LukeTokenizer'],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase	=							[
			    'LUKE_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'LukeForEntityClassification',
			    'LukeForEntityPairClassification',
			    'LukeForEntitySpanClassification',
			    'LukeForMultipleChoice',
			    'LukeForQuestionAnswering',
			    'LukeForSequenceClassification',
			    'LukeForTokenClassification',
			    'LukeForMaskedLM',
			    'LukeModel',
			    'LukePreTrainedModel',
			]


if TYPE_CHECKING:
			from .configuration_luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig
			from .tokenization_luke import LukeTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_luke import (
						    LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
						    LukeForEntityClassification,
						    LukeForEntityPairClassification,
						    LukeForEntitySpanClassification,
						    LukeForMaskedLM,
						    LukeForMultipleChoice,
						    LukeForQuestionAnswering,
						    LukeForSequenceClassification,
						    LukeForTokenClassification,
						    LukeModel,
						    LukePreTrainedModel,
						)

else:
			import sys

			lowerCAmelCase	=							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


