


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			):

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





									"""simple docstring"""







									lowercase__            =      tempfile.mkdtemp()

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

									lowercase__            =      InstructBlipProcessor(UpperCamelCase_    ,						UpperCamelCase_    ,						UpperCamelCase_							)

									processor.save_pretrained(self.tmpdirname							)

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





									"""simple docstring"""







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

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





									"""simple docstring"""







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

							def       lowerCamelCase_ (							self:	Tuple    ,						**UpperCamelCase_:	Optional[Any]							)			->   Optional[Any]:





									"""simple docstring"""







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

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





									"""simple docstring"""







									shutil.rmtree(self.tmpdirname							)

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





									"""simple docstring"""







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

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

									return image_inputs

							def       lowerCamelCase_ (							self:	Union[str, Any]							)			->   Dict:





									"""simple docstring"""







									lowercase__            =      InstructBlipProcessor(
									    tokenizer=self.get_tokenizer()    ,						image_processor=self.get_image_processor()    ,						qformer_tokenizer=self.get_qformer_tokenizer()    ,						)
									processor.save_pretrained(self.tmpdirname							)

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

									lowercase__            =      InstructBlipProcessor.from_pretrained(
									    self.tmpdirname    ,						bos_token='''(BOS)'''    ,						eos_token='''(EOS)'''    ,						do_normalize=UpperCamelCase_    ,						padding_value=1.0							)

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

									self.assertEqual(processor.image_processor.to_json_string()    ,						image_processor_add_kwargs.to_json_string()							)
									self.assertIsInstance(processor.image_processor    ,						UpperCamelCase_							)
									self.assertIsInstance(processor.qformer_tokenizer    ,						UpperCamelCase_							)

							def       lowerCamelCase_ (							self:	Any							)			->   Tuple:





									"""simple docstring"""







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

									lowercase__            =      InstructBlipProcessor(
									    tokenizer=UpperCamelCase_    ,						image_processor=UpperCamelCase_    ,						qformer_tokenizer=UpperCamelCase_							)

									lowercase__            =      self.prepare_image_inputs()

									lowercase__            =      image_processor(UpperCamelCase_    ,						return_tensors='''np'''							)
									lowercase__            =      processor(images=UpperCamelCase_    ,						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:	int							)			->   Any:





									"""simple docstring"""







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

									lowercase__            =      InstructBlipProcessor(
									    tokenizer=UpperCamelCase_    ,						image_processor=UpperCamelCase_    ,						qformer_tokenizer=UpperCamelCase_							)

									lowercase__            =      '''lower newer'''

									lowercase__            =      processor(text=UpperCamelCase_							)

									lowercase__            =      tokenizer(UpperCamelCase_    ,						return_token_type_ids=UpperCamelCase_							)
									lowercase__            =      qformer_tokenizer(UpperCamelCase_    ,						return_token_type_ids=UpperCamelCase_							)

									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:	List[Any]							)			->   Union[str, Any]:





									"""simple docstring"""







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

									lowercase__            =      InstructBlipProcessor(
									    tokenizer=UpperCamelCase_    ,						image_processor=UpperCamelCase_    ,						qformer_tokenizer=UpperCamelCase_							)

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

									lowercase__            =      processor(text=UpperCamelCase_    ,						images=UpperCamelCase_							)

									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(UpperCamelCase_							):
											processor()

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





									"""simple docstring"""







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

									lowercase__            =      InstructBlipProcessor(
									    tokenizer=UpperCamelCase_    ,						image_processor=UpperCamelCase_    ,						qformer_tokenizer=UpperCamelCase_							)

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

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

									self.assertListEqual(UpperCamelCase_    ,						UpperCamelCase_							)


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





									"""simple docstring"""







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

									lowercase__            =      InstructBlipProcessor(
									    tokenizer=UpperCamelCase_    ,						image_processor=UpperCamelCase_    ,						qformer_tokenizer=UpperCamelCase_							)

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

									lowercase__            =      processor(text=UpperCamelCase_    ,						images=UpperCamelCase_							)

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










'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


_A	:			Union[str, Any]         ={
    '''configuration_swiftformer''': [
        '''SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''SwiftFormerConfig''',
        '''SwiftFormerOnnxConfig''',
    ]
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A	:			Optional[Any]         =[
			    '''SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''SwiftFormerForImageClassification''',
			    '''SwiftFormerModel''',
			    '''SwiftFormerPreTrainedModel''',
			]

if TYPE_CHECKING:
			from .configuration_swiftformer import (
			    SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    SwiftFormerConfig,
			    SwiftFormerOnnxConfig,
			)

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_swiftformer import (
						    SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
						    SwiftFormerForImageClassification,
						    SwiftFormerModel,
						    SwiftFormerPreTrainedModel,
						)

else:
			import sys

			_A	:			Tuple         =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

