






'''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 _lowercase	(				unittest.TestCase		):




							def 		lowerCamelCase_				(							self:				List[str]     ):
														lowerCamelCase__					:	Optional[int]         =       tempfile.mkdtemp()

														lowerCamelCase__					:	Union[str, Any]         =       BlipImageProcessor()
														lowerCamelCase__					:	str         =       BertTokenizer.from_pretrained("""hf-internal-testing/tiny-random-BertModel"""     )

														lowerCamelCase__					:	Tuple         =       BlipProcessor(UpperCamelCase__			,							UpperCamelCase__     )

														processor.save_pretrained(self.tmpdirname     )




							def 		lowerCamelCase_				(							self:				str			,							**UpperCamelCase__:				Dict     ):
														return AutoProcessor.from_pretrained(self.tmpdirname			,							**UpperCamelCase__     ).tokenizer




							def 		lowerCamelCase_				(							self:				List[Any]			,							**UpperCamelCase__:				Optional[int]     ):
														return AutoProcessor.from_pretrained(self.tmpdirname			,							**UpperCamelCase__     ).image_processor




							def 		lowerCamelCase_				(							self:				str     ):
														shutil.rmtree(self.tmpdirname     )




							def 		lowerCamelCase_				(							self:				Optional[int]     ):
														lowerCamelCase__					:	str         =       [np.random.randint(255			,							size=(3, 30, 400)			,							dtype=np.uinta     )]

														lowerCamelCase__					:	List[Any]         =       [Image.fromarray(np.moveaxis(UpperCamelCase__			,							0			,							-1     )     ) for x in image_inputs]

														return image_inputs




							def 		lowerCamelCase_				(							self:				Dict     ):
														lowerCamelCase__					:	Union[str, Any]         =       BlipProcessor(tokenizer=self.get_tokenizer()			,							image_processor=self.get_image_processor()     )
														processor.save_pretrained(self.tmpdirname     )

														lowerCamelCase__					:	Dict         =       self.get_tokenizer(bos_token="""(BOS)"""			,							eos_token="""(EOS)"""     )
														lowerCamelCase__					:	Dict         =       self.get_image_processor(do_normalize=UpperCamelCase__			,							padding_value=1.0     )

														lowerCamelCase__					:	Optional[Any]         =       BlipProcessor.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__     )




							def 		lowerCamelCase_				(							self:				int     ):
														lowerCamelCase__					:	Any         =       self.get_image_processor()
														lowerCamelCase__					:	Tuple         =       self.get_tokenizer()

														lowerCamelCase__					:	List[str]         =       BlipProcessor(tokenizer=UpperCamelCase__			,							image_processor=UpperCamelCase__     )

														lowerCamelCase__					:	str         =       self.prepare_image_inputs()

														lowerCamelCase__					:	Any         =       image_processor(UpperCamelCase__			,							return_tensors="""np"""     )
														lowerCamelCase__					:	Any         =       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:				List[str]     ):
														lowerCamelCase__					:	str         =       self.get_image_processor()
														lowerCamelCase__					:	List[Any]         =       self.get_tokenizer()

														lowerCamelCase__					:	List[str]         =       BlipProcessor(tokenizer=UpperCamelCase__			,							image_processor=UpperCamelCase__     )

														lowerCamelCase__					:	Tuple         =       """lower newer"""

														lowerCamelCase__					:	Union[str, Any]         =       processor(text=UpperCamelCase__     )

														lowerCamelCase__					:	int         =       tokenizer(UpperCamelCase__			,							return_token_type_ids=UpperCamelCase__     )

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




							def 		lowerCamelCase_				(							self:				Optional[Any]     ):
														lowerCamelCase__					:	List[str]         =       self.get_image_processor()
														lowerCamelCase__					:	List[Any]         =       self.get_tokenizer()

														lowerCamelCase__					:	Tuple         =       BlipProcessor(tokenizer=UpperCamelCase__			,							image_processor=UpperCamelCase__     )

														lowerCamelCase__					:	Any         =       """lower newer"""
														lowerCamelCase__					:	Optional[Any]         =       self.prepare_image_inputs()

														lowerCamelCase__					:	List[str]         =       processor(text=UpperCamelCase__			,							images=UpperCamelCase__     )

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

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




							def 		lowerCamelCase_				(							self:				Dict     ):
														lowerCamelCase__					:	Optional[Any]         =       self.get_image_processor()
														lowerCamelCase__					:	Union[str, Any]         =       self.get_tokenizer()

														lowerCamelCase__					:	Any         =       BlipProcessor(tokenizer=UpperCamelCase__			,							image_processor=UpperCamelCase__     )

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

														lowerCamelCase__					:	Dict         =       processor.batch_decode(UpperCamelCase__     )
														lowerCamelCase__					:	List[Any]         =       tokenizer.batch_decode(UpperCamelCase__     )

														self.assertListEqual(UpperCamelCase__			,							UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				Optional[int]     ):
														lowerCamelCase__					:	Any         =       self.get_image_processor()
														lowerCamelCase__					:	str         =       self.get_tokenizer()

														lowerCamelCase__					:	Union[str, Any]         =       BlipProcessor(tokenizer=UpperCamelCase__			,							image_processor=UpperCamelCase__     )

														lowerCamelCase__					:	Tuple         =       """lower newer"""
														lowerCamelCase__					:	int         =       self.prepare_image_inputs()

														lowerCamelCase__					:	Optional[int]         =       processor(text=UpperCamelCase__			,							images=UpperCamelCase__     )

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









'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_A	:			Dict         ={
    '''configuration_swinv2''': ['''SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Swinv2Config'''],
}


try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A	:			List[Any]         =[
			    '''SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''Swinv2ForImageClassification''',
			    '''Swinv2ForMaskedImageModeling''',
			    '''Swinv2Model''',
			    '''Swinv2PreTrainedModel''',
			]


if TYPE_CHECKING:
			from .configuration_swinva import SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinvaConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_swinva import (
						    SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST,
						    SwinvaForImageClassification,
						    SwinvaForMaskedImageModeling,
						    SwinvaModel,
						    SwinvaPreTrainedModel,
						)


else:
			import sys

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

