





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 			__UpperCamelCase    (							unittest.TestCase    ):





     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   tempfile.mkdtemp()

        _UpperCAmelCase           =   BlipImageProcessor()
        _UpperCAmelCase           =   BertTokenizer.from_pretrained('''hf-internal-testing/tiny-random-BertModel'''					)

        _UpperCAmelCase           =   BlipProcessor(_UpperCamelCase				,							_UpperCamelCase					)

        processor.save_pretrained(self.tmpdirname					)





     def 		UpperCamelCase(     self				,							**_UpperCamelCase					):
        return AutoProcessor.from_pretrained(self.tmpdirname				,							**_UpperCamelCase					).tokenizer





     def 		UpperCamelCase(     self				,							**_UpperCamelCase					):
        return AutoProcessor.from_pretrained(self.tmpdirname				,							**_UpperCamelCase					).image_processor





     def 		UpperCamelCase(     self					):
        shutil.rmtree(self.tmpdirname					)





     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   [np.random.randint(255				,							size=(3, 30, 400)				,							dtype=np.uinta					)]

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

        return image_inputs





     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   BlipProcessor(tokenizer=self.get_tokenizer()				,							image_processor=self.get_image_processor()					)
        processor.save_pretrained(self.tmpdirname					)

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

        _UpperCAmelCase           =   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 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   self.get_image_processor()
        _UpperCAmelCase           =   self.get_tokenizer()

        _UpperCAmelCase           =   BlipProcessor(tokenizer=_UpperCamelCase				,							image_processor=_UpperCamelCase					)

        _UpperCAmelCase           =   self.prepare_image_inputs()

        _UpperCAmelCase           =   image_processor(_UpperCamelCase				,							return_tensors='''np'''					)
        _UpperCAmelCase           =   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 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   self.get_image_processor()
        _UpperCAmelCase           =   self.get_tokenizer()

        _UpperCAmelCase           =   BlipProcessor(tokenizer=_UpperCamelCase				,							image_processor=_UpperCamelCase					)

        _UpperCAmelCase           =   '''lower newer'''

        _UpperCAmelCase           =   processor(text=_UpperCamelCase					)

        _UpperCAmelCase           =   tokenizer(_UpperCamelCase				,							return_token_type_ids=_UpperCamelCase					)

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





     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   self.get_image_processor()
        _UpperCAmelCase           =   self.get_tokenizer()

        _UpperCAmelCase           =   BlipProcessor(tokenizer=_UpperCamelCase				,							image_processor=_UpperCamelCase					)

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

        _UpperCAmelCase           =   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 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   self.get_image_processor()
        _UpperCAmelCase           =   self.get_tokenizer()

        _UpperCAmelCase           =   BlipProcessor(tokenizer=_UpperCamelCase				,							image_processor=_UpperCamelCase					)

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

        _UpperCAmelCase           =   processor.batch_decode(_UpperCamelCase					)
        _UpperCAmelCase           =   tokenizer.batch_decode(_UpperCamelCase					)

        self.assertListEqual(_UpperCamelCase				,							_UpperCamelCase					)





     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   self.get_image_processor()
        _UpperCAmelCase           =   self.get_tokenizer()

        _UpperCAmelCase           =   BlipProcessor(tokenizer=_UpperCamelCase				,							image_processor=_UpperCamelCase					)

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

        _UpperCAmelCase           =   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''']					)
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


_lowercase:	Any       			=		{'''configuration_swin''': ['''SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SwinConfig''', '''SwinOnnxConfig''']}


try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowercase:	str       			=		[
				    '''SWIN_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''SwinForImageClassification''',
				    '''SwinForMaskedImageModeling''',
				    '''SwinModel''',
				    '''SwinPreTrainedModel''',
				    '''SwinBackbone''',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowercase:	str       			=		[
				    '''TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''TFSwinForImageClassification''',
				    '''TFSwinForMaskedImageModeling''',
				    '''TFSwinModel''',
				    '''TFSwinPreTrainedModel''',
				]

if TYPE_CHECKING:
				from .configuration_swin import SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinConfig, SwinOnnxConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_swin import (
								    SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
								    SwinBackbone,
								    SwinForImageClassification,
								    SwinForMaskedImageModeling,
								    SwinModel,
								    SwinPreTrainedModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_swin import (
								    TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFSwinForImageClassification,
								    TFSwinForMaskedImageModeling,
								    TFSwinModel,
								    TFSwinPreTrainedModel,
								)

else:
				import sys

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






