




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							):






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      tempfile.mkdtemp()

							SCREAMING_SNAKE_CASE_   : str					  =      BlipImageProcessor()
							SCREAMING_SNAKE_CASE_   : List[str]					  =      BertTokenizer.from_pretrained('hf-internal-testing/tiny-random-BertModel' )

							SCREAMING_SNAKE_CASE_   : Dict					  =      BlipProcessor(lowerCAmelCase__      ,lowerCAmelCase__ )

							processor.save_pretrained(self.tmpdirname )






				def 		snake_case	(			self      ,**snake_case__ ):
							return AutoProcessor.from_pretrained(self.tmpdirname      ,**lowerCAmelCase__ ).tokenizer






				def 		snake_case	(			self      ,**snake_case__ ):
							return AutoProcessor.from_pretrained(self.tmpdirname      ,**lowerCAmelCase__ ).image_processor






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






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

							SCREAMING_SNAKE_CASE_   : Tuple					  =      [Image.fromarray(np.moveaxis(lowerCAmelCase__      ,0      ,-1 ) ) for x in image_inputs]

							return image_inputs






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : str					  =      BlipProcessor(tokenizer=self.get_tokenizer()      ,image_processor=self.get_image_processor() )
							processor.save_pretrained(self.tmpdirname )

							SCREAMING_SNAKE_CASE_   : Tuple					  =      self.get_tokenizer(bos_token='(BOS)'      ,eos_token='(EOS)' )
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      self.get_image_processor(do_normalize=lowerCAmelCase__      ,padding_value=1.0 )

							SCREAMING_SNAKE_CASE_   : List[str]					  =      BlipProcessor.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 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      self.get_image_processor()
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      self.get_tokenizer()

							SCREAMING_SNAKE_CASE_   : Tuple					  =      BlipProcessor(tokenizer=lowerCAmelCase__      ,image_processor=lowerCAmelCase__ )

							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      self.prepare_image_inputs()

							SCREAMING_SNAKE_CASE_   : int					  =      image_processor(lowerCAmelCase__      ,return_tensors='np' )
							SCREAMING_SNAKE_CASE_   : List[str]					  =      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 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : Any					  =      self.get_image_processor()
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      self.get_tokenizer()

							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      BlipProcessor(tokenizer=lowerCAmelCase__      ,image_processor=lowerCAmelCase__ )

							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      "lower newer"

							SCREAMING_SNAKE_CASE_   : int					  =      processor(text=lowerCAmelCase__ )

							SCREAMING_SNAKE_CASE_   : Dict					  =      tokenizer(lowerCAmelCase__      ,return_token_type_ids=lowerCAmelCase__ )

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






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : Optional[int]					  =      self.get_image_processor()
							SCREAMING_SNAKE_CASE_   : Union[str, Any]					  =      self.get_tokenizer()

							SCREAMING_SNAKE_CASE_   : Optional[Any]					  =      BlipProcessor(tokenizer=lowerCAmelCase__      ,image_processor=lowerCAmelCase__ )

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

							SCREAMING_SNAKE_CASE_   : Dict					  =      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 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      self.get_image_processor()
							SCREAMING_SNAKE_CASE_   : Any					  =      self.get_tokenizer()

							SCREAMING_SNAKE_CASE_   : List[str]					  =      BlipProcessor(tokenizer=lowerCAmelCase__      ,image_processor=lowerCAmelCase__ )

							SCREAMING_SNAKE_CASE_   : Tuple					  =      [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

							SCREAMING_SNAKE_CASE_   : List[Any]					  =      processor.batch_decode(lowerCAmelCase__ )
							SCREAMING_SNAKE_CASE_   : List[Any]					  =      tokenizer.batch_decode(lowerCAmelCase__ )

							self.assertListEqual(lowerCAmelCase__      ,lowerCAmelCase__ )






				def 		snake_case	(			self ):
							SCREAMING_SNAKE_CASE_   : int					  =      self.get_image_processor()
							SCREAMING_SNAKE_CASE_   : Tuple					  =      self.get_tokenizer()

							SCREAMING_SNAKE_CASE_   : List[str]					  =      BlipProcessor(tokenizer=lowerCAmelCase__      ,image_processor=lowerCAmelCase__ )

							SCREAMING_SNAKE_CASE_   : List[Any]					  =      "lower newer"
							SCREAMING_SNAKE_CASE_   : int					  =      self.prepare_image_inputs()

							SCREAMING_SNAKE_CASE_   : int					  =      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'] )










"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


a		:					Optional[Any]	      =     {
    '''configuration_owlvit''': [
        '''OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''OwlViTConfig''',
        '''OwlViTOnnxConfig''',
        '''OwlViTTextConfig''',
        '''OwlViTVisionConfig''',
    ],
    '''processing_owlvit''': ['''OwlViTProcessor'''],
}


try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a		:					str	      =     ['''OwlViTFeatureExtractor''']
		a		:					List[Any]	      =     ['''OwlViTImageProcessor''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a		:					List[Any]	      =     [
		    '''OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''OwlViTModel''',
		    '''OwlViTPreTrainedModel''',
		    '''OwlViTTextModel''',
		    '''OwlViTVisionModel''',
		    '''OwlViTForObjectDetection''',
		]

if TYPE_CHECKING:
		from .configuration_owlvit import (
		    OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    OwlViTConfig,
		    OwlViTOnnxConfig,
		    OwlViTTextConfig,
		    OwlViTVisionConfig,
		)
		from .processing_owlvit import OwlViTProcessor

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_owlvit import OwlViTFeatureExtractor
				from .image_processing_owlvit import OwlViTImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_owlvit import (
				    OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    OwlViTForObjectDetection,
				    OwlViTModel,
				    OwlViTPreTrainedModel,
				    OwlViTTextModel,
				    OwlViTVisionModel,
				)

else:
		import sys

		a		:					List[str]	      =     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


