



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 			_snake_case     (   unittest.TestCase):




				def 							A__ (  self   :  Tuple  ):
							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(__lowercase,   __lowercase,   __lowercase  )

							processor.save_pretrained(self.tmpdirname  )




				def 							A__ (  self   :  List[str],   **__lowercase   :  int  ):
							return AutoProcessor.from_pretrained(self.tmpdirname,   **__lowercase  ).tokenizer




				def 							A__ (  self   :  Any,   **__lowercase   :  Dict  ):
							return AutoProcessor.from_pretrained(self.tmpdirname,   **__lowercase  ).image_processor




				def 							A__ (  self   :  Any,   **__lowercase   :  List[str]  ):
							return AutoProcessor.from_pretrained(self.tmpdirname,   **__lowercase  ).qformer_tokenizer




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




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

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

							return image_inputs




				def 							A__ (  self   :  List[Any]  ):
							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=__lowercase,   padding_value=1.0  )

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

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

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




				def 							A__ (  self   :  Dict  ):
							lowercase__   					=			self.get_image_processor()
							lowercase__   					=			self.get_tokenizer()
							lowercase__   					=			self.get_qformer_tokenizer()

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

							lowercase__   					=			self.prepare_image_inputs()

							lowercase__   					=			image_processor(__lowercase,   return_tensors="np"  )
							lowercase__   					=			processor(images=__lowercase,   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 							A__ (  self   :  Dict  ):
							lowercase__   					=			self.get_image_processor()
							lowercase__   					=			self.get_tokenizer()
							lowercase__   					=			self.get_qformer_tokenizer()

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

							lowercase__   					=			"lower newer"

							lowercase__   					=			processor(text=__lowercase  )

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

							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 							A__ (  self   :  Tuple  ):
							lowercase__   					=			self.get_image_processor()
							lowercase__   					=			self.get_tokenizer()
							lowercase__   					=			self.get_qformer_tokenizer()

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

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

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

							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(__lowercase  ):
										processor()




				def 							A__ (  self   :  List[str]  ):
							lowercase__   					=			self.get_image_processor()
							lowercase__   					=			self.get_tokenizer()
							lowercase__   					=			self.get_qformer_tokenizer()

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

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

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

							self.assertListEqual(__lowercase,   __lowercase  )




				def 							A__ (  self   :  str  ):
							lowercase__   					=			self.get_image_processor()
							lowercase__   					=			self.get_tokenizer()
							lowercase__   					=			self.get_qformer_tokenizer()

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

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

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

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









from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
							if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.25.0""")):
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							from ...utils.dummy_torch_and_transformers_objects import (
							    VersatileDiffusionDualGuidedPipeline,
							    VersatileDiffusionImageVariationPipeline,
							    VersatileDiffusionPipeline,
							    VersatileDiffusionTextToImagePipeline,
							)
else:
							from .modeling_text_unet import UNetFlatConditionModel
							from .pipeline_versatile_diffusion import VersatileDiffusionPipeline
							from .pipeline_versatile_diffusion_dual_guided import VersatileDiffusionDualGuidedPipeline
							from .pipeline_versatile_diffusion_image_variation import VersatileDiffusionImageVariationPipeline
							from .pipeline_versatile_diffusion_text_to_image import VersatileDiffusionTextToImagePipeline




