






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     lowerCamelCase_					(   unittest.TestCase			):







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:int   =   tempfile.mkdtemp()

								__magic_name__		:Optional[int]   =   BlipImageProcessor()
								__magic_name__		:Optional[Any]   =   GPTaTokenizer.from_pretrained('''hf-internal-testing/tiny-random-GPT2Model'''  )
								__magic_name__		:Union[str, Any]   =   BertTokenizerFast.from_pretrained('''hf-internal-testing/tiny-random-bert'''  )

								__magic_name__		:List[str]   =   InstructBlipProcessor(__lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase  )

								processor.save_pretrained(self.tmpdirname  )







				def       A						(    self							,       **__lowerCAmelCase  ):
								"""simple docstring"""
								return AutoProcessor.from_pretrained(self.tmpdirname							,       **__lowerCAmelCase  ).tokenizer







				def       A						(    self							,       **__lowerCAmelCase  ):
								"""simple docstring"""
								return AutoProcessor.from_pretrained(self.tmpdirname							,       **__lowerCAmelCase  ).image_processor







				def       A						(    self							,       **__lowerCAmelCase  ):
								"""simple docstring"""
								return AutoProcessor.from_pretrained(self.tmpdirname							,       **__lowerCAmelCase  ).qformer_tokenizer







				def       A						(    self  ):
								"""simple docstring"""
								shutil.rmtree(self.tmpdirname  )







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Optional[int]   =   [np.random.randint(2_5_5							,       size=(3, 3_0, 4_0_0)							,       dtype=np.uinta  )]

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

								return image_inputs







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Optional[Any]   =   InstructBlipProcessor(
								    tokenizer=self.get_tokenizer()							,       image_processor=self.get_image_processor()							,       qformer_tokenizer=self.get_qformer_tokenizer()							,       )
								processor.save_pretrained(self.tmpdirname  )

								__magic_name__		:Tuple   =   self.get_tokenizer(bos_token='''(BOS)'''							,       eos_token='''(EOS)'''  )
								__magic_name__		:Optional[int]   =   self.get_image_processor(do_normalize=__lowerCAmelCase							,       padding_value=1.0  )

								__magic_name__		:Optional[int]   =   InstructBlipProcessor.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  )
								self.assertIsInstance(processor.qformer_tokenizer							,       __lowerCAmelCase  )







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:List[str]   =   self.get_image_processor()
								__magic_name__		:List[Any]   =   self.get_tokenizer()
								__magic_name__		:str   =   self.get_qformer_tokenizer()

								__magic_name__		:Any   =   InstructBlipProcessor(
								    tokenizer=__lowerCAmelCase							,       image_processor=__lowerCAmelCase							,       qformer_tokenizer=__lowerCAmelCase  )

								__magic_name__		:List[Any]   =   self.prepare_image_inputs()

								__magic_name__		:Optional[int]   =   image_processor(__lowerCAmelCase							,       return_tensors='''np'''  )
								__magic_name__		:List[Any]   =   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       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:int   =   self.get_image_processor()
								__magic_name__		:Optional[Any]   =   self.get_tokenizer()
								__magic_name__		:str   =   self.get_qformer_tokenizer()

								__magic_name__		:str   =   InstructBlipProcessor(
								    tokenizer=__lowerCAmelCase							,       image_processor=__lowerCAmelCase							,       qformer_tokenizer=__lowerCAmelCase  )

								__magic_name__		:Optional[int]   =   '''lower newer'''

								__magic_name__		:Any   =   processor(text=__lowerCAmelCase  )

								__magic_name__		:str   =   tokenizer(__lowerCAmelCase							,       return_token_type_ids=__lowerCAmelCase  )
								__magic_name__		:Any   =   qformer_tokenizer(__lowerCAmelCase							,       return_token_type_ids=__lowerCAmelCase  )

								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  ):
								"""simple docstring"""
								__magic_name__		:Optional[int]   =   self.get_image_processor()
								__magic_name__		:Union[str, Any]   =   self.get_tokenizer()
								__magic_name__		:Union[str, Any]   =   self.get_qformer_tokenizer()

								__magic_name__		:List[Any]   =   InstructBlipProcessor(
								    tokenizer=__lowerCAmelCase							,       image_processor=__lowerCAmelCase							,       qformer_tokenizer=__lowerCAmelCase  )

								__magic_name__		:str   =   '''lower newer'''
								__magic_name__		:Any   =   self.prepare_image_inputs()

								__magic_name__		:Optional[Any]   =   processor(text=__lowerCAmelCase							,       images=__lowerCAmelCase  )

								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(__lowerCAmelCase  ):
												processor()







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Any   =   self.get_image_processor()
								__magic_name__		:List[Any]   =   self.get_tokenizer()
								__magic_name__		:int   =   self.get_qformer_tokenizer()

								__magic_name__		:str   =   InstructBlipProcessor(
								    tokenizer=__lowerCAmelCase							,       image_processor=__lowerCAmelCase							,       qformer_tokenizer=__lowerCAmelCase  )

								__magic_name__		:int   =   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

								__magic_name__		:Dict   =   processor.batch_decode(__lowerCAmelCase  )
								__magic_name__		:Tuple   =   tokenizer.batch_decode(__lowerCAmelCase  )

								self.assertListEqual(__lowerCAmelCase							,       __lowerCAmelCase  )







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Union[str, Any]   =   self.get_image_processor()
								__magic_name__		:Dict   =   self.get_tokenizer()
								__magic_name__		:str   =   self.get_qformer_tokenizer()

								__magic_name__		:Any   =   InstructBlipProcessor(
								    tokenizer=__lowerCAmelCase							,       image_processor=__lowerCAmelCase							,       qformer_tokenizer=__lowerCAmelCase  )

								__magic_name__		:Tuple   =   '''lower newer'''
								__magic_name__		:Tuple   =   self.prepare_image_inputs()

								__magic_name__		:int   =   processor(text=__lowerCAmelCase							,       images=__lowerCAmelCase  )

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













import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


UpperCamelCase__           =			False

class 							__SCREAMING_SNAKE_CASE       (  unittest.TestCase							):
							pass






@nightly
@require_torch_gpu
class 							__SCREAMING_SNAKE_CASE       (  unittest.TestCase							):






							def 					_lowerCamelCase		(    self	):
														# clean up the VRAM after each test
														super().tearDown()
														gc.collect()
														torch.cuda.empty_cache()






							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      VersatileDiffusionTextToImagePipeline.from_pretrained("""shi-labs/versatile-diffusion"""	)
														# remove text_unet
														pipe.remove_unused_weights()
														pipe.to(__lowerCAmelCase	)
														pipe.set_progress_bar_config(disable=__lowerCAmelCase	)

														UpperCamelCase__					  =      """A painting of a squirrel eating a burger """
														UpperCamelCase__					  =      torch.manual_seed(0	)
														UpperCamelCase__					  =      pipe(
														    prompt=__lowerCAmelCase	,			generator=__lowerCAmelCase	,			guidance_scale=7.5	,			num_inference_steps=2	,			output_type="""numpy"""	).images

														with tempfile.TemporaryDirectory() as tmpdirname:
																					pipe.save_pretrained(__lowerCAmelCase	)
																					UpperCamelCase__					  =      VersatileDiffusionTextToImagePipeline.from_pretrained(__lowerCAmelCase	)
														pipe.to(__lowerCAmelCase	)
														pipe.set_progress_bar_config(disable=__lowerCAmelCase	)

														UpperCamelCase__					  =      generator.manual_seed(0	)
														UpperCamelCase__					  =      pipe(
														    prompt=__lowerCAmelCase	,			generator=__lowerCAmelCase	,			guidance_scale=7.5	,			num_inference_steps=2	,			output_type="""numpy"""	).images

														assert np.abs(image - new_image	).sum() < 1E-5, "Models don't have the same forward pass"







							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      VersatileDiffusionTextToImagePipeline.from_pretrained(
														    """shi-labs/versatile-diffusion"""	,			torch_dtype=torch.floataa	)
														pipe.to(__lowerCAmelCase	)
														pipe.set_progress_bar_config(disable=__lowerCAmelCase	)

														UpperCamelCase__					  =      """A painting of a squirrel eating a burger """
														UpperCamelCase__					  =      torch.manual_seed(0	)
														UpperCamelCase__					  =      pipe(
														    prompt=__lowerCAmelCase	,			generator=__lowerCAmelCase	,			guidance_scale=7.5	,			num_inference_steps=50	,			output_type="""numpy"""	).images

														UpperCamelCase__					  =      image[0, 253:256, 253:256, -1]

														assert image.shape == (1, 512, 512, 3)
														UpperCamelCase__					  =      np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778]	)

														assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-2




