


from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding


class 					lowerCAmelCase_ (       _a):
						lowerCamelCase_         =     ['image_processor', 'tokenizer']
						lowerCamelCase_         =     'AutoImageProcessor'
						lowerCamelCase_         =     'AutoTokenizer'




						def __init__(	self						:   Any   ,     __A						:   List[Any]   ,     __A						:   Optional[int]						)      ->Union[str, Any]:






									"""simple docstring"""






									super().__init__(__A   ,     __A						)
									a__ :Optional[int]						  =    self.image_processor




						def __call__(	self						:   List[Any]   ,     __A						:   Tuple=None   ,     __A						:   Union[str, Any]=None   ,     __A						:   Optional[int]=None   ,     **__A						:   Union[str, Any]						)      ->List[Any]:






									"""simple docstring"""






									if text is None and images is None:
												raise ValueError("You have to specify either text or images. Both cannot be none."						)

									if text is not None:
												a__ :Dict						  =    self.tokenizer(__A   ,     return_tensors=__A   ,     **__A						)

									if images is not None:
												a__ :Dict						  =    self.image_processor(__A   ,     return_tensors=__A   ,     **__A						)

									if text is not None and images is not None:
												a__ :Optional[Any]						  =    image_features.pixel_values
												return encoding
									elif text is not None:
												return encoding
									else:
												return BatchEncoding(data=dict(**__A						)   ,     tensor_type=__A						)




						def  _snake_case					(	self						:   int   ,     *__A						:   Optional[int]   ,     **__A						:   List[str]						)      ->List[str]:






									"""simple docstring"""






									return self.tokenizer.batch_decode(*__A   ,     **__A						)




						def  _snake_case					(	self						:   List[Any]   ,     *__A						:   Optional[Any]   ,     **__A						:   Dict						)      ->Tuple:






									"""simple docstring"""






									return self.tokenizer.decode(*__A   ,     **__A						)






						@property
						def  _snake_case					(	self						:   List[Any]						)      ->str:






									"""simple docstring"""






									return ["input_ids", "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


snake_case__			 =						False


class 					lowerCAmelCase_ (       unittest.TestCase):
						pass






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




						def  _snake_case					(	self						:   Tuple						)      ->Optional[int]:






									"""simple docstring"""






									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()




						def  _snake_case					(	self						:   Dict						)      ->Any:






									"""simple docstring"""






									a__ :Union[str, Any]						  =    VersatileDiffusionTextToImagePipeline.from_pretrained("shi-labs/versatile-diffusion"						)
									# remove text_unet
									pipe.remove_unused_weights()
									pipe.to(__A						)
									pipe.set_progress_bar_config(disable=__A						)

									a__ :List[Any]						  =    "A painting of a squirrel eating a burger "
									a__ :Optional[Any]						  =    torch.manual_seed(0						)
									a__ :List[Any]						  =    pipe(
									    prompt=__A   ,     generator=__A   ,     guidance_scale=7.5   ,     num_inference_steps=2   ,     output_type="numpy"						).images

									with tempfile.TemporaryDirectory() as tmpdirname:
												pipe.save_pretrained(__A						)
												a__ :List[Any]						  =    VersatileDiffusionTextToImagePipeline.from_pretrained(__A						)
									pipe.to(__A						)
									pipe.set_progress_bar_config(disable=__A						)

									a__ :Optional[int]						  =    generator.manual_seed(0						)
									a__ :List[Any]						  =    pipe(
									    prompt=__A   ,     generator=__A   ,     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  _snake_case					(	self						:   Optional[Any]						)      ->List[Any]:






									"""simple docstring"""






									a__ :Tuple						  =    VersatileDiffusionTextToImagePipeline.from_pretrained(
									    "shi-labs/versatile-diffusion"   ,     torch_dtype=torch.floataa						)
									pipe.to(__A						)
									pipe.set_progress_bar_config(disable=__A						)

									a__ :Tuple						  =    "A painting of a squirrel eating a burger "
									a__ :Tuple						  =    torch.manual_seed(0						)
									a__ :Optional[Any]						  =    pipe(
									    prompt=__A   ,     generator=__A   ,     guidance_scale=7.5   ,     num_inference_steps=50   ,     output_type="numpy"						).images

									a__ :Tuple						  =    image[0, 253:256, 253:256, -1]

									assert image.shape == (1, 512, 512, 3)
									a__ :Tuple						  =    np.array([0.3_367, 0.3_169, 0.2_656, 0.3_870, 0.4_790, 0.3_796, 0.4_009, 0.4_878, 0.4_778]						)

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


