

"""simple docstring"""




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      __lowercase			(    unittest.TestCase					):
		pass







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

		def    __lowercase       (  self		:			Optional[Any]    ):






			'''simple docstring'''
			super().tearDown()
			gc.collect()
			torch.cuda.empty_cache()

		def    __lowercase       (  self		:			Optional[Any]    ):






			'''simple docstring'''
			UpperCAmelCase__					:  Tuple					   =					VersatileDiffusionTextToImagePipeline.from_pretrained("""shi-labs/versatile-diffusion"""    )
			# remove text_unet
			pipe.remove_unused_weights()
			pipe.to(lowercase_    )
			pipe.set_progress_bar_config(disable=lowercase_    )

			UpperCAmelCase__					:  int					   =					"""A painting of a squirrel eating a burger """
			UpperCAmelCase__					:  int					   =					torch.manual_seed(0    )
			UpperCAmelCase__					:  Dict					   =					pipe(
			    prompt=lowercase_					,generator=lowercase_					,guidance_scale=7.5					,num_inference_steps=2					,output_type="""numpy"""    ).images

			with tempfile.TemporaryDirectory() as tmpdirname:
				pipe.save_pretrained(lowercase_    )
				UpperCAmelCase__					:  Any					   =					VersatileDiffusionTextToImagePipeline.from_pretrained(lowercase_    )
			pipe.to(lowercase_    )
			pipe.set_progress_bar_config(disable=lowercase_    )

			UpperCAmelCase__					:  int					   =					generator.manual_seed(0    )
			UpperCAmelCase__					:  Any					   =					pipe(
			    prompt=lowercase_					,generator=lowercase_					,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    __lowercase       (  self		:			Dict    ):






			'''simple docstring'''
			UpperCAmelCase__					:  Dict					   =					VersatileDiffusionTextToImagePipeline.from_pretrained(
			    """shi-labs/versatile-diffusion"""					,torch_dtype=torch.floataa    )
			pipe.to(lowercase_    )
			pipe.set_progress_bar_config(disable=lowercase_    )

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

			UpperCAmelCase__					:  List[str]					   =					image[0, 253:256, 253:256, -1]

			assert image.shape == (1, 512, 512, 3)
			UpperCAmelCase__					:  str					   =					np.array([0.3_3_6_7, 0.3_1_6_9, 0.2_6_5_6, 0.3_8_7_0, 0.4_7_9_0, 0.3_7_9_6, 0.4_0_0_9, 0.4_8_7_8, 0.4_7_7_8]    )

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








"""simple docstring"""




from typing import Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
      from PIL import Image

      from ..image_utils import load_image

if is_torch_available():
      from ..models.auto.modeling_auto import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING

__UpperCAmelCase =				logging.get_logger(__name__)







@add_end_docstrings(__lowerCamelCase					)
class      __lowercase			(    __lowerCamelCase					):

  def __init__(  self		:			List[str]					,*A		:			Optional[int]					,**A		:			List[str]    ):






   '''simple docstring'''
   super().__init__(*A					,**A    )
   self.check_model_type(A    )

  def    __lowercase       (  self		:			Optional[int]					,A		:			Dict=None					,A		:			Optional[int]=None					,A		:			Tuple=None					,**A		:			str    ):






   '''simple docstring'''
   UpperCAmelCase__   ,      UpperCAmelCase__					:  Any					   =					{}, {}
   if padding is not None:
    UpperCAmelCase__					:  Union[str, Any]					   =					padding
   if truncation is not None:
    UpperCAmelCase__					:  List[str]					   =					truncation
   if top_k is not None:
    UpperCAmelCase__					:  List[Any]					   =					top_k
   return preprocess_params, {}, postprocess_params

  def __call__(  self		:			List[Any]					,A		:			Union["Image.Image", str]					,A		:			str = None					,**A		:			Union[str, Any]    ):






   '''simple docstring'''
   if isinstance(A					,(Image.Image, str)    ) and isinstance(A					,A    ):
    UpperCAmelCase__					:  Any					   =					{"""image""": image, """question""": question}
   else:
    UpperCAmelCase__					:  Dict					   =					image
   UpperCAmelCase__					:  Dict					   =					super().__call__(A					,**A    )
   return results

  def    __lowercase       (  self		:			Optional[int]					,A		:			Optional[Any]					,A		:			Optional[Any]=False					,A		:			List[Any]=False    ):






   '''simple docstring'''
   UpperCAmelCase__					:  List[Any]					   =					load_image(inputs["""image"""]    )
   UpperCAmelCase__					:  Optional[Any]					   =					self.tokenizer(
       inputs["""question"""]					,return_tensors=self.framework					,padding=A					,truncation=A    )
   UpperCAmelCase__					:  int					   =					self.image_processor(images=A					,return_tensors=self.framework    )
   model_inputs.update(A    )
   return model_inputs

  def    __lowercase       (  self		:			Dict					,A		:			Tuple    ):






   '''simple docstring'''
   UpperCAmelCase__					:  List[Any]					   =					self.model(**A    )
   return model_outputs




  def    __lowercase       (  self		:			int					,A		:			List[Any]					,A		:			List[str]=5    ):






   '''simple docstring'''
   if top_k > self.model.config.num_labels:
    UpperCAmelCase__					:  Any					   =					self.model.config.num_labels

   if self.framework == "pt":
    UpperCAmelCase__					:  Optional[int]					   =					model_outputs.logits.sigmoid()[0]
    UpperCAmelCase__   ,      UpperCAmelCase__					:  Optional[int]					   =					probs.topk(A    )
   else:
    raise ValueError(f"Unsupported framework: {self.framework}"    )

   UpperCAmelCase__					:  List[str]					   =					scores.tolist()
   UpperCAmelCase__					:  Optional[int]					   =					ids.tolist()
   return [{"score": score, "answer": self.model.config.idalabel[_id]} for score, _id in zip(A					,A    )]





