"""simple docstring"""
import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionModelEditingPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()




@skip_mps
class   __snake_case			(	__lowerCAmelCase			, __lowerCAmelCase			, __lowerCAmelCase			, unittest.TestCase							):
      a__								=						StableDiffusionModelEditingPipeline
      a__								=						TEXT_TO_IMAGE_PARAMS
      a__								=						TEXT_TO_IMAGE_BATCH_PARAMS
      a__								=						TEXT_TO_IMAGE_IMAGE_PARAMS
      a__								=						TEXT_TO_IMAGE_IMAGE_PARAMS



      def     lowerCamelCase_  (		self)      ->     List[str]:





        '''simple docstring'''





        torch.manual_seed(0)
        a__:	Tuple     				=      UNetaDConditionModel(
            block_out_channels=(32, 64)       ,     layers_per_block=2       ,     sample_size=32       ,     in_channels=4       ,     out_channels=4       ,     down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')       ,     up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')       ,     cross_attention_dim=32       ,     )
        a__:	Tuple     				=      DDIMScheduler()
        torch.manual_seed(0)
        a__:	List[str]     				=      AutoencoderKL(
            block_out_channels=[32, 64]       ,     in_channels=3       ,     out_channels=3       ,     down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']       ,     up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']       ,     latent_channels=4       ,     )
        torch.manual_seed(0)
        a__:	Any     				=      CLIPTextConfig(
            bos_token_id=0       ,     eos_token_id=2       ,     hidden_size=32       ,     intermediate_size=37       ,     layer_norm_eps=1e-05       ,     num_attention_heads=4       ,     num_hidden_layers=5       ,     pad_token_id=1       ,     vocab_size=10_00       ,     )
        a__:	str     				=      CLIPTextModel(lowercase)
        a__:	Any     				=      CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip')

        a__:	List[Any]     				=      {
            'unet': unet,
            'scheduler': scheduler,
            'vae': vae,
            'text_encoder': text_encoder,
            'tokenizer': tokenizer,
            'safety_checker': None,
            'feature_extractor': None,
        }
        return components



      def     lowerCamelCase_  (		self       ,     lowercase       ,     lowercase=0)      ->     int:





        '''simple docstring'''





        a__:	Union[str, Any]     				=      torch.manual_seed(lowercase)
        a__:	Optional[int]     				=      {
            'prompt': 'A field of roses',
            'generator': generator,
            # Setting height and width to None to prevent OOMs on CPU.
            'height': None,
            'width': None,
            'num_inference_steps': 2,
            'guidance_scale': 6.0,
            'output_type': 'numpy',
        }
        return inputs



      def     lowerCamelCase_  (		self)      ->     List[str]:





        '''simple docstring'''





        a__:	List[str]     				=      'cpu'  # ensure determinism for the device-dependent torch.Generator
        a__:	Union[str, Any]     				=      self.get_dummy_components()
        a__:	List[Any]     				=      StableDiffusionModelEditingPipeline(**lowercase)
        a__:	int     				=      sd_pipe.to(lowercase)
        sd_pipe.set_progress_bar_config(disable=lowercase)

        a__:	str     				=      self.get_dummy_inputs(lowercase)
        a__:	str     				=      sd_pipe(**lowercase).images
        a__:	Optional[Any]     				=      image[0, -3:, -3:, -1]
        assert image.shape == (1, 64, 64, 3)

        a__:	Dict     				=      np.array([0.4755, 0.5132, 0.4976, 0.3904, 0.3554, 0.4765, 0.5139, 0.5158, 0.4889])

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



      def     lowerCamelCase_  (		self)      ->     Tuple:





        '''simple docstring'''





        a__:	List[Any]     				=      'cpu'  # ensure determinism for the device-dependent torch.Generator
        a__:	List[Any]     				=      self.get_dummy_components()
        a__:	Union[str, Any]     				=      StableDiffusionModelEditingPipeline(**lowercase)
        a__:	Union[str, Any]     				=      sd_pipe.to(lowercase)
        sd_pipe.set_progress_bar_config(disable=lowercase)

        a__:	Dict     				=      self.get_dummy_inputs(lowercase)
        a__:	Optional[int]     				=      'french fries'
        a__:	List[str]     				=      sd_pipe(**lowercase       ,     negative_prompt=lowercase)
        a__:	str     				=      output.images
        a__:	Optional[int]     				=      image[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)

        a__:	List[Any]     				=      np.array([0.4992, 0.5101, 0.5004, 0.3949, 0.3604, 0.4735, 0.5216, 0.5204, 0.4913])

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



      def     lowerCamelCase_  (		self)      ->     List[Any]:





        '''simple docstring'''





        a__:	Optional[Any]     				=      'cpu'  # ensure determinism for the device-dependent torch.Generator
        a__:	str     				=      self.get_dummy_components()
        a__:	List[Any]     				=      EulerAncestralDiscreteScheduler(
            beta_start=0.00085       ,     beta_end=0.012       ,     beta_schedule='scaled_linear')
        a__:	List[Any]     				=      StableDiffusionModelEditingPipeline(**lowercase)
        a__:	str     				=      sd_pipe.to(lowercase)
        sd_pipe.set_progress_bar_config(disable=lowercase)

        a__:	Optional[Any]     				=      self.get_dummy_inputs(lowercase)
        a__:	Optional[Any]     				=      sd_pipe(**lowercase).images
        a__:	List[str]     				=      image[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)

        a__:	Any     				=      np.array([0.4747, 0.5372, 0.4779, 0.4982, 0.5543, 0.4816, 0.5238, 0.4904, 0.5027])

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



      def     lowerCamelCase_  (		self)      ->     List[Any]:





        '''simple docstring'''





        a__:	str     				=      'cpu'  # ensure determinism for the device-dependent torch.Generator
        a__:	Union[str, Any]     				=      self.get_dummy_components()
        a__:	str     				=      PNDMScheduler()
        a__:	Dict     				=      StableDiffusionModelEditingPipeline(**lowercase)
        a__:	Tuple     				=      sd_pipe.to(lowercase)
        sd_pipe.set_progress_bar_config(disable=lowercase)

        a__:	Optional[Any]     				=      self.get_dummy_inputs(lowercase)
        # the pipeline does not expect pndm so test if it raises error.
        with self.assertRaises(lowercase):
          a__:	str     				=      sd_pipe(**lowercase).images



      def     lowerCamelCase_  (		self)      ->     int:





        '''simple docstring'''





        super().test_inference_batch_single_identical(expected_max_diff=5e-3)



      def     lowerCamelCase_  (		self)      ->     List[str]:





        '''simple docstring'''





        super().test_attention_slicing_forward_pass(expected_max_diff=5e-3)







@slow
@require_torch_gpu
class   __snake_case			(	unittest.TestCase							):



      def     lowerCamelCase_  (		self)      ->     int:





        '''simple docstring'''





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



      def     lowerCamelCase_  (		self       ,     lowercase=0)      ->     int:





        '''simple docstring'''





        a__:	Any     				=      torch.manual_seed(lowercase)
        a__:	Any     				=      {
            'prompt': 'A field of roses',
            'generator': generator,
            'num_inference_steps': 3,
            'guidance_scale': 7.5,
            'output_type': 'numpy',
        }
        return inputs



      def     lowerCamelCase_  (		self)      ->     Optional[int]:





        '''simple docstring'''





        a__:	Optional[Any]     				=      'CompVis/stable-diffusion-v1-4'
        a__:	List[str]     				=      StableDiffusionModelEditingPipeline.from_pretrained(lowercase       ,     safety_checker=lowercase)
        pipe.to(lowercase)
        pipe.set_progress_bar_config(disable=lowercase)
        pipe.enable_attention_slicing()

        a__:	Optional[int]     				=      self.get_inputs()
        a__:	Optional[int]     				=      pipe(**lowercase).images
        a__:	int     				=      image[0, -3:, -3:, -1].flatten()

        assert image.shape == (1, 5_12, 5_12, 3)

        a__:	Optional[Any]     				=      np.array(
            [0.6749496, 0.6386453, 0.51443267, 0.66094905, 0.61921215, 0.5491332, 0.5744417, 0.58075106, 0.5174658])

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

        # make sure image changes after editing
        pipe.edit_model('A pack of roses'       ,     'A pack of blue roses')

        a__:	Dict     				=      pipe(**lowercase).images
        a__:	str     				=      image[0, -3:, -3:, -1].flatten()

        assert image.shape == (1, 5_12, 5_12, 3)

        assert np.abs(expected_slice - image_slice).max() > 1e-1



      def     lowerCamelCase_  (		self)      ->     Optional[int]:





        '''simple docstring'''





        torch.cuda.empty_cache()
        torch.cuda.reset_max_memory_allocated()
        torch.cuda.reset_peak_memory_stats()

        a__:	str     				=      'CompVis/stable-diffusion-v1-4'
        a__:	int     				=      DDIMScheduler.from_pretrained(lowercase       ,     subfolder='scheduler')
        a__:	Any     				=      StableDiffusionModelEditingPipeline.from_pretrained(
            lowercase       ,     scheduler=lowercase       ,     safety_checker=lowercase)
        a__:	List[str]     				=      pipe.to(lowercase)
        pipe.set_progress_bar_config(disable=lowercase)
        pipe.enable_attention_slicing(1)
        pipe.enable_sequential_cpu_offload()

        a__:	Dict     				=      self.get_inputs()
        a__:	Tuple     				=      pipe(**lowercase)

        a__:	Any     				=      torch.cuda.max_memory_allocated()
        # make sure that less than 4.4 GB is allocated
        assert mem_bytes < 4.4 * 10**9






'''simple docstring'''

import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
       import torch

if is_vision_available():
       from PIL import Image

       from transformers import OwlViTImageProcessor







class 			UpperCAmelCase			(							unittest.TestCase):

 def __init__(			self			:						Optional[Any],       a_			:						Optional[int],       a_			:						Dict=7,       a_			:						List[str]=3,       a_			:						int=18,       a_			:						Tuple=30,       a_			:						Any=400,       a_			:						Optional[int]=True,       a_			:						Optional[int]=None,       a_			:						Any=True,       a_			:						Optional[int]=None,       a_			:						int=True,       a_			:						Optional[int]=[0.48_145_466, 0.4_578_275, 0.40_821_073],       a_			:						Optional[Any]=[0.26_862_954, 0.26_130_258, 0.27_577_711],       a_			:						Optional[int]=True,       ):





    """simple docstring"""



    UpperCamelCase__			    =    parent
    UpperCamelCase__			    =    batch_size
    UpperCamelCase__			    =    num_channels
    UpperCamelCase__			    =    image_size
    UpperCamelCase__			    =    min_resolution
    UpperCamelCase__			    =    max_resolution
    UpperCamelCase__			    =    do_resize
    UpperCamelCase__			    =    size if size is not None else {"height": 18, "width": 18}
    UpperCamelCase__			    =    do_center_crop
    UpperCamelCase__			    =    crop_size if crop_size is not None else {"height": 18, "width": 18}
    UpperCamelCase__			    =    do_normalize
    UpperCamelCase__			    =    image_mean
    UpperCamelCase__			    =    image_std
    UpperCamelCase__			    =    do_convert_rgb

 def 		lowercase_    (			self			:						List[str]       ):





    """simple docstring"""



    return {
        "do_resize": self.do_resize,
        "size": self.size,
        "do_center_crop": self.do_center_crop,
        "crop_size": self.crop_size,
        "do_normalize": self.do_normalize,
        "image_mean": self.image_mean,
        "image_std": self.image_std,
        "do_convert_rgb": self.do_convert_rgb,
    }







@require_torch
@require_vision
class 			UpperCAmelCase			(							SCREAMING_SNAKE_CASE__		,      unittest.TestCase):
 _lowerCamelCase			:       Tuple            =       OwlViTImageProcessor if is_vision_available() else None

 def 		lowercase_    (			self			:						int       ):





    """simple docstring"""



    UpperCamelCase__			    =    OwlViTImageProcessingTester(self       )

 @property
 def 		lowercase_    (			self			:						str       ):





    """simple docstring"""



    return self.image_processor_tester.prepare_image_processor_dict()

 def 		lowercase_    (			self			:						Tuple       ):





    """simple docstring"""



    UpperCamelCase__			    =    self.image_processing_class(**self.image_processor_dict       )
    self.assertTrue(hasattr(a_,       "do_resize"       )       )
    self.assertTrue(hasattr(a_,       "size"       )       )
    self.assertTrue(hasattr(a_,       "do_center_crop"       )       )
    self.assertTrue(hasattr(a_,       "center_crop"       )       )
    self.assertTrue(hasattr(a_,       "do_normalize"       )       )
    self.assertTrue(hasattr(a_,       "image_mean"       )       )
    self.assertTrue(hasattr(a_,       "image_std"       )       )
    self.assertTrue(hasattr(a_,       "do_convert_rgb"       )       )

 def 		lowercase_    (			self			:						Any       ):





    """simple docstring"""



    UpperCamelCase__			    =    self.image_processing_class.from_dict(self.image_processor_dict       )
    self.assertEqual(image_processor.size,       {"height": 18, "width": 18}       )
    self.assertEqual(image_processor.crop_size,       {"height": 18, "width": 18}       )

    UpperCamelCase__			    =    self.image_processing_class.from_dict(self.image_processor_dict,       size=42,       crop_size=84       )
    self.assertEqual(image_processor.size,       {"height": 42, "width": 42}       )
    self.assertEqual(image_processor.crop_size,       {"height": 84, "width": 84}       )

 def 		lowercase_    (			self			:						int       ):





    """simple docstring"""



    UpperCamelCase__			    =    self.image_processing_class(**self.image_processor_dict       )
    # create random PIL images
    UpperCamelCase__			    =    prepare_image_inputs(self.image_processor_tester,       equal_resolution=a_       )

    for image in image_inputs:
       self.assertIsInstance(a_,       Image.Image       )

    # Test not batched input
    UpperCamelCase__			    =    image_processing(image_inputs[0],       return_tensors="pt"       ).pixel_values
    self.assertEqual(
        encoded_images.shape,       (
            1,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size["height"],
            self.image_processor_tester.crop_size["width"],
        ),       )

    # Test batched
    UpperCamelCase__			    =    image_processing(a_,       return_tensors="pt"       ).pixel_values
    self.assertEqual(
        encoded_images.shape,       (
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size["height"],
            self.image_processor_tester.crop_size["width"],
        ),       )

 def 		lowercase_    (			self			:						Any       ):





    """simple docstring"""



    UpperCamelCase__			    =    self.image_processing_class(**self.image_processor_dict       )
    # create random numpy tensors
    UpperCamelCase__			    =    prepare_image_inputs(self.image_processor_tester,       equal_resolution=a_,       numpify=a_       )
    for image in image_inputs:
       self.assertIsInstance(a_,       np.ndarray       )

    # Test not batched input
    UpperCamelCase__			    =    image_processing(image_inputs[0],       return_tensors="pt"       ).pixel_values
    self.assertEqual(
        encoded_images.shape,       (
            1,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size["height"],
            self.image_processor_tester.crop_size["width"],
        ),       )

    # Test batched
    UpperCamelCase__			    =    image_processing(a_,       return_tensors="pt"       ).pixel_values
    self.assertEqual(
        encoded_images.shape,       (
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size["height"],
            self.image_processor_tester.crop_size["width"],
        ),       )

 def 		lowercase_    (			self			:						Any       ):





    """simple docstring"""



    UpperCamelCase__			    =    self.image_processing_class(**self.image_processor_dict       )
    # create random PyTorch tensors
    UpperCamelCase__			    =    prepare_image_inputs(self.image_processor_tester,       equal_resolution=a_,       torchify=a_       )
    for image in image_inputs:
       self.assertIsInstance(a_,       torch.Tensor       )

    # Test not batched input
    UpperCamelCase__			    =    image_processing(image_inputs[0],       return_tensors="pt"       ).pixel_values
    self.assertEqual(
        encoded_images.shape,       (
            1,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size["height"],
            self.image_processor_tester.crop_size["width"],
        ),       )

    # Test batched
    UpperCamelCase__			    =    image_processing(a_,       return_tensors="pt"       ).pixel_values
    self.assertEqual(
        encoded_images.shape,       (
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size["height"],
            self.image_processor_tester.crop_size["width"],
        ),       )