
"""simple docstring"""

import random
import unittest

import numpy as np

from diffusers import (
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionImgaImgPipeline,
    PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
      import onnxruntime as ort

class __lowerCAmelCase	(   __SCREAMING_SNAKE_CASE    ,						unittest.TestCase    ):
     lowercase		     =		"hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase=0   ):

      '''simple docstring'''




      __UpperCamelCase       						= floats_tensor((1, 3, 128, 128)			,  rng=random.Random(__UpperCAmelCase   )   )
      __UpperCamelCase       						= np.random.RandomState(__UpperCAmelCase   )
      __UpperCamelCase       						= {
          'prompt': 'A painting of a squirrel eating a burger',
          'image': image,
          'generator': generator,
          'num_inference_steps': 3,
          'strength': 0.7_5,
          'guidance_scale': 7.5,
          'output_type': 'numpy',
      }
      return inputs





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint			,  provider='CPUExecutionProvider'   )
      pipe.set_progress_bar_config(disable=__UpperCAmelCase   )

      __UpperCamelCase       						= self.get_dummy_inputs()
      __UpperCamelCase       						= pipe(**__UpperCAmelCase   ).images
      __UpperCamelCase       						= image[0, -3:, -3:, -1].flatten()

      assert image.shape == (1, 128, 128, 3)
      __UpperCamelCase       						= np.array([0.6_9_6_4_3, 0.5_8_4_8_4, 0.5_0_3_1_4, 0.5_8_7_6_0, 0.5_5_3_6_8, 0.5_9_6_4_3, 0.5_1_5_2_9, 0.4_1_2_1_7, 0.4_9_0_8_7]   )
      assert np.abs(image_slice - expected_slice   ).max() < 1E-1





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint			,  provider='CPUExecutionProvider'   )
      __UpperCamelCase       						= PNDMScheduler.from_config(pipe.scheduler.config			,  skip_prk_steps=__UpperCAmelCase   )
      pipe.set_progress_bar_config(disable=__UpperCAmelCase   )

      __UpperCamelCase       						= self.get_dummy_inputs()
      __UpperCamelCase       						= pipe(**__UpperCAmelCase   ).images
      __UpperCamelCase       						= image[0, -3:, -3:, -1]

      assert image.shape == (1, 128, 128, 3)
      __UpperCamelCase       						= np.array([0.6_1_7_3_7, 0.5_4_6_4_2, 0.5_3_1_8_3, 0.5_4_4_6_5, 0.5_2_7_4_2, 0.6_0_5_2_5, 0.4_9_9_6_9, 0.4_0_6_5_5, 0.4_8_1_5_4]   )

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





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint			,  provider='CPUExecutionProvider'   )
      __UpperCamelCase       						= LMSDiscreteScheduler.from_config(pipe.scheduler.config   )
      pipe.set_progress_bar_config(disable=__UpperCAmelCase   )

      # warmup pass to apply optimizations
      __UpperCamelCase       						= pipe(**self.get_dummy_inputs()   )

      __UpperCamelCase       						= self.get_dummy_inputs()
      __UpperCamelCase       						= pipe(**__UpperCAmelCase   ).images
      __UpperCamelCase       						= image[0, -3:, -3:, -1]

      assert image.shape == (1, 128, 128, 3)
      __UpperCamelCase       						= np.array([0.5_2_7_6_1, 0.5_9_9_7_7, 0.4_9_0_3_3, 0.4_9_6_1_9, 0.5_4_2_8_2, 0.5_0_3_1_1, 0.4_7_6_0_0, 0.4_0_9_1_8, 0.4_5_2_0_3]   )

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





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint			,  provider='CPUExecutionProvider'   )
      __UpperCamelCase       						= EulerDiscreteScheduler.from_config(pipe.scheduler.config   )
      pipe.set_progress_bar_config(disable=__UpperCAmelCase   )

      __UpperCamelCase       						= self.get_dummy_inputs()
      __UpperCamelCase       						= pipe(**__UpperCAmelCase   ).images
      __UpperCamelCase       						= image[0, -3:, -3:, -1]

      assert image.shape == (1, 128, 128, 3)
      __UpperCamelCase       						= np.array([0.5_2_9_1_1, 0.6_0_0_0_4, 0.4_9_2_2_9, 0.4_9_8_0_5, 0.5_4_5_0_2, 0.5_0_6_8_0, 0.4_7_7_7_7, 0.4_1_0_2_8, 0.4_5_3_0_4]   )

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





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint			,  provider='CPUExecutionProvider'   )
      __UpperCamelCase       						= EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config   )
      pipe.set_progress_bar_config(disable=__UpperCAmelCase   )

      __UpperCamelCase       						= self.get_dummy_inputs()
      __UpperCamelCase       						= pipe(**__UpperCAmelCase   ).images
      __UpperCamelCase       						= image[0, -3:, -3:, -1]

      assert image.shape == (1, 128, 128, 3)
      __UpperCamelCase       						= np.array([0.5_2_9_1_1, 0.6_0_0_0_4, 0.4_9_2_2_9, 0.4_9_8_0_5, 0.5_4_5_0_2, 0.5_0_6_8_0, 0.4_7_7_7_7, 0.4_1_0_2_8, 0.4_5_3_0_4]   )

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







     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint			,  provider='CPUExecutionProvider'   )
      __UpperCamelCase       						= DPMSolverMultistepScheduler.from_config(pipe.scheduler.config   )
      pipe.set_progress_bar_config(disable=__UpperCAmelCase   )

      __UpperCamelCase       						= self.get_dummy_inputs()
      __UpperCamelCase       						= pipe(**__UpperCAmelCase   ).images
      __UpperCamelCase       						= image[0, -3:, -3:, -1]

      assert image.shape == (1, 128, 128, 3)
      __UpperCamelCase       						= np.array([0.6_5_3_3_1, 0.5_8_2_7_7, 0.4_8_2_0_4, 0.5_6_0_5_9, 0.5_3_6_6_5, 0.5_6_2_3_5, 0.5_0_9_6_9, 0.4_0_0_0_9, 0.4_6_5_5_2]   )

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


@nightly
@require_onnxruntime
@require_torch_gpu
class __lowerCAmelCase	(   unittest.TestCase    ):





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return (
          "CUDAExecutionProvider",
          {
              "gpu_mem_limit": "15000000000",  # 15GB
              "arena_extend_strategy": "kSameAsRequested",
          },
      )





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= ort.SessionOptions()
      __UpperCamelCase       						= False
      return options





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
          '/img2img/sketch-mountains-input.jpg'   )
      __UpperCamelCase       						= init_image.resize((768, 512)   )
      # using the PNDM scheduler by default
      __UpperCamelCase       						= OnnxStableDiffusionImgaImgPipeline.from_pretrained(
          'CompVis/stable-diffusion-v1-4'			,  revision='onnx'			,  safety_checker=__UpperCAmelCase			,  feature_extractor=__UpperCAmelCase			,  provider=self.gpu_provider			,  sess_options=self.gpu_options			,  )
      pipe.set_progress_bar_config(disable=__UpperCAmelCase   )

      __UpperCamelCase       						= 'A fantasy landscape, trending on artstation'

      __UpperCamelCase       						= np.random.RandomState(0   )
      __UpperCamelCase       						= pipe(
          prompt=__UpperCAmelCase			,  image=__UpperCAmelCase			,  strength=0.7_5			,  guidance_scale=7.5			,  num_inference_steps=10			,  generator=__UpperCAmelCase			,  output_type='np'			,  )
      __UpperCamelCase       						= output.images
      __UpperCamelCase       						= images[0, 255:258, 383:386, -1]

      assert images.shape == (1, 512, 768, 3)
      __UpperCamelCase       						= np.array([0.4_9_0_9, 0.5_0_5_9, 0.5_3_7_2, 0.4_6_2_3, 0.4_8_7_6, 0.5_0_4_9, 0.4_8_2_0, 0.4_9_5_6, 0.5_0_1_9]   )
      # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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







     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
          '/img2img/sketch-mountains-input.jpg'   )
      __UpperCamelCase       						= init_image.resize((768, 512)   )
      __UpperCamelCase       						= LMSDiscreteScheduler.from_pretrained(
          'runwayml/stable-diffusion-v1-5'			,  subfolder='scheduler'			,  revision='onnx'   )
      __UpperCamelCase       						= OnnxStableDiffusionImgaImgPipeline.from_pretrained(
          'runwayml/stable-diffusion-v1-5'			,  revision='onnx'			,  scheduler=__UpperCAmelCase			,  safety_checker=__UpperCAmelCase			,  feature_extractor=__UpperCAmelCase			,  provider=self.gpu_provider			,  sess_options=self.gpu_options			,  )
      pipe.set_progress_bar_config(disable=__UpperCAmelCase   )

      __UpperCamelCase       						= 'A fantasy landscape, trending on artstation'

      __UpperCamelCase       						= np.random.RandomState(0   )
      __UpperCamelCase       						= pipe(
          prompt=__UpperCAmelCase			,  image=__UpperCAmelCase			,  strength=0.7_5			,  guidance_scale=7.5			,  num_inference_steps=20			,  generator=__UpperCAmelCase			,  output_type='np'			,  )
      __UpperCamelCase       						= output.images
      __UpperCamelCase       						= images[0, 255:258, 383:386, -1]

      assert images.shape == (1, 512, 768, 3)
      __UpperCamelCase       						= np.array([0.8_0_4_3, 0.9_2_6, 0.9_5_8_1, 0.8_1_1_9, 0.8_9_5_4, 0.9_1_3, 0.7_2_0_9, 0.7_4_6_3, 0.7_4_3_1]   )
      # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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





"""simple docstring"""

import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()

class __lowerCAmelCase	(   __SCREAMING_SNAKE_CASE    ,						unittest.TestCase    ):
     lowercase		     =		KandinskyInpaintPipeline
     lowercase		     =		["prompt", "image_embeds", "negative_image_embeds", "image", "mask_image"]
     lowercase		     =		[
         "prompt",
         "negative_prompt",
         "image_embeds",
         "negative_image_embeds",
         "image",
         "mask_image",
     ]
     lowercase		     =		[
         "generator",
         "height",
         "width",
         "latents",
         "guidance_scale",
         "negative_prompt",
         "num_inference_steps",
         "return_dict",
         "guidance_scale",
         "num_images_per_prompt",
         "output_type",
         "return_dict",
     ]
     lowercase		     =		False





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return 32





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return 32





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return self.time_input_dim





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return self.time_input_dim * 4





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return 100





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'   )
      return tokenizer





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      torch.manual_seed(0   )
      __UpperCamelCase       						= MCLIPConfig(
          numDims=self.cross_attention_dim			,  transformerDimensions=self.text_embedder_hidden_size			,  hidden_size=self.text_embedder_hidden_size			,  intermediate_size=37			,  num_attention_heads=4			,  num_hidden_layers=5			,  vocab_size=1005			,  )

      __UpperCamelCase       						= MultilingualCLIP(__UpperCAmelCase   )
      __UpperCamelCase       						= text_encoder.eval()

      return text_encoder





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      torch.manual_seed(0   )

      __UpperCamelCase       						= {
          'in_channels': 9,
          # Out channels is double in channels because predicts mean and variance
          'out_channels': 8,
          'addition_embed_type': 'text_image',
          'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
          'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
          'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
          'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
          'layers_per_block': 1,
          'encoder_hid_dim': self.text_embedder_hidden_size,
          'encoder_hid_dim_type': 'text_image_proj',
          'cross_attention_dim': self.cross_attention_dim,
          'attention_head_dim': 4,
          'resnet_time_scale_shift': 'scale_shift',
          'class_embed_type': None,
      }

      __UpperCamelCase       						= UNetaDConditionModel(**__UpperCAmelCase   )
      return model





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return {
          "block_out_channels": [32, 64],
          "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
          "in_channels": 3,
          "latent_channels": 4,
          "layers_per_block": 1,
          "norm_num_groups": 8,
          "norm_type": "spatial",
          "num_vq_embeddings": 12,
          "out_channels": 3,
          "up_block_types": [
              "AttnUpDecoderBlock2D",
              "UpDecoderBlock2D",
          ],
          "vq_embed_dim": 4,
      }





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      torch.manual_seed(0   )
      __UpperCamelCase       						= VQModel(**self.dummy_movq_kwargs   )
      return model





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= self.dummy_text_encoder
      __UpperCamelCase       						= self.dummy_tokenizer
      __UpperCamelCase       						= self.dummy_unet
      __UpperCamelCase       						= self.dummy_movq

      __UpperCamelCase       						= DDIMScheduler(
          num_train_timesteps=1000			,  beta_schedule='linear'			,  beta_start=0.0_0_0_8_5			,  beta_end=0.0_1_2			,  clip_sample=__UpperCAmelCase			,  set_alpha_to_one=__UpperCAmelCase			,  steps_offset=1			,  prediction_type='epsilon'			,  thresholding=__UpperCAmelCase			,  )

      __UpperCamelCase       						= {
          'text_encoder': text_encoder,
          'tokenizer': tokenizer,
          'unet': unet,
          'scheduler': scheduler,
          'movq': movq,
      }

      return components





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  __UpperCAmelCase=0   ):

      '''simple docstring'''




      __UpperCamelCase       						= floats_tensor((1, self.cross_attention_dim)			,  rng=random.Random(__UpperCAmelCase   )   ).to(__UpperCAmelCase   )
      __UpperCamelCase       						= floats_tensor((1, self.cross_attention_dim)			,  rng=random.Random(seed + 1   )   ).to(__UpperCAmelCase   )
      # create init_image
      __UpperCamelCase       						= floats_tensor((1, 3, 64, 64)			,  rng=random.Random(__UpperCAmelCase   )   ).to(__UpperCAmelCase   )
      __UpperCamelCase       						= image.cpu().permute(0			,  2			,  3			,  1   )[0]
      __UpperCamelCase       						= Image.fromarray(np.uinta(__UpperCAmelCase   )   ).convert('RGB'   ).resize((256, 256)   )
      # create mask
      __UpperCamelCase       						= np.ones((64, 64)			,  dtype=np.floataa   )
      __UpperCamelCase       						= 0

      if str(__UpperCAmelCase   ).startswith('mps'   ):
       __UpperCamelCase       						= torch.manual_seed(__UpperCAmelCase   )
      else:
       __UpperCamelCase       						= torch.Generator(device=__UpperCAmelCase   ).manual_seed(__UpperCAmelCase   )
      __UpperCamelCase       						= {
          'prompt': 'horse',
          'image': init_image,
          'mask_image': mask,
          'image_embeds': image_embeds,
          'negative_image_embeds': negative_image_embeds,
          'generator': generator,
          'height': 64,
          'width': 64,
          'num_inference_steps': 2,
          'guidance_scale': 4.0,
          'output_type': 'np',
      }
      return inputs





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= 'cpu'

      __UpperCamelCase       						= self.get_dummy_components()

      __UpperCamelCase       						= self.pipeline_class(**__UpperCAmelCase   )
      __UpperCamelCase       						= pipe.to(__UpperCAmelCase   )

      pipe.set_progress_bar_config(disable=__UpperCAmelCase   )

      __UpperCamelCase       						= pipe(**self.get_dummy_inputs(__UpperCAmelCase   )   )
      __UpperCamelCase       						= output.images

      __UpperCamelCase       						= pipe(
          **self.get_dummy_inputs(__UpperCAmelCase   )			,  return_dict=__UpperCAmelCase			,  )[0]

      __UpperCamelCase       						= image[0, -3:, -3:, -1]
      __UpperCamelCase       						= image_from_tuple[0, -3:, -3:, -1]

      print(F'image.shape {image.shape}'   )

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

      __UpperCamelCase       						= np.array(
          [0.8_3_2_6_9_1_9, 0.7_3_7_9_0_4_6_7, 0.2_0_9_1_8_5_8_1, 0.9_3_0_9_6_1_2, 0.5_5_1_1_7_9_1, 0.4_3_7_1_3_3_2_8, 0.5_5_1_3_3_2_1, 0.4_9_9_2_2_9_3_4, 0.5_9_4_9_7_7_8_6]   )

      assert (
          np.abs(image_slice.flatten() - expected_slice   ).max() < 1E-2
      ), F' expected_slice {expected_slice}, but got {image_slice.flatten()}'
      assert (
          np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1E-2
      ), F' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'







     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      super().test_inference_batch_single_identical(expected_max_diff=3E-3   )


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





     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




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







     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= load_numpy(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
          '/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy'   )

      __UpperCamelCase       						= load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'   )
      __UpperCamelCase       						= np.ones((768, 768)			,  dtype=np.floataa   )
      __UpperCamelCase       						= 0

      __UpperCamelCase       						= 'a hat'

      __UpperCamelCase       						= KandinskyPriorPipeline.from_pretrained(
          'kandinsky-community/kandinsky-2-1-prior'			,  torch_dtype=torch.floataa   )
      pipe_prior.to(__UpperCAmelCase   )

      __UpperCamelCase       						= KandinskyInpaintPipeline.from_pretrained(
          'kandinsky-community/kandinsky-2-1-inpaint'			,  torch_dtype=torch.floataa   )
      __UpperCamelCase       						= pipeline.to(__UpperCAmelCase   )
      pipeline.set_progress_bar_config(disable=__UpperCAmelCase   )

      __UpperCamelCase       						= torch.Generator(device='cpu'   ).manual_seed(0   )
      __UpperCamelCase						,			__UpperCamelCase       						= pipe_prior(
          __UpperCAmelCase			,  generator=__UpperCAmelCase			,  num_inference_steps=5			,  negative_prompt=''			,  ).to_tuple()

      __UpperCamelCase       						= pipeline(
          __UpperCAmelCase			,  image=__UpperCAmelCase			,  mask_image=__UpperCAmelCase			,  image_embeds=__UpperCAmelCase			,  negative_image_embeds=__UpperCAmelCase			,  generator=__UpperCAmelCase			,  num_inference_steps=100			,  height=768			,  width=768			,  output_type='np'			,  )

      __UpperCamelCase       						= output.images[0]

      assert image.shape == (768, 768, 3)

      assert_mean_pixel_difference(__UpperCAmelCase			,  __UpperCAmelCase   )



