
'''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, KandinskyImgaImgPipeline, 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   (   __snake_case					,							unittest.TestCase				):







							'''simple docstring'''

							lowerCAmelCase_ 						=						KandinskyImgaImgPipeline
							lowerCAmelCase_ 						=						["prompt", "image_embeds", "negative_image_embeds", "image"]
							lowerCAmelCase_ 						=						[
							    "prompt",
							    "negative_prompt",
							    "image_embeds",
							    "negative_image_embeds",
							    "image",
							]
							lowerCAmelCase_ 						=						[
							    "generator",
							    "height",
							    "width",
							    "strength",
							    "guidance_scale",
							    "negative_prompt",
							    "num_inference_steps",
							    "return_dict",
							    "guidance_scale",
							    "num_images_per_prompt",
							    "output_type",
							    "return_dict",
							]
							lowerCAmelCase_ 						=						False

							@property
							def 	lowercase							(self  )					->       List[Any]:
														return 32

							@property
							def 	lowercase							(self  )					->       Tuple:
														return 32

							@property
							def 	lowercase							(self  )					->       Optional[int]:
														return self.time_input_dim

							@property
							def 	lowercase							(self  )					->       Optional[int]:
														return self.time_input_dim * 4

							@property
							def 	lowercase							(self  )					->       Any:
														return 100

							@property
							def 	lowercase							(self  )					->       Any:
														_snake_case       =  XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base"""  )
														return tokenizer

							@property
							def 	lowercase							(self  )					->       Optional[Any]:
														torch.manual_seed(0  )
														_snake_case       =  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  ,  )

														_snake_case       =  MultilingualCLIP(UpperCAmelCase  )
														_snake_case       =  text_encoder.eval()

														return text_encoder

							@property
							def 	lowercase							(self  )					->       Union[str, Any]:
														torch.manual_seed(0  )

														_snake_case       =  {
														    """in_channels""": 4,
														    # 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,
														}

														_snake_case       =  UNetaDConditionModel(**UpperCAmelCase  )
														return model

							@property
							def 	lowercase							(self  )					->       Any:
														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 	lowercase							(self  )					->       List[str]:
														torch.manual_seed(0  )
														_snake_case       =  VQModel(**self.dummy_movq_kwargs  )
														return model

							def 	lowercase							(self  )					->       Tuple:
														_snake_case       =  self.dummy_text_encoder
														_snake_case       =  self.dummy_tokenizer
														_snake_case       =  self.dummy_unet
														_snake_case       =  self.dummy_movq

														_snake_case       =  {
														    """num_train_timesteps""": 1000,
														    """beta_schedule""": """linear""",
														    """beta_start""": 0.0_0085,
														    """beta_end""": 0.012,
														    """clip_sample""": False,
														    """set_alpha_to_one""": False,
														    """steps_offset""": 0,
														    """prediction_type""": """epsilon""",
														    """thresholding""": False,
														}

														_snake_case       =  DDIMScheduler(**UpperCAmelCase  )

														_snake_case       =  {
														    """text_encoder""": text_encoder,
														    """tokenizer""": tokenizer,
														    """unet""": unet,
														    """scheduler""": scheduler,
														    """movq""": movq,
														}

														return components

							def 	lowercase							(self  ,  UpperCAmelCase  ,  UpperCAmelCase=0  )					->       Dict:
														_snake_case       =  floats_tensor((1, self.cross_attention_dim)  ,  rng=random.Random(UpperCAmelCase  )  ).to(UpperCAmelCase  )
														_snake_case       =  floats_tensor((1, self.cross_attention_dim)  ,  rng=random.Random(seed + 1  )  ).to(UpperCAmelCase  )
														# create init_image
														_snake_case       =  floats_tensor((1, 3, 64, 64)  ,  rng=random.Random(UpperCAmelCase  )  ).to(UpperCAmelCase  )
														_snake_case       =  image.cpu().permute(0  ,  2  ,  3  ,  1  )[0]
														_snake_case       =  Image.fromarray(np.uinta(UpperCAmelCase  )  ).convert("""RGB"""  ).resize((256, 256)  )

														if str(UpperCAmelCase  ).startswith("""mps"""  ):
																					_snake_case       =  torch.manual_seed(UpperCAmelCase  )
														else:
																					_snake_case       =  torch.Generator(device=UpperCAmelCase  ).manual_seed(UpperCAmelCase  )
														_snake_case       =  {
														    """prompt""": """horse""",
														    """image""": init_image,
														    """image_embeds""": image_embeds,
														    """negative_image_embeds""": negative_image_embeds,
														    """generator""": generator,
														    """height""": 64,
														    """width""": 64,
														    """num_inference_steps""": 10,
														    """guidance_scale""": 7.0,
														    """strength""": 0.2,
														    """output_type""": """np""",
														}
														return inputs





							def 	lowercase							(self  )					->       Tuple:
														_snake_case       =  """cpu"""

														_snake_case       =  self.get_dummy_components()

														_snake_case       =  self.pipeline_class(**UpperCAmelCase  )
														_snake_case       =  pipe.to(UpperCAmelCase  )

														pipe.set_progress_bar_config(disable=UpperCAmelCase  )

														_snake_case       =  pipe(**self.get_dummy_inputs(UpperCAmelCase  )  )
														_snake_case       =  output.images

														_snake_case       =  pipe(
														    **self.get_dummy_inputs(UpperCAmelCase  )  ,  return_dict=UpperCAmelCase  ,  )[0]

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

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

														_snake_case       =  np.array(
														    [0.6147_4943, 0.607_3539, 0.4330_8544, 0.592_8269, 0.4749_3595, 0.4675_5973, 0.461_3838, 0.4536_8797, 0.5011_9233]  )
														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()}"""


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







							'''simple docstring'''

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





							def 	lowercase							(self  )					->       Optional[int]:
														_snake_case       =  load_numpy(
														    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
														    """/kandinsky/kandinsky_img2img_frog.npy"""  )

														_snake_case       =  load_image(
														    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""  )
														_snake_case       =  """A red cartoon frog, 4k"""

														_snake_case       =  KandinskyPriorPipeline.from_pretrained(
														    """kandinsky-community/kandinsky-2-1-prior"""  ,  torch_dtype=torch.floataa  )
														pipe_prior.to(UpperCAmelCase  )

														_snake_case       =  KandinskyImgaImgPipeline.from_pretrained(
														    """kandinsky-community/kandinsky-2-1"""  ,  torch_dtype=torch.floataa  )
														_snake_case       =  pipeline.to(UpperCAmelCase  )

														pipeline.set_progress_bar_config(disable=UpperCAmelCase  )

														_snake_case       =  torch.Generator(device="""cpu"""  ).manual_seed(0  )
														_snake_case,							_snake_case       =  pipe_prior(
														    UpperCAmelCase  ,  generator=UpperCAmelCase  ,  num_inference_steps=5  ,  negative_prompt=""""""  ,  ).to_tuple()

														_snake_case       =  pipeline(
														    UpperCAmelCase  ,  image=UpperCAmelCase  ,  image_embeds=UpperCAmelCase  ,  negative_image_embeds=UpperCAmelCase  ,  generator=UpperCAmelCase  ,  num_inference_steps=100  ,  height=768  ,  width=768  ,  strength=0.2  ,  output_type="""np"""  ,  )

														_snake_case       =  output.images[0]

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

														assert_mean_pixel_difference(UpperCAmelCase  ,  UpperCAmelCase  )

'''simple docstring'''
import gc
import unittest

from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
				import jax
				import jax.numpy as jnp
				from flax.jax_utils import replicate
				from flax.training.common_utils import shard


@slow
@require_flax
class       _lowerCAmelCase   (   unittest.TestCase				):







							'''simple docstring'''

							def 	lowercase							(self  )					->       Optional[int]:
														# clean up the VRAM after each test
														super().tearDown()
														gc.collect()

							def 	lowercase							(self  )					->       Dict:
														_snake_case,							_snake_case       =  FlaxControlNetModel.from_pretrained(
														    """lllyasviel/sd-controlnet-canny"""  ,  from_pt=UpperCAmelCase  ,  dtype=jnp.bfloataa  )
														_snake_case,							_snake_case       =  FlaxStableDiffusionControlNetPipeline.from_pretrained(
														    """runwayml/stable-diffusion-v1-5"""  ,  controlnet=UpperCAmelCase  ,  from_pt=UpperCAmelCase  ,  dtype=jnp.bfloataa  )
														_snake_case       =  controlnet_params

														_snake_case       =  """bird"""
														_snake_case       =  jax.device_count()
														_snake_case       =  pipe.prepare_text_inputs([prompts] * num_samples  )

														_snake_case       =  load_image(
														    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"""  )
														_snake_case       =  pipe.prepare_image_inputs([canny_image] * num_samples  )

														_snake_case       =  jax.random.PRNGKey(0  )
														_snake_case       =  jax.random.split(UpperCAmelCase  ,  jax.device_count()  )

														_snake_case       =  replicate(UpperCAmelCase  )
														_snake_case       =  shard(UpperCAmelCase  )
														_snake_case       =  shard(UpperCAmelCase  )

														_snake_case       =  pipe(
														    prompt_ids=UpperCAmelCase  ,  image=UpperCAmelCase  ,  params=UpperCAmelCase  ,  prng_seed=UpperCAmelCase  ,  num_inference_steps=50  ,  jit=UpperCAmelCase  ,  ).images
														assert images.shape == (jax.device_count(), 1, 768, 512, 3)

														_snake_case       =  images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]  )
														_snake_case       =  images[0, 253:256, 253:256, -1]

														_snake_case       =  jnp.asarray(jax.device_get(image_slice.flatten()  )  )
														_snake_case       =  jnp.array(
														    [0.16_7969, 0.11_6699, 0.08_1543, 0.15_4297, 0.13_2812, 0.10_8887, 0.16_9922, 0.16_9922, 0.20_5078]  )
														print(f"""output_slice: {output_slice}"""  )
														assert jnp.abs(output_slice - expected_slice  ).max() < 1e-2





							def 	lowercase							(self  )					->       Optional[int]:
														_snake_case,							_snake_case       =  FlaxControlNetModel.from_pretrained(
														    """lllyasviel/sd-controlnet-openpose"""  ,  from_pt=UpperCAmelCase  ,  dtype=jnp.bfloataa  )
														_snake_case,							_snake_case       =  FlaxStableDiffusionControlNetPipeline.from_pretrained(
														    """runwayml/stable-diffusion-v1-5"""  ,  controlnet=UpperCAmelCase  ,  from_pt=UpperCAmelCase  ,  dtype=jnp.bfloataa  )
														_snake_case       =  controlnet_params

														_snake_case       =  """Chef in the kitchen"""
														_snake_case       =  jax.device_count()
														_snake_case       =  pipe.prepare_text_inputs([prompts] * num_samples  )

														_snake_case       =  load_image(
														    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png"""  )
														_snake_case       =  pipe.prepare_image_inputs([pose_image] * num_samples  )

														_snake_case       =  jax.random.PRNGKey(0  )
														_snake_case       =  jax.random.split(UpperCAmelCase  ,  jax.device_count()  )

														_snake_case       =  replicate(UpperCAmelCase  )
														_snake_case       =  shard(UpperCAmelCase  )
														_snake_case       =  shard(UpperCAmelCase  )

														_snake_case       =  pipe(
														    prompt_ids=UpperCAmelCase  ,  image=UpperCAmelCase  ,  params=UpperCAmelCase  ,  prng_seed=UpperCAmelCase  ,  num_inference_steps=50  ,  jit=UpperCAmelCase  ,  ).images
														assert images.shape == (jax.device_count(), 1, 768, 512, 3)

														_snake_case       =  images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]  )
														_snake_case       =  images[0, 253:256, 253:256, -1]

														_snake_case       =  jnp.asarray(jax.device_get(image_slice.flatten()  )  )
														_snake_case       =  jnp.array(
														    [[0.27_1484, 0.26_1719, 0.27_5391, 0.27_7344, 0.27_9297, 0.29_1016, 0.29_4922, 0.30_2734, 0.30_2734]]  )
														print(f"""output_slice: {output_slice}"""  )
														assert jnp.abs(output_slice - expected_slice  ).max() < 1e-2