





'''simple docstring'''


import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaImgaImgPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
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       a_   (				_lowerCAmelCase	,						unittest.TestCase							):
     __A      =			KandinskyVaaImgaImgPipeline
     __A      =			["image_embeds", "negative_image_embeds", "image"]
     __A      =			[
         "image_embeds",
         "negative_image_embeds",
         "image",
     ]
     __A      =			[
         "generator",
         "height",
         "width",
         "strength",
         "guidance_scale",
         "num_inference_steps",
         "return_dict",
         "guidance_scale",
         "num_images_per_prompt",
         "output_type",
         "return_dict",
     ]
     __A      =			False





     @property
     def 							lowercase__      ( self		:					Optional[int]						):





        """simple docstring"""




        return 32





     @property
     def 							lowercase__      ( self		:					List[Any]						):





        """simple docstring"""




        return 32





     @property
     def 							lowercase__      ( self		:					List[str]						):





        """simple docstring"""




        return self.time_input_dim





     @property
     def 							lowercase__      ( self		:					Union[str, Any]						):





        """simple docstring"""




        return self.time_input_dim * 4





     @property
     def 							lowercase__      ( self		:					List[Any]						):





        """simple docstring"""




        return 100





     @property
     def 							lowercase__      ( self		:					List[Any]						):





        """simple docstring"""




        torch.manual_seed(0						)

        lowercase_			:Optional[Any]    			= {
            "in_channels": 4,
            # Out channels is double in channels because predicts mean and variance
            "out_channels": 8,
            "addition_embed_type": "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": "image_proj",
            "cross_attention_dim": self.cross_attention_dim,
            "attention_head_dim": 4,
            "resnet_time_scale_shift": "scale_shift",
            "class_embed_type": None,
        }

        lowercase_			:int    			= UNetaDConditionModel(**lowercase						)
        return model





     @property
     def 							lowercase__      ( self		:					str						):





        """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 							lowercase__      ( self		:					int						):





        """simple docstring"""




        torch.manual_seed(0						)
        lowercase_			:List[Any]    			= VQModel(**self.dummy_movq_kwargs						)
        return model





     def 							lowercase__      ( self		:					Dict						):





        """simple docstring"""




        lowercase_			:Optional[Any]    			= self.dummy_unet
        lowercase_			:str    			= self.dummy_movq

        lowercase_			:List[str]    			= {
            "num_train_timesteps": 1_000,
            "beta_schedule": "linear",
            "beta_start": 0.0_00_85,
            "beta_end": 0.0_12,
            "clip_sample": False,
            "set_alpha_to_one": False,
            "steps_offset": 0,
            "prediction_type": "epsilon",
            "thresholding": False,
        }

        lowercase_			:Any    			= DDIMScheduler(**lowercase						)

        lowercase_			:List[str]    			= {
            "unet": unet,
            "scheduler": scheduler,
            "movq": movq,
        }

        return components





     def 							lowercase__      ( self		:					Optional[Any]	,      lowercase		:					Union[str, Any]	,      lowercase		:					List[str]=0						):





        """simple docstring"""




        lowercase_			:List[str]    			= floats_tensor((1, self.text_embedder_hidden_size)	,      rng=random.Random(lowercase						)						).to(lowercase						)
        lowercase_			:Union[str, Any]    			= floats_tensor((1, self.text_embedder_hidden_size)	,      rng=random.Random(seed + 1						)						).to(
            lowercase						)
        # create init_image
        lowercase_			:Optional[int]    			= floats_tensor((1, 3, 64, 64)	,      rng=random.Random(lowercase						)						).to(lowercase						)
        lowercase_			:Tuple    			= image.cpu().permute(0	,      2	,      3	,      1						)[0]
        lowercase_			:str    			= Image.fromarray(np.uinta(lowercase						)						).convert("RGB"						).resize((256, 256)						)

        if str(lowercase						).startswith("mps"						):
           lowercase_			:Tuple    			= torch.manual_seed(lowercase						)
        else:
           lowercase_			:int    			= torch.Generator(device=lowercase						).manual_seed(lowercase						)
        lowercase_			:Any    			= {
            "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		:					str						):





        """simple docstring"""




        lowercase_			:Optional[Any]    			= "cpu"

        lowercase_			:Optional[Any]    			= self.get_dummy_components()

        lowercase_			:Optional[Any]    			= self.pipeline_class(**lowercase						)
        lowercase_			:Any    			= pipe.to(lowercase						)

        pipe.set_progress_bar_config(disable=lowercase						)

        lowercase_			:Union[str, Any]    			= pipe(**self.get_dummy_inputs(lowercase						)						)
        lowercase_			:int    			= output.images

        lowercase_			:Optional[int]    			= pipe(
            **self.get_dummy_inputs(lowercase						)	,      return_dict=lowercase	,      )[0]

        lowercase_			:Tuple    			= image[0, -3:, -3:, -1]
        lowercase_			:Optional[int]    			= image_from_tuple[0, -3:, -3:, -1]

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

        lowercase_			:int    			= np.array(
            [0.6_19_97_78, 0.63_98_44_06, 0.46_14_57_85, 0.62_94_49_84, 0.5_62_22_15, 0.47_30_61_32, 0.47_44_14_56, 0.4_60_76_06, 0.48_71_92_63]						)
        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       a_   (				unittest.TestCase							):





     def 							lowercase__      ( self		:					Any						):





        """simple docstring"""




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







     def 							lowercase__      ( self		:					Optional[Any]						):





        """simple docstring"""




        lowercase_			:Union[str, Any]    			= load_numpy(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
            "/kandinskyv22/kandinskyv22_img2img_frog.npy"						)

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

        lowercase_			:List[Any]    			= KandinskyVaaPriorPipeline.from_pretrained(
            "kandinsky-community/kandinsky-2-2-prior"	,      torch_dtype=torch.floataa						)
        pipe_prior.to(lowercase						)

        lowercase_			:List[Any]    			= KandinskyVaaImgaImgPipeline.from_pretrained(
            "kandinsky-community/kandinsky-2-2-decoder"	,      torch_dtype=torch.floataa						)
        lowercase_			:Union[str, Any]    			= pipeline.to(lowercase						)

        pipeline.set_progress_bar_config(disable=lowercase						)

        lowercase_			:Any    			= torch.Generator(device="cpu"						).manual_seed(0						)
        lowercase_			,							lowercase_			:Tuple    			= pipe_prior(
            lowercase	,      generator=lowercase	,      num_inference_steps=5	,      negative_prompt=""	,      ).to_tuple()

        lowercase_			:Any    			= pipeline(
            image=lowercase	,      image_embeds=lowercase	,      negative_image_embeds=lowercase	,      generator=lowercase	,      num_inference_steps=100	,      height=768	,      width=768	,      strength=0.2	,      output_type="np"	,      )

        lowercase_			:Union[str, Any]    			= output.images[0]

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

        assert_mean_pixel_difference(lowercase	,      lowercase						)









'''simple docstring'''


import pytest

from datasets import inspect_metric, list_metrics, load_metric
@pytest.fixture
def 							UpperCAmelCase_  (						__lowerCamelCase					:					List[str] ):
   monkeypatch.setattr("datasets.utils.deprecation_utils._emitted_deprecation_warnings"	,set() )
@pytest.fixture
def 							UpperCAmelCase_  (						__lowerCamelCase					:					Any ):
   class       a_   :





        def __init__( self		:					int	,      lowercase		:					int						):





           """simple docstring"""




           lowercase_			:Optional[Any]    			= metric_id







   class       a_   :
        __A      =			[MetricMock(_lowerCAmelCase							) for metric_id in ["accuracy", "mse", "precision", "codeparrot/apps_metric"]]





        def 							lowercase__      ( self		:					Union[str, Any]						):





           """simple docstring"""




           return self._metrics







   monkeypatch.setattr("datasets.inspect.huggingface_hub"	,HfhMock() )


@pytest.mark.parametrize(
    "func, args"	,[(load_metric, ("metrics/mse",)), (list_metrics, ()), (inspect_metric, ("metrics/mse", "tmp_path"))] )
def 							UpperCAmelCase_  (						__lowerCamelCase					:					Union[str, Any]	,__lowerCamelCase					:					int	,__lowerCamelCase					:					Optional[int]	,__lowerCamelCase					:					Union[str, Any]	,__lowerCamelCase					:					Tuple ):
   if "tmp_path" in args:
      lowercase_			:Union[str, Any]    			= tuple(arg if arg != "tmp_path" else tmp_path for arg in args )
   with pytest.warns(__lowerCamelCase	,match="https://huggingface.co/docs/evaluate" ):
      func(*__lowerCamelCase )


