
import gc
import random
import unittest

import numpy as np
import torch

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetPipeline,
    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		(   __SCREAMING_SNAKE_CASE      ,	unittest.TestCase					):
       lowercase_		:						Optional[int]   							=  KandinskyVaaControlnetPipeline
       lowercase_		:						List[str]   							=  ['image_embeds', 'negative_image_embeds', 'hint']
       lowercase_		:						Dict   							=  ['image_embeds', 'negative_image_embeds', 'hint']
       lowercase_		:						Optional[Any]   							=  [
           'generator',
           'height',
           'width',
           'latents',
           'guidance_scale',
           'num_inference_steps',
           'return_dict',
           'guidance_scale',
           'num_images_per_prompt',
           'output_type',
           'return_dict',
       ]
       lowercase_		:						str   							=  False







       @property
       def       UpperCAmelCase__					(						self  :  Union[str, Any] ):
        """simple docstring"""







        return 32







       @property
       def       UpperCAmelCase__					(						self  :  Dict ):
        """simple docstring"""







        return 32







       @property
       def       UpperCAmelCase__					(						self  :  Optional[Any] ):
        """simple docstring"""







        return self.time_input_dim







       @property
       def       UpperCAmelCase__					(						self  :  List[Any] ):
        """simple docstring"""







        return self.time_input_dim * 4







       @property
       def       UpperCAmelCase__					(						self  :  Optional[Any] ):
        """simple docstring"""







        return 100







       @property
       def       UpperCAmelCase__					(						self  :  Dict ):
        """simple docstring"""







        torch.manual_seed(0 )

        __lowerCAmelCase         =							{
            "in_channels": 8,
            # Out channels is double in channels because predicts mean and variance
            "out_channels": 8,
            "addition_embed_type": "image_hint",
            "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,
        }

        __lowerCAmelCase         =							UNetaDConditionModel(**_a )
        return model







       @property
       def       UpperCAmelCase__					(						self  :  Optional[int] ):
        """simple docstring"""







        return {
            "block_out_channels": [32, 32, 64, 64],
            "down_block_types": [
                "DownEncoderBlock2D",
                "DownEncoderBlock2D",
                "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", "UpDecoderBlock2D", "UpDecoderBlock2D"],
            "vq_embed_dim": 4,
        }







       @property
       def       UpperCAmelCase__					(						self  :  List[Any] ):
        """simple docstring"""







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







       def       UpperCAmelCase__					(						self  :  str ):
        """simple docstring"""







        __lowerCAmelCase         =							self.dummy_unet
        __lowerCAmelCase         =							self.dummy_movq

        __lowerCAmelCase         =							DDIMScheduler(
            num_train_timesteps=1_000      ,				beta_schedule="linear"      ,				beta_start=0.0_0_0_8_5      ,				beta_end=0.0_1_2      ,				clip_sample=_a      ,				set_alpha_to_one=_a      ,				steps_offset=1      ,				prediction_type="epsilon"      ,				thresholding=_a      ,				)

        __lowerCAmelCase         =							{
            "unet": unet,
            "scheduler": scheduler,
            "movq": movq,
        }
        return components







       def       UpperCAmelCase__					(						self  :  Union[str, Any]      ,				snake_case__  :  Union[str, Any]      ,				snake_case__  :  List[str]=0 ):
        """simple docstring"""







        __lowerCAmelCase         =							floats_tensor((1, self.text_embedder_hidden_size)      ,				rng=random.Random(_a ) ).to(_a )
        __lowerCAmelCase         =							floats_tensor((1, self.text_embedder_hidden_size)      ,				rng=random.Random(seed + 1 ) ).to(
            _a )

        # create hint
        __lowerCAmelCase         =							floats_tensor((1, 3, 64, 64)      ,				rng=random.Random(_a ) ).to(_a )

        if str(_a ).startswith("mps" ):
         __lowerCAmelCase         =							torch.manual_seed(_a )
        else:
         __lowerCAmelCase         =							torch.Generator(device=_a ).manual_seed(_a )
        __lowerCAmelCase         =							{
            "image_embeds": image_embeds,
            "negative_image_embeds": negative_image_embeds,
            "hint": hint,
            "generator": generator,
            "height": 64,
            "width": 64,
            "guidance_scale": 4.0,
            "num_inference_steps": 2,
            "output_type": "np",
        }
        return inputs







       def       UpperCAmelCase__					(						self  :  List[Any] ):
        """simple docstring"""







        __lowerCAmelCase         =							"cpu"

        __lowerCAmelCase         =							self.get_dummy_components()

        __lowerCAmelCase         =							self.pipeline_class(**_a )
        __lowerCAmelCase         =							pipe.to(_a )

        pipe.set_progress_bar_config(disable=_a )

        __lowerCAmelCase         =							pipe(**self.get_dummy_inputs(_a ) )
        __lowerCAmelCase         =							output.images

        __lowerCAmelCase         =							pipe(
            **self.get_dummy_inputs(_a )      ,				return_dict=_a      ,				)[0]

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

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

        __lowerCAmelCase         =							np.array(
            [0.6_9_5_9_8_2_6, 0.8_6_8_2_7_9, 0.7_5_5_8_0_9_2, 0.6_8_7_6_9_4_6_7, 0.8_5_8_0_5_8_0_4, 0.6_5_9_7_7_4_9_6, 0.4_4_8_8_5_3_0_2, 0.5_9_5_9_1_1_1, 0.4_2_5_1_5_9_5] )

        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       UpperCAmelCase__					(						self  :  Optional[Any] ):
        """simple docstring"""







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







       def       UpperCAmelCase__					(						self  :  Tuple ):
        """simple docstring"""







        __lowerCAmelCase         =							load_numpy(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
            "/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy" )

        __lowerCAmelCase         =							load_image(
            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
            "/kandinskyv22/hint_image_cat.png" )
        __lowerCAmelCase         =							torch.from_numpy(np.array(_a ) ).float() / 2_5_5.0
        __lowerCAmelCase         =							hint.permute(2      ,				0      ,				1 ).unsqueeze(0 )

        __lowerCAmelCase         =							KandinskyVaaPriorPipeline.from_pretrained(
            "kandinsky-community/kandinsky-2-2-prior"      ,				torch_dtype=torch.floataa )
        pipe_prior.to(_a )

        __lowerCAmelCase         =							KandinskyVaaControlnetPipeline.from_pretrained(
            "kandinsky-community/kandinsky-2-2-controlnet-depth"      ,				torch_dtype=torch.floataa )
        __lowerCAmelCase         =							pipeline.to(_a )
        pipeline.set_progress_bar_config(disable=_a )

        __lowerCAmelCase         =							"A robot, 4k photo"

        __lowerCAmelCase         =							torch.Generator(device="cuda" ).manual_seed(0 )
        __lowerCAmelCase   ,						__lowerCAmelCase         =							pipe_prior(
            _a      ,				generator=_a      ,				num_inference_steps=5      ,				negative_prompt=""      ,				).to_tuple()

        __lowerCAmelCase         =							torch.Generator(device="cuda" ).manual_seed(0 )
        __lowerCAmelCase         =							pipeline(
            image_embeds=_a      ,				negative_image_embeds=_a      ,				hint=_a      ,				generator=_a      ,				num_inference_steps=100      ,				output_type="np"      ,				)

        __lowerCAmelCase         =							output.images[0]

        assert image.shape == (512, 512, 3)

        assert_mean_pixel_difference(_a      ,				_a )








import warnings
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCamelCase_   	=	logging.get_logger(__name__)

UpperCamelCase_   	=	{
    "nvidia/segformer-b0-finetuned-ade-512-512": (
        "https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512/resolve/main/config.json"
    ),
    # See all SegFormer models at https://huggingface.co/models?filter=segformer
}

class       a		(   __UpperCAmelCase					):
							lowercase_		:						Union[str, Any]   							=  'segformer'







							def __init__(						self  :  List[str]      ,				snake_case__  :  Any=3      ,				snake_case__  :  int=4      ,				snake_case__  :  Tuple=[2, 2, 2, 2]      ,				snake_case__  :  Optional[int]=[8, 4, 2, 1]      ,				snake_case__  :  Union[str, Any]=[32, 64, 160, 256]      ,				snake_case__  :  str=[7, 3, 3, 3]      ,				snake_case__  :  List[Any]=[4, 2, 2, 2]      ,				snake_case__  :  Tuple=[1, 2, 5, 8]      ,				snake_case__  :  List[str]=[4, 4, 4, 4]      ,				snake_case__  :  Optional[Any]="gelu"      ,				snake_case__  :  Optional[Any]=0.0      ,				snake_case__  :  Any=0.0      ,				snake_case__  :  str=0.1      ,				snake_case__  :  List[Any]=0.0_2      ,				snake_case__  :  Any=0.1      ,				snake_case__  :  List[Any]=1E-6      ,				snake_case__  :  Any=256      ,				snake_case__  :  Optional[Any]=255      ,				**snake_case__  :  Union[str, Any]      ,				):
								"""simple docstring"""







								super().__init__(**snake_case__ )

								if "reshape_last_stage" in kwargs and kwargs["reshape_last_stage"] is False:
									warnings.warn(
									    "Reshape_last_stage is set to False in this config. This argument is deprecated and will soon be"
									    " removed, as the behaviour will default to that of reshape_last_stage = True."      ,				snake_case__      ,				)

								__lowerCAmelCase         =							num_channels
								__lowerCAmelCase         =							num_encoder_blocks
								__lowerCAmelCase         =							depths
								__lowerCAmelCase         =							sr_ratios
								__lowerCAmelCase         =							hidden_sizes
								__lowerCAmelCase         =							patch_sizes
								__lowerCAmelCase         =							strides
								__lowerCAmelCase         =							mlp_ratios
								__lowerCAmelCase         =							num_attention_heads
								__lowerCAmelCase         =							hidden_act
								__lowerCAmelCase         =							hidden_dropout_prob
								__lowerCAmelCase         =							attention_probs_dropout_prob
								__lowerCAmelCase         =							classifier_dropout_prob
								__lowerCAmelCase         =							initializer_range
								__lowerCAmelCase         =							drop_path_rate
								__lowerCAmelCase         =							layer_norm_eps
								__lowerCAmelCase         =							decoder_hidden_size
								__lowerCAmelCase         =							kwargs.get("reshape_last_stage"      ,				snake_case__ )
								__lowerCAmelCase         =							semantic_loss_ignore_index

class       a		(   __UpperCAmelCase					):
							lowercase_		:						List[str]   							=  version.parse('1.11'					)







							@property
							def       UpperCAmelCase__					(						self  :  Any ):
								"""simple docstring"""







								return OrderedDict(
								    [
								        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
								    ] )







							@property
							def       UpperCAmelCase__					(						self  :  Union[str, Any] ):
								"""simple docstring"""







								return 1E-4







							@property
							def       UpperCAmelCase__					(						self  :  Optional[Any] ):
								"""simple docstring"""







								return 12






