



"""simple docstring"""







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 				UpperCamelCase__       (     lowercase_, unittest.TestCase  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE      =					KandinskyVaaControlnetPipeline
      _SCREAMING_SNAKE_CASE      =					["""image_embeds""", """negative_image_embeds""", """hint"""]
      _SCREAMING_SNAKE_CASE      =					["""image_embeds""", """negative_image_embeds""", """hint"""]
      _SCREAMING_SNAKE_CASE      =					[
          """generator""",
          """height""",
          """width""",
          """latents""",
          """guidance_scale""",
          """num_inference_steps""",
          """return_dict""",
          """guidance_scale""",
          """num_images_per_prompt""",
          """output_type""",
          """return_dict""",
      ]
      _SCREAMING_SNAKE_CASE      =					False



      @property
      def 					SCREAMING_SNAKE_CASE__			(				self    :				Union[str, Any]	):
        return 3_2



      @property
      def 					SCREAMING_SNAKE_CASE__			(				self    :				Optional[int]	):
        return 3_2



      @property
      def 					SCREAMING_SNAKE_CASE__			(				self    :				Dict	):
        return self.time_input_dim



      @property
      def 					SCREAMING_SNAKE_CASE__			(				self    :				Union[str, Any]	):
        return self.time_input_dim * 4



      @property
      def 					SCREAMING_SNAKE_CASE__			(				self    :				Dict	):
        return 1_0_0



      @property
      def 					SCREAMING_SNAKE_CASE__			(				self    :				List[Any]	):
        torch.manual_seed(0	)

        lowerCAmelCase_       :    Any								=			{
            '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_       :    Any								=			UNetaDConditionModel(**SCREAMING_SNAKE_CASE_	)
        return model



      @property
      def 					SCREAMING_SNAKE_CASE__			(				self    :				Union[str, Any]	):
        return {
            "block_out_channels": [3_2, 3_2, 6_4, 6_4],
            "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": 1_2,
            "out_channels": 3,
            "up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
            "vq_embed_dim": 4,
        }



      @property
      def 					SCREAMING_SNAKE_CASE__			(				self    :				Optional[Any]	):
        torch.manual_seed(0	)
        lowerCAmelCase_       :    int								=			VQModel(**self.dummy_movq_kwargs	)
        return model



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Optional[Any]	):
        lowerCAmelCase_       :    List[str]								=			self.dummy_unet
        lowerCAmelCase_       :    List[str]								=			self.dummy_movq

        lowerCAmelCase_       :    str								=			DDIMScheduler(
            num_train_timesteps=1_0_0_0   ,      beta_schedule='linear'   ,      beta_start=0.0_00_85   ,      beta_end=0.0_12   ,      clip_sample=SCREAMING_SNAKE_CASE_   ,      set_alpha_to_one=SCREAMING_SNAKE_CASE_   ,      steps_offset=1   ,      prediction_type='epsilon'   ,      thresholding=SCREAMING_SNAKE_CASE_   ,      )

        lowerCAmelCase_       :    str								=			{
            'unet': unet,
            'scheduler': scheduler,
            'movq': movq,
        }
        return components



      def 					SCREAMING_SNAKE_CASE__			(				self    :				Optional[int]   ,      SCREAMING_SNAKE_CASE_    :				List[Any]   ,      SCREAMING_SNAKE_CASE_    :				Optional[Any]=0	):
        lowerCAmelCase_       :    Tuple								=			floats_tensor((1, self.text_embedder_hidden_size)   ,      rng=random.Random(SCREAMING_SNAKE_CASE_	)	).to(SCREAMING_SNAKE_CASE_	)
        lowerCAmelCase_       :    Union[str, Any]								=			floats_tensor((1, self.text_embedder_hidden_size)   ,      rng=random.Random(seed + 1	)	).to(
            SCREAMING_SNAKE_CASE_	)

        # create hint
        lowerCAmelCase_       :    Tuple								=			floats_tensor((1, 3, 6_4, 6_4)   ,      rng=random.Random(SCREAMING_SNAKE_CASE_	)	).to(SCREAMING_SNAKE_CASE_	)

        if str(SCREAMING_SNAKE_CASE_	).startswith('mps'	):
          lowerCAmelCase_       :    Tuple								=			torch.manual_seed(SCREAMING_SNAKE_CASE_	)
        else:
          lowerCAmelCase_       :    str								=			torch.Generator(device=SCREAMING_SNAKE_CASE_	).manual_seed(SCREAMING_SNAKE_CASE_	)
        lowerCAmelCase_       :    Any								=			{
            'image_embeds': image_embeds,
            'negative_image_embeds': negative_image_embeds,
            'hint': hint,
            'generator': generator,
            'height': 6_4,
            'width': 6_4,
            'guidance_scale': 4.0,
            'num_inference_steps': 2,
            'output_type': 'np',
        }
        return inputs





      def 					SCREAMING_SNAKE_CASE__			(				self    :				List[Any]	):
        lowerCAmelCase_       :    List[Any]								=			'cpu'

        lowerCAmelCase_       :    Dict								=			self.get_dummy_components()

        lowerCAmelCase_       :    List[str]								=			self.pipeline_class(**SCREAMING_SNAKE_CASE_	)
        lowerCAmelCase_       :    Tuple								=			pipe.to(SCREAMING_SNAKE_CASE_	)

        pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_	)

        lowerCAmelCase_       :    List[Any]								=			pipe(**self.get_dummy_inputs(SCREAMING_SNAKE_CASE_	)	)
        lowerCAmelCase_       :    Tuple								=			output.images

        lowerCAmelCase_       :    List[Any]								=			pipe(
            **self.get_dummy_inputs(SCREAMING_SNAKE_CASE_	)   ,      return_dict=SCREAMING_SNAKE_CASE_   ,      )[0]

        lowerCAmelCase_       :    Any								=			image[0, -3:, -3:, -1]
        lowerCAmelCase_       :    Union[str, Any]								=			image_from_tuple[0, -3:, -3:, -1]

        assert image.shape == (1, 6_4, 6_4, 3)

        lowerCAmelCase_       :    Optional[Any]								=			np.array(
            [0.6_95_98_26, 0.86_82_79, 0.7_55_80_92, 0.68_76_94_67, 0.85_80_58_04, 0.65_97_74_96, 0.44_88_53_02, 0.5_95_91_11, 0.4_25_15_95]	)

        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 				UpperCamelCase__       (     unittest.TestCase  ):




      """simple docstring"""




      def 					SCREAMING_SNAKE_CASE__			(				self    :				Dict	):
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()





      def 					SCREAMING_SNAKE_CASE__			(				self    :				List[str]	):
        lowerCAmelCase_       :    List[Any]								=			load_numpy(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
            '/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy'	)

        lowerCAmelCase_       :    Dict								=			load_image(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
            '/kandinskyv22/hint_image_cat.png'	)
        lowerCAmelCase_       :    Dict								=			torch.from_numpy(np.array(SCREAMING_SNAKE_CASE_	)	).float() / 2_55.0
        lowerCAmelCase_       :    Union[str, Any]								=			hint.permute(2   ,      0   ,      1	).unsqueeze(0	)

        lowerCAmelCase_       :    Dict								=			KandinskyVaaPriorPipeline.from_pretrained(
            'kandinsky-community/kandinsky-2-2-prior'   ,      torch_dtype=torch.floataa	)
        pipe_prior.to(SCREAMING_SNAKE_CASE_	)

        lowerCAmelCase_       :    Any								=			KandinskyVaaControlnetPipeline.from_pretrained(
            'kandinsky-community/kandinsky-2-2-controlnet-depth'   ,      torch_dtype=torch.floataa	)
        lowerCAmelCase_       :    Optional[Any]								=			pipeline.to(SCREAMING_SNAKE_CASE_	)
        pipeline.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_	)

        lowerCAmelCase_       :    Optional[Any]								=			'A robot, 4k photo'

        lowerCAmelCase_       :    Tuple								=			torch.Generator(device='cuda'	).manual_seed(0	)
        lowerCAmelCase_						,lowerCAmelCase_       :    Any								=			pipe_prior(
            SCREAMING_SNAKE_CASE_   ,      generator=SCREAMING_SNAKE_CASE_   ,      num_inference_steps=5   ,      negative_prompt=''   ,      ).to_tuple()

        lowerCAmelCase_       :    str								=			torch.Generator(device='cuda'	).manual_seed(0	)
        lowerCAmelCase_       :    Dict								=			pipeline(
            image_embeds=SCREAMING_SNAKE_CASE_   ,      negative_image_embeds=SCREAMING_SNAKE_CASE_   ,      hint=SCREAMING_SNAKE_CASE_   ,      generator=SCREAMING_SNAKE_CASE_   ,      num_inference_steps=1_0_0   ,      output_type='np'   ,      )

        lowerCAmelCase_       :    str								=			output.images[0]

        assert image.shape == (5_1_2, 5_1_2, 3)

        assert_mean_pixel_difference(SCREAMING_SNAKE_CASE_   ,      SCREAMING_SNAKE_CASE_	)










"""simple docstring"""







import logging

from transformers.configuration_utils import PretrainedConfig


lowercase__		:    List[Any]				   =     logging.getLogger(__name__)







class 				UpperCamelCase__       (     lowercase_  ):




      """simple docstring"""




      _SCREAMING_SNAKE_CASE      =					"""masked_bert"""



      def __init__(				self    :				Any   ,      SCREAMING_SNAKE_CASE_    :				Union[str, Any]=3_0_5_2_2   ,      SCREAMING_SNAKE_CASE_    :				int=7_6_8   ,      SCREAMING_SNAKE_CASE_    :				Tuple=1_2   ,      SCREAMING_SNAKE_CASE_    :				List[Any]=1_2   ,      SCREAMING_SNAKE_CASE_    :				Tuple=3_0_7_2   ,      SCREAMING_SNAKE_CASE_    :				List[str]="gelu"   ,      SCREAMING_SNAKE_CASE_    :				Tuple=0.1   ,      SCREAMING_SNAKE_CASE_    :				List[Any]=0.1   ,      SCREAMING_SNAKE_CASE_    :				List[Any]=5_1_2   ,      SCREAMING_SNAKE_CASE_    :				Optional[Any]=2   ,      SCREAMING_SNAKE_CASE_    :				Dict=0.02   ,      SCREAMING_SNAKE_CASE_    :				Any=1E-12   ,      SCREAMING_SNAKE_CASE_    :				List[str]=0   ,      SCREAMING_SNAKE_CASE_    :				Optional[int]="topK"   ,      SCREAMING_SNAKE_CASE_    :				Optional[int]="constant"   ,      SCREAMING_SNAKE_CASE_    :				Union[str, Any]=0.0   ,      **SCREAMING_SNAKE_CASE_    :				Any   ,      ):
        super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_   ,      **SCREAMING_SNAKE_CASE_	)

        lowerCAmelCase_       :    List[Any]								=			vocab_size
        lowerCAmelCase_       :    str								=			hidden_size
        lowerCAmelCase_       :    Optional[int]								=			num_hidden_layers
        lowerCAmelCase_       :    Dict								=			num_attention_heads
        lowerCAmelCase_       :    List[str]								=			hidden_act
        lowerCAmelCase_       :    List[Any]								=			intermediate_size
        lowerCAmelCase_       :    Any								=			hidden_dropout_prob
        lowerCAmelCase_       :    str								=			attention_probs_dropout_prob
        lowerCAmelCase_       :    Any								=			max_position_embeddings
        lowerCAmelCase_       :    Dict								=			type_vocab_size
        lowerCAmelCase_       :    Tuple								=			initializer_range
        lowerCAmelCase_       :    List[Any]								=			layer_norm_eps
        lowerCAmelCase_       :    str								=			pruning_method
        lowerCAmelCase_       :    Optional[Any]								=			mask_init
        lowerCAmelCase_       :    int								=			mask_scale





