






"""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      _lowercase (     snake_case__		,  unittest.TestCase						):
      _a   :     Dict								=   KandinskyVaaControlnetPipeline
      _a   :     str								=   ["""image_embeds""", """negative_image_embeds""", """hint"""]
      _a   :     List[str]								=   ["""image_embeds""", """negative_image_embeds""", """hint"""]
      _a   :     Union[str, Any]								=   [
          """generator""",
          """height""",
          """width""",
          """latents""",
          """guidance_scale""",
          """num_inference_steps""",
          """return_dict""",
          """guidance_scale""",
          """num_images_per_prompt""",
          """output_type""",
          """return_dict""",
      ]
      _a   :     Tuple								=   False


      @property
      def _UpperCamelCase						(    self				:	Any						):





            """simple docstring"""







            return 3_2


      @property
      def _UpperCamelCase						(    self				:	Optional[Any]						):





            """simple docstring"""







            return 3_2


      @property
      def _UpperCamelCase						(    self				:	int						):





            """simple docstring"""







            return self.time_input_dim


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





            """simple docstring"""







            return self.time_input_dim * 4


      @property
      def _UpperCamelCase						(    self				:	Dict						):





            """simple docstring"""







            return 1_0_0


      @property
      def _UpperCamelCase						(    self				:	str						):





            """simple docstring"""







            torch.manual_seed(0						)

            __snake_case      :    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,
            }

            __snake_case      :    Dict  			=UNetaDConditionModel(**UpperCAmelCase_						)
            return model


      @property
      def _UpperCamelCase						(    self				:	Any						):





            """simple docstring"""







            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 _UpperCamelCase						(    self				:	Any						):





            """simple docstring"""







            torch.manual_seed(0						)
            __snake_case      :    str  			=VQModel(**self.dummy_movq_kwargs						)
            return model


      def _UpperCamelCase						(    self				:	Dict						):





            """simple docstring"""







            __snake_case      :    int  			=self.dummy_unet
            __snake_case      :    List[str]  			=self.dummy_movq

            __snake_case      :    Dict  			=DDIMScheduler(
                num_train_timesteps=1_0_0_0							,   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_							,   )

            __snake_case      :    Optional[int]  			={
                '''unet''': unet,
                '''scheduler''': scheduler,
                '''movq''': movq,
            }
            return components


      def _UpperCamelCase						(    self				:	Union[str, Any]							,   a				:	List[Any]							,   a				:	Optional[Any]=0						):





            """simple docstring"""







            __snake_case      :    Optional[Any]  			=floats_tensor((1, self.text_embedder_hidden_size)							,   rng=random.Random(UpperCAmelCase_						)						).to(UpperCAmelCase_						)
            __snake_case      :    str  			=floats_tensor((1, self.text_embedder_hidden_size)							,   rng=random.Random(seed + 1						)						).to(
                UpperCAmelCase_						)

            # create hint
            __snake_case      :    Union[str, Any]  			=floats_tensor((1, 3, 6_4, 6_4)							,   rng=random.Random(UpperCAmelCase_						)						).to(UpperCAmelCase_						)

            if str(UpperCAmelCase_						).startswith('''mps'''						):
                  __snake_case      :    Optional[Any]  			=torch.manual_seed(UpperCAmelCase_						)
            else:
                  __snake_case      :    Union[str, Any]  			=torch.Generator(device=UpperCAmelCase_						).manual_seed(UpperCAmelCase_						)
            __snake_case      :    int  			={
                '''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 _UpperCamelCase						(    self				:	List[str]						):





            """simple docstring"""







            __snake_case      :    Optional[int]  			='''cpu'''

            __snake_case      :    str  			=self.get_dummy_components()

            __snake_case      :    Dict  			=self.pipeline_class(**UpperCAmelCase_						)
            __snake_case      :    Tuple  			=pipe.to(UpperCAmelCase_						)

            pipe.set_progress_bar_config(disable=UpperCAmelCase_						)

            __snake_case      :    Any  			=pipe(**self.get_dummy_inputs(UpperCAmelCase_						)						)
            __snake_case      :    str  			=output.images

            __snake_case      :    int  			=pipe(
                **self.get_dummy_inputs(UpperCAmelCase_						)							,   return_dict=UpperCAmelCase_							,   )[0]

            __snake_case      :    List[Any]  			=image[0, -3:, -3:, -1]
            __snake_case      :    int  			=image_from_tuple[0, -3:, -3:, -1]

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

            __snake_case      :    List[str]  			=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      _lowercase (     unittest.TestCase						):


      def _UpperCamelCase						(    self				:	List[Any]						):





            """simple docstring"""







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







      def _UpperCamelCase						(    self				:	List[str]						):





            """simple docstring"""







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

            __snake_case      :    Union[str, Any]  			=load_image(
                '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
                '''/kandinskyv22/hint_image_cat.png'''						)
            __snake_case      :    Any  			=torch.from_numpy(np.array(UpperCAmelCase_						)						).float() / 2_5_5.0
            __snake_case      :    int  			=hint.permute(2							,   0							,   1						).unsqueeze(0						)

            __snake_case      :    List[str]  			=KandinskyVaaPriorPipeline.from_pretrained(
                '''kandinsky-community/kandinsky-2-2-prior'''							,   torch_dtype=torch.floataa						)
            pipe_prior.to(UpperCAmelCase_						)

            __snake_case      :    Union[str, Any]  			=KandinskyVaaControlnetPipeline.from_pretrained(
                '''kandinsky-community/kandinsky-2-2-controlnet-depth'''							,   torch_dtype=torch.floataa						)
            __snake_case      :    Optional[int]  			=pipeline.to(UpperCAmelCase_						)
            pipeline.set_progress_bar_config(disable=UpperCAmelCase_						)

            __snake_case      :    Any  			='''A robot, 4k photo'''

            __snake_case      :    int  			=torch.Generator(device='''cuda'''						).manual_seed(0						)
            __snake_case			,				__snake_case      :    Optional[int]  			=pipe_prior(
                UpperCAmelCase_							,   generator=UpperCAmelCase_							,   num_inference_steps=5							,   negative_prompt=''''''							,   ).to_tuple()

            __snake_case      :    int  			=torch.Generator(device='''cuda'''						).manual_seed(0						)
            __snake_case      :    Optional[Any]  			=pipeline(
                image_embeds=UpperCAmelCase_							,   negative_image_embeds=UpperCAmelCase_							,   hint=UpperCAmelCase_							,   generator=UpperCAmelCase_							,   num_inference_steps=1_0_0							,   output_type='''np'''							,   )

            __snake_case      :    str  			=output.images[0]

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

            assert_mean_pixel_difference(UpperCAmelCase_							,   UpperCAmelCase_						)










"""simple docstring"""





from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase_	:				Optional[int]										= logging.get_logger(__name__)

UpperCamelCase_	:				Union[str, Any]										= {"""ctrl""": """https://huggingface.co/ctrl/resolve/main/config.json"""}


class      _lowercase (     lowerCAmelCase						):
      _a   :     str								=   '''ctrl'''
      _a   :     Optional[int]								=   ['''past_key_values''']
      _a   :     List[Any]								=   {
          '''max_position_embeddings''': '''n_positions''',
          '''hidden_size''': '''n_embd''',
          '''num_attention_heads''': '''n_head''',
          '''num_hidden_layers''': '''n_layer''',
      }


      def __init__(    self				:	str							,   a				:	Optional[int]=2_4_6_5_3_4							,   a				:	Optional[int]=2_5_6							,   a				:	Optional[int]=1_2_8_0							,   a				:	Any=8_1_9_2							,   a				:	int=4_8							,   a				:	Optional[Any]=1_6							,   a				:	str=0.1							,   a				:	str=0.1							,   a				:	Any=1e-6							,   a				:	Optional[int]=0.0_2							,   a				:	int=True							,   **a				:	str							,   ):





            """simple docstring"""







            __snake_case      :    Tuple  			=vocab_size
            __snake_case      :    Optional[Any]  			=n_positions
            __snake_case      :    List[Any]  			=n_embd
            __snake_case      :    Any  			=n_layer
            __snake_case      :    Any  			=n_head
            __snake_case      :    str  			=dff
            __snake_case      :    List[str]  			=resid_pdrop
            __snake_case      :    str  			=embd_pdrop
            __snake_case      :    Union[str, Any]  			=layer_norm_epsilon
            __snake_case      :    List[Any]  			=initializer_range

            __snake_case      :    Any  			=use_cache

            super().__init__(**a						)


