






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 			UpperCAmelCase_						(					snake_case__,	unittest.TestCase ):



      """simple docstring"""
      UpperCamelCase_				:					int           =KandinskyImgaImgPipeline
      UpperCamelCase_				:					Optional[int]           =["prompt", "image_embeds", "negative_image_embeds", "image"]
      UpperCamelCase_				:					Optional[Any]           =[
          "prompt",
          "negative_prompt",
          "image_embeds",
          "negative_image_embeds",
          "image",
      ]
      UpperCamelCase_				:					int           =[
          "generator",
          "height",
          "width",
          "strength",
          "guidance_scale",
          "negative_prompt",
          "num_inference_steps",
          "return_dict",
          "guidance_scale",
          "num_images_per_prompt",
          "output_type",
          "return_dict",
      ]
      UpperCamelCase_				:					Tuple           =False
      @property
      def     UpperCAmelCase						(				self )				->      int:
          return 32
      @property
      def     UpperCAmelCase						(				self )				->      List[str]:
          return 32
      @property
      def     UpperCAmelCase						(				self )				->      Optional[int]:
          return self.time_input_dim
      @property
      def     UpperCAmelCase						(				self )				->      int:
          return self.time_input_dim * 4
      @property
      def     UpperCAmelCase						(				self )				->      List[Any]:
          return 100
      @property
      def     UpperCAmelCase						(				self )				->      int:
          UpperCamelCase       :Optional[int]								=   XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base''' )
          return tokenizer
      @property
      def     UpperCAmelCase						(				self )				->      Optional[Any]:
          torch.manual_seed(0 )
          UpperCamelCase       :Any								=   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    ,				)

          UpperCamelCase       :Optional[int]								=   MultilingualCLIP(SCREAMING_SNAKE_CASE_ )
          UpperCamelCase       :Dict								=   text_encoder.eval()

          return text_encoder
      @property
      def     UpperCAmelCase						(				self )				->      List[Any]:
          torch.manual_seed(0 )

          UpperCamelCase       :str								=   {
              "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,
          }

          UpperCamelCase       :str								=   UNetaDConditionModel(**SCREAMING_SNAKE_CASE_ )
          return model
      @property
      def     UpperCAmelCase						(				self )				->      Dict:
          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     UpperCAmelCase						(				self )				->      Any:
          torch.manual_seed(0 )
          UpperCamelCase       :str								=   VQModel(**self.dummy_movq_kwargs )
          return model
      def     UpperCAmelCase						(				self )				->      Tuple:
          UpperCamelCase       :Optional[Any]								=   self.dummy_text_encoder
          UpperCamelCase       :str								=   self.dummy_tokenizer
          UpperCamelCase       :int								=   self.dummy_unet
          UpperCamelCase       :List[str]								=   self.dummy_movq

          UpperCamelCase       :int								=   {
              "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,
          }

          UpperCamelCase       :List[Any]								=   DDIMScheduler(**SCREAMING_SNAKE_CASE_ )

          UpperCamelCase       :List[Any]								=   {
              "text_encoder": text_encoder,
              "tokenizer": tokenizer,
              "unet": unet,
              "scheduler": scheduler,
              "movq": movq,
          }

          return components
      def     UpperCAmelCase						(				self    ,				SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_=0 )				->      int:
          UpperCamelCase       :Dict								=   floats_tensor((1, self.cross_attention_dim)    ,				rng=random.Random(SCREAMING_SNAKE_CASE_ ) ).to(SCREAMING_SNAKE_CASE_ )
          UpperCamelCase       :int								=   floats_tensor((1, self.cross_attention_dim)    ,				rng=random.Random(seed + 1 ) ).to(SCREAMING_SNAKE_CASE_ )
          # create init_image
          UpperCamelCase       :int								=   floats_tensor((1, 3, 64, 64)    ,				rng=random.Random(SCREAMING_SNAKE_CASE_ ) ).to(SCREAMING_SNAKE_CASE_ )
          UpperCamelCase       :Union[str, Any]								=   image.cpu().permute(0    ,				2    ,				3    ,				1 )[0]
          UpperCamelCase       :Union[str, Any]								=   Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE_ ) ).convert('''RGB''' ).resize((256, 256) )

          if str(SCREAMING_SNAKE_CASE_ ).startswith('''mps''' ):
              UpperCamelCase       :str								=   torch.manual_seed(SCREAMING_SNAKE_CASE_ )
          else:
              UpperCamelCase       :Dict								=   torch.Generator(device=SCREAMING_SNAKE_CASE_ ).manual_seed(SCREAMING_SNAKE_CASE_ )
          UpperCamelCase       :Union[str, Any]								=   {
              "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     UpperCAmelCase						(				self )				->      List[Any]:
          UpperCamelCase       :str								=   "cpu"

          UpperCamelCase       :Tuple								=   self.get_dummy_components()

          UpperCamelCase       :Optional[Any]								=   self.pipeline_class(**SCREAMING_SNAKE_CASE_ )
          UpperCamelCase       :Dict								=   pipe.to(SCREAMING_SNAKE_CASE_ )

          pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )

          UpperCamelCase       :Optional[Any]								=   pipe(**self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ ) )
          UpperCamelCase       :Union[str, Any]								=   output.images

          UpperCamelCase       :str								=   pipe(
              **self.get_dummy_inputs(SCREAMING_SNAKE_CASE_ )    ,				return_dict=SCREAMING_SNAKE_CASE_    ,				)[0]

          UpperCamelCase       :Optional[Any]								=   image[0, -3:, -3:, -1]
          UpperCamelCase       :Optional[Any]								=   image_from_tuple[0, -3:, -3:, -1]

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

          UpperCamelCase       :int								=   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 			UpperCAmelCase_						(					unittest.TestCase ):



      """simple docstring"""
      def     UpperCAmelCase						(				self )				->      Optional[int]:
          # clean up the VRAM after each test
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()




      def     UpperCAmelCase						(				self )				->      Dict:
          UpperCamelCase       :Any								=   load_numpy(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
              '''/kandinsky/kandinsky_img2img_frog.npy''' )

          UpperCamelCase       :int								=   load_image(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png''' )
          UpperCamelCase       :Optional[Any]								=   "A red cartoon frog, 4k"

          UpperCamelCase       :Any								=   KandinskyPriorPipeline.from_pretrained(
              '''kandinsky-community/kandinsky-2-1-prior'''    ,				torch_dtype=torch.floataa )
          pipe_prior.to(SCREAMING_SNAKE_CASE_ )

          UpperCamelCase       :List[str]								=   KandinskyImgaImgPipeline.from_pretrained(
              '''kandinsky-community/kandinsky-2-1'''    ,				torch_dtype=torch.floataa )
          UpperCamelCase       :List[Any]								=   pipeline.to(SCREAMING_SNAKE_CASE_ )

          pipeline.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_ )

          UpperCamelCase       :int								=   torch.Generator(device='''cpu''' ).manual_seed(0 )
          UpperCamelCase       :Any								=   pipe_prior(
              SCREAMING_SNAKE_CASE_    ,				generator=SCREAMING_SNAKE_CASE_    ,				num_inference_steps=5    ,				negative_prompt=''''''    ,				).to_tuple()

          UpperCamelCase       :int								=   pipeline(
              SCREAMING_SNAKE_CASE_    ,				image=SCREAMING_SNAKE_CASE_    ,				image_embeds=SCREAMING_SNAKE_CASE_    ,				negative_image_embeds=SCREAMING_SNAKE_CASE_    ,				generator=SCREAMING_SNAKE_CASE_    ,				num_inference_steps=100    ,				height=768    ,				width=768    ,				strength=0.2    ,				output_type='''np'''    ,				)

          UpperCamelCase       :List[Any]								=   output.images[0]

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

          assert_mean_pixel_difference(SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_ )






'''simple docstring'''




import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union

import torch
from torch import nn

from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging


__SCREAMING_SNAKE_CASE       :     str     =  logging.get_logger(__name__)

class 	lowerCamelCase_	(snake_case__    ):
      '''simple docstring'''

      def __init__(       self						:				Optional[int]						,					A						:				Union[List[ControlNetModel], Tuple[ControlNetModel]]       ):
             super().__init__()
             _UpperCAmelCase     :  Optional[int]							      =    nn.ModuleList(A       )




      def      _A	(       self						:				Dict						,					A						:				torch.FloatTensor						,					A						:				Union[torch.Tensor, float, int]						,					A						:				torch.Tensor						,					A						:				List[torch.tensor]						,					A						:				List[float]						,					A						:				Optional[torch.Tensor] = None						,					A						:				Optional[torch.Tensor] = None						,					A						:				Optional[torch.Tensor] = None						,					A						:				Optional[Dict[str, Any]] = None						,					A						:				bool = False						,					A						:				bool = True						,					):
             for i, (image, scale, controlnet) in enumerate(zip(A						,					A						,					self.nets       )       ):
                    _UpperCAmelCase      ,  _UpperCAmelCase     :  str							      =    controlnet(
                        A						,					A						,					A						,					A						,					A						,					A						,					A						,					A						,					A						,					A						,					A						,					)

                    # merge samples
                    if i == 0:
                           _UpperCAmelCase      ,  _UpperCAmelCase     :  List[Any]							      =    down_samples, mid_sample
                    else:
                           _UpperCAmelCase     :  Optional[int]							      =    [
                               samples_prev + samples_curr
                               for samples_prev, samples_curr in zip(A						,					A       )
                           ]
                           mid_block_res_sample += mid_sample

             return down_block_res_samples, mid_block_res_sample




      def      _A	(       self						:				List[str]						,					A						:				Union[str, os.PathLike]						,					A						:				bool = True						,					A						:				Callable = None						,					A						:				bool = False						,					A						:				Optional[str] = None						,					):
             _UpperCAmelCase     :  str							      =    0
             _UpperCAmelCase     :  str							      =    save_directory
             for controlnet in self.nets:
                    controlnet.save_pretrained(
                        A						,					is_main_process=A						,					save_function=A						,					safe_serialization=A						,					variant=A						,					)

                    idx += 1
                    _UpperCAmelCase     :  Tuple							      =    model_path_to_save + F"""_{idx}"""





      @classmethod
      def      _A	(       cls						:				int						,					A						:				Optional[Union[str, os.PathLike]]						,					**A						:				Tuple       ):
             _UpperCAmelCase     :  str							      =    0
             _UpperCAmelCase     :  int							      =    []

             # load controlnet and append to list until no controlnet directory exists anymore
             # first controlnet has to be saved under `./mydirectory/controlnet` to be compliant with `DiffusionPipeline.from_prertained`
             # second, third, ... controlnets have to be saved under `./mydirectory/controlnet_1`, `./mydirectory/controlnet_2`, ...
             _UpperCAmelCase     :  int							      =    pretrained_model_path
             while os.path.isdir(A       ):
                    _UpperCAmelCase     :  List[str]							      =    ControlNetModel.from_pretrained(A						,					**A       )
                    controlnets.append(A       )

                    idx += 1
                    _UpperCAmelCase     :  Dict							      =    pretrained_model_path + F"""_{idx}"""

             logger.info(F"""{len(A       )} controlnets loaded from {pretrained_model_path}."""       )

             if len(A       ) == 0:
                    raise ValueError(
                        F"""No ControlNets found under {os.path.dirname(A       )}. Expected at least {pretrained_model_path + '_0'}."""       )

             return cls(A       )

