
import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetImgaImgPipeline,
    KandinskyVaaPriorEmbaEmbPipeline,
    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     __lowerCAmelCase						(						lowerCAmelCase_			,			unittest.TestCase   ):



      """simple docstring"""







      A__						:      Union[str, Any]   =     KandinskyVaaControlnetImgaImgPipeline
      A__						:      Tuple   =     ["""image_embeds""", """negative_image_embeds""", """image""", """hint"""]
      A__						:      Dict   =     ["""image_embeds""", """negative_image_embeds""", """image""", """hint"""]
      A__						:      str   =     [
          """generator""",
          """height""",
          """width""",
          """strength""",
          """guidance_scale""",
          """num_inference_steps""",
          """return_dict""",
          """guidance_scale""",
          """num_images_per_prompt""",
          """output_type""",
          """return_dict""",
      ]
      A__						:      Union[str, Any]   =     False




      @property
      def 		snake_case_      (   self		:							Optional[int]					):
          return 32




      @property
      def 		snake_case_      (   self		:							int					):
          return 32




      @property
      def 		snake_case_      (   self		:							Tuple					):
          return self.time_input_dim




      @property
      def 		snake_case_      (   self		:							Tuple					):
          return self.time_input_dim * 4




      @property
      def 		snake_case_      (   self		:							Dict					):
          return 100




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

          __lowercase		:      Optional[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,
          }

          __lowercase		:      List[str]						     =							UNetaDConditionModel(**_snake_case					)
          return model




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




      def 		snake_case_      (   self		:							Dict					):
          __lowercase		:      Union[str, Any]						     =							self.dummy_unet
          __lowercase		:      Dict						     =							self.dummy_movq

          __lowercase		:      Optional[int]						     =							{
              "num_train_timesteps": 1000,
              "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		:      str						     =							DDIMScheduler(**_snake_case					)

          __lowercase		:      Union[str, Any]						     =							{
              "unet": unet,
              "scheduler": scheduler,
              "movq": movq,
          }

          return components




      def 		snake_case_      (   self		:							Optional[int]					,							_snake_case		:							str					,							_snake_case		:							Any=0					):
          __lowercase		:      Tuple						     =							floats_tensor((1, self.text_embedder_hidden_size)					,							rng=random.Random(_snake_case					)					).to(_snake_case					)
          __lowercase		:      Optional[int]						     =							floats_tensor((1, self.text_embedder_hidden_size)					,							rng=random.Random(seed + 1					)					).to(
              _snake_case					)
          # create init_image
          __lowercase		:      Tuple						     =							floats_tensor((1, 3, 64, 64)					,							rng=random.Random(_snake_case					)					).to(_snake_case					)
          __lowercase		:      Optional[Any]						     =							image.cpu().permute(0					,							2					,							3					,							1					)[0]
          __lowercase		:      List[Any]						     =							Image.fromarray(np.uinta(_snake_case					)					).convert('''RGB'''					).resize((256, 256)					)
          # create hint
          __lowercase		:      Dict						     =							floats_tensor((1, 3, 64, 64)					,							rng=random.Random(_snake_case					)					).to(_snake_case					)

          if str(_snake_case					).startswith('''mps'''					):
              __lowercase		:      Any						     =							torch.manual_seed(_snake_case					)
          else:
              __lowercase		:      Optional[int]						     =							torch.Generator(device=_snake_case					).manual_seed(_snake_case					)
          __lowercase		:      Optional[int]						     =							{
              "image": init_image,
              "image_embeds": image_embeds,
              "negative_image_embeds": negative_image_embeds,
              "hint": hint,
              "generator": generator,
              "height": 64,
              "width": 64,
              "num_inference_steps": 10,
              "guidance_scale": 7.0,
              "strength": 0.2,
              "output_type": "np",
          }
          return inputs




      def 		snake_case_      (   self		:							List[Any]					):
          __lowercase		:      int						     =							"cpu"

          __lowercase		:      int						     =							self.get_dummy_components()

          __lowercase		:      Optional[int]						     =							self.pipeline_class(**_snake_case					)
          __lowercase		:      Tuple						     =							pipe.to(_snake_case					)

          pipe.set_progress_bar_config(disable=_snake_case					)

          __lowercase		:      Union[str, Any]						     =							pipe(**self.get_dummy_inputs(_snake_case					)					)
          __lowercase		:      Dict						     =							output.images

          __lowercase		:      str						     =							pipe(
              **self.get_dummy_inputs(_snake_case					)					,							return_dict=_snake_case					,							)[0]

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

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

          __lowercase		:      List[Any]						     =							np.array(
              [0.54_98_50_34, 0.55_50_93_65, 0.52_56_15_04, 0.5_57_04_94, 0.5_59_38_18, 0.5_26_39_79, 0.50_28_56_43, 0.5_06_98_46, 0.51_19_67_36]					)
          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     __lowerCAmelCase						(						unittest.TestCase   ):



      """simple docstring"""







      def 		snake_case_      (   self		:							List[Any]					):
          # clean up the VRAM after each test
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()




      def 		snake_case_      (   self		:							List[str]					):
          __lowercase		:      Any						     =							load_numpy(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
              '''/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy'''					)

          __lowercase		:      Any						     =							load_image(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''					)
          __lowercase		:      Optional[Any]						     =							init_image.resize((512, 512)					)

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

          __lowercase		:      Tuple						     =							"A robot, 4k photo"

          __lowercase		:      Any						     =							KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
              '''kandinsky-community/kandinsky-2-2-prior'''					,							torch_dtype=torch.floataa					)
          pipe_prior.to(_snake_case					)

          __lowercase		:      Tuple						     =							KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
              '''kandinsky-community/kandinsky-2-2-controlnet-depth'''					,							torch_dtype=torch.floataa					)
          __lowercase		:      Optional[int]						     =							pipeline.to(_snake_case					)

          pipeline.set_progress_bar_config(disable=_snake_case					)

          __lowercase		:      Optional[int]						     =							torch.Generator(device='''cpu'''					).manual_seed(0					)

          __lowercase		:      Tuple						     =							pipe_prior(
              _snake_case					,							image=_snake_case					,							strength=0.85					,							generator=_snake_case					,							negative_prompt=''''''					,							).to_tuple()

          __lowercase		:      List[Any]						     =							pipeline(
              image=_snake_case					,							image_embeds=_snake_case					,							negative_image_embeds=_snake_case					,							hint=_snake_case					,							generator=_snake_case					,							num_inference_steps=100					,							height=512					,							width=512					,							strength=0.5					,							output_type='''np'''					,							)

          __lowercase		:      str						     =							output.images[0]

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

          assert_mean_pixel_difference(_snake_case					,							_snake_case					)






from multiprocessing import Lock, Pipe, Process

# lock used to ensure that two processes do not access a pipe at the same time
SCREAMING_SNAKE_CASE__     					=			Lock()


def     lowercase	(    a     , a     , a     , a     , a     , a     , a ):



		'''simple docstring'''




		global process_lock

		# we perform n swaps since after n swaps we know we are sorted
		# we *could* stop early if we are sorted already, but it takes as long to
		# find out we are sorted as it does to sort the list with this algorithm
		for i in range(0     , 10 ):
				if (i + position) % 2 == 0 and r_send is not None:
						# send your value to your right neighbor
						process_lock.acquire()
						r_send[1].send(a )
						process_lock.release()

						# receive your right neighbor's value
						process_lock.acquire()
						SCREAMING_SNAKE_CASE_				:str				     =						rr_cv[0].recv()
						process_lock.release()

						# take the lower value since you are on the left
						SCREAMING_SNAKE_CASE_				:int				     =						min(a     , a )
				elif (i + position) % 2 != 0 and l_send is not None:
						# send your value to your left neighbor
						process_lock.acquire()
						l_send[1].send(a )
						process_lock.release()

						# receive your left neighbor's value
						process_lock.acquire()
						SCREAMING_SNAKE_CASE_				:int				     =						lr_cv[0].recv()
						process_lock.release()

						# take the higher value since you are on the right
						SCREAMING_SNAKE_CASE_				:Dict				     =						max(a     , a )
    # after all swaps are performed, send the values back to main
		result_pipe[1].send(a )


def     lowercase	(    a ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:Tuple				     =						[]
		SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						[]
		# initialize the list of pipes where the values will be retrieved
		for _ in arr:
				result_pipe.append(Pipe() )
		# creates the processes
		# the first and last process only have one neighbor so they are made outside
		# of the loop
		SCREAMING_SNAKE_CASE_				:str				     =						Pipe()
		SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						Pipe()
		process_array_.append(
		    Process(
		        target=a     , args=(0, arr[0], None, temp_rs, None, temp_rr, result_pipe[0])     , ) )
		SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						temp_rs
		SCREAMING_SNAKE_CASE_				:Any				     =						temp_rr

		for i in range(1     , len(a ) - 1 ):
				SCREAMING_SNAKE_CASE_				:int				     =						Pipe()
				SCREAMING_SNAKE_CASE_				:Dict				     =						Pipe()
				process_array_.append(
				    Process(
				        target=a     , args=(i, arr[i], temp_ls, temp_rs, temp_lr, temp_rr, result_pipe[i])     , ) )
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						temp_rs
				SCREAMING_SNAKE_CASE_				:int				     =						temp_rr

		process_array_.append(
		    Process(
		        target=a     , args=(
		            len(a ) - 1,
		            arr[len(a ) - 1],
		            temp_ls,
		            None,
		            temp_lr,
		            None,
		            result_pipe[len(a ) - 1],
		        )     , ) )

		# start the processes
		for p in process_array_:
				p.start()

		# wait for the processes to end and write their values to the list
		for p in range(0     , len(a ) ):
				SCREAMING_SNAKE_CASE_				:Tuple				     =						result_pipe[p][0].recv()
				process_array_[p].join()
		return arr


def     lowercase	(    ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:List[str]				     =						list(range(10     , 0     , -1 ) )
		print("Initial List" )
		print(*a )
		SCREAMING_SNAKE_CASE_				:int				     =						odd_even_transposition(a )
		print("Sorted List\n" )
		print(*a )


if __name__ == "__main__":
		main()






