


"""simple docstring"""
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 	lowercase__				(					A_ ,unittest.TestCase				):
   __UpperCAmelCase							       =					KandinskyVaaControlnetImgaImgPipeline
   __UpperCAmelCase							       =					['''image_embeds''', '''negative_image_embeds''', '''image''', '''hint''']
   __UpperCAmelCase							       =					['''image_embeds''', '''negative_image_embeds''', '''image''', '''hint''']
   __UpperCAmelCase							       =					[
       '''generator''',
       '''height''',
       '''width''',
       '''strength''',
       '''guidance_scale''',
       '''num_inference_steps''',
       '''return_dict''',
       '''guidance_scale''',
       '''num_images_per_prompt''',
       '''output_type''',
       '''return_dict''',
   ]
   __UpperCAmelCase							       =					False





   @property
   def 		UpperCamelCase_  (							self)  ->  Optional[Any]:
      return 32





   @property
   def 		UpperCamelCase_  (							self)  ->  str:
      return 32





   @property
   def 		UpperCamelCase_  (							self)  ->  str:
      return self.time_input_dim





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





   @property
   def 		UpperCamelCase_  (							self)  ->  str:
      return 100





   @property
   def 		UpperCamelCase_  (							self)  ->  Optional[int]:
      torch.manual_seed(0)

      _lowerCamelCase	:					str   =		{
          """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	:					Dict   =		UNetaDConditionModel(**SCREAMING_SNAKE_CASE)
      return model





   @property
   def 		UpperCamelCase_  (							self)  ->  Optional[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 		UpperCamelCase_  (							self)  ->  str:
      torch.manual_seed(0)
      _lowerCamelCase	:					Optional[Any]   =		VQModel(**self.dummy_movq_kwargs)
      return model





   def 		UpperCamelCase_  (							self)  ->  Tuple:
      _lowerCamelCase	:					int   =		self.dummy_unet
      _lowerCamelCase	:					Optional[Any]   =		self.dummy_movq

      _lowerCamelCase	:					Any   =		{
          """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,
      }

      _lowerCamelCase	:					List[Any]   =		DDIMScheduler(**SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					List[str]   =		{
          """unet""": unet,
          """scheduler""": scheduler,
          """movq""": movq,
      }

      return components





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE=0)  ->  str:
      _lowerCamelCase	:					Optional[Any]   =		floats_tensor((1, self.text_embedder_hidden_size)      ,   rng=random.Random(SCREAMING_SNAKE_CASE)).to(SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					Any   =		floats_tensor((1, self.text_embedder_hidden_size)      ,   rng=random.Random(seed + 1)).to(
          SCREAMING_SNAKE_CASE)
      # create init_image
      _lowerCamelCase	:					Union[str, Any]   =		floats_tensor((1, 3, 64, 64)      ,   rng=random.Random(SCREAMING_SNAKE_CASE)).to(SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					str   =		image.cpu().permute(0      ,   2      ,   3      ,   1)[0]
      _lowerCamelCase	:					Optional[int]   =		Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE)).convert("""RGB""").resize((256, 256))
      # create hint
      _lowerCamelCase	:					int   =		floats_tensor((1, 3, 64, 64)      ,   rng=random.Random(SCREAMING_SNAKE_CASE)).to(SCREAMING_SNAKE_CASE)

      if str(SCREAMING_SNAKE_CASE).startswith("""mps"""):
         _lowerCamelCase	:					List[Any]   =		torch.manual_seed(SCREAMING_SNAKE_CASE)
      else:
         _lowerCamelCase	:					int   =		torch.Generator(device=SCREAMING_SNAKE_CASE).manual_seed(SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					Tuple   =		{
          """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 		UpperCamelCase_  (							self)  ->  List[Any]:
      _lowerCamelCase	:					List[str]   =		"""cpu"""

      _lowerCamelCase	:					Any   =		self.get_dummy_components()

      _lowerCamelCase	:					int   =		self.pipeline_class(**SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					Dict   =		pipe.to(SCREAMING_SNAKE_CASE)

      pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					Optional[Any]   =		pipe(**self.get_dummy_inputs(SCREAMING_SNAKE_CASE))
      _lowerCamelCase	:					Dict   =		output.images

      _lowerCamelCase	:					Optional[int]   =		pipe(
          **self.get_dummy_inputs(SCREAMING_SNAKE_CASE)      ,   return_dict=SCREAMING_SNAKE_CASE      ,   )[0]

      _lowerCamelCase	:					List[str]   =		image[0, -3:, -3:, -1]
      _lowerCamelCase	:					int   =		image_from_tuple[0, -3:, -3:, -1]

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

      _lowerCamelCase	:					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 	lowercase__				(					unittest.TestCase				):





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





   def 		UpperCamelCase_  (							self)  ->  Tuple:
      _lowerCamelCase	:					Dict   =		load_numpy(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
          """/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy""")

      _lowerCamelCase	:					List[str]   =		load_image(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png""")
      _lowerCamelCase	:					Dict   =		init_image.resize((512, 512))

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

      _lowerCamelCase	:					Optional[int]   =		"""A robot, 4k photo"""

      _lowerCamelCase	:					Tuple   =		KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
          """kandinsky-community/kandinsky-2-2-prior"""      ,   torch_dtype=torch.floataa)
      pipe_prior.to(SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					List[str]   =		KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
          """kandinsky-community/kandinsky-2-2-controlnet-depth"""      ,   torch_dtype=torch.floataa)
      _lowerCamelCase	:					Dict   =		pipeline.to(SCREAMING_SNAKE_CASE)

      pipeline.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					Dict   =		torch.Generator(device="""cpu""").manual_seed(0)

      _lowerCamelCase   ,    _lowerCamelCase	:					List[Any]   =		pipe_prior(
          SCREAMING_SNAKE_CASE      ,   image=SCREAMING_SNAKE_CASE      ,   strength=0.85      ,   generator=SCREAMING_SNAKE_CASE      ,   negative_prompt=""""""      ,   ).to_tuple()

      _lowerCamelCase	:					List[str]   =		pipeline(
          image=SCREAMING_SNAKE_CASE      ,   image_embeds=SCREAMING_SNAKE_CASE      ,   negative_image_embeds=SCREAMING_SNAKE_CASE      ,   hint=SCREAMING_SNAKE_CASE      ,   generator=SCREAMING_SNAKE_CASE      ,   num_inference_steps=100      ,   height=512      ,   width=512      ,   strength=0.5      ,   output_type="""np"""      ,   )

      _lowerCamelCase	:					Any   =		output.images[0]

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

      assert_mean_pixel_difference(SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)




"""simple docstring"""





import warnings

from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor


__UpperCamelCase    : List[str]				 =						logging.get_logger(__name__)







class 							SCREAMING_SNAKE_CASE   (						a_			):






					"""simple docstring"""



					def __init__(			self     :							Any  ,*lowercase_     :							Dict  ,**lowercase_     :							List[str]						):
						warnings.warn(
						    '''The class ImageGPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
						    ''' Please use ImageGPTImageProcessor instead.'''  ,lowercase_  ,)
						super().__init__(*lowercase_  ,**lowercase_						)




