

import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..utils import BaseOutput


__UpperCAmelCase    				=     'scheduler_config.json'





class A__    ( A ):



 """simple docstring"""







 _lowercase     :					Optional[Any]      =						1
 _lowercase     :					List[str]      =						2
 _lowercase     :					Dict      =						3
 _lowercase     :					str      =						4
 _lowercase     :					str      =						5





@dataclass
class A__    ( A ):



 """simple docstring"""







 _lowercase     :					jnp.ndarray





class A__    :



 """simple docstring"""







 _lowercase     :					Union[str, Any]      =						SCHEDULER_CONFIG_NAME
 _lowercase     :					int      =						['''dtype''']
 _lowercase     :					int      =						[]
 _lowercase     :					Tuple      =						True

 @classmethod
 def    __magic_name__       (		cls :	int     ,						A_ :	Dict[str, Any] = None     ,						A_ :	Optional[str] = None     ,						A_ :	Optional[Any]=False     ,						**A_ :	Optional[int]     ,						):






      '''simple docstring'''




      _lowerCAmelCase      ,			_lowerCAmelCase   :					int        =    cls.load_config(
          pretrained_model_name_or_path=A_     ,						subfolder=A_     ,						return_unused_kwargs=A_     ,						**A_     ,						)
      _lowerCAmelCase      ,			_lowerCAmelCase   :					Optional[Any]        =    cls.from_config(A_     ,						return_unused_kwargs=A_     ,						**A_       )

      if hasattr(A_     ,						"create_state"       ) and getattr(A_     ,						"has_state"     ,						A_       ):
           _lowerCAmelCase   :					Dict        =    scheduler.create_state()

      if return_unused_kwargs:
           return scheduler, state, unused_kwargs

      return scheduler, state

 def    __magic_name__       (		self :	int     ,						A_ :	Union[str, os.PathLike]     ,						A_ :	bool = False     ,						**A_ :	List[Any]       ):






      '''simple docstring'''




      self.save_config(save_directory=A_     ,						push_to_hub=A_     ,						**A_       )

 @property
 def    __magic_name__       (		self :	int       ):






      '''simple docstring'''




      return self._get_compatibles()


 @classmethod
 def    __magic_name__       (		cls :	List[str]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					List[str]        =    list(set([cls.__name__] + cls._compatibles       )       )
      _lowerCAmelCase   :					List[Any]        =    importlib.import_module(__name__.split("."       )[0]       )
      _lowerCAmelCase   :					Any        =    [
          getattr(A_     ,						A_       ) for c in compatible_classes_str if hasattr(A_     ,						A_       )
      ]
      return compatible_classes







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      jnp.ndarray:

     """simple docstring"""







     assert len(SCREAMING_SNAKE_CASE				) >= x.ndim
     return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(SCREAMING_SNAKE_CASE				) - x.ndim)				)		,							SCREAMING_SNAKE_CASE				)







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE=0.999		,							SCREAMING_SNAKE_CASE=jnp.floataa				)       ->      jnp.ndarray:

     """simple docstring"""







     def alpha_bar(SCREAMING_SNAKE_CASE				):
          return math.cos((time_step + 0.008) / 1.008 * math.pi / 2				) ** 2

     _lowerCAmelCase   :					int        =    []
     for i in range(SCREAMING_SNAKE_CASE				):
          _lowerCAmelCase   :					Union[str, Any]        =    i / num_diffusion_timesteps
          _lowerCAmelCase   :					Tuple        =    (i + 1) / num_diffusion_timesteps
          betas.append(min(1 - alpha_bar(SCREAMING_SNAKE_CASE				) / alpha_bar(SCREAMING_SNAKE_CASE				)		,							SCREAMING_SNAKE_CASE				)				)
     return jnp.array(SCREAMING_SNAKE_CASE		,							dtype=SCREAMING_SNAKE_CASE				)





@flax.struct.dataclass
class A__    :



 """simple docstring"""







 _lowercase     :					jnp.ndarray
 _lowercase     :					jnp.ndarray
 _lowercase     :					jnp.ndarray

 @classmethod
 def    __magic_name__       (		cls :	Optional[Any]     ,						A_ :	Dict       ):






      '''simple docstring'''




      _lowerCAmelCase   :					str        =    scheduler.config

      if config.trained_betas is not None:
           _lowerCAmelCase   :					Any        =    jnp.asarray(config.trained_betas     ,						dtype=scheduler.dtype       )
      elif config.beta_schedule == "linear":
           _lowerCAmelCase   :					Union[str, Any]        =    jnp.linspace(config.beta_start     ,						config.beta_end     ,						config.num_train_timesteps     ,						dtype=scheduler.dtype       )
      elif config.beta_schedule == "scaled_linear":
           # this schedule is very specific to the latent diffusion model.
           _lowerCAmelCase   :					Dict        =    (
               jnp.linspace(
                   config.beta_start**0.5     ,						config.beta_end**0.5     ,						config.num_train_timesteps     ,						dtype=scheduler.dtype       )
               ** 2
           )
      elif config.beta_schedule == "squaredcos_cap_v2":
           # Glide cosine schedule
           _lowerCAmelCase   :					List[Any]        =    betas_for_alpha_bar(config.num_train_timesteps     ,						dtype=scheduler.dtype       )
      else:
           raise NotImplementedError(
               F'''beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}'''       )

      _lowerCAmelCase   :					Dict        =    1.0 - betas

      _lowerCAmelCase   :					Optional[Any]        =    jnp.cumprod(A_     ,						axis=0       )

      return cls(
          alphas=A_     ,						betas=A_     ,						alphas_cumprod=A_     ,						)







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      Optional[Any]:

     """simple docstring"""







     _lowerCAmelCase   :					Optional[int]        =    state.alphas_cumprod

     _lowerCAmelCase   :					Tuple        =    alphas_cumprod[timesteps] ** 0.5
     _lowerCAmelCase   :					Dict        =    sqrt_alpha_prod.flatten()
     _lowerCAmelCase   :					Tuple        =    broadcast_to_shape_from_left(SCREAMING_SNAKE_CASE		,							original_samples.shape				)

     _lowerCAmelCase   :					int        =    (1 - alphas_cumprod[timesteps]) ** 0.5
     _lowerCAmelCase   :					Dict        =    sqrt_one_minus_alpha_prod.flatten()
     _lowerCAmelCase   :					Any        =    broadcast_to_shape_from_left(SCREAMING_SNAKE_CASE		,							original_samples.shape				)

     return sqrt_alpha_prod, sqrt_one_minus_alpha_prod







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      List[str]:

     """simple docstring"""







     _lowerCAmelCase      ,			_lowerCAmelCase   :					Union[str, Any]        =    get_sqrt_alpha_prod(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)
     _lowerCAmelCase   :					Dict        =    sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
     return noisy_samples







def 	_snake_case		(    SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)       ->      Tuple:

     """simple docstring"""







     _lowerCAmelCase      ,			_lowerCAmelCase   :					Any        =    get_sqrt_alpha_prod(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE				)
     _lowerCAmelCase   :					str        =    sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
     return velocity









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 A__    ( A  ,		unittest.TestCase ):



 """simple docstring"""







 _lowercase     :					List[str]      =						KandinskyVaaControlnetImgaImgPipeline
 _lowercase     :					Tuple      =						['''image_embeds''', '''negative_image_embeds''', '''image''', '''hint''']
 _lowercase     :					str      =						['''image_embeds''', '''negative_image_embeds''', '''image''', '''hint''']
 _lowercase     :					Optional[Any]      =						[
     '''generator''',
     '''height''',
     '''width''',
     '''strength''',
     '''guidance_scale''',
     '''num_inference_steps''',
     '''return_dict''',
     '''guidance_scale''',
     '''num_images_per_prompt''',
     '''output_type''',
     '''return_dict''',
 ]
 _lowercase     :					Tuple      =						False

 @property
 def    __magic_name__       (		self :	Any       ):






      '''simple docstring'''




      return 3_2

 @property
 def    __magic_name__       (		self :	Tuple       ):






      '''simple docstring'''




      return 3_2

 @property
 def    __magic_name__       (		self :	List[Any]       ):






      '''simple docstring'''




      return self.time_input_dim

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






      '''simple docstring'''




      return self.time_input_dim * 4

 @property
 def    __magic_name__       (		self :	Any       ):






      '''simple docstring'''




      return 1_0_0

 @property
 def    __magic_name__       (		self :	Any       ):






      '''simple docstring'''




      torch.manual_seed(0       )

      _lowerCAmelCase   :					Tuple        =    {
          "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   :					Optional[int]        =    UNetaDConditionModel(**A_       )
      return model

 @property
 def    __magic_name__       (		self :	Dict       ):






      '''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    __magic_name__       (		self :	Optional[int]       ):






      '''simple docstring'''




      torch.manual_seed(0       )
      _lowerCAmelCase   :					List[str]        =    VQModel(**self.dummy_movq_kwargs       )
      return model

 def    __magic_name__       (		self :	int       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Tuple        =    self.dummy_unet
      _lowerCAmelCase   :					List[Any]        =    self.dummy_movq

      _lowerCAmelCase   :					Tuple        =    {
          "num_train_timesteps": 1_0_0_0,
          "beta_schedule": "linear",
          "beta_start": 0.00085,
          "beta_end": 0.012,
          "clip_sample": False,
          "set_alpha_to_one": False,
          "steps_offset": 0,
          "prediction_type": "epsilon",
          "thresholding": False,
      }

      _lowerCAmelCase   :					int        =    DDIMScheduler(**A_       )

      _lowerCAmelCase   :					Tuple        =    {
          "unet": unet,
          "scheduler": scheduler,
          "movq": movq,
      }

      return components

 def    __magic_name__       (		self :	Union[str, Any]     ,						A_ :	Union[str, Any]     ,						A_ :	Optional[int]=0       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Tuple        =    floats_tensor((1, self.text_embedder_hidden_size)     ,						rng=random.Random(A_       )       ).to(A_       )
      _lowerCAmelCase   :					str        =    floats_tensor((1, self.text_embedder_hidden_size)     ,						rng=random.Random(seed + 1       )       ).to(
          A_       )
      # create init_image
      _lowerCAmelCase   :					Dict        =    floats_tensor((1, 3, 6_4, 6_4)     ,						rng=random.Random(A_       )       ).to(A_       )
      _lowerCAmelCase   :					Dict        =    image.cpu().permute(0     ,						2     ,						3     ,						1       )[0]
      _lowerCAmelCase   :					Tuple        =    Image.fromarray(np.uinta(A_       )       ).convert("RGB"       ).resize((2_5_6, 2_5_6)       )
      # create hint
      _lowerCAmelCase   :					Union[str, Any]        =    floats_tensor((1, 3, 6_4, 6_4)     ,						rng=random.Random(A_       )       ).to(A_       )

      if str(A_       ).startswith("mps"       ):
           _lowerCAmelCase   :					Tuple        =    torch.manual_seed(A_       )
      else:
           _lowerCAmelCase   :					Any        =    torch.Generator(device=A_       ).manual_seed(A_       )
      _lowerCAmelCase   :					Union[str, Any]        =    {
          "image": init_image,
          "image_embeds": image_embeds,
          "negative_image_embeds": negative_image_embeds,
          "hint": hint,
          "generator": generator,
          "height": 6_4,
          "width": 6_4,
          "num_inference_steps": 1_0,
          "guidance_scale": 7.0,
          "strength": 0.2,
          "output_type": "np",
      }
      return inputs


 def    __magic_name__       (		self :	Optional[int]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Union[str, Any]        =    "cpu"

      _lowerCAmelCase   :					Tuple        =    self.get_dummy_components()

      _lowerCAmelCase   :					Optional[int]        =    self.pipeline_class(**A_       )
      _lowerCAmelCase   :					Optional[Any]        =    pipe.to(A_       )

      pipe.set_progress_bar_config(disable=A_       )

      _lowerCAmelCase   :					List[str]        =    pipe(**self.get_dummy_inputs(A_       )       )
      _lowerCAmelCase   :					List[Any]        =    output.images

      _lowerCAmelCase   :					str        =    pipe(
          **self.get_dummy_inputs(A_       )     ,						return_dict=A_     ,						)[0]

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

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

      _lowerCAmelCase   :					List[Any]        =    np.array(
          [0.54985034, 0.55509365, 0.52561504, 0.5570494, 0.5593818, 0.5263979, 0.50285643, 0.5069846, 0.51196736]       )
      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 A__    ( unittest.TestCase ):



 """simple docstring"""







 def    __magic_name__       (		self :	Any       ):






      '''simple docstring'''




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


 def    __magic_name__       (		self :	List[Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					List[str]        =    load_numpy(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
          "/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy"       )

      _lowerCAmelCase   :					str        =    load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"       )
      _lowerCAmelCase   :					Union[str, Any]        =    init_image.resize((5_1_2, 5_1_2)       )

      _lowerCAmelCase   :					Dict        =    load_image(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
          "/kandinskyv22/hint_image_cat.png"       )
      _lowerCAmelCase   :					Tuple        =    torch.from_numpy(np.array(A_       )       ).float() / 255.0
      _lowerCAmelCase   :					Optional[int]        =    hint.permute(2     ,						0     ,						1       ).unsqueeze(0       )

      _lowerCAmelCase   :					List[str]        =    "A robot, 4k photo"

      _lowerCAmelCase   :					Dict        =    KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
          "kandinsky-community/kandinsky-2-2-prior"     ,						torch_dtype=torch.floataa       )
      pipe_prior.to(A_       )

      _lowerCAmelCase   :					str        =    KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
          "kandinsky-community/kandinsky-2-2-controlnet-depth"     ,						torch_dtype=torch.floataa       )
      _lowerCAmelCase   :					Optional[int]        =    pipeline.to(A_       )

      pipeline.set_progress_bar_config(disable=A_       )

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

      _lowerCAmelCase      ,			_lowerCAmelCase   :					Tuple        =    pipe_prior(
          A_     ,						image=A_     ,						strength=0.85     ,						generator=A_     ,						negative_prompt=""     ,						).to_tuple()

      _lowerCAmelCase   :					List[Any]        =    pipeline(
          image=A_     ,						image_embeds=A_     ,						negative_image_embeds=A_     ,						hint=A_     ,						generator=A_     ,						num_inference_steps=1_0_0     ,						height=5_1_2     ,						width=5_1_2     ,						strength=0.5     ,						output_type="np"     ,						)

      _lowerCAmelCase   :					str        =    output.images[0]

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

      assert_mean_pixel_difference(A_     ,						A_       )






