



from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin






@flax.struct.dataclass
class 		lowercase						:






	"""simple docstring"""


	a__				: Optional[int]		       =							None
	a__				: Optional[jnp.ndarray]		       =							None
	a__				: Optional[jnp.ndarray]		       =							None  # sigma(t_i)




	@classmethod
	def   _SCREAMING_SNAKE_CASE		(     cls     :						Tuple					)      ->			Union[str, Any]:
							return cls()






@dataclass
class 		lowercase						(			snake_case__):






	"""simple docstring"""


	a__				: jnp.ndarray
	a__				: jnp.ndarray
	a__				: KarrasVeSchedulerState






class 		lowercase						(			snake_case__			,   snake_case__):






	"""simple docstring"""


	@property
	def   _SCREAMING_SNAKE_CASE		(     self     :						Union[str, Any]					)      ->			List[Any]:
							return True




	@register_to_config
	def __init__(     self     :						Tuple						,  __UpperCAmelCase     :						float = 0.02						,  __UpperCAmelCase     :						float = 100						,  __UpperCAmelCase     :						float = 1.007						,  __UpperCAmelCase     :						float = 80						,  __UpperCAmelCase     :						float = 0.05						,  __UpperCAmelCase     :						float = 50						,  )      ->			Any:
							pass




	def   _SCREAMING_SNAKE_CASE		(     self     :						Tuple					)      ->			Union[str, Any]:
							return KarrasVeSchedulerState.create()




	def   _SCREAMING_SNAKE_CASE		(     self     :						Tuple						,  __UpperCAmelCase     :						KarrasVeSchedulerState						,  __UpperCAmelCase     :						int						,  __UpperCAmelCase     :						Tuple = ()					)      ->			KarrasVeSchedulerState:
							UpperCAmelCase_=       jnp.arange(0						,  __UpperCAmelCase					)[::-1].copy()
							UpperCAmelCase_=       [
							    (
							        self.config.sigma_max**2
							        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
							    )
							    for i in timesteps
							]

							return state.replace(
							    num_inference_steps=__UpperCAmelCase						,  schedule=jnp.array(__UpperCAmelCase						,  dtype=jnp.floataa					)						,  timesteps=__UpperCAmelCase						,  )




	def   _SCREAMING_SNAKE_CASE		(     self     :						int						,  __UpperCAmelCase     :						KarrasVeSchedulerState						,  __UpperCAmelCase     :						jnp.ndarray						,  __UpperCAmelCase     :						float						,  __UpperCAmelCase     :						random.KeyArray						,  )      ->			Tuple[jnp.ndarray, float]:
							if self.config.s_min <= sigma <= self.config.s_max:
													UpperCAmelCase_=       min(self.config.s_churn / state.num_inference_steps						,  2**0.5 - 1					)
							else:
													UpperCAmelCase_=       0

							# sample eps ~ N(0, S_noise^2 * I)
							UpperCAmelCase_=       random.split(__UpperCAmelCase						,  num=1					)
							UpperCAmelCase_=       self.config.s_noise * random.normal(key=__UpperCAmelCase						,  shape=sample.shape					)
							UpperCAmelCase_=       sigma + gamma * sigma
							UpperCAmelCase_=       sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

							return sample_hat, sigma_hat




	def   _SCREAMING_SNAKE_CASE		(     self     :						List[Any]						,  __UpperCAmelCase     :						KarrasVeSchedulerState						,  __UpperCAmelCase     :						jnp.ndarray						,  __UpperCAmelCase     :						float						,  __UpperCAmelCase     :						float						,  __UpperCAmelCase     :						jnp.ndarray						,  __UpperCAmelCase     :						bool = True						,  )      ->			Union[FlaxKarrasVeOutput, Tuple]:
							UpperCAmelCase_=       sample_hat + sigma_hat * model_output
							UpperCAmelCase_=       (sample_hat - pred_original_sample) / sigma_hat
							UpperCAmelCase_=       sample_hat + (sigma_prev - sigma_hat) * derivative

							if not return_dict:
													return (sample_prev, derivative, state)

							return FlaxKarrasVeOutput(prev_sample=__UpperCAmelCase						,  derivative=__UpperCAmelCase						,  state=__UpperCAmelCase					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						List[Any]						,  __UpperCAmelCase     :						KarrasVeSchedulerState						,  __UpperCAmelCase     :						jnp.ndarray						,  __UpperCAmelCase     :						float						,  __UpperCAmelCase     :						float						,  __UpperCAmelCase     :						jnp.ndarray						,  __UpperCAmelCase     :						jnp.ndarray						,  __UpperCAmelCase     :						jnp.ndarray						,  __UpperCAmelCase     :						bool = True						,  )      ->			Union[FlaxKarrasVeOutput, Tuple]:
							UpperCAmelCase_=       sample_prev + sigma_prev * model_output
							UpperCAmelCase_=       (sample_prev - pred_original_sample) / sigma_prev
							UpperCAmelCase_=       sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

							if not return_dict:
													return (sample_prev, derivative, state)

							return FlaxKarrasVeOutput(prev_sample=__UpperCAmelCase						,  derivative=__UpperCAmelCase						,  state=__UpperCAmelCase					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						List[Any]						,  __UpperCAmelCase     :						KarrasVeSchedulerState						,  __UpperCAmelCase     :						Tuple						,  __UpperCAmelCase     :						Optional[Any]						,  __UpperCAmelCase     :						Any					)      ->			Optional[int]:
							raise NotImplementedError()









import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, 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 		lowercase						(			snake_case__			,   unittest.TestCase):






	"""simple docstring"""


	a__				: Tuple		       =							KandinskyInpaintPipeline
	a__				: Tuple		       =							["prompt", "image_embeds", "negative_image_embeds", "image", "mask_image"]
	a__				: str		       =							[
	    "prompt",
	    "negative_prompt",
	    "image_embeds",
	    "negative_image_embeds",
	    "image",
	    "mask_image",
	]
	a__				: List[Any]		       =							[
	    "generator",
	    "height",
	    "width",
	    "latents",
	    "guidance_scale",
	    "negative_prompt",
	    "num_inference_steps",
	    "return_dict",
	    "guidance_scale",
	    "num_images_per_prompt",
	    "output_type",
	    "return_dict",
	]
	a__				: Union[str, Any]		       =							False




	@property
	def   _SCREAMING_SNAKE_CASE		(     self     :						Any					)      ->			Any:
							return 32




	@property
	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[int]					)      ->			Union[str, Any]:
							return 32




	@property
	def   _SCREAMING_SNAKE_CASE		(     self     :						str					)      ->			Optional[Any]:
							return self.time_input_dim




	@property
	def   _SCREAMING_SNAKE_CASE		(     self     :						Dict					)      ->			Tuple:
							return self.time_input_dim * 4




	@property
	def   _SCREAMING_SNAKE_CASE		(     self     :						int					)      ->			List[str]:
							return 100




	@property
	def   _SCREAMING_SNAKE_CASE		(     self     :						Tuple					)      ->			List[str]:
							UpperCAmelCase_=       XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base"""					)
							return tokenizer




	@property
	def   _SCREAMING_SNAKE_CASE		(     self     :						Any					)      ->			List[str]:
							torch.manual_seed(0					)
							UpperCAmelCase_=       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=1_005						,  )

							UpperCAmelCase_=       MultilingualCLIP(__UpperCAmelCase					)
							UpperCAmelCase_=       text_encoder.eval()

							return text_encoder




	@property
	def   _SCREAMING_SNAKE_CASE		(     self     :						str					)      ->			Dict:
							torch.manual_seed(0					)

							UpperCAmelCase_=       {
							    """in_channels""": 9,
							    # 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_=       UNetaDConditionModel(**__UpperCAmelCase					)
							return model




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




	def   _SCREAMING_SNAKE_CASE		(     self     :						List[str]					)      ->			Optional[int]:
							UpperCAmelCase_=       self.dummy_text_encoder
							UpperCAmelCase_=       self.dummy_tokenizer
							UpperCAmelCase_=       self.dummy_unet
							UpperCAmelCase_=       self.dummy_movq

							UpperCAmelCase_=       DDIMScheduler(
							    num_train_timesteps=1_000						,  beta_schedule="""linear"""						,  beta_start=0.00_085						,  beta_end=0.012						,  clip_sample=__UpperCAmelCase						,  set_alpha_to_one=__UpperCAmelCase						,  steps_offset=1						,  prediction_type="""epsilon"""						,  thresholding=__UpperCAmelCase						,  )

							UpperCAmelCase_=       {
							    """text_encoder""": text_encoder,
							    """tokenizer""": tokenizer,
							    """unet""": unet,
							    """scheduler""": scheduler,
							    """movq""": movq,
							}

							return components




	def   _SCREAMING_SNAKE_CASE		(     self     :						Union[str, Any]						,  __UpperCAmelCase     :						Tuple						,  __UpperCAmelCase     :						Dict=0					)      ->			Dict:
							UpperCAmelCase_=       floats_tensor((1, self.cross_attention_dim)						,  rng=random.Random(__UpperCAmelCase					)					).to(__UpperCAmelCase					)
							UpperCAmelCase_=       floats_tensor((1, self.cross_attention_dim)						,  rng=random.Random(seed + 1					)					).to(__UpperCAmelCase					)
							# create init_image
							UpperCAmelCase_=       floats_tensor((1, 3, 64, 64)						,  rng=random.Random(__UpperCAmelCase					)					).to(__UpperCAmelCase					)
							UpperCAmelCase_=       image.cpu().permute(0						,  2						,  3						,  1					)[0]
							UpperCAmelCase_=       Image.fromarray(np.uinta(__UpperCAmelCase					)					).convert("""RGB"""					).resize((256, 256)					)
							# create mask
							UpperCAmelCase_=       np.ones((64, 64)						,  dtype=np.floataa					)
							UpperCAmelCase_=       0

							if str(__UpperCAmelCase					).startswith("""mps"""					):
													UpperCAmelCase_=       torch.manual_seed(__UpperCAmelCase					)
							else:
													UpperCAmelCase_=       torch.Generator(device=__UpperCAmelCase					).manual_seed(__UpperCAmelCase					)
							UpperCAmelCase_=       {
							    """prompt""": """horse""",
							    """image""": init_image,
							    """mask_image""": mask,
							    """image_embeds""": image_embeds,
							    """negative_image_embeds""": negative_image_embeds,
							    """generator""": generator,
							    """height""": 64,
							    """width""": 64,
							    """num_inference_steps""": 2,
							    """guidance_scale""": 4.0,
							    """output_type""": """np""",
							}
							return inputs




	def   _SCREAMING_SNAKE_CASE		(     self     :						str					)      ->			List[str]:
							UpperCAmelCase_=       """cpu"""

							UpperCAmelCase_=       self.get_dummy_components()

							UpperCAmelCase_=       self.pipeline_class(**__UpperCAmelCase					)
							UpperCAmelCase_=       pipe.to(__UpperCAmelCase					)

							pipe.set_progress_bar_config(disable=__UpperCAmelCase					)

							UpperCAmelCase_=       pipe(**self.get_dummy_inputs(__UpperCAmelCase					)					)
							UpperCAmelCase_=       output.images

							UpperCAmelCase_=       pipe(
							    **self.get_dummy_inputs(__UpperCAmelCase					)						,  return_dict=__UpperCAmelCase						,  )[0]

							UpperCAmelCase_=       image[0, -3:, -3:, -1]
							UpperCAmelCase_=       image_from_tuple[0, -3:, -3:, -1]

							print(F"""image.shape {image.shape}"""					)

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

							UpperCAmelCase_=       np.array(
							    [0.8_326_919, 0.73_790_467, 0.20_918_581, 0.9_309_612, 0.5_511_791, 0.43_713_328, 0.5_513_321, 0.49_922_934, 0.59_497_786]					)

							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()}"""




	def   _SCREAMING_SNAKE_CASE		(     self     :						List[str]					)      ->			Any:
							super().test_inference_batch_single_identical(expected_max_diff=3E-3					)






@slow
@require_torch_gpu
class 		lowercase						(			unittest.TestCase):






	"""simple docstring"""


	def   _SCREAMING_SNAKE_CASE		(     self     :						Dict					)      ->			Any:
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()




	def   _SCREAMING_SNAKE_CASE		(     self     :						Dict					)      ->			Optional[Any]:
							UpperCAmelCase_=       load_numpy(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
							    """/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy"""					)

							UpperCAmelCase_=       load_image(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""					)
							UpperCAmelCase_=       np.ones((768, 768)						,  dtype=np.floataa					)
							UpperCAmelCase_=       0

							UpperCAmelCase_=       """a hat"""

							UpperCAmelCase_=       KandinskyPriorPipeline.from_pretrained(
							    """kandinsky-community/kandinsky-2-1-prior"""						,  torch_dtype=torch.floataa					)
							pipe_prior.to(__UpperCAmelCase					)

							UpperCAmelCase_=       KandinskyInpaintPipeline.from_pretrained(
							    """kandinsky-community/kandinsky-2-1-inpaint"""						,  torch_dtype=torch.floataa					)
							UpperCAmelCase_=       pipeline.to(__UpperCAmelCase					)
							pipeline.set_progress_bar_config(disable=__UpperCAmelCase					)

							UpperCAmelCase_=       torch.Generator(device="""cpu"""					).manual_seed(0					)
							UpperCAmelCase_,       UpperCAmelCase_=       pipe_prior(
							    __UpperCAmelCase						,  generator=__UpperCAmelCase						,  num_inference_steps=5						,  negative_prompt=""""""						,  ).to_tuple()

							UpperCAmelCase_=       pipeline(
							    __UpperCAmelCase						,  image=__UpperCAmelCase						,  mask_image=__UpperCAmelCase						,  image_embeds=__UpperCAmelCase						,  negative_image_embeds=__UpperCAmelCase						,  generator=__UpperCAmelCase						,  num_inference_steps=100						,  height=768						,  width=768						,  output_type="""np"""						,  )

							UpperCAmelCase_=       output.images[0]

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

							assert_mean_pixel_difference(__UpperCAmelCase						,  __UpperCAmelCase					)




