






"""simple docstring"""







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

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin





@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class 	UpperCamelCase_			(							UpperCamelCase__	):
  _A							:       Tuple					=						42
  _A							:       Dict					=						None






def    _lowerCAmelCase							(   lowerCAmelCase						,   lowerCAmelCase=0.9_99						,   lowerCAmelCase="cosine"						,   ):







       '''simple docstring'''
       if alpha_transform_type == "cosine":

              def alpha_bar_fn(lowerCAmelCase		):
                     return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2		) ** 2

       elif alpha_transform_type == "exp":

              def alpha_bar_fn(lowerCAmelCase		):
                     return math.exp(t * -12.0		)

       else:
              raise ValueError(F'''Unsupported alpha_tranform_type: {alpha_transform_type}'''		)

       UpperCAmelCase						     =      []
       for i in range(lowerCAmelCase		):
              UpperCAmelCase						     =      i / num_diffusion_timesteps
              UpperCAmelCase						     =      (i + 1) / num_diffusion_timesteps
              betas.append(min(1 - alpha_bar_fn(lowerCAmelCase		) / alpha_bar_fn(lowerCAmelCase		)						,   lowerCAmelCase		)		)
       return torch.tensor(lowerCAmelCase						,   dtype=torch.floataa		)





class 	UpperCamelCase_			(							UpperCamelCase__ ,       UpperCamelCase__	):



  @register_to_config
  def __init__(   self			,		snake_case__ = 10_00			,		snake_case__ = "fixed_small_log"			,		snake_case__ = True			,		snake_case__ = 1.0			,		snake_case__ = "epsilon"			,		snake_case__ = "squaredcos_cap_v2"			,		)      ->      Any:



         """simple docstring"""





         if beta_schedule != "squaredcos_cap_v2":
                raise ValueError("""UnCLIPScheduler only supports `beta_schedule`: 'squaredcos_cap_v2'"""					)

         UpperCAmelCase						     =      betas_for_alpha_bar(__snake_case					)

         UpperCAmelCase						     =      1.0 - self.betas
         UpperCAmelCase						     =      torch.cumprod(self.alphas			,		dim=0					)
         UpperCAmelCase						     =      torch.tensor(1.0					)

         # standard deviation of the initial noise distribution
         UpperCAmelCase						     =      1.0

         # setable values
         UpperCAmelCase						     =      None
         UpperCAmelCase						     =      torch.from_numpy(np.arange(0			,		__snake_case					)[::-1].copy()					)

         UpperCAmelCase						     =      variance_type



  def 		UpperCamelCase_					(   self			,		snake_case__			,		snake_case__ = None					)      ->      Any:



         """simple docstring"""





         return sample



  def 		UpperCamelCase_					(   self			,		snake_case__			,		snake_case__ = None					)      ->      Union[str, Any]:



         """simple docstring"""





         UpperCAmelCase						     =      num_inference_steps
         UpperCAmelCase						     =      (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
         UpperCAmelCase						     =      (np.arange(0			,		__snake_case					) * step_ratio).round()[::-1].copy().astype(np.intaa					)
         UpperCAmelCase						     =      torch.from_numpy(__snake_case					).to(__snake_case					)



  def 		UpperCamelCase_					(   self			,		snake_case__			,		snake_case__=None			,		snake_case__=None			,		snake_case__=None					)      ->      Optional[Any]:



         """simple docstring"""





         if prev_timestep is None:
                UpperCAmelCase						     =      t - 1

         UpperCAmelCase						     =      self.alphas_cumprod[t]
         UpperCAmelCase						     =      self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
         UpperCAmelCase						     =      1 - alpha_prod_t
         UpperCAmelCase						     =      1 - alpha_prod_t_prev

         if prev_timestep == t - 1:
                UpperCAmelCase						     =      self.betas[t]
         else:
                UpperCAmelCase						     =      1 - alpha_prod_t / alpha_prod_t_prev

         # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
         # and sample from it to get previous sample
         # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
         UpperCAmelCase						     =      beta_prod_t_prev / beta_prod_t * beta

         if variance_type is None:
                UpperCAmelCase						     =      self.config.variance_type

         # hacks - were probably added for training stability
         if variance_type == "fixed_small_log":
                UpperCAmelCase						     =      torch.log(torch.clamp(__snake_case			,		min=1e-20					)					)
                UpperCAmelCase						     =      torch.exp(0.5 * variance					)
         elif variance_type == "learned_range":
                # NOTE difference with DDPM scheduler
                UpperCAmelCase						     =      variance.log()
                UpperCAmelCase						     =      beta.log()

                UpperCAmelCase						     =      (predicted_variance + 1) / 2
                UpperCAmelCase						     =      frac * max_log + (1 - frac) * min_log

         return variance



  def 		UpperCamelCase_					(   self			,		snake_case__			,		snake_case__			,		snake_case__			,		snake_case__ = None			,		snake_case__=None			,		snake_case__ = True			,		)      ->      List[str]:



         """simple docstring"""





         UpperCAmelCase						     =      timestep

         if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
                UpperCAmelCase						     =      torch.split(__snake_case			,		sample.shape[1]			,		dim=1					)
         else:
                UpperCAmelCase						     =      None

         # 1. compute alphas, betas
         if prev_timestep is None:
                UpperCAmelCase						     =      t - 1

         UpperCAmelCase						     =      self.alphas_cumprod[t]
         UpperCAmelCase						     =      self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
         UpperCAmelCase						     =      1 - alpha_prod_t
         UpperCAmelCase						     =      1 - alpha_prod_t_prev

         if prev_timestep == t - 1:
                UpperCAmelCase						     =      self.betas[t]
                UpperCAmelCase						     =      self.alphas[t]
         else:
                UpperCAmelCase						     =      1 - alpha_prod_t / alpha_prod_t_prev
                UpperCAmelCase						     =      1 - beta

         # 2. compute predicted original sample from predicted noise also called
         # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
         if self.config.prediction_type == "epsilon":
                UpperCAmelCase						     =      (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
         elif self.config.prediction_type == "sample":
                UpperCAmelCase						     =      model_output
         else:
                raise ValueError(
                    f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`'''
                    """ for the UnCLIPScheduler."""					)

         # 3. Clip "predicted x_0"
         if self.config.clip_sample:
                UpperCAmelCase						     =      torch.clamp(
                    __snake_case			,		-self.config.clip_sample_range			,		self.config.clip_sample_range					)

         # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
         # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
         UpperCAmelCase						     =      (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
         UpperCAmelCase						     =      alpha ** 0.5 * beta_prod_t_prev / beta_prod_t

         # 5. Compute predicted previous sample µ_t
         # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
         UpperCAmelCase						     =      pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

         # 6. Add noise
         UpperCAmelCase						     =      0
         if t > 0:
                UpperCAmelCase						     =      randn_tensor(
                    model_output.shape			,		dtype=model_output.dtype			,		generator=__snake_case			,		device=model_output.device					)

                UpperCAmelCase						     =      self._get_variance(
                    __snake_case			,		predicted_variance=__snake_case			,		prev_timestep=__snake_case			,		)

                if self.variance_type == "fixed_small_log":
                       UpperCAmelCase						     =      variance
                elif self.variance_type == "learned_range":
                       UpperCAmelCase						     =      (0.5 * variance).exp()
                else:
                       raise ValueError(
                           f'''variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`'''
                           """ for the UnCLIPScheduler."""					)

                UpperCAmelCase						     =      variance * variance_noise

         UpperCAmelCase						     =      pred_prev_sample + variance

         if not return_dict:
                return (pred_prev_sample,)

         return UnCLIPSchedulerOutput(prev_sample=__snake_case			,		pred_original_sample=__snake_case					)



  def 		UpperCamelCase_					(   self			,		snake_case__			,		snake_case__			,		snake_case__			,		)      ->      List[Any]:



         """simple docstring"""





         UpperCAmelCase						     =      self.alphas_cumprod.to(device=original_samples.device			,		dtype=original_samples.dtype					)
         UpperCAmelCase						     =      timesteps.to(original_samples.device					)

         UpperCAmelCase						     =      alphas_cumprod[timesteps] ** 0.5
         UpperCAmelCase						     =      sqrt_alpha_prod.flatten()
         while len(sqrt_alpha_prod.shape					) < len(original_samples.shape					):
                UpperCAmelCase						     =      sqrt_alpha_prod.unsqueeze(-1					)

         UpperCAmelCase						     =      (1 - alphas_cumprod[timesteps]) ** 0.5
         UpperCAmelCase						     =      sqrt_one_minus_alpha_prod.flatten()
         while len(sqrt_one_minus_alpha_prod.shape					) < len(original_samples.shape					):
                UpperCAmelCase						     =      sqrt_one_minus_alpha_prod.unsqueeze(-1					)

         UpperCAmelCase						     =      sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
         return noisy_samples






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 		__A   (      UpperCamelCase__       ,    unittest.TestCase      ):
						UpperCamelCase      =       KandinskyInpaintPipeline
						UpperCamelCase      =       ["""prompt""", """image_embeds""", """negative_image_embeds""", """image""", """mask_image"""]
						UpperCamelCase      =       [
						    """prompt""",
						    """negative_prompt""",
						    """image_embeds""",
						    """negative_image_embeds""",
						    """image""",
						    """mask_image""",
						]
						UpperCamelCase      =       [
						    """generator""",
						    """height""",
						    """width""",
						    """latents""",
						    """guidance_scale""",
						    """negative_prompt""",
						    """num_inference_steps""",
						    """return_dict""",
						    """guidance_scale""",
						    """num_images_per_prompt""",
						    """output_type""",
						    """return_dict""",
						]
						UpperCamelCase      =       False

						@property
						def      A__    (		self :Union[str, Any]			):
										'''simple docstring'''

										return 32

						@property
						def      A__    (		self :Optional[Any]			):
										'''simple docstring'''

										return 32

						@property
						def      A__    (		self :List[Any]			):
										'''simple docstring'''

										return self.time_input_dim

						@property
						def      A__    (		self :Dict			):
										'''simple docstring'''

										return self.time_input_dim * 4

						@property
						def      A__    (		self :List[Any]			):
										'''simple docstring'''

										return 1_00

						@property
						def      A__    (		self :Dict			):
										'''simple docstring'''

										__magic_name__						:						Dict										=XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base"""			)
										return tokenizer

						@property
						def      A__    (		self :str			):
										'''simple docstring'''

										torch.manual_seed(0			)
										__magic_name__						:						str										=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=10_05     ,			)

										__magic_name__						:						Tuple										=MultilingualCLIP(__snake_case			)
										__magic_name__						:						Optional[int]										=text_encoder.eval()

										return text_encoder

						@property
						def      A__    (		self :Dict			):
										'''simple docstring'''

										torch.manual_seed(0			)

										__magic_name__						:						Optional[Any]										={
										    """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,
										}

										__magic_name__						:						Union[str, Any]										=UNetaDConditionModel(**__snake_case			)
										return model

						@property
						def      A__    (		self :List[str]			):
										'''simple docstring'''

										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      A__    (		self :Tuple			):
										'''simple docstring'''

										torch.manual_seed(0			)
										__magic_name__						:						Dict										=VQModel(**self.dummy_movq_kwargs			)
										return model

						def      A__    (		self :Optional[Any]			):
										'''simple docstring'''

										__magic_name__						:						List[str]										=self.dummy_text_encoder
										__magic_name__						:						Optional[Any]										=self.dummy_tokenizer
										__magic_name__						:						Optional[Any]										=self.dummy_unet
										__magic_name__						:						Tuple										=self.dummy_movq

										__magic_name__						:						List[str]										=DDIMScheduler(
										    num_train_timesteps=10_00     ,			beta_schedule="""linear"""     ,			beta_start=0.00085     ,			beta_end=0.012     ,			clip_sample=__snake_case     ,			set_alpha_to_one=__snake_case     ,			steps_offset=1     ,			prediction_type="""epsilon"""     ,			thresholding=__snake_case     ,			)

										__magic_name__						:						str										={
										    """text_encoder""": text_encoder,
										    """tokenizer""": tokenizer,
										    """unet""": unet,
										    """scheduler""": scheduler,
										    """movq""": movq,
										}

										return components

						def      A__    (		self :str     ,			__snake_case :Optional[Any]     ,			__snake_case :int=0			):
										'''simple docstring'''

										__magic_name__						:						Union[str, Any]										=floats_tensor((1, self.cross_attention_dim)     ,			rng=random.Random(__snake_case			)			).to(__snake_case			)
										__magic_name__						:						Dict										=floats_tensor((1, self.cross_attention_dim)     ,			rng=random.Random(seed + 1			)			).to(__snake_case			)
										# create init_image
										__magic_name__						:						str										=floats_tensor((1, 3, 64, 64)     ,			rng=random.Random(__snake_case			)			).to(__snake_case			)
										__magic_name__						:						int										=image.cpu().permute(0     ,			2     ,			3     ,			1			)[0]
										__magic_name__						:						str										=Image.fromarray(np.uinta(__snake_case			)			).convert("""RGB"""			).resize((2_56, 2_56)			)
										# create mask
										__magic_name__						:						Dict										=np.ones((64, 64)     ,			dtype=np.floataa			)
										__magic_name__						:						Any										=0

										if str(__snake_case			).startswith("""mps"""			):
														__magic_name__						:						Dict										=torch.manual_seed(__snake_case			)
										else:
														__magic_name__						:						Tuple										=torch.Generator(device=__snake_case			).manual_seed(__snake_case			)
										__magic_name__						:						List[Any]										={
										    """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      A__    (		self :List[str]			):
										'''simple docstring'''

										__magic_name__						:						Tuple										="""cpu"""

										__magic_name__						:						List[Any]										=self.get_dummy_components()

										__magic_name__						:						Union[str, Any]										=self.pipeline_class(**__snake_case			)
										__magic_name__						:						Tuple										=pipe.to(__snake_case			)

										pipe.set_progress_bar_config(disable=__snake_case			)

										__magic_name__						:						Tuple										=pipe(**self.get_dummy_inputs(__snake_case			)			)
										__magic_name__						:						List[Any]										=output.images

										__magic_name__						:						Any										=pipe(
										    **self.get_dummy_inputs(__snake_case			)     ,			return_dict=__snake_case     ,			)[0]

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

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

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

										__magic_name__						:						Optional[Any]										=np.array(
										    [0.8326919, 0.73790467, 0.20918581, 0.9309612, 0.5511791, 0.43713328, 0.5513321, 0.49922934, 0.59497786]			)

										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      A__    (		self :Dict			):
										'''simple docstring'''

										super().test_inference_batch_single_identical(expected_max_diff=3E-3			)




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

						def      A__    (		self :List[Any]			):
										'''simple docstring'''

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


						def      A__    (		self :Union[str, Any]			):
										'''simple docstring'''

										__magic_name__						:						List[str]										=load_numpy(
										    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
										    """/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy"""			)

										__magic_name__						:						int										=load_image(
										    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""			)
										__magic_name__						:						List[Any]										=np.ones((7_68, 7_68)     ,			dtype=np.floataa			)
										__magic_name__						:						Any										=0

										__magic_name__						:						int										="""a hat"""

										__magic_name__						:						int										=KandinskyPriorPipeline.from_pretrained(
										    """kandinsky-community/kandinsky-2-1-prior"""     ,			torch_dtype=torch.floataa			)
										pipe_prior.to(__snake_case			)

										__magic_name__						:						Dict										=KandinskyInpaintPipeline.from_pretrained(
										    """kandinsky-community/kandinsky-2-1-inpaint"""     ,			torch_dtype=torch.floataa			)
										__magic_name__						:						int										=pipeline.to(__snake_case			)
										pipeline.set_progress_bar_config(disable=__snake_case			)

										__magic_name__						:						Union[str, Any]										=torch.Generator(device="""cpu"""			).manual_seed(0			)
										__magic_name__					,			__magic_name__						:						Dict										=pipe_prior(
										    __snake_case     ,			generator=__snake_case     ,			num_inference_steps=5     ,			negative_prompt=""""""     ,			).to_tuple()

										__magic_name__						:						Optional[Any]										=pipeline(
										    __snake_case     ,			image=__snake_case     ,			mask_image=__snake_case     ,			image_embeds=__snake_case     ,			negative_image_embeds=__snake_case     ,			generator=__snake_case     ,			num_inference_steps=1_00     ,			height=7_68     ,			width=7_68     ,			output_type="""np"""     ,			)

										__magic_name__						:						Optional[int]										=output.images[0]

										assert image.shape == (7_68, 7_68, 3)

										assert_mean_pixel_difference(__snake_case     ,			__snake_case			)




