





'''simple docstring'''

import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaInpaintPipeline,
    KandinskyVaaPriorPipeline,
    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 (   _snake_case,   unittest.TestCase			):
   __UpperCamelCase   : int	      =	KandinskyVaaInpaintPipeline
   __UpperCamelCase   : List[Any]	      =	['image_embeds', 'negative_image_embeds', 'image', 'mask_image']
   __UpperCamelCase   : Any	      =	[
       'image_embeds',
       'negative_image_embeds',
       'image',
       'mask_image',
   ]
   __UpperCamelCase   : List[str]	      =	[
       'generator',
       'height',
       'width',
       'latents',
       'guidance_scale',
       'num_inference_steps',
       'return_dict',
       'guidance_scale',
       'num_images_per_prompt',
       'output_type',
       'return_dict',
   ]
   __UpperCamelCase   : Optional[int]	      =	False





   @property
   def      UpperCAmelCase__     (					self				:			Union[str, Any]    ):

      '''simple docstring'''

      return 32





   @property
   def      UpperCAmelCase__     (					self				:			str    ):

      '''simple docstring'''

      return 32





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

      '''simple docstring'''

      return self.time_input_dim





   @property
   def      UpperCAmelCase__     (					self				:			Tuple    ):

      '''simple docstring'''

      return self.time_input_dim * 4





   @property
   def      UpperCAmelCase__     (					self				:			Any    ):

      '''simple docstring'''

      return 100





   @property
   def      UpperCAmelCase__     (					self				:			Tuple    ):

      '''simple docstring'''

      torch.manual_seed(0    )

      __SCREAMING_SNAKE_CASE      							=       {
          """in_channels""": 9,
          # Out channels is double in channels because predicts mean and variance
          """out_channels""": 8,
          """addition_embed_type""": """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""": """image_proj""",
          """cross_attention_dim""": self.cross_attention_dim,
          """attention_head_dim""": 4,
          """resnet_time_scale_shift""": """scale_shift""",
          """class_embed_type""": None,
      }

      __SCREAMING_SNAKE_CASE      							=       UNetaDConditionModel(**lowerCamelCase    )
      return model





   @property
   def      UpperCAmelCase__     (					self				:			int    ):

      '''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      UpperCAmelCase__     (					self				:			Dict    ):

      '''simple docstring'''

      torch.manual_seed(0    )
      __SCREAMING_SNAKE_CASE      							=       VQModel(**self.dummy_movq_kwargs    )
      return model





   def      UpperCAmelCase__     (					self				:			Union[str, Any]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       self.dummy_unet
      __SCREAMING_SNAKE_CASE      							=       self.dummy_movq

      __SCREAMING_SNAKE_CASE      							=       DDIMScheduler(
          num_train_timesteps=1000				,beta_schedule="""linear"""				,beta_start=0.00_085				,beta_end=0.012				,clip_sample=lowerCamelCase				,set_alpha_to_one=lowerCamelCase				,steps_offset=1				,prediction_type="""epsilon"""				,thresholding=lowerCamelCase				,)

      __SCREAMING_SNAKE_CASE      							=       {
          """unet""": unet,
          """scheduler""": scheduler,
          """movq""": movq,
      }

      return components





   def      UpperCAmelCase__     (					self				:			Optional[Any]				,lowerCamelCase				:			Union[str, Any]				,lowerCamelCase				:			Tuple=0    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       floats_tensor((1, self.text_embedder_hidden_size)				,rng=random.Random(lowerCamelCase    )    ).to(lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       floats_tensor((1, self.text_embedder_hidden_size)				,rng=random.Random(seed + 1    )    ).to(
          lowerCamelCase    )
      # create init_image
      __SCREAMING_SNAKE_CASE      							=       floats_tensor((1, 3, 64, 64)				,rng=random.Random(lowerCamelCase    )    ).to(lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       image.cpu().permute(0				,2				,3				,1    )[0]
      __SCREAMING_SNAKE_CASE      							=       Image.fromarray(np.uinta(lowerCamelCase    )    ).convert("""RGB"""    ).resize((256, 256)    )
      # create mask
      __SCREAMING_SNAKE_CASE      							=       np.ones((64, 64)				,dtype=np.floataa    )
      __SCREAMING_SNAKE_CASE      							=       0

      if str(lowerCamelCase    ).startswith("""mps"""    ):
         __SCREAMING_SNAKE_CASE      							=       torch.manual_seed(lowerCamelCase    )
      else:
         __SCREAMING_SNAKE_CASE      							=       torch.Generator(device=lowerCamelCase    ).manual_seed(lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       {
          """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      UpperCAmelCase__     (					self				:			List[Any]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       """cpu"""

      __SCREAMING_SNAKE_CASE      							=       self.get_dummy_components()

      __SCREAMING_SNAKE_CASE      							=       self.pipeline_class(**lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       pipe.to(lowerCamelCase    )

      pipe.set_progress_bar_config(disable=lowerCamelCase    )

      __SCREAMING_SNAKE_CASE      							=       pipe(**self.get_dummy_inputs(lowerCamelCase    )    )
      __SCREAMING_SNAKE_CASE      							=       output.images

      __SCREAMING_SNAKE_CASE      							=       pipe(
          **self.get_dummy_inputs(lowerCamelCase    )				,return_dict=lowerCamelCase				,)[0]

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

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

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

      __SCREAMING_SNAKE_CASE      							=       np.array(
          [0.50_775_903, 0.49_527_195, 0.48_824_543, 0.50_192_237, 0.48_644_906, 0.49_373_814, 0.4_780_598, 0.47_234_827, 0.48_327_848]    )

      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      UpperCAmelCase__     (					self				:			Dict    ):

      '''simple docstring'''

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







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





   def      UpperCAmelCase__     (					self				:			Dict    ):

      '''simple docstring'''

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





   def      UpperCAmelCase__     (					self				:			Union[str, Any]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       load_numpy(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
          """/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy"""    )

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

      __SCREAMING_SNAKE_CASE      							=       """a hat"""

      __SCREAMING_SNAKE_CASE      							=       KandinskyVaaPriorPipeline.from_pretrained(
          """kandinsky-community/kandinsky-2-2-prior"""				,torch_dtype=torch.floataa    )
      pipe_prior.to(lowerCamelCase    )

      __SCREAMING_SNAKE_CASE      							=       KandinskyVaaInpaintPipeline.from_pretrained(
          """kandinsky-community/kandinsky-2-2-decoder-inpaint"""				,torch_dtype=torch.floataa    )
      __SCREAMING_SNAKE_CASE      							=       pipeline.to(lowerCamelCase    )
      pipeline.set_progress_bar_config(disable=lowerCamelCase    )

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

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

      __SCREAMING_SNAKE_CASE      							=       output.images[0]

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

      assert_mean_pixel_difference(lowerCamelCase				,lowerCamelCase    )












'''simple docstring'''

import math
from enum import Enum
from typing import Optional, Union

from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR

from .utils import logging


a        =						logging.get_logger(__name__)







class __a (   _snake_case			):
   __UpperCamelCase   : int	      =	'linear'
   __UpperCamelCase   : Tuple	      =	'cosine'
   __UpperCamelCase   : Tuple	      =	'cosine_with_restarts'
   __UpperCamelCase   : List[Any]	      =	'polynomial'
   __UpperCamelCase   : Optional[Any]	      =	'constant'
   __UpperCamelCase   : Optional[int]	      =	'constant_with_warmup'
   __UpperCamelCase   : List[Any]	      =	'piecewise_constant'





def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase = -1      )						->					int:

   '''simple docstring'''







   return LambdaLR(__UpperCAmelCase							,	lambda __UpperCAmelCase      : 1							,	last_epoch=__UpperCAmelCase      )

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase = -1      )						->					List[Any]:

   '''simple docstring'''







   def lr_lambda(__UpperCAmelCase      ):
      if current_step < num_warmup_steps:
         return float(__UpperCAmelCase      ) / float(max(1.0							,	__UpperCAmelCase      )      )
      return 1.0

   return LambdaLR(__UpperCAmelCase							,	__UpperCAmelCase							,	last_epoch=__UpperCAmelCase      )

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase = -1      )						->					int:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       {}
   __SCREAMING_SNAKE_CASE      							=       step_rules.split(""","""      )
   for rule_str in rule_list[:-1]:
      __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE      							=       rule_str.split(""":"""      )
      __SCREAMING_SNAKE_CASE      							=       int(__UpperCAmelCase      )
      __SCREAMING_SNAKE_CASE      							=       float(__UpperCAmelCase      )
      __SCREAMING_SNAKE_CASE      							=       value
   __SCREAMING_SNAKE_CASE      							=       float(rule_list[-1]      )

   def create_rules_function(__UpperCAmelCase							,	__UpperCAmelCase      ):
      def rule_func(__UpperCAmelCase      ) -> float:
         __SCREAMING_SNAKE_CASE      							=       sorted(rules_dict.keys()      )
         for i, sorted_step in enumerate(__UpperCAmelCase      ):
            if steps < sorted_step:
               return rules_dict[sorted_steps[i]]
         return last_lr_multiple

      return rule_func

   __SCREAMING_SNAKE_CASE      							=       create_rules_function(__UpperCAmelCase							,	__UpperCAmelCase      )

   return LambdaLR(__UpperCAmelCase							,	__UpperCAmelCase							,	last_epoch=__UpperCAmelCase      )

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase=-1      )						->					int:

   '''simple docstring'''







   def lr_lambda(__UpperCAmelCase      ):
      if current_step < num_warmup_steps:
         return float(__UpperCAmelCase      ) / float(max(1							,	__UpperCAmelCase      )      )
      return max(
          0.0							,	float(num_training_steps - current_step      ) / float(max(1							,	num_training_steps - num_warmup_steps      )      )      )

   return LambdaLR(__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase      )

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase = 0.5							,	__UpperCAmelCase = -1      )						->					Dict:

   '''simple docstring'''







   def lr_lambda(__UpperCAmelCase      ):
      if current_step < num_warmup_steps:
         return float(__UpperCAmelCase      ) / float(max(1							,	__UpperCAmelCase      )      )
      __SCREAMING_SNAKE_CASE      							=       float(current_step - num_warmup_steps      ) / float(max(1							,	num_training_steps - num_warmup_steps      )      )
      return max(0.0							,	0.5 * (1.0 + math.cos(math.pi * float(__UpperCAmelCase      ) * 2.0 * progress      ))      )

   return LambdaLR(__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase      )

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase = 1							,	__UpperCAmelCase = -1      )						->					Tuple:

   '''simple docstring'''







   def lr_lambda(__UpperCAmelCase      ):
      if current_step < num_warmup_steps:
         return float(__UpperCAmelCase      ) / float(max(1							,	__UpperCAmelCase      )      )
      __SCREAMING_SNAKE_CASE      							=       float(current_step - num_warmup_steps      ) / float(max(1							,	num_training_steps - num_warmup_steps      )      )
      if progress >= 1.0:
         return 0.0
      return max(0.0							,	0.5 * (1.0 + math.cos(math.pi * ((float(__UpperCAmelCase      ) * progress) % 1.0)      ))      )

   return LambdaLR(__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase      )

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase=1e-7							,	__UpperCAmelCase=1.0							,	__UpperCAmelCase=-1      )						->					Tuple:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       optimizer.defaults["""lr"""]
   if not (lr_init > lr_end):
      raise ValueError(f"""lr_end ({lr_end}) must be be smaller than initial lr ({lr_init})"""      )

   def lr_lambda(__UpperCAmelCase      ):
      if current_step < num_warmup_steps:
         return float(__UpperCAmelCase      ) / float(max(1							,	__UpperCAmelCase      )      )
      elif current_step > num_training_steps:
         return lr_end / lr_init  # as LambdaLR multiplies by lr_init
      else:
         __SCREAMING_SNAKE_CASE      							=       lr_init - lr_end
         __SCREAMING_SNAKE_CASE      							=       num_training_steps - num_warmup_steps
         __SCREAMING_SNAKE_CASE      							=       1 - (current_step - num_warmup_steps) / decay_steps
         __SCREAMING_SNAKE_CASE      							=       lr_range * pct_remaining**power + lr_end
         return decay / lr_init  # as LambdaLR multiplies by lr_init

   return LambdaLR(__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase      )


a        =						{
    SchedulerType.LINEAR: get_linear_schedule_with_warmup,
    SchedulerType.COSINE: get_cosine_schedule_with_warmup,
    SchedulerType.COSINE_WITH_RESTARTS: get_cosine_with_hard_restarts_schedule_with_warmup,
    SchedulerType.POLYNOMIAL: get_polynomial_decay_schedule_with_warmup,
    SchedulerType.CONSTANT: get_constant_schedule,
    SchedulerType.CONSTANT_WITH_WARMUP: get_constant_schedule_with_warmup,
    SchedulerType.PIECEWISE_CONSTANT: get_piecewise_constant_schedule,
}







def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase							,	__UpperCAmelCase = None							,	__UpperCAmelCase = None							,	__UpperCAmelCase = None							,	__UpperCAmelCase = 1							,	__UpperCAmelCase = 1.0							,	__UpperCAmelCase = -1							,	)						->					str:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       SchedulerType(__UpperCAmelCase      )
   __SCREAMING_SNAKE_CASE      							=       TYPE_TO_SCHEDULER_FUNCTION[name]
   if name == SchedulerType.CONSTANT:
      return schedule_func(__UpperCAmelCase							,	last_epoch=__UpperCAmelCase      )

   if name == SchedulerType.PIECEWISE_CONSTANT:
      return schedule_func(__UpperCAmelCase							,	step_rules=__UpperCAmelCase							,	last_epoch=__UpperCAmelCase      )

   # All other schedulers require `num_warmup_steps`
   if num_warmup_steps is None:
      raise ValueError(f"""{name} requires `num_warmup_steps`, please provide that argument."""      )

   if name == SchedulerType.CONSTANT_WITH_WARMUP:
      return schedule_func(__UpperCAmelCase							,	num_warmup_steps=__UpperCAmelCase							,	last_epoch=__UpperCAmelCase      )

   # All other schedulers require `num_training_steps`
   if num_training_steps is None:
      raise ValueError(f"""{name} requires `num_training_steps`, please provide that argument."""      )

   if name == SchedulerType.COSINE_WITH_RESTARTS:
      return schedule_func(
          __UpperCAmelCase							,	num_warmup_steps=__UpperCAmelCase							,	num_training_steps=__UpperCAmelCase							,	num_cycles=__UpperCAmelCase							,	last_epoch=__UpperCAmelCase							,	)

   if name == SchedulerType.POLYNOMIAL:
      return schedule_func(
          __UpperCAmelCase							,	num_warmup_steps=__UpperCAmelCase							,	num_training_steps=__UpperCAmelCase							,	power=__UpperCAmelCase							,	last_epoch=__UpperCAmelCase							,	)

   return schedule_func(
       __UpperCAmelCase							,	num_warmup_steps=__UpperCAmelCase							,	num_training_steps=__UpperCAmelCase							,	last_epoch=__UpperCAmelCase      )





