


'''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   snake_case					(					lowercase    ,		unittest.TestCase    ):





  """simple docstring"""




  _lowerCamelCase						=						KandinskyVaaInpaintPipeline
  _lowerCamelCase						=						["image_embeds", "negative_image_embeds", "image", "mask_image"]
  _lowerCamelCase						=						[
      "image_embeds",
      "negative_image_embeds",
      "image",
      "mask_image",
  ]
  _lowerCamelCase						=						[
      "generator",
      "height",
      "width",
      "latents",
      "guidance_scale",
      "num_inference_steps",
      "return_dict",
      "guidance_scale",
      "num_images_per_prompt",
      "output_type",
      "return_dict",
  ]
  _lowerCamelCase						=						False






  @property
  def 						snake_case	(					self  ):







   """simple docstring"""
   return 32






  @property
  def 						snake_case	(					self  ):







   """simple docstring"""
   return 32






  @property
  def 						snake_case	(					self  ):







   """simple docstring"""
   return self.time_input_dim






  @property
  def 						snake_case	(					self  ):







   """simple docstring"""
   return self.time_input_dim * 4






  @property
  def 						snake_case	(					self  ):







   """simple docstring"""
   return 100






  @property
  def 						snake_case	(					self  ):







   """simple docstring"""
   torch.manual_seed(0  )

   lowerCamelCase_  				=						{
       "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,
   }

   lowerCamelCase_  				=						UNetaDConditionModel(**UpperCamelCase  )
   return model






  @property
  def 						snake_case	(					self  ):







   """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 						snake_case	(					self  ):







   """simple docstring"""
   torch.manual_seed(0  )
   lowerCamelCase_  				=						VQModel(**self.dummy_movq_kwargs  )
   return model






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						self.dummy_unet
   lowerCamelCase_  				=						self.dummy_movq

   lowerCamelCase_  				=						DDIMScheduler(
       num_train_timesteps=1000    ,						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    ,						)

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

   return components






  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase=0  ):







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

   if str(UpperCamelCase  ).startswith("mps"  ):
    lowerCamelCase_  				=						torch.manual_seed(UpperCamelCase  )
   else:
    lowerCamelCase_  				=						torch.Generator(device=UpperCamelCase  ).manual_seed(UpperCamelCase  )
   lowerCamelCase_  				=						{
       "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 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						"cpu"

   lowerCamelCase_  				=						self.get_dummy_components()

   lowerCamelCase_  				=						self.pipeline_class(**UpperCamelCase  )
   lowerCamelCase_  				=						pipe.to(UpperCamelCase  )

   pipe.set_progress_bar_config(disable=UpperCamelCase  )

   lowerCamelCase_  				=						pipe(**self.get_dummy_inputs(UpperCamelCase  )  )
   lowerCamelCase_  				=						output.images

   lowerCamelCase_  				=						pipe(
       **self.get_dummy_inputs(UpperCamelCase  )    ,						return_dict=UpperCamelCase    ,						)[0]

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

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

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

   lowerCamelCase_  				=						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 						snake_case	(					self  ):







   """simple docstring"""
   super().test_inference_batch_single_identical(expected_max_diff=3e-3  )





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





  """simple docstring"""




  def 						snake_case	(					self  ):







   """simple docstring"""
   # clean up the VRAM after each test
   super().tearDown()
   gc.collect()
   torch.cuda.empty_cache()







  def 						snake_case	(					self  ):







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

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

   lowerCamelCase_  				=						"a hat"

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

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

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

   lowerCamelCase_  				=						pipeline(
       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"    ,						)

   lowerCamelCase_  				=						output.images[0]

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

   assert_mean_pixel_difference(UpperCamelCase    ,						UpperCamelCase  )







'''simple docstring'''

import os
import tempfile
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
     import torch
     from torch import nn

     from transformers import (
         Adafactor,
         AdamW,
         get_constant_schedule,
         get_constant_schedule_with_warmup,
         get_cosine_schedule_with_warmup,
         get_cosine_with_hard_restarts_schedule_with_warmup,
         get_inverse_sqrt_schedule,
         get_linear_schedule_with_warmup,
         get_polynomial_decay_schedule_with_warmup,
     )





def __snake_case      (   UpperCAmelCase_		:		List[Any]      ,       UpperCAmelCase_		:		Union[str, Any]=10	):
 lowerCamelCase_  				=						[]
 for _ in range(UpperCAmelCase_	):
  lrs.append(scheduler.get_lr()[0]	)
  scheduler.step()
 return lrs





def __snake_case      (   UpperCAmelCase_		:		Tuple      ,       UpperCAmelCase_		:		List[Any]=10	):
 lowerCamelCase_  				=						[]
 for step in range(UpperCAmelCase_	):
  lrs.append(scheduler.get_lr()[0]	)
  scheduler.step()
  if step == num_steps // 2:
   with tempfile.TemporaryDirectory() as tmpdirname:
    lowerCamelCase_  				=						os.path.join(UpperCAmelCase_      ,       "schedule.bin"	)
    torch.save(scheduler.state_dict()      ,       UpperCAmelCase_	)

    lowerCamelCase_  				=						torch.load(UpperCAmelCase_	)
    scheduler.load_state_dict(UpperCAmelCase_	)
 return lrs





@require_torch
class   snake_case					(					unittest.TestCase    ):





  """simple docstring"""




  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase  ):







   """simple docstring"""
   self.assertEqual(len(UpperCamelCase  )    ,						len(UpperCamelCase  )  )
   for a, b in zip(UpperCamelCase    ,						UpperCamelCase  ):
    self.assertAlmostEqual(UpperCamelCase    ,						UpperCamelCase    ,						delta=UpperCamelCase  )






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						torch.tensor([0.1, -0.2, -0.1]    ,						requires_grad=UpperCamelCase  )
   lowerCamelCase_  				=						torch.tensor([0.4, 0.2, -0.5]  )
   lowerCamelCase_  				=						nn.MSELoss()
   # No warmup, constant schedule, no gradient clipping
   lowerCamelCase_  				=						AdamW(params=[w]    ,						lr=2e-1    ,						weight_decay=0.0  )
   for _ in range(100  ):
    lowerCamelCase_  				=						criterion(UpperCamelCase    ,						UpperCamelCase  )
    loss.backward()
    optimizer.step()
    w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
    w.grad.zero_()
   self.assertListAlmostEqual(w.tolist()    ,						[0.4, 0.2, -0.5]    ,						tol=1e-2  )







  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						torch.tensor([0.1, -0.2, -0.1]    ,						requires_grad=UpperCamelCase  )
   lowerCamelCase_  				=						torch.tensor([0.4, 0.2, -0.5]  )
   lowerCamelCase_  				=						nn.MSELoss()
   # No warmup, constant schedule, no gradient clipping
   lowerCamelCase_  				=						Adafactor(
       params=[w]    ,						lr=1e-2    ,						eps=(1e-30, 1e-3)    ,						clip_threshold=1.0    ,						decay_rate=-0.8    ,						betaa=UpperCamelCase    ,						weight_decay=0.0    ,						relative_step=UpperCamelCase    ,						scale_parameter=UpperCamelCase    ,						warmup_init=UpperCamelCase    ,						)
   for _ in range(1000  ):
    lowerCamelCase_  				=						criterion(UpperCamelCase    ,						UpperCamelCase  )
    loss.backward()
    optimizer.step()
    w.grad.detach_()  # No zero_grad() function on simple tensors. we do it ourselves.
    w.grad.zero_()
   self.assertListAlmostEqual(w.tolist()    ,						[0.4, 0.2, -0.5]    ,						tol=1e-2  )





@require_torch
class   snake_case					(					unittest.TestCase    ):





  """simple docstring"""




  _lowerCamelCase						=						nn.Linear(50    ,		50    ) if is_torch_available() else None
  _lowerCamelCase						=						AdamW(m.parameters()    ,		lr=10.0    ) if is_torch_available() else None
  _lowerCamelCase						=						10






  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase    ,						UpperCamelCase=None  ):







   """simple docstring"""
   self.assertEqual(len(UpperCamelCase  )    ,						len(UpperCamelCase  )  )
   for a, b in zip(UpperCamelCase    ,						UpperCamelCase  ):
    self.assertAlmostEqual(UpperCamelCase    ,						UpperCamelCase    ,						delta=UpperCamelCase    ,						msg=UpperCamelCase  )







  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						{"num_warmup_steps": 2, "num_training_steps": 10}
   # schedulers doct format
   # function: (sched_args_dict, expected_learning_rates)
   lowerCamelCase_  				=						{
       get_constant_schedule: ({}, [10.0] * self.num_steps),
       get_constant_schedule_with_warmup: (
           {"num_warmup_steps": 4},
           [0.0, 2.5, 5.0, 7.5, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0],
       ),
       get_linear_schedule_with_warmup: (
           {**common_kwargs},
           [0.0, 5.0, 10.0, 8.75, 7.5, 6.25, 5.0, 3.75, 2.5, 1.25],
       ),
       get_cosine_schedule_with_warmup: (
           {**common_kwargs},
           [0.0, 5.0, 10.0, 9.61, 8.53, 6.91, 5.0, 3.08, 1.46, 0.38],
       ),
       get_cosine_with_hard_restarts_schedule_with_warmup: (
           {**common_kwargs, "num_cycles": 2},
           [0.0, 5.0, 10.0, 8.53, 5.0, 1.46, 10.0, 8.53, 5.0, 1.46],
       ),
       get_polynomial_decay_schedule_with_warmup: (
           {**common_kwargs, "power": 2.0, "lr_end": 1e-7},
           [0.0, 5.0, 10.0, 7.656, 5.625, 3.906, 2.5, 1.406, 0.625, 0.156],
       ),
       get_inverse_sqrt_schedule: (
           {"num_warmup_steps": 2},
           [0.0, 5.0, 10.0, 8.165, 7.071, 6.325, 5.774, 5.345, 5.0, 4.714],
       ),
   }

   for scheduler_func, data in scheds.items():
    lowerCamelCase_				,lowerCamelCase_  				=						data

    lowerCamelCase_  				=						scheduler_func(self.optimizer    ,						**UpperCamelCase  )
    self.assertEqual(len([scheduler.get_lr()[0]]  )    ,						1  )
    lowerCamelCase_  				=						unwrap_schedule(UpperCamelCase    ,						self.num_steps  )
    self.assertListAlmostEqual(
        UpperCamelCase    ,						UpperCamelCase    ,						tol=1e-2    ,						msg=f'''failed for {scheduler_func} in normal scheduler'''    ,						)

    lowerCamelCase_  				=						scheduler_func(self.optimizer    ,						**UpperCamelCase  )
    if scheduler_func.__name__ != "get_constant_schedule":
     LambdaScheduleWrapper.wrap_scheduler(UpperCamelCase  )  # wrap to test picklability of the schedule
    lowerCamelCase_  				=						unwrap_and_save_reload_schedule(UpperCamelCase    ,						self.num_steps  )
    self.assertListEqual(UpperCamelCase    ,						UpperCamelCase    ,						msg=f'''failed for {scheduler_func} in save and reload'''  )





class   snake_case					:





  """simple docstring"""




  def __init__(					self    ,						UpperCamelCase  ):







   """simple docstring"""
   lowerCamelCase_  				=						fn






  def __call__(					self    ,						*UpperCamelCase    ,						**UpperCamelCase  ):







   """simple docstring"""
   return self.fn(*UpperCamelCase    ,						**UpperCamelCase  )







  @classmethod
  def 						snake_case	(					self    ,						UpperCamelCase  ):







   """simple docstring"""
   lowerCamelCase_  				=						list(map(self    ,						scheduler.lr_lambdas  )  )



