






'''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  _a(  UpperCamelCase__				:						Any,	UpperCamelCase__				:						Tuple=1_0							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=[]
      for _ in range(UpperCamelCase__							):
            lrs.append(scheduler.get_lr()[0]							)
            scheduler.step()
      return lrs





def  _a(  UpperCamelCase__				:						Optional[int],	UpperCamelCase__				:						Union[str, Any]=1_0							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__     :				str									=[]
      for step in range(UpperCamelCase__							):
            lrs.append(scheduler.get_lr()[0]							)
            scheduler.step()
            if step == num_steps // 2:
                  with tempfile.TemporaryDirectory() as tmpdirname:
                        SCREAMING_SNAKE_CASE__     :				str									=os.path.join(UpperCamelCase__,	'''schedule.bin'''							)
                        torch.save(scheduler.state_dict(),	UpperCamelCase__							)

                        SCREAMING_SNAKE_CASE__     :				Dict									=torch.load(UpperCamelCase__							)
                        scheduler.load_state_dict(UpperCamelCase__							)
      return lrs






@require_torch
class      __SCREAMING_SNAKE_CASE      (			unittest.TestCase				):




 def      __magic_name__	(      self	:							Optional[int]					,      __lowercase	:							Union[str, Any]					,      __lowercase	:							Optional[int]					,      __lowercase	:							List[str]      )	->						int:
       self.assertEqual(len(__lowercase      )					,      len(__lowercase      )      )
       for a, b in zip(__lowercase					,      __lowercase      ):
             self.assertAlmostEqual(__lowercase					,      __lowercase					,      delta=__lowercase      )




 def      __magic_name__	(      self	:							Optional[int]      )	->						List[Any]:
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=torch.tensor([0.1, -0.2, -0.1]					,      requires_grad=__lowercase      )
       SCREAMING_SNAKE_CASE__     :				Any									=torch.tensor([0.4, 0.2, -0.5]      )
       SCREAMING_SNAKE_CASE__     :				Dict									=nn.MSELoss()
       # No warmup, constant schedule, no gradient clipping
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=AdamW(params=[w]					,      lr=2e-1					,      weight_decay=0.0      )
       for _ in range(1_00      ):
             SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=criterion(__lowercase					,      __lowercase      )
             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      __magic_name__	(      self	:							Optional[int]      )	->						Tuple:
       SCREAMING_SNAKE_CASE__     :				List[str]									=torch.tensor([0.1, -0.2, -0.1]					,      requires_grad=__lowercase      )
       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=torch.tensor([0.4, 0.2, -0.5]      )
       SCREAMING_SNAKE_CASE__     :				List[str]									=nn.MSELoss()
       # No warmup, constant schedule, no gradient clipping
       SCREAMING_SNAKE_CASE__     :				Dict									=Adafactor(
           params=[w]					,      lr=1e-2					,      eps=(1e-30, 1e-3)					,      clip_threshold=1.0					,      decay_rate=-0.8					,      betaa=__lowercase					,      weight_decay=0.0					,      relative_step=__lowercase					,      scale_parameter=__lowercase					,      warmup_init=__lowercase					,      )
       for _ in range(10_00      ):
             SCREAMING_SNAKE_CASE__     :				List[Any]									=criterion(__lowercase					,      __lowercase      )
             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      __SCREAMING_SNAKE_CASE      (			unittest.TestCase				):
 snake_case_  							=							nn.Linear(50	,				50				) if is_torch_available() else None
 snake_case_  							=							AdamW(m.parameters()	,				lr=10.0				) if is_torch_available() else None
 snake_case_  							=							10




 def      __magic_name__	(      self	:							List[Any]					,      __lowercase	:							Optional[Any]					,      __lowercase	:							List[Any]					,      __lowercase	:							List[Any]					,      __lowercase	:							Optional[int]=None      )	->						Union[str, Any]:
       self.assertEqual(len(__lowercase      )					,      len(__lowercase      )      )
       for a, b in zip(__lowercase					,      __lowercase      ):
             self.assertAlmostEqual(__lowercase					,      __lowercase					,      delta=__lowercase					,      msg=__lowercase      )




 def      __magic_name__	(      self	:							List[Any]      )	->						str:
       SCREAMING_SNAKE_CASE__     :				int									={'''num_warmup_steps''': 2, '''num_training_steps''': 10}
       # schedulers doct format
       # function: (sched_args_dict, expected_learning_rates)
       SCREAMING_SNAKE_CASE__     :				Any									={
           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():
             SCREAMING_SNAKE_CASE__     :				Optional[int]									=data

             SCREAMING_SNAKE_CASE__     :				List[Any]									=scheduler_func(self.optimizer					,      **__lowercase      )
             self.assertEqual(len([scheduler.get_lr()[0]]      )					,      1      )
             SCREAMING_SNAKE_CASE__     :				List[str]									=unwrap_schedule(__lowercase					,      self.num_steps      )
             self.assertListAlmostEqual(
                 __lowercase					,      __lowercase					,      tol=1e-2					,      msg=F"failed for {scheduler_func} in normal scheduler"					,      )

             SCREAMING_SNAKE_CASE__     :				str									=scheduler_func(self.optimizer					,      **__lowercase      )
             if scheduler_func.__name__ != "get_constant_schedule":
                   LambdaScheduleWrapper.wrap_scheduler(__lowercase      )  # wrap to test picklability of the schedule
             SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=unwrap_and_save_reload_schedule(__lowercase					,      self.num_steps      )
             self.assertListEqual(__lowercase					,      __lowercase					,      msg=F"failed for {scheduler_func} in save and reload"      )






class      __SCREAMING_SNAKE_CASE      :




 def __init__(      self	:							str					,      __lowercase	:							Any      )	->						Union[str, Any]:
       SCREAMING_SNAKE_CASE__     :				str									=fn




 def __call__(      self	:							Union[str, Any]					,      *__lowercase	:							Any					,      **__lowercase	:							Optional[Any]      )	->						int:
       return self.fn(*__lowercase					,      **__lowercase      )




 @classmethod
 def      __magic_name__	(      self	:							Tuple					,      __lowercase	:							Dict      )	->						List[str]:
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=list(map(self					,      scheduler.lr_lambdas      )      )







'''simple docstring'''
import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaImgaImgPipeline,
    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      __SCREAMING_SNAKE_CASE      (			lowerCamelCase	,				unittest.TestCase				):
 snake_case_  							=							KandinskyVaaImgaImgPipeline
 snake_case_  							=							["""image_embeds""", """negative_image_embeds""", """image"""]
 snake_case_  							=							[
     """image_embeds""",
     """negative_image_embeds""",
     """image""",
 ]
 snake_case_  							=							[
     """generator""",
     """height""",
     """width""",
     """strength""",
     """guidance_scale""",
     """num_inference_steps""",
     """return_dict""",
     """guidance_scale""",
     """num_images_per_prompt""",
     """output_type""",
     """return_dict""",
 ]
 snake_case_  							=							False




 @property
 def      __magic_name__	(      self	:							List[str]      )	->						Tuple:
       return 32




 @property
 def      __magic_name__	(      self	:							List[str]      )	->						str:
       return 32




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




 @property
 def      __magic_name__	(      self	:							List[Any]      )	->						int:
       return self.time_input_dim * 4




 @property
 def      __magic_name__	(      self	:							Tuple      )	->						Optional[int]:
       return 1_00




 @property
 def      __magic_name__	(      self	:							Union[str, Any]      )	->						Tuple:
       torch.manual_seed(0      )

       SCREAMING_SNAKE_CASE__     :				Optional[Any]									={
           '''in_channels''': 4,
           # 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__     :				Optional[int]									=UNetaDConditionModel(**__lowercase      )
       return model




 @property
 def      __magic_name__	(      self	:							Dict      )	->						Any:
       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      __magic_name__	(      self	:							Tuple      )	->						Optional[Any]:
       torch.manual_seed(0      )
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=VQModel(**self.dummy_movq_kwargs      )
       return model




 def      __magic_name__	(      self	:							str      )	->						Tuple:
       SCREAMING_SNAKE_CASE__     :				List[str]									=self.dummy_unet
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=self.dummy_movq

       SCREAMING_SNAKE_CASE__     :				Optional[Any]									={
           '''num_train_timesteps''': 10_00,
           '''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,
       }

       SCREAMING_SNAKE_CASE__     :				str									=DDIMScheduler(**__lowercase      )

       SCREAMING_SNAKE_CASE__     :				Any									={
           '''unet''': unet,
           '''scheduler''': scheduler,
           '''movq''': movq,
       }

       return components




 def      __magic_name__	(      self	:							str					,      __lowercase	:							Optional[Any]					,      __lowercase	:							Any=0      )	->						int:
       SCREAMING_SNAKE_CASE__     :				Optional[int]									=floats_tensor((1, self.text_embedder_hidden_size)					,      rng=random.Random(__lowercase      )      ).to(__lowercase      )
       SCREAMING_SNAKE_CASE__     :				Any									=floats_tensor((1, self.text_embedder_hidden_size)					,      rng=random.Random(seed + 1      )      ).to(
           __lowercase      )
       # create init_image
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=floats_tensor((1, 3, 64, 64)					,      rng=random.Random(__lowercase      )      ).to(__lowercase      )
       SCREAMING_SNAKE_CASE__     :				Dict									=image.cpu().permute(0					,      2					,      3					,      1      )[0]
       SCREAMING_SNAKE_CASE__     :				Any									=Image.fromarray(np.uinta(__lowercase      )      ).convert('''RGB'''      ).resize((2_56, 2_56)      )

       if str(__lowercase      ).startswith('''mps'''      ):
             SCREAMING_SNAKE_CASE__     :				Dict									=torch.manual_seed(__lowercase      )
       else:
             SCREAMING_SNAKE_CASE__     :				Tuple									=torch.Generator(device=__lowercase      ).manual_seed(__lowercase      )
       SCREAMING_SNAKE_CASE__     :				str									={
           '''image''': init_image,
           '''image_embeds''': image_embeds,
           '''negative_image_embeds''': negative_image_embeds,
           '''generator''': generator,
           '''height''': 64,
           '''width''': 64,
           '''num_inference_steps''': 10,
           '''guidance_scale''': 7.0,
           '''strength''': 0.2,
           '''output_type''': '''np''',
       }
       return inputs




 def      __magic_name__	(      self	:							int      )	->						Union[str, Any]:
       SCREAMING_SNAKE_CASE__     :				List[Any]									='''cpu'''

       SCREAMING_SNAKE_CASE__     :				Tuple									=self.get_dummy_components()

       SCREAMING_SNAKE_CASE__     :				Dict									=self.pipeline_class(**__lowercase      )
       SCREAMING_SNAKE_CASE__     :				Optional[Any]									=pipe.to(__lowercase      )

       pipe.set_progress_bar_config(disable=__lowercase      )

       SCREAMING_SNAKE_CASE__     :				Dict									=pipe(**self.get_dummy_inputs(__lowercase      )      )
       SCREAMING_SNAKE_CASE__     :				Tuple									=output.images

       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=pipe(
           **self.get_dummy_inputs(__lowercase      )					,      return_dict=__lowercase					,      )[0]

       SCREAMING_SNAKE_CASE__     :				List[Any]									=image[0, -3:, -3:, -1]
       SCREAMING_SNAKE_CASE__     :				List[str]									=image_from_tuple[0, -3:, -3:, -1]

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

       SCREAMING_SNAKE_CASE__     :				Tuple									=np.array(
           [0.6199778, 0.63984406, 0.46145785, 0.62944984, 0.5622215, 0.47306132, 0.47441456, 0.4607606, 0.48719263]      )
       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      __SCREAMING_SNAKE_CASE      (			unittest.TestCase				):




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




 def      __magic_name__	(      self	:							Union[str, Any]      )	->						Tuple:
       SCREAMING_SNAKE_CASE__     :				str									=load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/kandinskyv22/kandinskyv22_img2img_frog.npy'''      )

       SCREAMING_SNAKE_CASE__     :				Union[str, Any]									=load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''      )
       SCREAMING_SNAKE_CASE__     :				List[Any]									='''A red cartoon frog, 4k'''

       SCREAMING_SNAKE_CASE__     :				Optional[int]									=KandinskyVaaPriorPipeline.from_pretrained(
           '''kandinsky-community/kandinsky-2-2-prior'''					,      torch_dtype=torch.floataa      )
       pipe_prior.to(__lowercase      )

       SCREAMING_SNAKE_CASE__     :				Any									=KandinskyVaaImgaImgPipeline.from_pretrained(
           '''kandinsky-community/kandinsky-2-2-decoder'''					,      torch_dtype=torch.floataa      )
       SCREAMING_SNAKE_CASE__     :				Dict									=pipeline.to(__lowercase      )

       pipeline.set_progress_bar_config(disable=__lowercase      )

       SCREAMING_SNAKE_CASE__     :				Tuple									=torch.Generator(device='''cpu'''      ).manual_seed(0      )
       SCREAMING_SNAKE_CASE__		,	SCREAMING_SNAKE_CASE__     :				List[str]									=pipe_prior(
           __lowercase					,      generator=__lowercase					,      num_inference_steps=5					,      negative_prompt=''''''					,      ).to_tuple()

       SCREAMING_SNAKE_CASE__     :				List[Any]									=pipeline(
           image=__lowercase					,      image_embeds=__lowercase					,      negative_image_embeds=__lowercase					,      generator=__lowercase					,      num_inference_steps=1_00					,      height=7_68					,      width=7_68					,      strength=0.2					,      output_type='''np'''					,      )

       SCREAMING_SNAKE_CASE__     :				int									=output.images[0]

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

       assert_mean_pixel_difference(__lowercase					,      __lowercase      )