




'''simple docstring'''



import gc
import random
import unittest

import numpy as np
import torch

from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, 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_   (			A					,  unittest.TestCase       ):


   """simple docstring"""

   lowerCamelCase_      =						KandinskyVaaPipeline
   lowerCamelCase_      =						[
       '''image_embeds''',
       '''negative_image_embeds''',
   ]
   lowerCamelCase_      =						['''image_embeds''', '''negative_image_embeds''']
   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 							lowerCAmelCase_      (   self :     int   ):

      """simple docstring"""
      return 3_2



   @property
   def 							lowerCAmelCase_      (   self :     Dict   ):

      """simple docstring"""
      return 3_2



   @property
   def 							lowerCAmelCase_      (   self :     str   ):

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



   @property
   def 							lowerCAmelCase_      (   self :     str   ):

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



   @property
   def 							lowerCAmelCase_      (   self :     Tuple   ):

      """simple docstring"""
      return 1_0_0



   @property
   def 							lowerCAmelCase_      (   self :     Any   ):

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

      _SCREAMING_SNAKE_CASE	=				{
          "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	=				UNetaDConditionModel(**__lowerCamelCase   )
      return model



   @property
   def 							lowerCAmelCase_      (   self :     Dict   ):

      """simple docstring"""
      return {
          "block_out_channels": [3_2, 6_4],
          "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": 1_2,
          "out_channels": 3,
          "up_block_types": [
              "AttnUpDecoderBlock2D",
              "UpDecoderBlock2D",
          ],
          "vq_embed_dim": 4,
      }



   @property
   def 							lowerCAmelCase_      (   self :     str   ):

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



   def 							lowerCAmelCase_      (   self :     Optional[Any]   ):

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

      _SCREAMING_SNAKE_CASE	=				DDIMScheduler(
          num_train_timesteps=1_0_0_0  ,				beta_schedule="linear"  ,				beta_start=0.0_0_0_8_5  ,				beta_end=0.0_1_2  ,				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 							lowerCAmelCase_      (   self :     Tuple  ,				__lowerCamelCase :     Tuple  ,				__lowerCamelCase :     List[Any]=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   )
      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_embeds": image_embeds,
          "negative_image_embeds": negative_image_embeds,
          "generator": generator,
          "height": 6_4,
          "width": 6_4,
          "guidance_scale": 4.0,
          "num_inference_steps": 2,
          "output_type": "np",
      }
      return inputs



   def 							lowerCAmelCase_      (   self :     Dict   ):

      """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]

      assert image.shape == (1, 6_4, 6_4, 3)

      _SCREAMING_SNAKE_CASE	=				np.array(
          [0.6_2_3_7_9_7_6, 1.0, 0.3_6_4_4_1_3_3_2, 1.0, 0.7_0_6_3_9_6_3_4, 0.2_9_8_7_7_1_8_6, 0.8_5_6_5_2_1_2_5, 0.5_2_1_6_8_4_3, 0.5_4_4_5_4_0_4_6]   )

      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 		lowercase_   (			unittest.TestCase       ):


   """simple docstring"""

   def 							lowerCAmelCase_      (   self :     Any   ):

      """simple docstring"""
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()



   def 							lowerCAmelCase_      (   self :     str   ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE	=				load_numpy(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
          "/kandinskyv22/kandinskyv22_text2img_cat_fp16.npy"   )

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

      _SCREAMING_SNAKE_CASE	=				KandinskyVaaPipeline.from_pretrained(
          "kandinsky-community/kandinsky-2-2-decoder"  ,				torch_dtype=torch.floataa   )
      _SCREAMING_SNAKE_CASE	=				pipeline.to(__lowerCamelCase   )
      pipeline.set_progress_bar_config(disable=__lowerCamelCase   )

      _SCREAMING_SNAKE_CASE	=				"red cat, 4k photo"

      _SCREAMING_SNAKE_CASE	=				torch.Generator(device="cuda"   ).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	=				torch.Generator(device="cuda"   ).manual_seed(0   )
      _SCREAMING_SNAKE_CASE	=				pipeline(
          image_embeds=__lowerCamelCase  ,				negative_image_embeds=__lowerCamelCase  ,				generator=__lowerCamelCase  ,				num_inference_steps=1_0_0  ,				output_type="np"  ,				)

      _SCREAMING_SNAKE_CASE	=				output.images[0]

      assert image.shape == (5_1_2, 5_1_2, 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


lowerCamelCase_			=						logging.get_logger(__name__)







class 		lowercase_   (			A       ):


   """simple docstring"""

   lowerCamelCase_      =						'''linear'''
   lowerCamelCase_      =						'''cosine'''
   lowerCamelCase_      =						'''cosine_with_restarts'''
   lowerCamelCase_      =						'''polynomial'''
   lowerCamelCase_      =						'''constant'''
   lowerCamelCase_      =						'''constant_with_warmup'''
   lowerCamelCase_      =						'''piecewise_constant'''


def 			SCREAMING_SNAKE_CASE_			(							__A    :			Optimizer	,					__A    :			int = -1  )			->			Optional[int]:
   return LambdaLR(__A	,					lambda __A  : 1	,					last_epoch=__A  )
def 			SCREAMING_SNAKE_CASE_			(							__A    :			Optimizer	,					__A    :			int	,					__A    :			int = -1  )			->			Dict:

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

   return LambdaLR(__A	,					__A	,					last_epoch=__A  )
def 			SCREAMING_SNAKE_CASE_			(							__A    :			Optimizer	,					__A    :			str	,					__A    :			int = -1  )			->			Tuple:
   _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(__A  )
      _SCREAMING_SNAKE_CASE	=				float(__A  )
      _SCREAMING_SNAKE_CASE	=				value
   _SCREAMING_SNAKE_CASE	=				float(rule_list[-1]  )

   def create_rules_function(__A    :			Tuple	,					__A    :			List[Any]  ):
      def rule_func(__A    :			int  ) -> float:
         _SCREAMING_SNAKE_CASE	=				sorted(rules_dict.keys()  )
         for i, sorted_step in enumerate(__A  ):
            if steps < sorted_step:
               return rules_dict[sorted_steps[i]]
         return last_lr_multiple

      return rule_func

   _SCREAMING_SNAKE_CASE	=				create_rules_function(__A	,					__A  )

   return LambdaLR(__A	,					__A	,					last_epoch=__A  )
def 			SCREAMING_SNAKE_CASE_			(							__A    :			Union[str, Any]	,					__A    :			Optional[Any]	,					__A    :			List[str]	,					__A    :			Union[str, Any]=-1  )			->			str:

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

   return LambdaLR(__A	,					__A	,					__A  )
def 			SCREAMING_SNAKE_CASE_			(							__A    :			Optimizer	,					__A    :			int	,					__A    :			int	,					__A    :			float = 0.5	,					__A    :			int = -1  )			->			Any:

   def lr_lambda(__A    :			int  ):
      if current_step < num_warmup_steps:
         return float(__A  ) / float(max(1	,					__A  )  )
      _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(__A  ) * 2.0 * progress  ))  )

   return LambdaLR(__A	,					__A	,					__A  )
def 			SCREAMING_SNAKE_CASE_			(							__A    :			Optimizer	,					__A    :			int	,					__A    :			int	,					__A    :			int = 1	,					__A    :			int = -1  )			->			str:

   def lr_lambda(__A    :			Optional[int]  ):
      if current_step < num_warmup_steps:
         return float(__A  ) / float(max(1	,					__A  )  )
      _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(__A  ) * progress) % 1.0)  ))  )

   return LambdaLR(__A	,					__A	,					__A  )
def 			SCREAMING_SNAKE_CASE_			(							__A    :			Optional[int]	,					__A    :			Optional[Any]	,					__A    :			List[str]	,					__A    :			Any=1e-7	,					__A    :			Optional[Any]=1.0	,					__A    :			List[Any]=-1  )			->			Tuple:
   _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(__A    :			int  ):
      if current_step < num_warmup_steps:
         return float(__A  ) / float(max(1	,					__A  )  )
      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(__A	,					__A	,					__A  )


lowerCamelCase_			=						{
    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 			SCREAMING_SNAKE_CASE_			(							__A    :			Union[str, SchedulerType]	,					__A    :			Optimizer	,					__A    :			Optional[str] = None	,					__A    :			Optional[int] = None	,					__A    :			Optional[int] = None	,					__A    :			int = 1	,					__A    :			float = 1.0	,					__A    :			int = -1	,					)			->			Optional[Any]:
   _SCREAMING_SNAKE_CASE	=				SchedulerType(__A  )
   _SCREAMING_SNAKE_CASE	=				TYPE_TO_SCHEDULER_FUNCTION[name]
   if name == SchedulerType.CONSTANT:
      return schedule_func(__A	,					last_epoch=__A  )

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

   # 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(__A	,					num_warmup_steps=__A	,					last_epoch=__A  )

   # 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(
          __A	,					num_warmup_steps=__A	,					num_training_steps=__A	,					num_cycles=__A	,					last_epoch=__A	,					)

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

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

