

'''simple docstring'''
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 						__magic_name__   (							__SCREAMING_SNAKE_CASE ,				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   ):
   return 32







 @property
 def 						_A(       self   ):
   return 32







 @property
 def 						_A(       self   ):
   return self.time_input_dim







 @property
 def 						_A(       self   ):
   return self.time_input_dim * 4







 @property
 def 						_A(       self   ):
   return 1_00







 @property
 def 						_A(       self   ):
   lowercase									=XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base'''   )
   return tokenizer







 @property
 def 						_A(       self   ):
   torch.manual_seed(0   )
   lowercase									=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  , )

   lowercase									=MultilingualCLIP(snake_case_   )
   lowercase									=text_encoder.eval()

   return text_encoder







 @property
 def 						_A(       self   ):
   torch.manual_seed(0   )

   lowercase									={
       '''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,
   }

   lowercase									=UNetaDConditionModel(**snake_case_   )
   return model







 @property
 def 						_A(       self   ):
   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   ):
   torch.manual_seed(0   )
   lowercase									=VQModel(**self.dummy_movq_kwargs   )
   return model







 def 						_A(       self   ):
   lowercase									=self.dummy_text_encoder
   lowercase									=self.dummy_tokenizer
   lowercase									=self.dummy_unet
   lowercase									=self.dummy_movq

   lowercase									=DDIMScheduler(
       num_train_timesteps=10_00  , beta_schedule='''linear'''  , beta_start=0.0_00_85  , beta_end=0.0_12  , clip_sample=snake_case_  , set_alpha_to_one=snake_case_  , steps_offset=1  , prediction_type='''epsilon'''  , thresholding=snake_case_  , )

   lowercase									={
       '''text_encoder''': text_encoder,
       '''tokenizer''': tokenizer,
       '''unet''': unet,
       '''scheduler''': scheduler,
       '''movq''': movq,
   }

   return components







 def 						_A(       self  , snake_case_  , snake_case_=0   ):
   lowercase									=floats_tensor((1, self.cross_attention_dim)  , rng=random.Random(snake_case_   )   ).to(snake_case_   )
   lowercase									=floats_tensor((1, self.cross_attention_dim)  , rng=random.Random(seed + 1   )   ).to(snake_case_   )
   # create init_image
   lowercase									=floats_tensor((1, 3, 64, 64)  , rng=random.Random(snake_case_   )   ).to(snake_case_   )
   lowercase									=image.cpu().permute(0  , 2  , 3  , 1   )[0]
   lowercase									=Image.fromarray(np.uinta(snake_case_   )   ).convert('''RGB'''   ).resize((2_56, 2_56)   )
   # create mask
   lowercase									=np.ones((64, 64)  , dtype=np.floataa   )
   lowercase									=0

   if str(snake_case_   ).startswith('''mps'''   ):
     lowercase									=torch.manual_seed(snake_case_   )
   else:
     lowercase									=torch.Generator(device=snake_case_   ).manual_seed(snake_case_   )
   lowercase									={
       '''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   ):
   lowercase									='''cpu'''

   lowercase									=self.get_dummy_components()

   lowercase									=self.pipeline_class(**snake_case_   )
   lowercase									=pipe.to(snake_case_   )

   pipe.set_progress_bar_config(disable=snake_case_   )

   lowercase									=pipe(**self.get_dummy_inputs(snake_case_   )   )
   lowercase									=output.images

   lowercase									=pipe(
       **self.get_dummy_inputs(snake_case_   )  , return_dict=snake_case_  , )[0]

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

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

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

   lowercase									=np.array(
       [0.8_32_69_19, 0.73_79_04_67, 0.20_91_85_81, 0.9_30_96_12, 0.5_51_17_91, 0.43_71_33_28, 0.5_51_33_21, 0.49_92_29_34, 0.59_49_77_86]   )

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

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







 def 						_A(       self   ):
   # clean up the VRAM after each test
   super().tearDown()
   gc.collect()
   torch.cuda.empty_cache()







 def 						_A(       self   ):
   lowercase									=load_numpy(
       '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
       '''/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy'''   )

   lowercase									=load_image(
       '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png'''   )
   lowercase									=np.ones((7_68, 7_68)  , dtype=np.floataa   )
   lowercase									=0

   lowercase									='''a hat'''

   lowercase									=KandinskyPriorPipeline.from_pretrained(
       '''kandinsky-community/kandinsky-2-1-prior'''  , torch_dtype=torch.floataa   )
   pipe_prior.to(snake_case_   )

   lowercase									=KandinskyInpaintPipeline.from_pretrained(
       '''kandinsky-community/kandinsky-2-1-inpaint'''  , torch_dtype=torch.floataa   )
   lowercase									=pipeline.to(snake_case_   )
   pipeline.set_progress_bar_config(disable=snake_case_   )

   lowercase									=torch.Generator(device='''cpu'''   ).manual_seed(0   )
   lowercase							, lowercase									=pipe_prior(
       snake_case_  , generator=snake_case_  , num_inference_steps=5  , negative_prompt=''''''  , ).to_tuple()

   lowercase									=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'''  , )

   lowercase									=output.images[0]

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

   assert_mean_pixel_difference(snake_case_  , snake_case_   )







import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class       __lowerCAmelCase					(       _UpperCamelCase      ):


	'''simple docstring'''





	_A													=				(EulerDiscreteScheduler,)
	_A													=				10

	def    snake_case__(						self:						List[Any],					**lowerCamelCase_:						Any			):
			lowercase__					:      Tuple      =		{
			    'num_train_timesteps': 1100,
			    'beta_start': 0.0_0_0_1,
			    'beta_end': 0.0_2,
			    'beta_schedule': 'linear',
			}

			config.update(**lowerCamelCase_			)
			return config

	def    snake_case__(						self:						Optional[Any]			):
			for timesteps in [10, 50, 100, 1000]:
					self.check_over_configs(num_train_timesteps=lowerCamelCase_			)

	def    snake_case__(						self:						Optional[int]			):
			for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1],					[0.0_0_0_2, 0.0_0_2, 0.0_2]			):
					self.check_over_configs(beta_start=lowerCamelCase_,					beta_end=lowerCamelCase_			)

	def    snake_case__(						self:						Optional[Any]			):
			for schedule in ["linear", "scaled_linear"]:
					self.check_over_configs(beta_schedule=lowerCamelCase_			)

	def    snake_case__(						self:						str			):
			for prediction_type in ["epsilon", "v_prediction"]:
					self.check_over_configs(prediction_type=lowerCamelCase_			)

	def    snake_case__(						self:						List[str]			):
			lowercase__					:      List[str]      =		self.scheduler_classes[0]
			lowercase__					:      Tuple      =		self.get_scheduler_config()
			lowercase__					:      List[str]      =		scheduler_class(**lowerCamelCase_			)

			scheduler.set_timesteps(self.num_inference_steps			)

			lowercase__					:      Any      =		torch.manual_seed(0			)

			lowercase__					:      int      =		self.dummy_model()
			lowercase__					:      Tuple      =		self.dummy_sample_deter * scheduler.init_noise_sigma
			lowercase__					:      Union[str, Any]      =		sample.to(lowerCamelCase_			)

			for i, t in enumerate(scheduler.timesteps			):
					lowercase__					:      Dict      =		scheduler.scale_model_input(lowerCamelCase_,					lowerCamelCase_			)

					lowercase__					:      Dict      =		model(lowerCamelCase_,					lowerCamelCase_			)

					lowercase__					:      Tuple      =		scheduler.step(lowerCamelCase_,					lowerCamelCase_,					lowerCamelCase_,					generator=lowerCamelCase_			)
					lowercase__					:      List[str]      =		output.prev_sample

			lowercase__					:      Any      =		torch.sum(torch.abs(lowerCamelCase_			)			)
			lowercase__					:      Optional[Any]      =		torch.mean(torch.abs(lowerCamelCase_			)			)

			assert abs(result_sum.item() - 1_0.0_8_0_7			) < 1E-2
			assert abs(result_mean.item() - 0.0_1_3_1			) < 1E-3

	def    snake_case__(						self:						Dict			):
			lowercase__					:      Dict      =		self.scheduler_classes[0]
			lowercase__					:      str      =		self.get_scheduler_config(prediction_type='v_prediction'			)
			lowercase__					:      Optional[int]      =		scheduler_class(**lowerCamelCase_			)

			scheduler.set_timesteps(self.num_inference_steps			)

			lowercase__					:      Optional[int]      =		torch.manual_seed(0			)

			lowercase__					:      str      =		self.dummy_model()
			lowercase__					:      Optional[int]      =		self.dummy_sample_deter * scheduler.init_noise_sigma
			lowercase__					:      Optional[int]      =		sample.to(lowerCamelCase_			)

			for i, t in enumerate(scheduler.timesteps			):
					lowercase__					:      int      =		scheduler.scale_model_input(lowerCamelCase_,					lowerCamelCase_			)

					lowercase__					:      str      =		model(lowerCamelCase_,					lowerCamelCase_			)

					lowercase__					:      Tuple      =		scheduler.step(lowerCamelCase_,					lowerCamelCase_,					lowerCamelCase_,					generator=lowerCamelCase_			)
					lowercase__					:      List[str]      =		output.prev_sample

			lowercase__					:      Dict      =		torch.sum(torch.abs(lowerCamelCase_			)			)
			lowercase__					:      Optional[Any]      =		torch.mean(torch.abs(lowerCamelCase_			)			)

			assert abs(result_sum.item() - 0.0_0_0_2			) < 1E-2
			assert abs(result_mean.item() - 2.2676E-06			) < 1E-3

	def    snake_case__(						self:						List[Any]			):
			lowercase__					:      List[str]      =		self.scheduler_classes[0]
			lowercase__					:      Dict      =		self.get_scheduler_config()
			lowercase__					:      List[str]      =		scheduler_class(**lowerCamelCase_			)

			scheduler.set_timesteps(self.num_inference_steps,					device=lowerCamelCase_			)

			lowercase__					:      Dict      =		torch.manual_seed(0			)

			lowercase__					:      Union[str, Any]      =		self.dummy_model()
			lowercase__					:      Any      =		self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
			lowercase__					:      List[Any]      =		sample.to(lowerCamelCase_			)

			for t in scheduler.timesteps:
					lowercase__					:      Optional[int]      =		scheduler.scale_model_input(lowerCamelCase_,					lowerCamelCase_			)

					lowercase__					:      Dict      =		model(lowerCamelCase_,					lowerCamelCase_			)

					lowercase__					:      List[Any]      =		scheduler.step(lowerCamelCase_,					lowerCamelCase_,					lowerCamelCase_,					generator=lowerCamelCase_			)
					lowercase__					:      Union[str, Any]      =		output.prev_sample

			lowercase__					:      Union[str, Any]      =		torch.sum(torch.abs(lowerCamelCase_			)			)
			lowercase__					:      Union[str, Any]      =		torch.mean(torch.abs(lowerCamelCase_			)			)

			assert abs(result_sum.item() - 1_0.0_8_0_7			) < 1E-2
			assert abs(result_mean.item() - 0.0_1_3_1			) < 1E-3





	def    snake_case__(						self:						Optional[int]			):
			lowercase__					:      Tuple      =		self.scheduler_classes[0]
			lowercase__					:      Tuple      =		self.get_scheduler_config()
			lowercase__					:      Dict      =		scheduler_class(**lowerCamelCase_,					use_karras_sigmas=lowerCamelCase_			)

			scheduler.set_timesteps(self.num_inference_steps,					device=lowerCamelCase_			)

			lowercase__					:      int      =		torch.manual_seed(0			)

			lowercase__					:      Optional[int]      =		self.dummy_model()
			lowercase__					:      str      =		self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
			lowercase__					:      List[str]      =		sample.to(lowerCamelCase_			)

			for t in scheduler.timesteps:
					lowercase__					:      Any      =		scheduler.scale_model_input(lowerCamelCase_,					lowerCamelCase_			)

					lowercase__					:      Optional[Any]      =		model(lowerCamelCase_,					lowerCamelCase_			)

					lowercase__					:      Tuple      =		scheduler.step(lowerCamelCase_,					lowerCamelCase_,					lowerCamelCase_,					generator=lowerCamelCase_			)
					lowercase__					:      Optional[Any]      =		output.prev_sample

			lowercase__					:      int      =		torch.sum(torch.abs(lowerCamelCase_			)			)
			lowercase__					:      Dict      =		torch.mean(torch.abs(lowerCamelCase_			)			)

			assert abs(result_sum.item() - 1_2_4.5_2_2_9_9_4_9_9_5_1_1_7_1_9			) < 1E-2
			assert abs(result_mean.item() - 0.1_6_2_1_3_9_3_2_6_3_3_3_9_9_9_6_3			) < 1E-3


