


"""simple docstring"""






import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
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     A(     snake_case_							,       unittest.TestCase		):
		A         =       KandinskyPipeline
		A         =       [
		    "prompt",
		    "image_embeds",
		    "negative_image_embeds",
		]
		A         =       ["prompt", "negative_prompt", "image_embeds", "negative_image_embeds"]
		A         =       [
		    "generator",
		    "height",
		    "width",
		    "latents",
		    "guidance_scale",
		    "negative_prompt",
		    "num_inference_steps",
		    "return_dict",
		    "guidance_scale",
		    "num_images_per_prompt",
		    "output_type",
		    "return_dict",
		]
		A         =       False
		@property
		def      _UpperCamelCase(   self      )			->  List[str]:

					"""simple docstring"""



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

					"""simple docstring"""



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

					"""simple docstring"""



					return self.time_input_dim
		@property
		def      _UpperCamelCase(   self      )			->  Union[str, Any]:

					"""simple docstring"""



					return self.time_input_dim * 4
		@property
		def      _UpperCamelCase(   self      )			->  int:

					"""simple docstring"""



					return 1_00
		@property
		def      _UpperCamelCase(   self      )			->  int:

					"""simple docstring"""



					_UpperCamelCase				:int				    =  XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base'''      )
					return tokenizer
		@property
		def      _UpperCamelCase(   self      )			->  Tuple:

					"""simple docstring"""



					torch.manual_seed(0      )
					_UpperCamelCase				:Optional[Any]				    =  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				,							)

					_UpperCamelCase				:List[str]				    =  MultilingualCLIP(SCREAMING_SNAKE_CASE__      )
					_UpperCamelCase				:Tuple				    =  text_encoder.eval()

					return text_encoder
		@property
		def      _UpperCamelCase(   self      )			->  Any:

					"""simple docstring"""



					torch.manual_seed(0      )

					_UpperCamelCase				:Any				    =  {
					    '''in_channels''': 4,
					    # 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,
					}

					_UpperCamelCase				:str				    =  UNetaDConditionModel(**SCREAMING_SNAKE_CASE__      )
					return model
		@property
		def      _UpperCamelCase(   self      )			->  Any:

					"""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      )
					_UpperCamelCase				:Optional[int]				    =  VQModel(**self.dummy_movq_kwargs      )
					return model
		def      _UpperCamelCase(   self      )			->  Union[str, Any]:

					"""simple docstring"""



					_UpperCamelCase				:List[str]				    =  self.dummy_text_encoder
					_UpperCamelCase				:Union[str, Any]				    =  self.dummy_tokenizer
					_UpperCamelCase				:Optional[int]				    =  self.dummy_unet
					_UpperCamelCase				:str				    =  self.dummy_movq

					_UpperCamelCase				:Tuple				    =  DDIMScheduler(
					    num_train_timesteps=10_00				,							beta_schedule='''linear'''				,							beta_start=0.0_0_0_8_5				,							beta_end=0.0_1_2				,							clip_sample=SCREAMING_SNAKE_CASE__				,							set_alpha_to_one=SCREAMING_SNAKE_CASE__				,							steps_offset=1				,							prediction_type='''epsilon'''				,							thresholding=SCREAMING_SNAKE_CASE__				,							)

					_UpperCamelCase				:List[Any]				    =  {
					    '''text_encoder''': text_encoder,
					    '''tokenizer''': tokenizer,
					    '''unet''': unet,
					    '''scheduler''': scheduler,
					    '''movq''': movq,
					}
					return components
		def      _UpperCamelCase(   self				,							SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__=0      )			->  List[str]:

					"""simple docstring"""



					_UpperCamelCase				:str				    =  floats_tensor((1, self.cross_attention_dim)				,							rng=random.Random(SCREAMING_SNAKE_CASE__      )      ).to(SCREAMING_SNAKE_CASE__      )
					_UpperCamelCase				:Optional[Any]				    =  floats_tensor((1, self.cross_attention_dim)				,							rng=random.Random(seed + 1      )      ).to(SCREAMING_SNAKE_CASE__      )
					if str(SCREAMING_SNAKE_CASE__      ).startswith('''mps'''      ):
								_UpperCamelCase				:Optional[Any]				    =  torch.manual_seed(SCREAMING_SNAKE_CASE__      )
					else:
								_UpperCamelCase				:List[Any]				    =  torch.Generator(device=SCREAMING_SNAKE_CASE__      ).manual_seed(SCREAMING_SNAKE_CASE__      )
					_UpperCamelCase				:Dict				    =  {
					    '''prompt''': '''horse''',
					    '''image_embeds''': image_embeds,
					    '''negative_image_embeds''': negative_image_embeds,
					    '''generator''': generator,
					    '''height''': 64,
					    '''width''': 64,
					    '''guidance_scale''': 4.0,
					    '''num_inference_steps''': 2,
					    '''output_type''': '''np''',
					}
					return inputs





		def      _UpperCamelCase(   self      )			->  Any:

					"""simple docstring"""



					_UpperCamelCase				:str				    =  '''cpu'''

					_UpperCamelCase				:int				    =  self.get_dummy_components()

					_UpperCamelCase				:str				    =  self.pipeline_class(**SCREAMING_SNAKE_CASE__      )
					_UpperCamelCase				:str				    =  pipe.to(SCREAMING_SNAKE_CASE__      )

					pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__      )

					_UpperCamelCase				:Optional[int]				    =  pipe(**self.get_dummy_inputs(SCREAMING_SNAKE_CASE__      )      )
					_UpperCamelCase				:Tuple				    =  output.images

					_UpperCamelCase				:Dict				    =  pipe(
					    **self.get_dummy_inputs(SCREAMING_SNAKE_CASE__      )				,							return_dict=SCREAMING_SNAKE_CASE__				,							)[0]

					_UpperCamelCase				:int				    =  image[0, -3:, -3:, -1]
					_UpperCamelCase				:List[Any]				    =  image_from_tuple[0, -3:, -3:, -1]

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

					_UpperCamelCase				:Union[str, Any]				    =  np.array(
					    [0.3_2_8_6_6_3, 1.0, 0.2_3_2_1_6_8_7_3, 1.0, 0.9_2_7_1_7_5_6_4, 0.4_6_3_9_0_4_6, 0.9_6_8_9_4_7_7_7, 0.3_1_7_1_3_3_7_8, 0.6_2_9_3_9_5_3]      )

					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     A(     unittest.TestCase		):
		def      _UpperCamelCase(   self      )			->  List[str]:

					"""simple docstring"""



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





		def      _UpperCamelCase(   self      )			->  Optional[int]:

					"""simple docstring"""



					_UpperCamelCase				:Optional[int]				    =  load_numpy(
					    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
					    '''/kandinsky/kandinsky_text2img_cat_fp16.npy'''      )

					_UpperCamelCase				:List[Any]				    =  KandinskyPriorPipeline.from_pretrained(
					    '''kandinsky-community/kandinsky-2-1-prior'''				,							torch_dtype=torch.floataa      )
					pipe_prior.to(SCREAMING_SNAKE_CASE__      )

					_UpperCamelCase				:Optional[Any]				    =  KandinskyPipeline.from_pretrained('''kandinsky-community/kandinsky-2-1'''				,							torch_dtype=torch.floataa      )
					_UpperCamelCase				:Any				    =  pipeline.to(SCREAMING_SNAKE_CASE__      )
					pipeline.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE__      )

					_UpperCamelCase				:Tuple				    =  '''red cat, 4k photo'''

					_UpperCamelCase				:str				    =  torch.Generator(device='''cuda'''      ).manual_seed(0      )
					_UpperCamelCase    ,			_UpperCamelCase				:Tuple				    =  pipe_prior(
					    SCREAMING_SNAKE_CASE__				,							generator=SCREAMING_SNAKE_CASE__				,							num_inference_steps=5				,							negative_prompt=''''''				,							).to_tuple()

					_UpperCamelCase				:Dict				    =  torch.Generator(device='''cuda'''      ).manual_seed(0      )
					_UpperCamelCase				:Any				    =  pipeline(
					    SCREAMING_SNAKE_CASE__				,							image_embeds=SCREAMING_SNAKE_CASE__				,							negative_image_embeds=SCREAMING_SNAKE_CASE__				,							generator=SCREAMING_SNAKE_CASE__				,							num_inference_steps=1_00				,							output_type='''np'''				,							)

					_UpperCamelCase				:Dict				    =  output.images[0]

					assert image.shape == (5_12, 5_12, 3)

					assert_mean_pixel_difference(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__      )





"""simple docstring"""






from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL

from ...utils import BaseOutput, is_invisible_watermark_available, is_torch_available, is_transformers_available






@dataclass
class     A(     lowerCamelCase__		):

		"""simple docstring"""





		A         =       42


if is_transformers_available() and is_torch_available() and is_invisible_watermark_available():
					from .pipeline_stable_diffusion_xl import StableDiffusionXLPipeline
					from .pipeline_stable_diffusion_xl_imgaimg import StableDiffusionXLImgaImgPipeline

