



import random
import unittest

import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyVaaPriorEmbaEmbPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()
class lowerCAmelCase_       (   __lowerCamelCase ,unittest.TestCase			):







	'''simple docstring'''




	A_       :     str									=		KandinskyVaaPriorEmbaEmbPipeline
	A_       :     Tuple									=		['prompt', 'image']
	A_       :     Any									=		['prompt', 'image']
	A_       :     Dict									=		[
	    'num_images_per_prompt',
	    'strength',
	    'generator',
	    'num_inference_steps',
	    'latents',
	    'negative_prompt',
	    'guidance_scale',
	    'output_type',
	    'return_dict',
	]
	A_       :     Any									=		False

	@property
	def 				_A			(	self       ):



				'''simple docstring'''


				return 32

	@property
	def 				_A			(	self       ):



				'''simple docstring'''


				return 32

	@property
	def 				_A			(	self       ):



				'''simple docstring'''


				return self.time_input_dim

	@property
	def 				_A			(	self       ):



				'''simple docstring'''


				return self.time_input_dim * 4

	@property
	def 				_A			(	self       ):



				'''simple docstring'''


				return 100

	@property
	def 				_A			(	self       ):



				'''simple docstring'''


				a__					=  CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""       )
				return tokenizer

	@property
	def 				_A			(	self       ):



				'''simple docstring'''


				torch.manual_seed(0       )
				a__					=  CLIPTextConfig(
				    bos_token_id=0   ,       eos_token_id=2   ,       hidden_size=self.text_embedder_hidden_size   ,       projection_dim=self.text_embedder_hidden_size   ,       intermediate_size=37   ,       layer_norm_eps=1e-05   ,       num_attention_heads=4   ,       num_hidden_layers=5   ,       pad_token_id=1   ,       vocab_size=1000   ,       )
				return CLIPTextModelWithProjection(__lowerCAmelCase       )

	@property
	def 				_A			(	self       ):



				'''simple docstring'''


				torch.manual_seed(0       )

				a__					=  {
				    """num_attention_heads""": 2,
				    """attention_head_dim""": 12,
				    """embedding_dim""": self.text_embedder_hidden_size,
				    """num_layers""": 1,
				}

				a__					=  PriorTransformer(**__lowerCAmelCase       )
				# clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
				a__					=  nn.Parameter(torch.ones(model.clip_std.shape       )       )
				return model

	@property
	def 				_A			(	self       ):



				'''simple docstring'''


				torch.manual_seed(0       )
				a__					=  CLIPVisionConfig(
				    hidden_size=self.text_embedder_hidden_size   ,       image_size=224   ,       projection_dim=self.text_embedder_hidden_size   ,       intermediate_size=37   ,       num_attention_heads=4   ,       num_channels=3   ,       num_hidden_layers=5   ,       patch_size=14   ,       )

				a__					=  CLIPVisionModelWithProjection(__lowerCAmelCase       )
				return model

	@property
	def 				_A			(	self       ):



				'''simple docstring'''


				a__					=  CLIPImageProcessor(
				    crop_size=224   ,       do_center_crop=__lowerCAmelCase   ,       do_normalize=__lowerCAmelCase   ,       do_resize=__lowerCAmelCase   ,       image_mean=[0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]   ,       image_std=[0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]   ,       resample=3   ,       size=224   ,       )

				return image_processor

	def 				_A			(	self       ):



				'''simple docstring'''


				a__					=  self.dummy_prior
				a__					=  self.dummy_image_encoder
				a__					=  self.dummy_text_encoder
				a__					=  self.dummy_tokenizer
				a__					=  self.dummy_image_processor

				a__					=  UnCLIPScheduler(
				    variance_type="""fixed_small_log"""   ,       prediction_type="""sample"""   ,       num_train_timesteps=1000   ,       clip_sample=__lowerCAmelCase   ,       clip_sample_range=1_0.0   ,       )

				a__					=  {
				    """prior""": prior,
				    """image_encoder""": image_encoder,
				    """text_encoder""": text_encoder,
				    """tokenizer""": tokenizer,
				    """scheduler""": scheduler,
				    """image_processor""": image_processor,
				}

				return components

	def 				_A			(	self   ,       lowerCamelCase   ,       lowerCamelCase=0       ):



				'''simple docstring'''


				if str(__lowerCAmelCase       ).startswith("""mps"""       ):
							a__					=  torch.manual_seed(__lowerCAmelCase       )
				else:
							a__					=  torch.Generator(device=__lowerCAmelCase       ).manual_seed(__lowerCAmelCase       )

				a__					=  floats_tensor((1, 3, 64, 64)   ,       rng=random.Random(__lowerCAmelCase       )       ).to(__lowerCAmelCase       )
				a__					=  image.cpu().permute(0   ,       2   ,       3   ,       1       )[0]
				a__					=  Image.fromarray(np.uinta(__lowerCAmelCase       )       ).convert("""RGB"""       ).resize((256, 256)       )

				a__					=  {
				    """prompt""": """horse""",
				    """image""": init_image,
				    """strength""": 0.5,
				    """generator""": generator,
				    """guidance_scale""": 4.0,
				    """num_inference_steps""": 2,
				    """output_type""": """np""",
				}
				return inputs

	def 				_A			(	self       ):



				'''simple docstring'''


				a__					=  """cpu"""

				a__					=  self.get_dummy_components()

				a__					=  self.pipeline_class(**__lowerCAmelCase       )
				a__					=  pipe.to(__lowerCAmelCase       )

				pipe.set_progress_bar_config(disable=__lowerCAmelCase       )

				a__					=  pipe(**self.get_dummy_inputs(__lowerCAmelCase       )       )
				a__					=  output.image_embeds

				a__					=  pipe(
				    **self.get_dummy_inputs(__lowerCAmelCase       )   ,       return_dict=__lowerCAmelCase   ,       )[0]

				a__					=  image[0, -10:]
				a__					=  image_from_tuple[0, -10:]

				assert image.shape == (1, 32)

				a__					=  np.array(
				    [
				        0.1_0_7_1_2_8_4,
				        1.3_3_3_0_2_7_1,
				        0.6_1_2_6_0_2_2_3,
				        -0.6_6_9_1_0_6_5,
				        -0.3_8_4_6_8_5_2,
				        -1.0_3_0_3_6_6_1,
				        0.2_2_7_1_6_1_1_1,
				        0.0_3_3_4_8_9_0_1,
				        0.3_0_0_4_0_6_7_5,
				        -0.2_4_8_0_5_0_2_9,
				    ]       )

				assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1e-2
				assert np.abs(image_from_tuple_slice.flatten() - expected_slice       ).max() < 1e-2

	@skip_mps
	def 				_A			(	self       ):



				'''simple docstring'''


				a__					=  torch_device == """cpu"""
				a__					=  True
				a__					=  False

				self._test_inference_batch_single_identical(
				    test_max_difference=__lowerCAmelCase   ,       relax_max_difference=__lowerCAmelCase   ,       test_mean_pixel_difference=__lowerCAmelCase   ,       )







	@skip_mps
	def 				_A			(	self       ):



				'''simple docstring'''


				a__					=  torch_device == """cpu"""
				a__					=  False

				self._test_attention_slicing_forward_pass(
				    test_max_difference=__lowerCAmelCase   ,       test_mean_pixel_difference=__lowerCAmelCase   ,       )


'''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 				lowercase    (    __lowerCamelCase			,					unittest.TestCase			):
   lowerCamelCase_            =KandinskyPipeline
   lowerCamelCase_            =[
       'prompt',
       'image_embeds',
       'negative_image_embeds',
   ]
   lowerCamelCase_            =['prompt', 'negative_prompt', 'image_embeds', 'negative_image_embeds']
   lowerCamelCase_            =[
       'generator',
       'height',
       'width',
       'latents',
       'guidance_scale',
       'negative_prompt',
       'num_inference_steps',
       'return_dict',
       'guidance_scale',
       'num_images_per_prompt',
       'output_type',
       'return_dict',
   ]
   lowerCamelCase_            =False


   @property
   def __UpperCAmelCase							( self					:						Optional[int])   ->       Union[str, Any]:
         return 32


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


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


   @property
   def __UpperCAmelCase							( self					:						List[str])   ->       str:
         return self.time_input_dim * 4


   @property
   def __UpperCAmelCase							( self					:						List[str])   ->       str:
         return 100


   @property
   def __UpperCAmelCase							( self					:						Tuple)   ->       List[str]:
         lowercase_     			=   XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base")
         return tokenizer


   @property
   def __UpperCAmelCase							( self					:						Any)   ->       Optional[int]:
         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=1005 ,		)

         lowercase_     			=   MultilingualCLIP(__lowerCAmelCase)
         lowercase_     			=   text_encoder.eval()

         return text_encoder


   @property
   def __UpperCAmelCase							( self					:						Tuple)   ->       Any:
         torch.manual_seed(0)

         lowercase_     			=   {
             "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,
         }

         lowercase_     			=   UNetaDConditionModel(**__lowerCAmelCase)
         return model


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


   def __UpperCAmelCase							( self					:						Tuple)   ->       Union[str, Any]:
         lowercase_     			=   self.dummy_text_encoder
         lowercase_     			=   self.dummy_tokenizer
         lowercase_     			=   self.dummy_unet
         lowercase_     			=   self.dummy_movq

         lowercase_     			=   DDIMScheduler(
             num_train_timesteps=1000 ,		beta_schedule="linear" ,		beta_start=0.0_0085 ,		beta_end=0.012 ,		clip_sample=__lowerCAmelCase ,		set_alpha_to_one=__lowerCAmelCase ,		steps_offset=1 ,		prediction_type="epsilon" ,		thresholding=__lowerCAmelCase ,		)

         lowercase_     			=   {
             "text_encoder": text_encoder,
             "tokenizer": tokenizer,
             "unet": unet,
             "scheduler": scheduler,
             "movq": movq,
         }
         return components


   def __UpperCAmelCase							( self					:						Union[str, Any] ,		__lowerCAmelCase					:						Optional[Any] ,		__lowerCAmelCase					:						List[str]=0)   ->       List[Any]:
         lowercase_     			=   floats_tensor((1, self.cross_attention_dim) ,		rng=random.Random(__lowerCAmelCase)).to(__lowerCAmelCase)
         lowercase_     			=   floats_tensor((1, self.cross_attention_dim) ,		rng=random.Random(seed + 1)).to(__lowerCAmelCase)
         if str(__lowerCAmelCase).startswith("mps"):
               lowercase_     			=   torch.manual_seed(__lowerCAmelCase)
         else:
               lowercase_     			=   torch.Generator(device=__lowerCAmelCase).manual_seed(__lowerCAmelCase)
         lowercase_     			=   {
             "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)   ->       Optional[int]:
         lowercase_     			=   "cpu"

         lowercase_     			=   self.get_dummy_components()

         lowercase_     			=   self.pipeline_class(**__lowerCAmelCase)
         lowercase_     			=   pipe.to(__lowerCAmelCase)

         pipe.set_progress_bar_config(disable=__lowerCAmelCase)

         lowercase_     			=   pipe(**self.get_dummy_inputs(__lowerCAmelCase))
         lowercase_     			=   output.images

         lowercase_     			=   pipe(
             **self.get_dummy_inputs(__lowerCAmelCase) ,		return_dict=__lowerCAmelCase ,		)[0]

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

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

         lowercase_     			=   np.array(
             [0.32_8663, 1.0, 0.2321_6873, 1.0, 0.9271_7564, 0.463_9046, 0.9689_4777, 0.3171_3378, 0.629_3953])

         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			):


   def __UpperCAmelCase							( self					:						str)   ->       Tuple:
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()


   def __UpperCAmelCase							( self					:						List[str])   ->       Tuple:
         lowercase_     			=   load_numpy(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
             "/kandinsky/kandinsky_text2img_cat_fp16.npy")

         lowercase_     			=   KandinskyPriorPipeline.from_pretrained(
             "kandinsky-community/kandinsky-2-1-prior" ,		torch_dtype=torch.floataa)
         pipe_prior.to(__lowerCAmelCase)

         lowercase_     			=   KandinskyPipeline.from_pretrained("kandinsky-community/kandinsky-2-1" ,		torch_dtype=torch.floataa)
         lowercase_     			=   pipeline.to(__lowerCAmelCase)
         pipeline.set_progress_bar_config(disable=__lowerCAmelCase)

         lowercase_     			=   "red cat, 4k photo"

         lowercase_     			=   torch.Generator(device="cuda").manual_seed(0)
         lowercase_			,				lowercase_     			=   pipe_prior(
             __lowerCAmelCase ,		generator=__lowerCAmelCase ,		num_inference_steps=5 ,		negative_prompt="" ,		).to_tuple()

         lowercase_     			=   torch.Generator(device="cuda").manual_seed(0)
         lowercase_     			=   pipeline(
             __lowerCAmelCase ,		image_embeds=__lowerCAmelCase ,		negative_image_embeds=__lowerCAmelCase ,		generator=__lowerCAmelCase ,		num_inference_steps=100 ,		output_type="np" ,		)

         lowercase_     			=   output.images[0]

         assert image.shape == (512, 512, 3)

         assert_mean_pixel_difference(__lowerCAmelCase ,		__lowerCAmelCase)






