


'''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  (  _SCREAMING_SNAKE_CASE	,					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    )       ->				List[str]:
      return 32
 @property
 def 				__UpperCAmelCase		(			self    )       ->				List[str]:
      return 32
 @property
 def 				__UpperCAmelCase		(			self    )       ->				Optional[Any]:
      return self.time_input_dim
 @property
 def 				__UpperCAmelCase		(			self    )       ->				Optional[Any]:
      return self.time_input_dim * 4
 @property
 def 				__UpperCAmelCase		(			self    )       ->				Union[str, Any]:
      return 1_00
 @property
 def 				__UpperCAmelCase		(			self    )       ->				Tuple:
      _a         =				XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'    )
      return tokenizer
 @property
 def 				__UpperCAmelCase		(			self    )       ->				int:
      torch.manual_seed(0    )
      _a         =				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   ,					)

      _a         =				MultilingualCLIP(A_    )
      _a         =				text_encoder.eval()

      return text_encoder
 @property
 def 				__UpperCAmelCase		(			self    )       ->				Dict:
      torch.manual_seed(0    )

      _a         =				{
          "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,
      }

      _a         =				UNetaDConditionModel(**A_    )
      return model
 @property
 def 				__UpperCAmelCase		(			self    )       ->				List[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 				__UpperCAmelCase		(			self    )       ->				str:
      torch.manual_seed(0    )
      _a         =				VQModel(**self.dummy_movq_kwargs    )
      return model
 def 				__UpperCAmelCase		(			self    )       ->				int:
      _a         =				self.dummy_text_encoder
      _a         =				self.dummy_tokenizer
      _a         =				self.dummy_unet
      _a         =				self.dummy_movq

      _a         =				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=A_   ,					set_alpha_to_one=A_   ,					steps_offset=1   ,					prediction_type='epsilon'   ,					thresholding=A_   ,					)

      _a         =				{
          "text_encoder": text_encoder,
          "tokenizer": tokenizer,
          "unet": unet,
          "scheduler": scheduler,
          "movq": movq,
      }
      return components
 def 				__UpperCAmelCase		(			self   ,					__magic_name__   ,					__magic_name__=0    )       ->				Dict:
      _a         =				floats_tensor((1, self.cross_attention_dim)   ,					rng=random.Random(A_    )    ).to(A_    )
      _a         =				floats_tensor((1, self.cross_attention_dim)   ,					rng=random.Random(seed + 1    )    ).to(A_    )
      if str(A_    ).startswith('mps'    ):
           _a         =				torch.manual_seed(A_    )
      else:
           _a         =				torch.Generator(device=A_    ).manual_seed(A_    )
      _a         =				{
          "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    )       ->				List[str]:
      _a         =				"cpu"

      _a         =				self.get_dummy_components()

      _a         =				self.pipeline_class(**A_    )
      _a         =				pipe.to(A_    )

      pipe.set_progress_bar_config(disable=A_    )

      _a         =				pipe(**self.get_dummy_inputs(A_    )    )
      _a         =				output.images

      _a         =				pipe(
          **self.get_dummy_inputs(A_    )   ,					return_dict=A_   ,					)[0]

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

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

      _a         =				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    )       ->				Any:
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()




 def 				__UpperCAmelCase		(			self    )       ->				str:
      _a         =				load_numpy(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
          '/kandinsky/kandinsky_text2img_cat_fp16.npy'    )

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

      _a         =				KandinskyPipeline.from_pretrained('kandinsky-community/kandinsky-2-1'   ,					torch_dtype=torch.floataa    )
      _a         =				pipeline.to(A_    )
      pipeline.set_progress_bar_config(disable=A_    )

      _a         =				"red cat, 4k photo"

      _a         =				torch.Generator(device='cuda'    ).manual_seed(0    )
      _a         =				pipe_prior(
          A_   ,					generator=A_   ,					num_inference_steps=5   ,					negative_prompt=''   ,					).to_tuple()

      _a         =				torch.Generator(device='cuda'    ).manual_seed(0    )
      _a         =				pipeline(
          A_   ,					image_embeds=A_   ,					negative_image_embeds=A_   ,					generator=A_   ,					num_inference_steps=1_00   ,					output_type='np'   ,					)

      _a         =				output.images[0]

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

      assert_mean_pixel_difference(A_   ,					A_    )




import numpy as np
import PIL
import torch
import torchvision.transforms as T
import torchvision.transforms.functional as TF
from PIL import Image







def 						_snake_case		(     SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE=256					)							->   Optional[int]:

     """simple docstring"""







     _lowerCAmelCase :       Any	   =     min(img.size					)

     if s < target_image_size:
          raise ValueError(f'''min dim for image {s} < {target_image_size}'''					)

     _lowerCAmelCase :       Dict	   =     target_image_size / s
     _lowerCAmelCase :       Dict	   =     (round(r * img.size[1]					), round(r * img.size[0]					))
     _lowerCAmelCase :       Any	   =     TF.resize(SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE		,				interpolation=PIL.Image.LANCZOS					)
     _lowerCAmelCase :       str	   =     TF.center_crop(SCREAMING_SNAKE_CASE		,				output_size=2 * [target_image_size]					)
     _lowerCAmelCase :       int	   =     torch.unsqueeze(T.ToTensor()(SCREAMING_SNAKE_CASE					)		,				0					)
     return img







def 						_snake_case		(     SCREAMING_SNAKE_CASE					)							->   List[str]:

     """simple docstring"""







     _lowerCAmelCase :       List[Any]	   =     2.0 * x - 1.0
     return x







def 						_snake_case		(     SCREAMING_SNAKE_CASE		,				SCREAMING_SNAKE_CASE=True		,				SCREAMING_SNAKE_CASE="RGB"					)							->   int:

     """simple docstring"""







     _lowerCAmelCase :       Optional[int]	   =     x.detach().cpu()
     if process:
          _lowerCAmelCase :       List[Any]	   =     post_process_tensor(SCREAMING_SNAKE_CASE					)
     _lowerCAmelCase :       Optional[int]	   =     x.numpy()
     if process:
          _lowerCAmelCase :       List[Any]	   =     (255 * x).astype(np.uinta					)
     _lowerCAmelCase :       Optional[int]	   =     Image.fromarray(SCREAMING_SNAKE_CASE					)
     if not x.mode == mode:
          _lowerCAmelCase :       str	   =     x.convert(SCREAMING_SNAKE_CASE					)
     return x







def 						_snake_case		(     SCREAMING_SNAKE_CASE					)							->   Union[str, Any]:

     """simple docstring"""







     _lowerCAmelCase :       Any	   =     torch.clamp(SCREAMING_SNAKE_CASE		,				-1.0		,				1.0					)
     _lowerCAmelCase :       Any	   =     (x + 1.0) / 2.0
     _lowerCAmelCase :       List[Any]	   =     x.permute(1		,				2		,				0					)
     return x







def 						_snake_case		(     SCREAMING_SNAKE_CASE					)							->   Tuple:

     """simple docstring"""







     _lowerCAmelCase :       str	   =     post_process_tensor(x.squeeze()					)
     return x.permute(2		,				0		,				1					).unsqueeze(0					)






