






"""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  __lowerCamelCase     (	_a				,						unittest.TestCase	):
   a   :					Tuple			    =KandinskyPipeline
   a   :					List[Any]			    =[
       """prompt""",
       """image_embeds""",
       """negative_image_embeds""",
   ]
   a   :					Any			    =["""prompt""", """negative_prompt""", """image_embeds""", """negative_image_embeds"""]
   a   :					Optional[Any]			    =[
       """generator""",
       """height""",
       """width""",
       """latents""",
       """guidance_scale""",
       """negative_prompt""",
       """num_inference_steps""",
       """return_dict""",
       """guidance_scale""",
       """num_images_per_prompt""",
       """output_type""",
       """return_dict""",
   ]
   a   :					List[Any]			    =False

   @property
   def     SCREAMING_SNAKE_CASE__			(       self   )		->							Dict:
       return 32

   @property
   def     SCREAMING_SNAKE_CASE__			(       self   )		->							Tuple:
       return 32

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

   @property
   def     SCREAMING_SNAKE_CASE__			(       self   )		->							str:
       return self.time_input_dim * 4

   @property
   def     SCREAMING_SNAKE_CASE__			(       self   )		->							Any:
       return 100

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

   @property
   def     SCREAMING_SNAKE_CASE__			(       self   )		->							Union[str, Any]:
       torch.manual_seed(0   )
       UpperCamelCase__								=     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			,					)

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

       return text_encoder

   @property
   def     SCREAMING_SNAKE_CASE__			(       self   )		->							str:
       torch.manual_seed(0   )

       UpperCamelCase__								=     {
           '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__								=     UNetaDConditionModel(**snake_case_   )
       return model

   @property
   def     SCREAMING_SNAKE_CASE__			(       self   )		->							Dict:
       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     SCREAMING_SNAKE_CASE__			(       self   )		->							List[str]:
       torch.manual_seed(0   )
       UpperCamelCase__								=     VQModel(**self.dummy_movq_kwargs   )
       return model

   def     SCREAMING_SNAKE_CASE__			(       self   )		->							Dict:
       UpperCamelCase__								=     self.dummy_text_encoder
       UpperCamelCase__								=     self.dummy_tokenizer
       UpperCamelCase__								=     self.dummy_unet
       UpperCamelCase__								=     self.dummy_movq

       UpperCamelCase__								=     DDIMScheduler(
           num_train_timesteps=1000			,					beta_schedule='linear'			,					beta_start=0.00_085			,					beta_end=0.012			,					clip_sample=snake_case_			,					set_alpha_to_one=snake_case_			,					steps_offset=1			,					prediction_type='epsilon'			,					thresholding=snake_case_			,					)

       UpperCamelCase__								=     {
           'text_encoder': text_encoder,
           'tokenizer': tokenizer,
           'unet': unet,
           'scheduler': scheduler,
           'movq': movq,
       }
       return components

   def     SCREAMING_SNAKE_CASE__			(       self			,					snake_case_			,					snake_case_=0   )		->							List[str]:
       UpperCamelCase__								=     floats_tensor((1, self.cross_attention_dim)			,					rng=random.Random(snake_case_   )   ).to(snake_case_   )
       UpperCamelCase__								=     floats_tensor((1, self.cross_attention_dim)			,					rng=random.Random(seed + 1   )   ).to(snake_case_   )
       if str(snake_case_   ).startswith('mps'   ):
           UpperCamelCase__								=     torch.manual_seed(snake_case_   )
       else:
           UpperCamelCase__								=     torch.Generator(device=snake_case_   ).manual_seed(snake_case_   )
       UpperCamelCase__								=     {
           '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     SCREAMING_SNAKE_CASE__			(       self   )		->							int:
       UpperCamelCase__								=     'cpu'

       UpperCamelCase__								=     self.get_dummy_components()

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

       pipe.set_progress_bar_config(disable=snake_case_   )

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

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

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

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

       UpperCamelCase__								=     np.array(
           [0.328_663, 1.0, 0.23_216_873, 1.0, 0.92_717_564, 0.4_639_046, 0.96_894_777, 0.31_713_378, 0.6_293_953]   )

       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  __lowerCamelCase     (	unittest.TestCase	):

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







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

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

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

       UpperCamelCase__								=     'red cat, 4k photo'

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

       UpperCamelCase__								=     torch.Generator(device='cuda'   ).manual_seed(0   )
       UpperCamelCase__								=     pipeline(
           snake_case_			,					image_embeds=snake_case_			,					negative_image_embeds=snake_case_			,					generator=snake_case_			,					num_inference_steps=100			,					output_type='np'			,					)

       UpperCamelCase__								=     output.images[0]

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

       assert_mean_pixel_difference(snake_case_			,					snake_case_   )












"""simple docstring"""





def 	lowerCAmelCase_(			SCREAMING_SNAKE_CASE       )       ->						int:
    """simple docstring"""

    if not isinstance(SCREAMING_SNAKE_CASE			,						SCREAMING_SNAKE_CASE       ):
        raise ValueError('double_factorial() only accepts integral values'       )
    if num < 0:
        raise ValueError('double_factorial() not defined for negative values'       )
    UpperCamelCase__								=     1
    for i in range(SCREAMING_SNAKE_CASE			,						0			,						-2       ):
        value *= i
    return value


if __name__ == "__main__":
  import doctest

  doctest.testmod()




