





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_   (     _lowercase					, 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 _lowerCAmelCase (self      :Optional[int]					)->   Union[str, Any]:
         return 32




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




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




   @property
   def _lowerCAmelCase (self      :Optional[int]					)->   Optional[Any]:
         return self.time_input_dim * 4




   @property
   def _lowerCAmelCase (self      :Optional[int]					)->   str:
         return 100




   @property
   def _lowerCAmelCase (self      :Any					)->   Optional[Any]:
         __A			  =							XLMRobertaTokenizerFast.from_pretrained('''YiYiXu/tiny-random-mclip-base'''					)
         return tokenizer




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

         __A			  =							MultilingualCLIP(__SCREAMING_SNAKE_CASE					)
         __A			  =							text_encoder.eval()

         return text_encoder




   @property
   def _lowerCAmelCase (self      :str					)->   List[Any]:
         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(**__SCREAMING_SNAKE_CASE					)
         return model




   @property
   def _lowerCAmelCase (self      :List[Any]					)->   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 _lowerCAmelCase (self      :Union[str, Any]					)->   Dict:
         torch.manual_seed(0					)
         __A			  =							VQModel(**self.dummy_movq_kwargs					)
         return model




   def _lowerCAmelCase (self      :Optional[int]					)->   Dict:
         __A			  =							self.dummy_text_encoder
         __A			  =							self.dummy_tokenizer
         __A			  =							self.dummy_unet
         __A			  =							self.dummy_movq

         __A			  =							DDIMScheduler(
             num_train_timesteps=1000       ,  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       ,  )

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




   def _lowerCAmelCase (self      :Optional[Any]       ,  _UpperCamelCase      :Any       ,  _UpperCamelCase      :Union[str, Any]=0					)->   int:
         __A			  =							floats_tensor((1, self.cross_attention_dim)       ,  rng=random.Random(__SCREAMING_SNAKE_CASE					)					).to(__SCREAMING_SNAKE_CASE					)
         __A			  =							floats_tensor((1, self.cross_attention_dim)       ,  rng=random.Random(seed + 1					)					).to(__SCREAMING_SNAKE_CASE					)
         if str(__SCREAMING_SNAKE_CASE					).startswith('''mps'''					):
               __A			  =							torch.manual_seed(__SCREAMING_SNAKE_CASE					)
         else:
               __A			  =							torch.Generator(device=__SCREAMING_SNAKE_CASE					).manual_seed(__SCREAMING_SNAKE_CASE					)
         __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 _lowerCAmelCase (self      :List[str]					)->   Optional[int]:
         __A			  =							'''cpu'''

         __A			  =							self.get_dummy_components()

         __A			  =							self.pipeline_class(**__SCREAMING_SNAKE_CASE					)
         __A			  =							pipe.to(__SCREAMING_SNAKE_CASE					)

         pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE					)

         __A			  =							pipe(**self.get_dummy_inputs(__SCREAMING_SNAKE_CASE					)					)
         __A			  =							output.images

         __A			  =							pipe(
             **self.get_dummy_inputs(__SCREAMING_SNAKE_CASE					)       ,  return_dict=__SCREAMING_SNAKE_CASE       ,  )[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 _lowerCAmelCase (self      :Dict					)->   List[str]:
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()






   def _lowerCAmelCase (self      :Union[str, Any]					)->   Union[str, Any]:
         __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(__SCREAMING_SNAKE_CASE					)

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

         __A			  =							'''red cat, 4k photo'''

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

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

         __A			  =							output.images[0]

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

         assert_mean_pixel_difference(__SCREAMING_SNAKE_CASE       ,  __SCREAMING_SNAKE_CASE					)






def  UpperCAmelCase__(    lowerCamelCase_ = 1000	):



			"""simple docstring"""





			return sum(i for i in range(lowerCamelCase_	) if i % 3 == 0 or i % 5 == 0	)


if __name__ == "__main__":
			print(F"{solution() = }")

