


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					( UpperCAmelCase__				,						unittest.TestCase							):
     '''simple docstring'''






     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 						lowerCamelCase__  (self					:		List[str]	)		->     List[Any]:




       """simple docstring"""


       return 32

     @property
     def 						lowerCamelCase__  (self					:		List[str]	)		->     List[str]:




       """simple docstring"""


       return 32

     @property
     def 						lowerCamelCase__  (self					:		str	)		->     Optional[Any]:




       """simple docstring"""


       return self.time_input_dim

     @property
     def 						lowerCamelCase__  (self					:		Any	)		->     Tuple:




       """simple docstring"""


       return self.time_input_dim * 4

     @property
     def 						lowerCamelCase__  (self					:		Any	)		->     List[str]:




       """simple docstring"""


       return 100

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




       """simple docstring"""


       lowercase__      =	XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base"""	)
       return tokenizer

     @property
     def 						lowerCamelCase__  (self					:		Any	)		->     Tuple:




       """simple docstring"""


       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(_UpperCAmelCase	)
       lowercase__      =	text_encoder.eval()

       return text_encoder

     @property
     def 						lowerCamelCase__  (self					:		int	)		->     Union[str, Any]:




       """simple docstring"""


       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(**_UpperCAmelCase	)
       return model

     @property
     def 						lowerCamelCase__  (self					:		str	)		->     List[str]:




       """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 						lowerCamelCase__  (self					:		int	)		->     Tuple:




       """simple docstring"""


       torch.manual_seed(0	)
       lowercase__      =	VQModel(**self.dummy_movq_kwargs	)
       return model

     def 						lowerCamelCase__  (self					:		Tuple	)		->     int:




       """simple docstring"""


       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=0.00_085   ,      beta_end=0.012   ,      clip_sample=_UpperCAmelCase   ,      set_alpha_to_one=_UpperCAmelCase   ,      steps_offset=1   ,      prediction_type="""epsilon"""   ,      thresholding=_UpperCAmelCase   ,      )

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

     def 						lowerCamelCase__  (self					:		List[Any]   ,      _UpperCAmelCase					:		Dict   ,      _UpperCAmelCase					:		Dict=0	)		->     Tuple:




       """simple docstring"""


       lowercase__      =	floats_tensor((1, self.cross_attention_dim)   ,      rng=random.Random(_UpperCAmelCase	)	).to(_UpperCAmelCase	)
       lowercase__      =	floats_tensor((1, self.cross_attention_dim)   ,      rng=random.Random(seed + 1	)	).to(_UpperCAmelCase	)
       if str(_UpperCAmelCase	).startswith("""mps"""	):
         lowercase__      =	torch.manual_seed(_UpperCAmelCase	)
       else:
         lowercase__      =	torch.Generator(device=_UpperCAmelCase	).manual_seed(_UpperCAmelCase	)
       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 						lowerCamelCase__  (self					:		Union[str, Any]	)		->     Tuple:




       """simple docstring"""


       lowercase__      =	"""cpu"""

       lowercase__      =	self.get_dummy_components()

       lowercase__      =	self.pipeline_class(**_UpperCAmelCase	)
       lowercase__      =	pipe.to(_UpperCAmelCase	)

       pipe.set_progress_bar_config(disable=_UpperCAmelCase	)

       lowercase__      =	pipe(**self.get_dummy_inputs(_UpperCAmelCase	)	)
       lowercase__      =	output.images

       lowercase__      =	pipe(
           **self.get_dummy_inputs(_UpperCAmelCase	)   ,      return_dict=_UpperCAmelCase   ,      )[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(
           [0.328_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 							A					( unittest.TestCase							):
     '''simple docstring'''






     def 						lowerCamelCase__  (self					:		Dict	)		->     Any:




       """simple docstring"""


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


     def 						lowerCamelCase__  (self					:		List[str]	)		->     Optional[int]:




       """simple docstring"""


       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(_UpperCAmelCase	)

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

       lowercase__      =	"""red cat, 4k photo"""

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

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

       lowercase__      =	output.images[0]

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

       assert_mean_pixel_difference(_UpperCAmelCase   ,      _UpperCAmelCase	)





from __future__ import annotations






def 				UpperCamelCase      (			__magic_name__  :							list[list[int]]					,					__magic_name__  :							list[list[int]]  )     ->						list[int]:
		"""simple docstring"""




		assert len(__magic_name__  ) == len(__magic_name__  )

		lowercase__      =	len(__magic_name__  )
		lowercase__      =	list(range(__magic_name__  )  )
		lowercase__      =	[-1] * n  # who the donor has donated to
		lowercase__      =	[-1] * n  # who the recipient has received from
		lowercase__      =	[0] * n

		while unmatched_donors:
				lowercase__      =	unmatched_donors[0]
				lowercase__      =	donor_pref[donor]
				lowercase__      =	donor_preference[num_donations[donor]]
				num_donations[donor] += 1
				lowercase__      =	recipient_pref[recipient]
				lowercase__      =	rec_record[recipient]

				if prev_donor != -1:
						if rec_preference.index(__magic_name__  ) > rec_preference.index(__magic_name__  ):
								lowercase__      =	donor
								lowercase__      =	recipient
								unmatched_donors.append(__magic_name__  )
								unmatched_donors.remove(__magic_name__  )
				else:
						lowercase__      =	donor
						lowercase__      =	recipient
						unmatched_donors.remove(__magic_name__  )
		return donor_record

