

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 		__magic_name__ (				snake_case       ,	unittest.TestCase							):
     UpperCamelCase_     :str									=	KandinskyPipeline
     UpperCamelCase_     :Dict									=	[
         """prompt""",
         """image_embeds""",
         """negative_image_embeds""",
     ]
     UpperCamelCase_     :int									=	["""prompt""", """negative_prompt""", """image_embeds""", """negative_image_embeds"""]
     UpperCamelCase_     :str									=	[
         """generator""",
         """height""",
         """width""",
         """latents""",
         """guidance_scale""",
         """negative_prompt""",
         """num_inference_steps""",
         """return_dict""",
         """guidance_scale""",
         """num_images_per_prompt""",
         """output_type""",
         """return_dict""",
     ]
     UpperCamelCase_     :int									=	False






     @property
     def      UpperCAmelCase_							(							self     )->			str:
         return 32






     @property
     def      UpperCAmelCase_							(							self     )->			Any:
         return 32






     @property
     def      UpperCAmelCase_							(							self     )->			Tuple:
         return self.time_input_dim






     @property
     def      UpperCAmelCase_							(							self     )->			Tuple:
         return self.time_input_dim * 4






     @property
     def      UpperCAmelCase_							(							self     )->			str:
         return 100






     @property
     def      UpperCAmelCase_							(							self     )->			Tuple:
         UpperCamelCase_          =		XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base"     )
         return tokenizer






     @property
     def      UpperCAmelCase_							(							self     )->			Tuple:
         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=1_005						,		)

         UpperCamelCase_          =		MultilingualCLIP(_lowercase     )
         UpperCamelCase_          =		text_encoder.eval()

         return text_encoder






     @property
     def      UpperCAmelCase_							(							self     )->			List[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(**_lowercase     )
         return model






     @property
     def      UpperCAmelCase_							(							self     )->			Tuple:
         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     )
         UpperCamelCase_          =		VQModel(**self.dummy_movq_kwargs     )
         return model






     def      UpperCAmelCase_							(							self     )->			Union[str, Any]:
         UpperCamelCase_          =		self.dummy_text_encoder
         UpperCamelCase_          =		self.dummy_tokenizer
         UpperCamelCase_          =		self.dummy_unet
         UpperCamelCase_          =		self.dummy_movq

         UpperCamelCase_          =		DDIMScheduler(
             num_train_timesteps=1_000						,		beta_schedule="linear"						,		beta_start=0.00_085						,		beta_end=0.012						,		clip_sample=_lowercase						,		set_alpha_to_one=_lowercase						,		steps_offset=1						,		prediction_type="epsilon"						,		thresholding=_lowercase						,		)

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






     def      UpperCAmelCase_							(							self						,		_lowercase						,		_lowercase=0     )->			Dict:
         UpperCamelCase_          =		floats_tensor((1, self.cross_attention_dim)						,		rng=random.Random(_lowercase     )     ).to(_lowercase     )
         UpperCamelCase_          =		floats_tensor((1, self.cross_attention_dim)						,		rng=random.Random(seed + 1     )     ).to(_lowercase     )
         if str(_lowercase     ).startswith("mps"     ):
             UpperCamelCase_          =		torch.manual_seed(_lowercase     )
         else:
             UpperCamelCase_          =		torch.Generator(device=_lowercase     ).manual_seed(_lowercase     )
         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      UpperCAmelCase_							(							self     )->			Any:
         UpperCamelCase_          =		"cpu"

         UpperCamelCase_          =		self.get_dummy_components()

         UpperCamelCase_          =		self.pipeline_class(**_lowercase     )
         UpperCamelCase_          =		pipe.to(_lowercase     )

         pipe.set_progress_bar_config(disable=_lowercase     )

         UpperCamelCase_          =		pipe(**self.get_dummy_inputs(_lowercase     )     )
         UpperCamelCase_          =		output.images

         UpperCamelCase_          =		pipe(
             **self.get_dummy_inputs(_lowercase     )						,		return_dict=_lowercase						,		)[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 		__magic_name__ (				unittest.TestCase							):






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






     def      UpperCAmelCase_							(							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(_lowercase     )

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

         UpperCamelCase_          =		"red cat, 4k photo"

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

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

         UpperCamelCase_          =		output.images[0]

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

         assert_mean_pixel_difference(_lowercase						,		_lowercase     )



from __future__ import annotations

from queue import Queue




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		tuple[int, int]:



    """simple docstring"""






    a ^= b
    b ^= a
    a ^= b
    return a, b




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		list[list[int]]:



    """simple docstring"""






    UpperCamelCase_          =		1
    while (1 << j) < max_node:
        for i in range(1       ,		max_node + 1       ):
            UpperCamelCase_          =		parent[j - 1][parent[j - 1][i]]
        j += 1
    return parent




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )->		int:



    """simple docstring"""






    if level[u] < level[v]:
        UpperCamelCase_   ,    UpperCamelCase_          =		swap(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )
    # making depth of u same as depth of v
    for i in range(1_8       ,		-1       ,		-1       ):
        if level[u] - (1 << i) >= level[v]:
            UpperCamelCase_          =		parent[i][u]
    # at the same depth if u==v that mean lca is found
    if u == v:
        return u
    # moving both nodes upwards till lca in found
    for i in range(1_8       ,		-1       ,		-1       ):
        if parent[i][u] not in [0, parent[i][v]]:
            UpperCamelCase_   ,    UpperCamelCase_          =		parent[i][u], parent[i][v]
    # returning longest common ancestor of u,v
    return parent[0][u]




def        lowerCAmelCase(	SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_ = 1       ,		)->		tuple[list[int], list[list[int]]]:



    """simple docstring"""






    UpperCamelCase_          =		0
    UpperCamelCase_          =		Queue(maxsize=SCREAMING_SNAKE_CASE_       )
    q.put(SCREAMING_SNAKE_CASE_       )
    while q.qsize() != 0:
        UpperCamelCase_          =		q.get()
        for v in graph[u]:
            if level[v] == -1:
                UpperCamelCase_          =		level[u] + 1
                q.put(SCREAMING_SNAKE_CASE_       )
                UpperCamelCase_          =		u
    return level, parent




def        lowerCAmelCase(	)->		None:



    """simple docstring"""






    UpperCamelCase_          =		1_3
    # initializing with 0
    UpperCamelCase_          =		[[0 for _ in range(max_node + 1_0       )] for _ in range(2_0       )]
    # initializing with -1 which means every node is unvisited
    UpperCamelCase_          =		[-1 for _ in range(max_node + 1_0       )]
    UpperCamelCase_          =		{
        1: [2, 3, 4],
        2: [5],
        3: [6, 7],
        4: [8],
        5: [9, 1_0],
        6: [1_1],
        7: [],
        8: [1_2, 1_3],
        9: [],
        1_0: [],
        1_1: [],
        1_2: [],
        1_3: [],
    }
    UpperCamelCase_   ,    UpperCamelCase_          =		breadth_first_search(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       ,		1       )
    UpperCamelCase_          =		create_sparse(SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )
    print("LCA of node 1 and 3 is: "       ,		lowest_common_ancestor(1       ,		3       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )       )
    print("LCA of node 5 and 6 is: "       ,		lowest_common_ancestor(5       ,		6       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )       )
    print("LCA of node 7 and 11 is: "       ,		lowest_common_ancestor(7       ,		1_1       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )       )
    print("LCA of node 6 and 7 is: "       ,		lowest_common_ancestor(6       ,		7       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )       )
    print("LCA of node 4 and 12 is: "       ,		lowest_common_ancestor(4       ,		1_2       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )       )
    print("LCA of node 8 and 8 is: "       ,		lowest_common_ancestor(8       ,		8       ,		SCREAMING_SNAKE_CASE_       ,		SCREAMING_SNAKE_CASE_       )       )


if __name__ == "__main__":
 main()
