
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	):
				_lowercase      		=KandinskyPipeline
				_lowercase      		=[
				    '''prompt''',
				    '''image_embeds''',
				    '''negative_image_embeds''',
				]
				_lowercase      		=['''prompt''', '''negative_prompt''', '''image_embeds''', '''negative_image_embeds''']
				_lowercase      		=[
				    '''generator''',
				    '''height''',
				    '''width''',
				    '''latents''',
				    '''guidance_scale''',
				    '''negative_prompt''',
				    '''num_inference_steps''',
				    '''return_dict''',
				    '''guidance_scale''',
				    '''num_images_per_prompt''',
				    '''output_type''',
				    '''return_dict''',
				]
				_lowercase      		=False




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




				@property
				def 					__a						( self					)    ->				Optional[int]:
							return 32




				@property
				def 					__a						( self					)    ->				str:
							return self.time_input_dim




				@property
				def 					__a						( self					)    ->				Union[str, Any]:
							return self.time_input_dim * 4




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




				@property
				def 					__a						( self					)    ->				str:
							lowerCAmelCase_      =     XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base"					)
							return tokenizer




				@property
				def 					__a						( self					)    ->				Optional[int]:
							torch.manual_seed(0					)
							lowerCAmelCase_      =     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    ,    )

							lowerCAmelCase_      =     MultilingualCLIP(_UpperCamelCase					)
							lowerCAmelCase_      =     text_encoder.eval()

							return text_encoder




				@property
				def 					__a						( self					)    ->				List[str]:
							torch.manual_seed(0					)

							lowerCAmelCase_      =     {
							    "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,
							}

							lowerCAmelCase_      =     UNetaDConditionModel(**_UpperCamelCase					)
							return model




				@property
				def 					__a						( self					)    ->				List[str]:
							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 					__a						( self					)    ->				Optional[int]:
							torch.manual_seed(0					)
							lowerCAmelCase_      =     VQModel(**self.dummy_movq_kwargs					)
							return model




				def 					__a						( self					)    ->				Optional[int]:
							lowerCAmelCase_      =     self.dummy_text_encoder
							lowerCAmelCase_      =     self.dummy_tokenizer
							lowerCAmelCase_      =     self.dummy_unet
							lowerCAmelCase_      =     self.dummy_movq

							lowerCAmelCase_      =     DDIMScheduler(
							    num_train_timesteps=1_000    ,    beta_schedule="linear"    ,    beta_start=0.00085    ,    beta_end=0.012    ,    clip_sample=_UpperCamelCase    ,    set_alpha_to_one=_UpperCamelCase    ,    steps_offset=1    ,    prediction_type="epsilon"    ,    thresholding=_UpperCamelCase    ,    )

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




				def 					__a						( self    ,    _UpperCamelCase    ,    _UpperCamelCase=0					)    ->				Optional[Any]:
							lowerCAmelCase_      =     floats_tensor((1, self.cross_attention_dim)    ,    rng=random.Random(_UpperCamelCase					)					).to(_UpperCamelCase					)
							lowerCAmelCase_      =     floats_tensor((1, self.cross_attention_dim)    ,    rng=random.Random(seed + 1					)					).to(_UpperCamelCase					)
							if str(_UpperCamelCase					).startswith("mps"					):
										lowerCAmelCase_      =     torch.manual_seed(_UpperCamelCase					)
							else:
										lowerCAmelCase_      =     torch.Generator(device=_UpperCamelCase					).manual_seed(_UpperCamelCase					)
							lowerCAmelCase_      =     {
							    "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 					__a						( self					)    ->				Any:
							lowerCAmelCase_      =     "cpu"

							lowerCAmelCase_      =     self.get_dummy_components()

							lowerCAmelCase_      =     self.pipeline_class(**_UpperCamelCase					)
							lowerCAmelCase_      =     pipe.to(_UpperCamelCase					)

							pipe.set_progress_bar_config(disable=_UpperCamelCase					)

							lowerCAmelCase_      =     pipe(**self.get_dummy_inputs(_UpperCamelCase					)					)
							lowerCAmelCase_      =     output.images

							lowerCAmelCase_      =     pipe(
							    **self.get_dummy_inputs(_UpperCamelCase					)    ,    return_dict=_UpperCamelCase    ,    )[0]

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

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

							lowerCAmelCase_      =     np.array(
							    [0.328663, 1.0, 0.23216873, 1.0, 0.92717564, 0.4639046, 0.96894777, 0.31713378, 0.6293953]					)

							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 					__a						( self					)    ->				Optional[int]:
							# clean up the VRAM after each test
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()




				def 					__a						( self					)    ->				int:
							lowerCAmelCase_      =     load_numpy(
							    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
							    "/kandinsky/kandinsky_text2img_cat_fp16.npy"					)

							lowerCAmelCase_      =     KandinskyPriorPipeline.from_pretrained(
							    "kandinsky-community/kandinsky-2-1-prior"    ,    torch_dtype=torch.floataa					)
							pipe_prior.to(_UpperCamelCase					)

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

							lowerCAmelCase_      =     "red cat, 4k photo"

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

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

							lowerCAmelCase_      =     output.images[0]

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

							assert_mean_pixel_difference(_UpperCamelCase    ,    _UpperCamelCase					)





import math





class      _lowerCAmelCase :




				def __init__( self    ,    _UpperCamelCase					)    ->				Dict:
							lowerCAmelCase_      =     len(_UpperCamelCase					)
							lowerCAmelCase_      =     [0] * (
							    4 * self.N
							)  # approximate the overall size of segment tree with array N
							self.build(1    ,    0    ,    self.N - 1					)




				def 					__a						( self    ,    _UpperCamelCase					)    ->				List[str]:
							return idx * 2




				def 					__a						( self    ,    _UpperCamelCase					)    ->				Tuple:
							return idx * 2 + 1




				def 					__a						( self    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase					)    ->				int:  # noqa: E741
							if l == r:
										lowerCAmelCase_      =     A[l]
							else:
										lowerCAmelCase_      =     (l + r) // 2
										self.build(self.left(_UpperCamelCase					)    ,    _UpperCamelCase    ,    _UpperCamelCase					)
										self.build(self.right(_UpperCamelCase					)    ,    mid + 1    ,    _UpperCamelCase					)
										lowerCAmelCase_      =     max(self.st[self.left(_UpperCamelCase					)]    ,    self.st[self.right(_UpperCamelCase					)]					)




				def 					__a						( self    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase					)    ->				int:
							return self.update_recursive(1    ,    0    ,    self.N - 1    ,    a - 1    ,    b - 1    ,    _UpperCamelCase					)




				def 					__a						( self    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase					)    ->				str:  # noqa: E741
							if r < a or l > b:
										return True
							if l == r:
										lowerCAmelCase_      =     val
										return True
							lowerCAmelCase_      =     (l + r) // 2
							self.update_recursive(self.left(_UpperCamelCase					)    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase					)
							self.update_recursive(self.right(_UpperCamelCase					)    ,    mid + 1    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase					)
							lowerCAmelCase_      =     max(self.st[self.left(_UpperCamelCase					)]    ,    self.st[self.right(_UpperCamelCase					)]					)
							return True




				def 					__a						( self    ,    _UpperCamelCase    ,    _UpperCamelCase					)    ->				Optional[Any]:
							return self.query_recursive(1    ,    0    ,    self.N - 1    ,    a - 1    ,    b - 1					)




				def 					__a						( self    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase					)    ->				Any:  # noqa: E741
							if r < a or l > b:
										return -math.inf
							if l >= a and r <= b:
										return self.st[idx]
							lowerCAmelCase_      =     (l + r) // 2
							lowerCAmelCase_      =     self.query_recursive(self.left(_UpperCamelCase					)    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase					)
							lowerCAmelCase_      =     self.query_recursive(self.right(_UpperCamelCase					)    ,    mid + 1    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase					)
							return max(_UpperCamelCase    ,    _UpperCamelCase					)




				def 					__a						( self					)    ->				int:
							lowerCAmelCase_      =     []
							for i in range(1    ,    N + 1					):
										show_list += [self.query(_UpperCamelCase    ,    _UpperCamelCase					)]
							print(_UpperCamelCase					)


if __name__ == "__main__":
			_A       =					[1, 2, -4, 7, 3, -5, 6, 11, -20, 9, 14, 15, 5, 2, -8]
			_A       =					15
			_A       =					SegmentTree(A)
			print(segt.query(4, 6))
			print(segt.query(7, 11))
			print(segt.query(7, 12))
			segt.update(1, 3, 1_11)
			print(segt.query(1, 15))
			segt.update(7, 8, 2_35)
			segt.show_data()



