



import gc
import os
import tempfile
import unittest

import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, StableDiffusionPipeline, UNetaDConditionModel
from diffusers.loaders import AttnProcsLayers, LoraLoaderMixin
from diffusers.models.attention_processor import (
    Attention,
    AttnProcessor,
    AttnProcessor2_0,
    LoRAAttnProcessor,
    LoRAAttnProcessor2_0,
    LoRAXFormersAttnProcessor,
    XFormersAttnProcessor,
)
from diffusers.utils import TEXT_ENCODER_ATTN_MODULE, floats_tensor, torch_device





def 				A      (	_UpperCAmelCase							:  nn.Module       )							->					Union[str, Any]:







			'''simple docstring'''
			_UpperCAmelCase									=  {}
			for name in unet.attn_processors.keys():
						_UpperCAmelCase									=  None if name.endswith('attn1.processor'       ) else unet.config.cross_attention_dim
						if name.startswith('mid_block'       ):
									_UpperCAmelCase									=  unet.config.block_out_channels[-1]
						elif name.startswith('up_blocks'       ):
									_UpperCAmelCase									=  int(name[len('up_blocks.'       )]       )
									_UpperCAmelCase									=  list(reversed(unet.config.block_out_channels       )       )[block_id]
						elif name.startswith('down_blocks'       ):
									_UpperCAmelCase									=  int(name[len('down_blocks.'       )]       )
									_UpperCAmelCase									=  unet.config.block_out_channels[block_id]
						_UpperCAmelCase									=  (
						    LoRAAttnProcessor2_0 if hasattr(_UpperCAmelCase				,  'scaled_dot_product_attention'       ) else LoRAAttnProcessor
						)
						_UpperCAmelCase									=  lora_attn_processor_class(
						    hidden_size=_UpperCAmelCase				,  cross_attention_dim=_UpperCAmelCase       )
			_UpperCAmelCase									=  AttnProcsLayers(_UpperCAmelCase       )
			return lora_attn_procs, unet_lora_layers





def 				A      (	_UpperCAmelCase							:  nn.Module       )							->					str:







			'''simple docstring'''
			_UpperCAmelCase									=  {}
			_UpperCAmelCase									=  (
			    LoRAAttnProcessor2_0 if hasattr(_UpperCAmelCase				,  'scaled_dot_product_attention'       ) else LoRAAttnProcessor
			)
			for name, module in text_encoder.named_modules():
						if name.endswith(_UpperCAmelCase       ):
									_UpperCAmelCase									=  lora_attn_processor_class(
									    hidden_size=module.out_proj.out_features				,  cross_attention_dim=_UpperCAmelCase       )
			return text_lora_attn_procs





def 				A      (	_UpperCAmelCase							:  nn.Module       )							->					List[Any]:







			'''simple docstring'''
			_UpperCAmelCase									=  create_text_encoder_lora_attn_procs(_UpperCAmelCase       )
			_UpperCAmelCase									=  AttnProcsLayers(_UpperCAmelCase       )
			return text_encoder_lora_layers





def 				A      (	_UpperCAmelCase							:  Tuple				,  _UpperCAmelCase							:  str=False       )							->					List[Any]:







			'''simple docstring'''
			for _, attn_proc in text_lora_attn_procs.items():
						# set up.weights
						for layer_name, layer_module in attn_proc.named_modules():
									if layer_name.endswith('_lora'       ):
												_UpperCAmelCase									=  (
												    torch.randn_like(layer_module.up.weight       )
												    if randn_weight
												    else torch.zeros_like(layer_module.up.weight       )
												)
												_UpperCAmelCase									=  torch.nn.Parameter(_UpperCAmelCase       )







class      __lowerCAmelCase       (     unittest.TestCase		):



			def        _lowerCamelCase			(		self  :    Dict)  ->       Optional[int]:



						"""simple docstring"""






						torch.manual_seed(0)
						_UpperCAmelCase									=  UNetaDConditionModel(
						    block_out_channels=(32, 64)					,						layers_per_block=2					,						sample_size=32					,						in_channels=4					,						out_channels=4					,						down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')					,						up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')					,						cross_attention_dim=32					,						)
						_UpperCAmelCase									=  DDIMScheduler(
						    beta_start=0.0_0_0_8_5					,						beta_end=0.0_1_2					,						beta_schedule='scaled_linear'					,						clip_sample=A					,						set_alpha_to_one=A					,						steps_offset=1					,						)
						torch.manual_seed(0)
						_UpperCAmelCase									=  AutoencoderKL(
						    block_out_channels=[32, 64]					,						in_channels=3					,						out_channels=3					,						down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']					,						up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']					,						latent_channels=4					,						)
						_UpperCAmelCase									=  CLIPTextConfig(
						    bos_token_id=0					,						eos_token_id=2					,						hidden_size=32					,						intermediate_size=37					,						layer_norm_eps=1E-05					,						num_attention_heads=4					,						num_hidden_layers=5					,						pad_token_id=1					,						vocab_size=10_00					,						)
						_UpperCAmelCase									=  CLIPTextModel(A)
						_UpperCAmelCase									=  CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip')

						_UpperCAmelCase			,	_UpperCAmelCase									=  create_unet_lora_layers(A)
						_UpperCAmelCase									=  create_text_encoder_lora_layers(A)

						_UpperCAmelCase									=  {
						    'unet': unet,
						    'scheduler': scheduler,
						    'vae': vae,
						    'text_encoder': text_encoder,
						    'tokenizer': tokenizer,
						    'safety_checker': None,
						    'feature_extractor': None,
						}
						_UpperCAmelCase									=  {
						    'unet_lora_layers': unet_lora_layers,
						    'text_encoder_lora_layers': text_encoder_lora_layers,
						    'unet_lora_attn_procs': unet_lora_attn_procs,
						}
						return pipeline_components, lora_components



			def        _lowerCamelCase			(		self  :    Dict)  ->       Union[str, Any]:



						"""simple docstring"""






						_UpperCAmelCase									=  1
						_UpperCAmelCase									=  10
						_UpperCAmelCase									=  4
						_UpperCAmelCase									=  (32, 32)

						_UpperCAmelCase									=  torch.manual_seed(0)
						_UpperCAmelCase									=  floats_tensor((batch_size, num_channels) + sizes)
						_UpperCAmelCase									=  torch.randint(1					,						A					,						size=(batch_size, sequence_length)					,						generator=A)

						_UpperCAmelCase									=  {
						    'prompt': 'A painting of a squirrel eating a burger',
						    'generator': generator,
						    'num_inference_steps': 2,
						    'guidance_scale': 6.0,
						    'output_type': 'numpy',
						}

						return noise, input_ids, pipeline_inputs

						# copied from: https://colab.research.google.com/gist/sayakpaul/df2ef6e1ae6d8c10a49d859883b10860/scratchpad.ipynb



			def        _lowerCamelCase			(		self  :    Union[str, Any])  ->       List[str]:



						"""simple docstring"""






						_UpperCAmelCase									=  77

						_UpperCAmelCase									=  torch.randint(2					,						56					,						size=(1, max_seq_length)					,						generator=torch.manual_seed(0))

						_UpperCAmelCase									=  {}
						_UpperCAmelCase									=  inputs
						return prepared_inputs



			def        _lowerCamelCase			(		self  :    Tuple					,						A  :    Union[str, Any])  ->       Optional[Any]:



						"""simple docstring"""






						_UpperCAmelCase			,	_UpperCAmelCase									=  self.get_dummy_components()
						LoraLoaderMixin.save_lora_weights(
						    save_directory=A					,						unet_lora_layers=lora_components['unet_lora_layers']					,						text_encoder_lora_layers=lora_components['text_encoder_lora_layers']					,						)
						self.assertTrue(os.path.isfile(os.path.join(A					,						'pytorch_lora_weights.bin')))



			def        _lowerCamelCase			(		self  :    List[Any])  ->       int:



						"""simple docstring"""






						_UpperCAmelCase			,	_UpperCAmelCase									=  self.get_dummy_components()
						_UpperCAmelCase									=  StableDiffusionPipeline(**A)
						_UpperCAmelCase									=  sd_pipe.to(A)
						sd_pipe.set_progress_bar_config(disable=A)

						_UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase									=  self.get_dummy_inputs()

						_UpperCAmelCase									=  sd_pipe(**A).images
						_UpperCAmelCase									=  original_images[0, -3:, -3:, -1]

						with tempfile.TemporaryDirectory() as tmpdirname:
									LoraLoaderMixin.save_lora_weights(
									    save_directory=A					,						unet_lora_layers=lora_components['unet_lora_layers']					,						text_encoder_lora_layers=lora_components['text_encoder_lora_layers']					,						)
									self.assertTrue(os.path.isfile(os.path.join(A					,						'pytorch_lora_weights.bin')))
									sd_pipe.load_lora_weights(A)

						_UpperCAmelCase									=  sd_pipe(**A).images
						_UpperCAmelCase									=  lora_images[0, -3:, -3:, -1]

						# Outputs shouldn't match.
						self.assertFalse(torch.allclose(torch.from_numpy(A)					,						torch.from_numpy(A)))



			def        _lowerCamelCase			(		self  :    Union[str, Any])  ->       Tuple:



						"""simple docstring"""






						_UpperCAmelCase			,	_UpperCAmelCase									=  self.get_dummy_components()
						_UpperCAmelCase									=  StableDiffusionPipeline(**A)
						_UpperCAmelCase									=  sd_pipe.to(A)
						sd_pipe.set_progress_bar_config(disable=A)

						_UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase									=  self.get_dummy_inputs()

						_UpperCAmelCase									=  sd_pipe(**A).images
						_UpperCAmelCase									=  original_images[0, -3:, -3:, -1]

						with tempfile.TemporaryDirectory() as tmpdirname:
									LoraLoaderMixin.save_lora_weights(
									    save_directory=A					,						unet_lora_layers=lora_components['unet_lora_layers']					,						text_encoder_lora_layers=lora_components['text_encoder_lora_layers']					,						safe_serialization=A					,						)
									self.assertTrue(os.path.isfile(os.path.join(A					,						'pytorch_lora_weights.safetensors')))
									sd_pipe.load_lora_weights(A)

						_UpperCAmelCase									=  sd_pipe(**A).images
						_UpperCAmelCase									=  lora_images[0, -3:, -3:, -1]

						# Outputs shouldn't match.
						self.assertFalse(torch.allclose(torch.from_numpy(A)					,						torch.from_numpy(A)))



			def        _lowerCamelCase			(		self  :    Tuple)  ->       str:



						"""simple docstring"""






						_UpperCAmelCase			,	_UpperCAmelCase									=  self.get_dummy_components()
						_UpperCAmelCase									=  lora_components['unet_lora_attn_procs']
						_UpperCAmelCase									=  StableDiffusionPipeline(**A)
						_UpperCAmelCase									=  sd_pipe.to(A)
						sd_pipe.set_progress_bar_config(disable=A)

						_UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase									=  self.get_dummy_inputs()

						_UpperCAmelCase									=  sd_pipe(**A).images
						_UpperCAmelCase									=  original_images[0, -3:, -3:, -1]

						with tempfile.TemporaryDirectory() as tmpdirname:
									_UpperCAmelCase									=  sd_pipe.unet
									unet.set_attn_processor(A)
									unet.save_attn_procs(A)
									self.assertTrue(os.path.isfile(os.path.join(A					,						'pytorch_lora_weights.bin')))
									sd_pipe.load_lora_weights(A)

						_UpperCAmelCase									=  sd_pipe(**A).images
						_UpperCAmelCase									=  lora_images[0, -3:, -3:, -1]

						# Outputs shouldn't match.
						self.assertFalse(torch.allclose(torch.from_numpy(A)					,						torch.from_numpy(A)))



			def        _lowerCamelCase			(		self  :    List[Any])  ->       Union[str, Any]:



						"""simple docstring"""






						_UpperCAmelCase			,	_UpperCAmelCase									=  self.get_dummy_components()
						_UpperCAmelCase									=  StableDiffusionPipeline(**A)

						_UpperCAmelCase									=  self.get_dummy_tokens()

						# inference without lora
						_UpperCAmelCase									=  pipe.text_encoder(**A)[0]
						assert outputs_without_lora.shape == (1, 77, 32)

						# create lora_attn_procs with zeroed out up.weights
						_UpperCAmelCase									=  create_text_encoder_lora_attn_procs(pipe.text_encoder)
						set_lora_up_weights(A					,						randn_weight=A)

						# monkey patch
						pipe._modify_text_encoder(A)

						# verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
						del text_attn_procs
						gc.collect()

						# inference with lora
						_UpperCAmelCase									=  pipe.text_encoder(**A)[0]
						assert outputs_with_lora.shape == (1, 77, 32)

						assert torch.allclose(
						    A					,						A), "lora_up_weight are all zero, so the lora outputs should be the same to without lora outputs"

						# create lora_attn_procs with randn up.weights
						_UpperCAmelCase									=  create_text_encoder_lora_attn_procs(pipe.text_encoder)
						set_lora_up_weights(A					,						randn_weight=A)

						# monkey patch
						pipe._modify_text_encoder(A)

						# verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
						del text_attn_procs
						gc.collect()

						# inference with lora
						_UpperCAmelCase									=  pipe.text_encoder(**A)[0]
						assert outputs_with_lora.shape == (1, 77, 32)

						assert not torch.allclose(
						    A					,						A), "lora_up_weight are not zero, so the lora outputs should be different to without lora outputs"



			def        _lowerCamelCase			(		self  :    Optional[int])  ->       Optional[int]:



						"""simple docstring"""






						_UpperCAmelCase			,	_UpperCAmelCase									=  self.get_dummy_components()
						_UpperCAmelCase									=  StableDiffusionPipeline(**A)

						_UpperCAmelCase									=  self.get_dummy_tokens()

						# inference without lora
						_UpperCAmelCase									=  pipe.text_encoder(**A)[0]
						assert outputs_without_lora.shape == (1, 77, 32)

						# create lora_attn_procs with randn up.weights
						_UpperCAmelCase									=  create_text_encoder_lora_attn_procs(pipe.text_encoder)
						set_lora_up_weights(A					,						randn_weight=A)

						# monkey patch
						pipe._modify_text_encoder(A)

						# verify that it's okay to release the text_attn_procs which holds the LoRAAttnProcessor.
						del text_attn_procs
						gc.collect()

						# inference with lora
						_UpperCAmelCase									=  pipe.text_encoder(**A)[0]
						assert outputs_with_lora.shape == (1, 77, 32)

						assert not torch.allclose(
						    A					,						A), "lora outputs should be different to without lora outputs"

						# remove monkey patch
						pipe._remove_text_encoder_monkey_patch()

						# inference with removed lora
						_UpperCAmelCase									=  pipe.text_encoder(**A)[0]
						assert outputs_without_lora_removed.shape == (1, 77, 32)

						assert torch.allclose(
						    A					,						A), "remove lora monkey patch should restore the original outputs"



			def        _lowerCamelCase			(		self  :    Optional[Any])  ->       List[Any]:



						"""simple docstring"""






						_UpperCAmelCase			,	_UpperCAmelCase									=  self.get_dummy_components()
						_UpperCAmelCase									=  StableDiffusionPipeline(**A)
						_UpperCAmelCase									=  sd_pipe.to(A)
						sd_pipe.set_progress_bar_config(disable=A)

						_UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase									=  self.get_dummy_inputs()

						with tempfile.TemporaryDirectory() as tmpdirname:
									LoraLoaderMixin.save_lora_weights(
									    save_directory=A					,						unet_lora_layers=lora_components['unet_lora_layers']					,						text_encoder_lora_layers=lora_components['text_encoder_lora_layers']					,						)
									self.assertTrue(os.path.isfile(os.path.join(A					,						'pytorch_lora_weights.bin')))
									sd_pipe.load_lora_weights(A)

						_UpperCAmelCase									=  sd_pipe(**A).images
						_UpperCAmelCase									=  lora_images[0, -3:, -3:, -1]

						_UpperCAmelCase									=  sd_pipe(**A					,						cross_attention_kwargs={'scale': 0.5}).images
						_UpperCAmelCase									=  lora_images_with_scale[0, -3:, -3:, -1]

						# Outputs shouldn't match.
						self.assertFalse(
						    torch.allclose(torch.from_numpy(A)					,						torch.from_numpy(A)))



			def        _lowerCamelCase			(		self  :    Any)  ->       Optional[int]:



						"""simple docstring"""






						with tempfile.TemporaryDirectory() as tmpdirname:
									self.create_lora_weight_file(A)

									_UpperCAmelCase			,	_UpperCAmelCase									=  self.get_dummy_components()
									_UpperCAmelCase									=  StableDiffusionPipeline(**A)
									_UpperCAmelCase									=  sd_pipe.to(A)
									sd_pipe.set_progress_bar_config(disable=A)

									# check if vanilla attention processors are used
									for _, module in sd_pipe.unet.named_modules():
												if isinstance(A					,						A):
															self.assertIsInstance(module.processor					,						(AttnProcessor, AttnProcessor2_0))

            # load LoRA weight file
									sd_pipe.load_lora_weights(A)

									# check if lora attention processors are used
									for _, module in sd_pipe.unet.named_modules():
												if isinstance(A					,						A):
															_UpperCAmelCase									=  (
															    LoRAAttnProcessor2_0 if hasattr(A					,						'scaled_dot_product_attention') else LoRAAttnProcessor
															)
															self.assertIsInstance(module.processor					,						A)



			@unittest.skipIf(torch_device != 'cuda'					,						'This test is supposed to run on GPU')
			def        _lowerCamelCase			(		self  :    Union[str, Any])  ->       Any:



						"""simple docstring"""






						with tempfile.TemporaryDirectory() as tmpdirname:
									self.create_lora_weight_file(A)

									_UpperCAmelCase			,	_UpperCAmelCase									=  self.get_dummy_components()
									_UpperCAmelCase									=  StableDiffusionPipeline(**A)
									_UpperCAmelCase									=  sd_pipe.to(A)
									sd_pipe.set_progress_bar_config(disable=A)

									# enable XFormers
									sd_pipe.enable_xformers_memory_efficient_attention()

									# check if xFormers attention processors are used
									for _, module in sd_pipe.unet.named_modules():
												if isinstance(A					,						A):
															self.assertIsInstance(module.processor					,						A)

            # load LoRA weight file
									sd_pipe.load_lora_weights(A)

									# check if lora attention processors are used
									for _, module in sd_pipe.unet.named_modules():
												if isinstance(A					,						A):
															self.assertIsInstance(module.processor					,						A)



			@unittest.skipIf(torch_device != 'cuda'					,						'This test is supposed to run on GPU')
			def        _lowerCamelCase			(		self  :    Dict)  ->       int:



						"""simple docstring"""






						_UpperCAmelCase			,	_UpperCAmelCase									=  self.get_dummy_components()
						_UpperCAmelCase									=  StableDiffusionPipeline(**A)
						_UpperCAmelCase									=  sd_pipe.to(A)
						sd_pipe.set_progress_bar_config(disable=A)

						_UpperCAmelCase			,	_UpperCAmelCase			,	_UpperCAmelCase									=  self.get_dummy_inputs()

						# enable XFormers
						sd_pipe.enable_xformers_memory_efficient_attention()

						_UpperCAmelCase									=  sd_pipe(**A).images
						_UpperCAmelCase									=  original_images[0, -3:, -3:, -1]

						with tempfile.TemporaryDirectory() as tmpdirname:
									LoraLoaderMixin.save_lora_weights(
									    save_directory=A					,						unet_lora_layers=lora_components['unet_lora_layers']					,						text_encoder_lora_layers=lora_components['text_encoder_lora_layers']					,						)
									self.assertTrue(os.path.isfile(os.path.join(A					,						'pytorch_lora_weights.bin')))
									sd_pipe.load_lora_weights(A)

						_UpperCAmelCase									=  sd_pipe(**A).images
						_UpperCAmelCase									=  lora_images[0, -3:, -3:, -1]

						# Outputs shouldn't match.
						self.assertFalse(torch.allclose(torch.from_numpy(A)					,						torch.from_numpy(A)))





UpperCAmelCase__  =			10**8





def 				A      (	_UpperCAmelCase							:  int       )							->					bool:







			'''simple docstring'''
			if n % 10 == 0:
						return False
			_UpperCAmelCase									=  str(_UpperCAmelCase       )
			return s == s[::-1]





def 				A      (	)							->					int:







			'''simple docstring'''
			_UpperCAmelCase									=  set()
			_UpperCAmelCase									=  1
			_UpperCAmelCase									=  5
			while sum_squares < LIMIT:
						_UpperCAmelCase									=  first_square + 1
						while sum_squares < LIMIT:
									if is_palindrome(_UpperCAmelCase       ):
												answer.add(_UpperCAmelCase       )
									last_square += 1
									sum_squares += last_square**2
						first_square += 1
						_UpperCAmelCase									=  first_square**2 + (first_square + 1) ** 2

			return sum(_UpperCAmelCase       )


if __name__ == "__main__":
							print(solution())
