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

def        SCREAMING_SNAKE_CASE_					(						snake_case__							)					->     Union[str, Any]:
							lowerCAmelCase     		=		{}
							lowerCAmelCase     		=		(
							    LoRAAttnProcessor2_0 if hasattr(snake_case__       ,  '''scaled_dot_product_attention'''							) else LoRAAttnProcessor
							)
							for name, module in text_encoder.named_modules():
														if name.endswith(snake_case__							):
																					lowerCAmelCase     		=		lora_attn_processor_class(
																					    hidden_size=module.out_proj.out_features       ,  cross_attention_dim=snake_case__							)
							return text_lora_attn_procs

def        SCREAMING_SNAKE_CASE_					(						snake_case__							)					->     Tuple:
							lowerCAmelCase     		=		create_text_encoder_lora_attn_procs(snake_case__							)
							lowerCAmelCase     		=		AttnProcsLayers(snake_case__							)
							return text_encoder_lora_layers

def        SCREAMING_SNAKE_CASE_					(						snake_case__       ,  snake_case__=False							)					->     str:
							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'''							):
																												lowerCAmelCase     		=		(
																												    torch.randn_like(layer_module.up.weight							)
																												    if randn_weight
																												    else torch.zeros_like(layer_module.up.weight							)
																												)
																												lowerCAmelCase     		=		torch.nn.Parameter(snake_case__							)






class        lowercase_  (  unittest.TestCase						):



	"""simple docstring"""





	def       SCREAMING_SNAKE_CASE_    (							self    )						->List[str]:
								torch.manual_seed(0    )
								lowerCAmelCase     		=		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		,      )
								lowerCAmelCase     		=		DDIMScheduler(
								    beta_start=0.0_0_0_8_5		,      beta_end=0.0_1_2		,      beta_schedule='''scaled_linear'''		,      clip_sample=__SCREAMING_SNAKE_CASE		,      set_alpha_to_one=__SCREAMING_SNAKE_CASE		,      steps_offset=1		,      )
								torch.manual_seed(0    )
								lowerCAmelCase     		=		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		,      )
								lowerCAmelCase     		=		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=1000		,      )
								lowerCAmelCase     		=		CLIPTextModel(__SCREAMING_SNAKE_CASE    )
								lowerCAmelCase     		=		CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''    )

								lowerCAmelCase		,				lowerCAmelCase     		=		create_unet_lora_layers(__SCREAMING_SNAKE_CASE    )
								lowerCAmelCase     		=		create_text_encoder_lora_layers(__SCREAMING_SNAKE_CASE    )

								lowerCAmelCase     		=		{
								    '''unet''': unet,
								    '''scheduler''': scheduler,
								    '''vae''': vae,
								    '''text_encoder''': text_encoder,
								    '''tokenizer''': tokenizer,
								    '''safety_checker''': None,
								    '''feature_extractor''': None,
								}
								lowerCAmelCase     		=		{
								    '''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       SCREAMING_SNAKE_CASE_    (							self    )						->List[str]:
								lowerCAmelCase     		=		1
								lowerCAmelCase     		=		10
								lowerCAmelCase     		=		4
								lowerCAmelCase     		=		(32, 32)

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

								lowerCAmelCase     		=		{
								    '''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       SCREAMING_SNAKE_CASE_    (							self    )						->Tuple:
								lowerCAmelCase     		=		77

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

								lowerCAmelCase     		=		{}
								lowerCAmelCase     		=		inputs
								return prepared_inputs


	def       SCREAMING_SNAKE_CASE_    (							self		,      __SCREAMING_SNAKE_CASE    )						->Tuple:
								lowerCAmelCase		,				lowerCAmelCase     		=		self.get_dummy_components()
								LoraLoaderMixin.save_lora_weights(
								    save_directory=__SCREAMING_SNAKE_CASE		,      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(__SCREAMING_SNAKE_CASE		,      '''pytorch_lora_weights.bin'''    )    )    )


	def       SCREAMING_SNAKE_CASE_    (							self    )						->Optional[Any]:
								lowerCAmelCase		,				lowerCAmelCase     		=		self.get_dummy_components()
								lowerCAmelCase     		=		StableDiffusionPipeline(**__SCREAMING_SNAKE_CASE    )
								lowerCAmelCase     		=		sd_pipe.to(__SCREAMING_SNAKE_CASE    )
								sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE    )

								lowerCAmelCase		,				lowerCAmelCase		,				lowerCAmelCase     		=		self.get_dummy_inputs()

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

								with tempfile.TemporaryDirectory() as tmpdirname:
															LoraLoaderMixin.save_lora_weights(
															    save_directory=__SCREAMING_SNAKE_CASE		,      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(__SCREAMING_SNAKE_CASE		,      '''pytorch_lora_weights.bin'''    )    )    )
															sd_pipe.load_lora_weights(__SCREAMING_SNAKE_CASE    )

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

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


	def       SCREAMING_SNAKE_CASE_    (							self    )						->List[str]:
								lowerCAmelCase		,				lowerCAmelCase     		=		self.get_dummy_components()
								lowerCAmelCase     		=		StableDiffusionPipeline(**__SCREAMING_SNAKE_CASE    )
								lowerCAmelCase     		=		sd_pipe.to(__SCREAMING_SNAKE_CASE    )
								sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE    )

								lowerCAmelCase		,				lowerCAmelCase		,				lowerCAmelCase     		=		self.get_dummy_inputs()

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

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

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

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


	def       SCREAMING_SNAKE_CASE_    (							self    )						->Dict:
								lowerCAmelCase		,				lowerCAmelCase     		=		self.get_dummy_components()
								lowerCAmelCase     		=		lora_components['''unet_lora_attn_procs''']
								lowerCAmelCase     		=		StableDiffusionPipeline(**__SCREAMING_SNAKE_CASE    )
								lowerCAmelCase     		=		sd_pipe.to(__SCREAMING_SNAKE_CASE    )
								sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE    )

								lowerCAmelCase		,				lowerCAmelCase		,				lowerCAmelCase     		=		self.get_dummy_inputs()

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

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

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

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


	def       SCREAMING_SNAKE_CASE_    (							self    )						->Union[str, Any]:
								lowerCAmelCase		,				lowerCAmelCase     		=		self.get_dummy_components()
								lowerCAmelCase     		=		StableDiffusionPipeline(**__SCREAMING_SNAKE_CASE    )

								lowerCAmelCase     		=		self.get_dummy_tokens()

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

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

								# monkey patch
								pipe._modify_text_encoder(__SCREAMING_SNAKE_CASE    )

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

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

								assert torch.allclose(
								    __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE    ), "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
								lowerCAmelCase     		=		create_text_encoder_lora_attn_procs(pipe.text_encoder    )
								set_lora_up_weights(__SCREAMING_SNAKE_CASE		,      randn_weight=__SCREAMING_SNAKE_CASE    )

								# monkey patch
								pipe._modify_text_encoder(__SCREAMING_SNAKE_CASE    )

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

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

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


	def       SCREAMING_SNAKE_CASE_    (							self    )						->str:
								lowerCAmelCase		,				lowerCAmelCase     		=		self.get_dummy_components()
								lowerCAmelCase     		=		StableDiffusionPipeline(**__SCREAMING_SNAKE_CASE    )

								lowerCAmelCase     		=		self.get_dummy_tokens()

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

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

								# monkey patch
								pipe._modify_text_encoder(__SCREAMING_SNAKE_CASE    )

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

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

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

								# remove monkey patch
								pipe._remove_text_encoder_monkey_patch()

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

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


	def       SCREAMING_SNAKE_CASE_    (							self    )						->int:
								lowerCAmelCase		,				lowerCAmelCase     		=		self.get_dummy_components()
								lowerCAmelCase     		=		StableDiffusionPipeline(**__SCREAMING_SNAKE_CASE    )
								lowerCAmelCase     		=		sd_pipe.to(__SCREAMING_SNAKE_CASE    )
								sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE    )

								lowerCAmelCase		,				lowerCAmelCase		,				lowerCAmelCase     		=		self.get_dummy_inputs()

								with tempfile.TemporaryDirectory() as tmpdirname:
															LoraLoaderMixin.save_lora_weights(
															    save_directory=__SCREAMING_SNAKE_CASE		,      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(__SCREAMING_SNAKE_CASE		,      '''pytorch_lora_weights.bin'''    )    )    )
															sd_pipe.load_lora_weights(__SCREAMING_SNAKE_CASE    )

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

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

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


	def       SCREAMING_SNAKE_CASE_    (							self    )						->Union[str, Any]:
								with tempfile.TemporaryDirectory() as tmpdirname:
															self.create_lora_weight_file(__SCREAMING_SNAKE_CASE    )

															lowerCAmelCase		,				lowerCAmelCase     		=		self.get_dummy_components()
															lowerCAmelCase     		=		StableDiffusionPipeline(**__SCREAMING_SNAKE_CASE    )
															lowerCAmelCase     		=		sd_pipe.to(__SCREAMING_SNAKE_CASE    )
															sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE    )

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

            # load LoRA weight file
															sd_pipe.load_lora_weights(__SCREAMING_SNAKE_CASE    )

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


	@unittest.skipIf(torch_device != '''cuda'''		,      '''This test is supposed to run on GPU'''    )
	def       SCREAMING_SNAKE_CASE_    (							self    )						->Any:
								with tempfile.TemporaryDirectory() as tmpdirname:
															self.create_lora_weight_file(__SCREAMING_SNAKE_CASE    )

															lowerCAmelCase		,				lowerCAmelCase     		=		self.get_dummy_components()
															lowerCAmelCase     		=		StableDiffusionPipeline(**__SCREAMING_SNAKE_CASE    )
															lowerCAmelCase     		=		sd_pipe.to(__SCREAMING_SNAKE_CASE    )
															sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE    )

															# 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(__SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE    ):
																													self.assertIsInstance(module.processor		,      __SCREAMING_SNAKE_CASE    )

            # load LoRA weight file
															sd_pipe.load_lora_weights(__SCREAMING_SNAKE_CASE    )

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




	@unittest.skipIf(torch_device != '''cuda'''		,      '''This test is supposed to run on GPU'''    )
	def       SCREAMING_SNAKE_CASE_    (							self    )						->Optional[Any]:
								lowerCAmelCase		,				lowerCAmelCase     		=		self.get_dummy_components()
								lowerCAmelCase     		=		StableDiffusionPipeline(**__SCREAMING_SNAKE_CASE    )
								lowerCAmelCase     		=		sd_pipe.to(__SCREAMING_SNAKE_CASE    )
								sd_pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE    )

								lowerCAmelCase		,				lowerCAmelCase		,				lowerCAmelCase     		=		self.get_dummy_inputs()

								# enable XFormers
								sd_pipe.enable_xformers_memory_efficient_attention()

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

								with tempfile.TemporaryDirectory() as tmpdirname:
															LoraLoaderMixin.save_lora_weights(
															    save_directory=__SCREAMING_SNAKE_CASE		,      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(__SCREAMING_SNAKE_CASE		,      '''pytorch_lora_weights.bin'''    )    )    )
															sd_pipe.load_lora_weights(__SCREAMING_SNAKE_CASE    )

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

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




import random




class        lowercase_  :



	"""simple docstring"""





	def __init__(							self		,      __SCREAMING_SNAKE_CASE		,      __SCREAMING_SNAKE_CASE    )						->None:
								lowerCAmelCase     		=		x
								lowerCAmelCase     		=		y


	def       SCREAMING_SNAKE_CASE_    (							self    )						->bool:
								return (self.x**2 + self.y**2) <= 1




	@classmethod
	def       SCREAMING_SNAKE_CASE_    (							cls    )						->Tuple:
								return cls(x=random.random()		,      y=random.random()    )

def        SCREAMING_SNAKE_CASE_					(						snake_case__							)					->     float:
							if number_of_simulations < 1:
														raise ValueError('''At least one simulation is necessary to estimate PI.'''							)

							lowerCAmelCase     		=		0
							for _ in range(snake_case__							):
														lowerCAmelCase     		=		Point.random_unit_square()

														if random_point.is_in_unit_circle():
																					number_in_unit_circle += 1

							return 4 * number_in_unit_circle / number_of_simulations


if __name__ == "__main__":
	# import doctest

	# doctest.testmod()
	from math import pi

	lowercase__      :    int          =     '''Please enter the desired number of Monte Carlo simulations: '''
	lowercase__      :    Dict          =     estimate_pi(int(input(prompt).strip()))
	print(f'An estimate of PI is {my_pi} with an error of {abs(my_pi - pi)}')



