


'''simple docstring'''






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					(lowerCAmelCase__	:nn.Module		) ->	Tuple:




     '''simple docstring'''



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






def 						_A					(lowerCAmelCase__	:nn.Module		) ->	int:




     '''simple docstring'''



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






def 						_A					(lowerCAmelCase__	:nn.Module		) ->	str:




     '''simple docstring'''



     _a         =				create_text_encoder_lora_attn_procs(lowerCAmelCase__		)
     _a         =				AttnProcsLayers(lowerCAmelCase__		)
     return text_encoder_lora_layers






def 						_A					(lowerCAmelCase__	:Dict					,    lowerCAmelCase__	:List[str]=False		) ->	Dict:




     '''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'		):
                    _a         =				(
                        torch.randn_like(layer_module.up.weight		)
                        if randn_weight
                        else torch.zeros_like(layer_module.up.weight		)
                    )
                    _a         =				torch.nn.Parameter(lowerCAmelCase__		)






class 			a  (  unittest.TestCase							):
 def 				__UpperCAmelCase		(			self    )       ->				str:
      torch.manual_seed(0    )
      _a         =				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   ,					)
      _a         =				DDIMScheduler(
          beta_start=0.0_0_0_8_5   ,					beta_end=0.0_1_2   ,					beta_schedule='scaled_linear'   ,					clip_sample=__magic_name__   ,					set_alpha_to_one=__magic_name__   ,					steps_offset=1   ,					)
      torch.manual_seed(0    )
      _a         =				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   ,					)
      _a         =				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   ,					)
      _a         =				CLIPTextModel(__magic_name__    )
      _a         =				CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'    )

      _a   , _a         =				create_unet_lora_layers(__magic_name__    )
      _a         =				create_text_encoder_lora_layers(__magic_name__    )

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

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

      _a         =				{
          '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 				__UpperCAmelCase		(			self    )       ->				int:
      _a         =				77

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

      _a         =				{}
      _a         =				inputs
      return prepared_inputs
 def 				__UpperCAmelCase		(			self   ,					__magic_name__    )       ->				Any:
      _a   , _a         =				self.get_dummy_components()
      LoraLoaderMixin.save_lora_weights(
          save_directory=__magic_name__   ,					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(__magic_name__   ,					'pytorch_lora_weights.bin'    )    )    )
 def 				__UpperCAmelCase		(			self    )       ->				int:
      _a   , _a         =				self.get_dummy_components()
      _a         =				StableDiffusionPipeline(**__magic_name__    )
      _a         =				sd_pipe.to(__magic_name__    )
      sd_pipe.set_progress_bar_config(disable=__magic_name__    )

      _a   , _a   , _a         =				self.get_dummy_inputs()

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

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

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

      # Outputs shouldn't match.
      self.assertFalse(torch.allclose(torch.from_numpy(__magic_name__    )   ,					torch.from_numpy(__magic_name__    )    )    )
 def 				__UpperCAmelCase		(			self    )       ->				List[Any]:
      _a   , _a         =				self.get_dummy_components()
      _a         =				StableDiffusionPipeline(**__magic_name__    )
      _a         =				sd_pipe.to(__magic_name__    )
      sd_pipe.set_progress_bar_config(disable=__magic_name__    )

      _a   , _a   , _a         =				self.get_dummy_inputs()

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

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

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

      # Outputs shouldn't match.
      self.assertFalse(torch.allclose(torch.from_numpy(__magic_name__    )   ,					torch.from_numpy(__magic_name__    )    )    )
 def 				__UpperCAmelCase		(			self    )       ->				Any:
      _a   , _a         =				self.get_dummy_components()
      _a         =				lora_components['unet_lora_attn_procs']
      _a         =				StableDiffusionPipeline(**__magic_name__    )
      _a         =				sd_pipe.to(__magic_name__    )
      sd_pipe.set_progress_bar_config(disable=__magic_name__    )

      _a   , _a   , _a         =				self.get_dummy_inputs()

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

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

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

      # Outputs shouldn't match.
      self.assertFalse(torch.allclose(torch.from_numpy(__magic_name__    )   ,					torch.from_numpy(__magic_name__    )    )    )
 def 				__UpperCAmelCase		(			self    )       ->				Dict:
      _a   , _a         =				self.get_dummy_components()
      _a         =				StableDiffusionPipeline(**__magic_name__    )

      _a         =				self.get_dummy_tokens()

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

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

      # monkey patch
      pipe._modify_text_encoder(__magic_name__    )

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

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

      assert torch.allclose(
          __magic_name__   ,					__magic_name__    ), "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
      _a         =				create_text_encoder_lora_attn_procs(pipe.text_encoder    )
      set_lora_up_weights(__magic_name__   ,					randn_weight=__magic_name__    )

      # monkey patch
      pipe._modify_text_encoder(__magic_name__    )

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

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

      assert not torch.allclose(
          __magic_name__   ,					__magic_name__    ), "lora_up_weight are not zero, so the lora outputs should be different to without lora outputs"
 def 				__UpperCAmelCase		(			self    )       ->				Optional[Any]:
      _a   , _a         =				self.get_dummy_components()
      _a         =				StableDiffusionPipeline(**__magic_name__    )

      _a         =				self.get_dummy_tokens()

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

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

      # monkey patch
      pipe._modify_text_encoder(__magic_name__    )

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

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

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

      # remove monkey patch
      pipe._remove_text_encoder_monkey_patch()

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

      assert torch.allclose(
          __magic_name__   ,					__magic_name__    ), "remove lora monkey patch should restore the original outputs"
 def 				__UpperCAmelCase		(			self    )       ->				List[Any]:
      _a   , _a         =				self.get_dummy_components()
      _a         =				StableDiffusionPipeline(**__magic_name__    )
      _a         =				sd_pipe.to(__magic_name__    )
      sd_pipe.set_progress_bar_config(disable=__magic_name__    )

      _a   , _a   , _a         =				self.get_dummy_inputs()

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

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

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

      # Outputs shouldn't match.
      self.assertFalse(
          torch.allclose(torch.from_numpy(__magic_name__    )   ,					torch.from_numpy(__magic_name__    )    )    )
 def 				__UpperCAmelCase		(			self    )       ->				Union[str, Any]:
      with tempfile.TemporaryDirectory() as tmpdirname:
           self.create_lora_weight_file(__magic_name__    )

           _a   , _a         =				self.get_dummy_components()
           _a         =				StableDiffusionPipeline(**__magic_name__    )
           _a         =				sd_pipe.to(__magic_name__    )
           sd_pipe.set_progress_bar_config(disable=__magic_name__    )

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

            # load LoRA weight file
           sd_pipe.load_lora_weights(__magic_name__    )

           # check if lora attention processors are used
           for _, module in sd_pipe.unet.named_modules():
                if isinstance(__magic_name__   ,					__magic_name__    ):
                     _a         =				(
                         LoRAAttnProcessor2_0 if hasattr(__magic_name__   ,					'scaled_dot_product_attention'    ) else LoRAAttnProcessor
                     )
                     self.assertIsInstance(module.processor   ,					__magic_name__    )
 @unittest.skipIf(torch_device != 'cuda'   ,					'This test is supposed to run on GPU'    )
 def 				__UpperCAmelCase		(			self    )       ->				int:
      with tempfile.TemporaryDirectory() as tmpdirname:
           self.create_lora_weight_file(__magic_name__    )

           _a   , _a         =				self.get_dummy_components()
           _a         =				StableDiffusionPipeline(**__magic_name__    )
           _a         =				sd_pipe.to(__magic_name__    )
           sd_pipe.set_progress_bar_config(disable=__magic_name__    )

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

            # load LoRA weight file
           sd_pipe.load_lora_weights(__magic_name__    )

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




 @unittest.skipIf(torch_device != 'cuda'   ,					'This test is supposed to run on GPU'    )
 def 				__UpperCAmelCase		(			self    )       ->				Tuple:
      _a   , _a         =				self.get_dummy_components()
      _a         =				StableDiffusionPipeline(**__magic_name__    )
      _a         =				sd_pipe.to(__magic_name__    )
      sd_pipe.set_progress_bar_config(disable=__magic_name__    )

      _a   , _a   , _a         =				self.get_dummy_inputs()

      # enable XFormers
      sd_pipe.enable_xformers_memory_efficient_attention()

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

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

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

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





'''simple docstring'''






from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available




@dataclass
class 			a  (  _SCREAMING_SNAKE_CASE							):
 _lowerCAmelCase			       =						{
     """sld_warmup_steps""": 1_5,
     """sld_guidance_scale""": 2_0,
     """sld_threshold""": 0.0,
     """sld_momentum_scale""": 0.0,
     """sld_mom_beta""": 0.0,
 }
 _lowerCAmelCase			       =						{
     """sld_warmup_steps""": 1_0,
     """sld_guidance_scale""": 1_0_0_0,
     """sld_threshold""": 0.01,
     """sld_momentum_scale""": 0.3,
     """sld_mom_beta""": 0.4,
 }
 _lowerCAmelCase			       =						{
     """sld_warmup_steps""": 7,
     """sld_guidance_scale""": 2_0_0_0,
     """sld_threshold""": 0.025,
     """sld_momentum_scale""": 0.5,
     """sld_mom_beta""": 0.7,
 }
 _lowerCAmelCase			       =						{
     """sld_warmup_steps""": 0,
     """sld_guidance_scale""": 5_0_0_0,
     """sld_threshold""": 1.0,
     """sld_momentum_scale""": 0.5,
     """sld_mom_beta""": 0.7,
 }





@dataclass
class 			a  (  _SCREAMING_SNAKE_CASE							):
 _lowerCAmelCase			       =						42
 _lowerCAmelCase			       =						42
 _lowerCAmelCase			       =						42
 _lowerCAmelCase			       =						42


if is_transformers_available() and is_torch_available():
 from .pipeline_stable_diffusion_safe import StableDiffusionPipelineSafe
 from .safety_checker import SafeStableDiffusionSafetyChecker

