import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNetaDConditionModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_numpy,
    nightly,
    require_torch_gpu,
    slow,
    torch_device,
)

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()




class  __magic_name__  (		__a      ,	unittest.TestCase    ):





   """simple docstring"""




   lowerCAmelCase      :    List[Any]  			=   LDMTextToImagePipeline
   lowerCAmelCase      :    Optional[int]  			=   TEXT_TO_IMAGE_PARAMS - {
       '''negative_prompt''',
       '''negative_prompt_embeds''',
       '''cross_attention_kwargs''',
       '''prompt_embeds''',
   }
   lowerCAmelCase      :    Any  			=   PipelineTesterMixin.required_optional_params - {
       '''num_images_per_prompt''',
       '''callback''',
       '''callback_steps''',
   }
   lowerCAmelCase      :    int  			=   TEXT_TO_IMAGE_BATCH_PARAMS
   lowerCAmelCase      :    Any  			=   False



   def   lowerCAmelCase						(	self					:  List[str]							):



    """simple docstring"""



    torch.manual_seed(0							)
    _UpperCamelCase:     List[Any]							= 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:     List[str]							= DDIMScheduler(
        beta_start=0.00085		,  beta_end=0.012		,  beta_schedule='''scaled_linear'''		,  clip_sample=_lowercase		,  set_alpha_to_one=_lowercase		,  )
    torch.manual_seed(0							)
    _UpperCamelCase:     Optional[Any]							= 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		,  )
    torch.manual_seed(0							)
    _UpperCamelCase:     str							= 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=1_000		,  )
    _UpperCamelCase:     List[Any]							= CLIPTextModel(_lowercase							)
    _UpperCamelCase:     Union[str, Any]							= CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''							)

    _UpperCamelCase:     List[Any]							= {
        '''unet''': unet,
        '''scheduler''': scheduler,
        '''vqvae''': vae,
        '''bert''': text_encoder,
        '''tokenizer''': tokenizer,
    }
    return components



   def   lowerCAmelCase						(	self					:  Dict		,  _lowercase					:  Any		,  _lowercase					:  Optional[Any]=0							):



    """simple docstring"""



    if str(_lowercase							).startswith('''mps'''							):
     _UpperCamelCase:     Tuple							= torch.manual_seed(_lowercase							)
    else:
     _UpperCamelCase:     Dict							= torch.Generator(device=_lowercase							).manual_seed(_lowercase							)
    _UpperCamelCase:     Any							= {
        '''prompt''': '''A painting of a squirrel eating a burger''',
        '''generator''': generator,
        '''num_inference_steps''': 2,
        '''guidance_scale''': 6.0,
        '''output_type''': '''numpy''',
    }
    return inputs



   def   lowerCAmelCase						(	self					:  Optional[int]							):



    """simple docstring"""



    _UpperCamelCase:     Dict							= '''cpu'''  # ensure determinism for the device-dependent torch.Generator

    _UpperCamelCase:     List[Any]							= self.get_dummy_components()
    _UpperCamelCase:     Any							= LDMTextToImagePipeline(**_lowercase							)
    pipe.to(_lowercase							)
    pipe.set_progress_bar_config(disable=_lowercase							)

    _UpperCamelCase:     Any							= self.get_dummy_inputs(_lowercase							)
    _UpperCamelCase:     Optional[int]							= pipe(**_lowercase							).images
    _UpperCamelCase:     Union[str, Any]							= image[0, -3:, -3:, -1]

    assert image.shape == (1, 16, 16, 3)
    _UpperCamelCase:     str							= np.array([0.6101, 0.6156, 0.5622, 0.4895, 0.6661, 0.3804, 0.5748, 0.6136, 0.5014]							)

    assert np.abs(image_slice.flatten() - expected_slice							).max() < 1E-3




@slow
@require_torch_gpu
class  __magic_name__  (		unittest.TestCase    ):





   """simple docstring"""




   def   lowerCAmelCase						(	self					:  List[Any]							):



    """simple docstring"""



    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()



   def   lowerCAmelCase						(	self					:  List[Any]		,  _lowercase					:  List[Any]		,  _lowercase					:  int=torch.floataa		,  _lowercase					:  Any=0							):



    """simple docstring"""



    _UpperCamelCase:     Optional[Any]							= torch.manual_seed(_lowercase							)
    _UpperCamelCase:     List[str]							= np.random.RandomState(_lowercase							).standard_normal((1, 4, 32, 32)							)
    _UpperCamelCase:     Dict							= torch.from_numpy(_lowercase							).to(device=_lowercase		,  dtype=_lowercase							)
    _UpperCamelCase:     Tuple							= {
        '''prompt''': '''A painting of a squirrel eating a burger''',
        '''latents''': latents,
        '''generator''': generator,
        '''num_inference_steps''': 3,
        '''guidance_scale''': 6.0,
        '''output_type''': '''numpy''',
    }
    return inputs



   def   lowerCAmelCase						(	self					:  Any							):



    """simple docstring"""



    _UpperCamelCase:     Union[str, Any]							= LDMTextToImagePipeline.from_pretrained('''CompVis/ldm-text2im-large-256'''							).to(_lowercase							)
    pipe.set_progress_bar_config(disable=_lowercase							)

    _UpperCamelCase:     Union[str, Any]							= self.get_inputs(_lowercase							)
    _UpperCamelCase:     Optional[int]							= pipe(**_lowercase							).images
    _UpperCamelCase:     List[Any]							= image[0, -3:, -3:, -1].flatten()

    assert image.shape == (1, 256, 256, 3)
    _UpperCamelCase:     Optional[Any]							= np.array([0.51825, 0.52850, 0.52543, 0.54258, 0.52304, 0.52569, 0.54363, 0.55276, 0.56878]							)
    _UpperCamelCase:     int							= np.abs(expected_slice - image_slice							).max()
    assert max_diff < 1E-3




@nightly
@require_torch_gpu
class  __magic_name__  (		unittest.TestCase    ):





   """simple docstring"""




   def   lowerCAmelCase						(	self					:  Optional[Any]							):



    """simple docstring"""



    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()



   def   lowerCAmelCase						(	self					:  Dict		,  _lowercase					:  List[str]		,  _lowercase					:  Dict=torch.floataa		,  _lowercase					:  Tuple=0							):



    """simple docstring"""



    _UpperCamelCase:     Optional[Any]							= torch.manual_seed(_lowercase							)
    _UpperCamelCase:     Optional[Any]							= np.random.RandomState(_lowercase							).standard_normal((1, 4, 32, 32)							)
    _UpperCamelCase:     Dict							= torch.from_numpy(_lowercase							).to(device=_lowercase		,  dtype=_lowercase							)
    _UpperCamelCase:     Dict							= {
        '''prompt''': '''A painting of a squirrel eating a burger''',
        '''latents''': latents,
        '''generator''': generator,
        '''num_inference_steps''': 50,
        '''guidance_scale''': 6.0,
        '''output_type''': '''numpy''',
    }
    return inputs



   def   lowerCAmelCase						(	self					:  Union[str, Any]							):



    """simple docstring"""



    _UpperCamelCase:     str							= LDMTextToImagePipeline.from_pretrained('''CompVis/ldm-text2im-large-256'''							).to(_lowercase							)
    pipe.set_progress_bar_config(disable=_lowercase							)

    _UpperCamelCase:     List[Any]							= self.get_inputs(_lowercase							)
    _UpperCamelCase:     Dict							= pipe(**_lowercase							).images[0]

    _UpperCamelCase:     List[str]							= load_numpy(
        '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/ldm_text2img/ldm_large_256_ddim.npy'''							)
    _UpperCamelCase:     str							= np.abs(expected_image - image							).max()
    assert max_diff < 1E-3
import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline




def       lowerCAmelCase_     (		lowercase:							Optional[int]  ,					lowercase:							Any  ,					lowercase:							str  ,					lowercase:							List[str]  ,					lowercase:							Optional[int]		)    ->				int:







 '''simple docstring'''
 # load base model
 _UpperCamelCase:     Optional[int]							= StableDiffusionPipeline.from_pretrained(lowercase  ,					torch_dtype=torch.floataa		)

 # load LoRA weight from .safetensors
 _UpperCamelCase:     List[str]							= load_file(lowercase		)

 _UpperCamelCase:     Optional[int]							= []

 # directly update weight in diffusers model
 for key in state_dict:
  # it is suggested to print out the key, it usually will be something like below
  # "lora_te_text_model_encoder_layers_0_self_attn_k_proj.lora_down.weight"

  # as we have set the alpha beforehand, so just skip
  if ".alpha" in key or key in visited:
   continue

  if "text" in key:
   _UpperCamelCase:     int							= key.split('''.'''		)[0].split(LORA_PREFIX_TEXT_ENCODER + '''_'''		)[-1].split('''_'''		)
   _UpperCamelCase:     str							= pipeline.text_encoder
  else:
   _UpperCamelCase:     Optional[Any]							= key.split('''.'''		)[0].split(LORA_PREFIX_UNET + '''_'''		)[-1].split('''_'''		)
   _UpperCamelCase:     Any							= pipeline.unet

  # find the target layer
  _UpperCamelCase:     Union[str, Any]							= layer_infos.pop(0		)
  while len(lowercase		) > -1:
   try:
    _UpperCamelCase:     Union[str, Any]							= curr_layer.__getattr__(lowercase		)
    if len(lowercase		) > 0:
     _UpperCamelCase:     Tuple							= layer_infos.pop(0		)
    elif len(lowercase		) == 0:
     break
   except Exception:
    if len(lowercase		) > 0:
     temp_name += "_" + layer_infos.pop(0		)
    else:
     _UpperCamelCase:     List[Any]							= layer_infos.pop(0		)

  _UpperCamelCase:     Any							= []
  if "lora_down" in key:
   pair_keys.append(key.replace('''lora_down'''  ,					'''lora_up'''		)		)
   pair_keys.append(lowercase		)
  else:
   pair_keys.append(lowercase		)
   pair_keys.append(key.replace('''lora_up'''  ,					'''lora_down'''		)		)

  # update weight
  if len(state_dict[pair_keys[0]].shape		) == 4:
   _UpperCamelCase:     str							= state_dict[pair_keys[0]].squeeze(3		).squeeze(2		).to(torch.floataa		)
   _UpperCamelCase:     Optional[int]							= state_dict[pair_keys[1]].squeeze(3		).squeeze(2		).to(torch.floataa		)
   curr_layer.weight.data += alpha * torch.mm(lowercase  ,					lowercase		).unsqueeze(2		).unsqueeze(3		)
  else:
   _UpperCamelCase:     str							= state_dict[pair_keys[0]].to(torch.floataa		)
   _UpperCamelCase:     Dict							= state_dict[pair_keys[1]].to(torch.floataa		)
   curr_layer.weight.data += alpha * torch.mm(lowercase  ,					lowercase		)

  # update visited list
  for item in pair_keys:
   visited.append(lowercase		)

 return pipeline


if __name__ == "__main__":
  UpperCAmelCase_			    =							argparse.ArgumentParser()

  parser.add_argument(
      '''--base_model_path''', default=None, type=str, required=True, help='''Path to the base model in diffusers format.'''
  )
  parser.add_argument(
      '''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
  )
  parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')
  parser.add_argument(
      '''--lora_prefix_unet''', default='''lora_unet''', type=str, help='''The prefix of UNet weight in safetensors'''
  )
  parser.add_argument(
      '''--lora_prefix_text_encoder''',
      default='''lora_te''',
      type=str,
      help='''The prefix of text encoder weight in safetensors''',
  )
  parser.add_argument('''--alpha''', default=0.7_5, type=float, help='''The merging ratio in W = W0 + alpha * deltaW''')
  parser.add_argument(
      '''--to_safetensors''', action='''store_true''', help='''Whether to store pipeline in safetensors format or not.'''
  )
  parser.add_argument('''--device''', type=str, help='''Device to use (e.g. cpu, cuda:0, cuda:1, etc.)''')

  UpperCAmelCase_			    =							parser.parse_args()

  UpperCAmelCase_			    =							args.base_model_path
  UpperCAmelCase_			    =							args.checkpoint_path
  UpperCAmelCase_			    =							args.dump_path
  UpperCAmelCase_			    =							args.lora_prefix_unet
  UpperCAmelCase_			    =							args.lora_prefix_text_encoder
  UpperCAmelCase_			    =							args.alpha

  UpperCAmelCase_			    =							convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)

  UpperCAmelCase_			    =							pipe.to(args.device)
  pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)