'''simple docstring'''
import argparse
import collections
from pathlib import Path

import torch
from torch.serialization import default_restore_location

from transformers import BertConfig, DPRConfig, DPRContextEncoder, DPRQuestionEncoder, DPRReader


a=	collections.namedtuple(
    '''CheckpointState''', ['''model_dict''', '''optimizer_dict''', '''scheduler_dict''', '''offset''', '''epoch''', '''encoder_params''']
)







def 			_UpperCamelCase			(  _a						:						str      ):




    """simple docstring"""



    print(f"""Reading saved model from {model_file}"""      )
    __UpperCamelCase						:						Dict		     =	torch.load(_a					,  map_location=lambda _a					,  _a      : default_restore_location(_a					,  'cpu'      )      )
    return CheckpointState(**_a      )






class       __lowercase		:







     """simple docstring"""






     def __init__(		self  ,      _lowerCamelCase     ):
         __UpperCamelCase						:						str		     =	src_file







     def lowerCAmelCase  (		self     ):
         raise NotImplementedError







     @staticmethod
     def lowerCAmelCase  (		_lowerCamelCase  ,      *_lowerCamelCase  ,      **_lowerCamelCase     ):
         if comp_type.startswith('c'     ):
             return DPRContextEncoderState(*_lowerCamelCase  ,      **_lowerCamelCase     )
         if comp_type.startswith('q'     ):
             return DPRQuestionEncoderState(*_lowerCamelCase  ,      **_lowerCamelCase     )
         if comp_type.startswith('r'     ):
             return DPRReaderState(*_lowerCamelCase  ,      **_lowerCamelCase     )
         else:
             raise ValueError('Component type must be either \'ctx_encoder\', \'question_encoder\' or \'reader\'.'     )






class       __lowercase		(    _lowerCamelCase    ):







     """simple docstring"""






     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						Dict		     =	DPRContextEncoder(DPRConfig(**BertConfig.get_config_dict('bert-base-uncased'     )[0]     )     )
         print(f"""Loading DPR biencoder from {self.src_file}"""     )
         __UpperCamelCase						:						Tuple		     =	load_states_from_checkpoint(self.src_file     )
         __UpperCamelCase     ,       __UpperCamelCase						:						Any		     =	model.ctx_encoder, 'ctx_model.'
         # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
         __UpperCamelCase						:						Optional[int]		     =	{'bert_model.embeddings.position_ids': model.ctx_encoder.bert_model.embeddings.position_ids}
         for key, value in saved_state.model_dict.items():
             if key.startswith(_lowerCamelCase     ):
                 __UpperCamelCase						:						int		     =	key[len(_lowerCamelCase     ) :]
                 if not key.startswith('encode_proj.'     ):
                     __UpperCamelCase						:						Union[str, Any]		     =	'bert_model.' + key
                 __UpperCamelCase						:						List[str]		     =	value
         encoder.load_state_dict(_lowerCamelCase     )
         return model






class       __lowercase		(    _lowerCamelCase    ):







     """simple docstring"""






     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						Dict		     =	DPRQuestionEncoder(DPRConfig(**BertConfig.get_config_dict('bert-base-uncased'     )[0]     )     )
         print(f"""Loading DPR biencoder from {self.src_file}"""     )
         __UpperCamelCase						:						Optional[Any]		     =	load_states_from_checkpoint(self.src_file     )
         __UpperCamelCase     ,       __UpperCamelCase						:						Optional[Any]		     =	model.question_encoder, 'question_model.'
         # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
         __UpperCamelCase						:						Optional[int]		     =	{'bert_model.embeddings.position_ids': model.question_encoder.bert_model.embeddings.position_ids}
         for key, value in saved_state.model_dict.items():
             if key.startswith(_lowerCamelCase     ):
                 __UpperCamelCase						:						Union[str, Any]		     =	key[len(_lowerCamelCase     ) :]
                 if not key.startswith('encode_proj.'     ):
                     __UpperCamelCase						:						List[str]		     =	'bert_model.' + key
                 __UpperCamelCase						:						str		     =	value
         encoder.load_state_dict(_lowerCamelCase     )
         return model






class       __lowercase		(    _lowerCamelCase    ):







     """simple docstring"""






     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						List[Any]		     =	DPRReader(DPRConfig(**BertConfig.get_config_dict('bert-base-uncased'     )[0]     )     )
         print(f"""Loading DPR reader from {self.src_file}"""     )
         __UpperCamelCase						:						Optional[int]		     =	load_states_from_checkpoint(self.src_file     )
         # Fix changes from https://github.com/huggingface/transformers/commit/614fef1691edb806de976756d4948ecbcd0c0ca3
         __UpperCamelCase						:						Dict		     =	{
             'encoder.bert_model.embeddings.position_ids': model.span_predictor.encoder.bert_model.embeddings.position_ids
         }
         for key, value in saved_state.model_dict.items():
             if key.startswith('encoder.'     ) and not key.startswith('encoder.encode_proj'     ):
                 __UpperCamelCase						:						Tuple		     =	'encoder.bert_model.' + key[len('encoder.'     ) :]
             __UpperCamelCase						:						List[Any]		     =	value
         model.span_predictor.load_state_dict(_lowerCamelCase     )
         return model







def 			_UpperCamelCase			(  _a						:						str					,  _a						:						Path					,  _a						:						Path      ):




    """simple docstring"""



    __UpperCamelCase						:						List[str]		     =	Path(_a      )
    dest_dir.mkdir(exist_ok=_a      )

    __UpperCamelCase						:						int		     =	DPRState.from_type(_a					,  src_file=_a      )
    __UpperCamelCase						:						int		     =	dpr_state.load_dpr_model()
    model.save_pretrained(_a      )
    model.from_pretrained(_a      )  # sanity check


if __name__ == "__main__":
  a=	argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '''--type''', type=str, help='''Type of the component to convert: \'ctx_encoder\', \'question_encoder\' or \'reader\'.'''
  )
  parser.add_argument(
      '''--src''',
      type=str,
      help=(
          '''Path to the dpr checkpoint file. They can be downloaded from the official DPR repo'''
          ''' https://github.com/facebookresearch/DPR. Note that in the official repo, both encoders are stored in the'''
          ''' \'retriever\' checkpoints.'''
      ),
  )
  parser.add_argument('''--dest''', type=str, default=None, help='''Path to the output PyTorch model directory.''')
  a=	parser.parse_args()

  a=	Path(args.src)
  a=	F"""converted-{src_file.name}""" if args.dest is None else args.dest
  a=	Path(dest_dir)
  assert src_file.exists()
  assert (
      args.type is not None
  ), "Please specify the component type of the DPR model to convert: 'ctx_encoder', 'question_encoder' or 'reader'."
  convert(args.type, src_file, dest_dir)




'''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 			_UpperCamelCase			(  _a						:						nn.Module      ):




    """simple docstring"""



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







def 			_UpperCamelCase			(  _a						:						nn.Module      ):




    """simple docstring"""



    __UpperCamelCase						:						Optional[int]		     =	{}
    __UpperCamelCase						:						List[str]		     =	(
        LoRAAttnProcessor2_0 if hasattr(_a					,  'scaled_dot_product_attention'      ) else LoRAAttnProcessor
    )
    for name, module in text_encoder.named_modules():
        if name.endswith(_a      ):
            __UpperCamelCase						:						List[Any]		     =	lora_attn_processor_class(
                hidden_size=module.out_proj.out_features					,  cross_attention_dim=_a      )
    return text_lora_attn_procs







def 			_UpperCamelCase			(  _a						:						nn.Module      ):




    """simple docstring"""



    __UpperCamelCase						:						Optional[Any]		     =	create_text_encoder_lora_attn_procs(_a      )
    __UpperCamelCase						:						Any		     =	AttnProcsLayers(_a      )
    return text_encoder_lora_layers







def 			_UpperCamelCase			(  _a						:						int					,  _a						:						List[Any]=False      ):




    """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						:						List[str]		     =	(
                    torch.randn_like(layer_module.up.weight      )
                    if randn_weight
                    else torch.zeros_like(layer_module.up.weight      )
                )
                __UpperCamelCase						:						Any		     =	torch.nn.Parameter(_a      )






class       __lowercase		(    unittest.TestCase    ):







     """simple docstring"""






     def lowerCAmelCase  (		self     ):
         torch.manual_seed(0     )
         __UpperCamelCase						:						Union[str, Any]		     =	UNetaDConditionModel(
             block_out_channels=(3_2, 6_4)  ,      layers_per_block=2  ,      sample_size=3_2  ,      in_channels=4  ,      out_channels=4  ,      down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')  ,      up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')  ,      cross_attention_dim=3_2  ,      )
         __UpperCamelCase						:						int		     =	DDIMScheduler(
             beta_start=0.0_0_0_8_5  ,      beta_end=0.0_1_2  ,      beta_schedule='scaled_linear'  ,      clip_sample=_lowerCamelCase  ,      set_alpha_to_one=_lowerCamelCase  ,      steps_offset=1  ,      )
         torch.manual_seed(0     )
         __UpperCamelCase						:						Union[str, Any]		     =	AutoencoderKL(
             block_out_channels=[3_2, 6_4]  ,      in_channels=3  ,      out_channels=3  ,      down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']  ,      up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']  ,      latent_channels=4  ,      )
         __UpperCamelCase						:						str		     =	CLIPTextConfig(
             bos_token_id=0  ,      eos_token_id=2  ,      hidden_size=3_2  ,      intermediate_size=3_7  ,      layer_norm_eps=1E-0_5  ,      num_attention_heads=4  ,      num_hidden_layers=5  ,      pad_token_id=1  ,      vocab_size=1_0_0_0  ,      )
         __UpperCamelCase						:						List[str]		     =	CLIPTextModel(_lowerCamelCase     )
         __UpperCamelCase						:						int		     =	CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'     )

         __UpperCamelCase     ,       __UpperCamelCase						:						Union[str, Any]		     =	create_unet_lora_layers(_lowerCamelCase     )
         __UpperCamelCase						:						Any		     =	create_text_encoder_lora_layers(_lowerCamelCase     )

         __UpperCamelCase						:						Dict		     =	{
             'unet': unet,
             'scheduler': scheduler,
             'vae': vae,
             'text_encoder': text_encoder,
             'tokenizer': tokenizer,
             'safety_checker': None,
             'feature_extractor': None,
         }
         __UpperCamelCase						:						List[str]		     =	{
             '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     ):
         __UpperCamelCase						:						Any		     =	1
         __UpperCamelCase						:						Union[str, Any]		     =	1_0
         __UpperCamelCase						:						Dict		     =	4
         __UpperCamelCase						:						Optional[Any]		     =	(3_2, 3_2)

         __UpperCamelCase						:						Any		     =	torch.manual_seed(0     )
         __UpperCamelCase						:						Union[str, Any]		     =	floats_tensor((batch_size, num_channels) + sizes     )
         __UpperCamelCase						:						List[str]		     =	torch.randint(1  ,      _lowerCamelCase  ,      size=(batch_size, sequence_length)  ,      generator=_lowerCamelCase     )

         __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 noise, input_ids, pipeline_inputs

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







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase						:						int		     =	7_7

         __UpperCamelCase						:						Any		     =	torch.randint(2  ,      5_6  ,      size=(1, max_seq_length)  ,      generator=torch.manual_seed(0     )     )

         __UpperCamelCase						:						List[str]		     =	{}
         __UpperCamelCase						:						Optional[int]		     =	inputs
         return prepared_inputs







     def lowerCAmelCase  (		self  ,      _lowerCamelCase     ):
         __UpperCamelCase     ,       __UpperCamelCase						:						int		     =	self.get_dummy_components()
         LoraLoaderMixin.save_lora_weights(
             save_directory=_lowerCamelCase  ,      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(_lowerCamelCase  ,      'pytorch_lora_weights.bin'     )     )     )







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase     ,       __UpperCamelCase						:						List[str]		     =	self.get_dummy_components()
         __UpperCamelCase						:						List[str]		     =	StableDiffusionPipeline(**_lowerCamelCase     )
         __UpperCamelCase						:						Dict		     =	sd_pipe.to(_lowerCamelCase     )
         sd_pipe.set_progress_bar_config(disable=_lowerCamelCase     )

         __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase						:						Union[str, Any]		     =	self.get_dummy_inputs()

         __UpperCamelCase						:						Any		     =	sd_pipe(**_lowerCamelCase     ).images
         __UpperCamelCase						:						int		     =	original_images[0, -3:, -3:, -1]

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

         __UpperCamelCase						:						Optional[int]		     =	sd_pipe(**_lowerCamelCase     ).images
         __UpperCamelCase						:						Optional[int]		     =	lora_images[0, -3:, -3:, -1]

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







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase     ,       __UpperCamelCase						:						Tuple		     =	self.get_dummy_components()
         __UpperCamelCase						:						str		     =	StableDiffusionPipeline(**_lowerCamelCase     )
         __UpperCamelCase						:						Union[str, Any]		     =	sd_pipe.to(_lowerCamelCase     )
         sd_pipe.set_progress_bar_config(disable=_lowerCamelCase     )

         __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase						:						Tuple		     =	self.get_dummy_inputs()

         __UpperCamelCase						:						Optional[Any]		     =	sd_pipe(**_lowerCamelCase     ).images
         __UpperCamelCase						:						Optional[int]		     =	original_images[0, -3:, -3:, -1]

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

         __UpperCamelCase						:						Dict		     =	sd_pipe(**_lowerCamelCase     ).images
         __UpperCamelCase						:						Dict		     =	lora_images[0, -3:, -3:, -1]

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







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase     ,       __UpperCamelCase						:						List[str]		     =	self.get_dummy_components()
         __UpperCamelCase						:						List[Any]		     =	lora_components['unet_lora_attn_procs']
         __UpperCamelCase						:						Union[str, Any]		     =	StableDiffusionPipeline(**_lowerCamelCase     )
         __UpperCamelCase						:						int		     =	sd_pipe.to(_lowerCamelCase     )
         sd_pipe.set_progress_bar_config(disable=_lowerCamelCase     )

         __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase						:						str		     =	self.get_dummy_inputs()

         __UpperCamelCase						:						Dict		     =	sd_pipe(**_lowerCamelCase     ).images
         __UpperCamelCase						:						Dict		     =	original_images[0, -3:, -3:, -1]

         with tempfile.TemporaryDirectory() as tmpdirname:
             __UpperCamelCase						:						Union[str, Any]		     =	sd_pipe.unet
             unet.set_attn_processor(_lowerCamelCase     )
             unet.save_attn_procs(_lowerCamelCase     )
             self.assertTrue(os.path.isfile(os.path.join(_lowerCamelCase  ,      'pytorch_lora_weights.bin'     )     )     )
             sd_pipe.load_lora_weights(_lowerCamelCase     )

         __UpperCamelCase						:						Union[str, Any]		     =	sd_pipe(**_lowerCamelCase     ).images
         __UpperCamelCase						:						Optional[Any]		     =	lora_images[0, -3:, -3:, -1]

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







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase     ,       __UpperCamelCase						:						Dict		     =	self.get_dummy_components()
         __UpperCamelCase						:						Any		     =	StableDiffusionPipeline(**_lowerCamelCase     )

         __UpperCamelCase						:						Any		     =	self.get_dummy_tokens()

         # inference without lora
         __UpperCamelCase						:						List[str]		     =	pipe.text_encoder(**_lowerCamelCase     )[0]
         assert outputs_without_lora.shape == (1, 7_7, 3_2)

         # create lora_attn_procs with zeroed out up.weights
         __UpperCamelCase						:						Any		     =	create_text_encoder_lora_attn_procs(pipe.text_encoder     )
         set_lora_up_weights(_lowerCamelCase  ,      randn_weight=_lowerCamelCase     )

         # monkey patch
         pipe._modify_text_encoder(_lowerCamelCase     )

         # 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						:						Tuple		     =	pipe.text_encoder(**_lowerCamelCase     )[0]
         assert outputs_with_lora.shape == (1, 7_7, 3_2)

         assert torch.allclose(
             _lowerCamelCase  ,      _lowerCamelCase     ), "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						:						Optional[int]		     =	create_text_encoder_lora_attn_procs(pipe.text_encoder     )
         set_lora_up_weights(_lowerCamelCase  ,      randn_weight=_lowerCamelCase     )

         # monkey patch
         pipe._modify_text_encoder(_lowerCamelCase     )

         # 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						:						Optional[int]		     =	pipe.text_encoder(**_lowerCamelCase     )[0]
         assert outputs_with_lora.shape == (1, 7_7, 3_2)

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







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase     ,       __UpperCamelCase						:						Optional[Any]		     =	self.get_dummy_components()
         __UpperCamelCase						:						Any		     =	StableDiffusionPipeline(**_lowerCamelCase     )

         __UpperCamelCase						:						List[Any]		     =	self.get_dummy_tokens()

         # inference without lora
         __UpperCamelCase						:						str		     =	pipe.text_encoder(**_lowerCamelCase     )[0]
         assert outputs_without_lora.shape == (1, 7_7, 3_2)

         # create lora_attn_procs with randn up.weights
         __UpperCamelCase						:						Any		     =	create_text_encoder_lora_attn_procs(pipe.text_encoder     )
         set_lora_up_weights(_lowerCamelCase  ,      randn_weight=_lowerCamelCase     )

         # monkey patch
         pipe._modify_text_encoder(_lowerCamelCase     )

         # 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						:						Any		     =	pipe.text_encoder(**_lowerCamelCase     )[0]
         assert outputs_with_lora.shape == (1, 7_7, 3_2)

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

         # remove monkey patch
         pipe._remove_text_encoder_monkey_patch()

         # inference with removed lora
         __UpperCamelCase						:						Dict		     =	pipe.text_encoder(**_lowerCamelCase     )[0]
         assert outputs_without_lora_removed.shape == (1, 7_7, 3_2)

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







     def lowerCAmelCase  (		self     ):
         __UpperCamelCase     ,       __UpperCamelCase						:						Optional[int]		     =	self.get_dummy_components()
         __UpperCamelCase						:						Any		     =	StableDiffusionPipeline(**_lowerCamelCase     )
         __UpperCamelCase						:						List[str]		     =	sd_pipe.to(_lowerCamelCase     )
         sd_pipe.set_progress_bar_config(disable=_lowerCamelCase     )

         __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase						:						int		     =	self.get_dummy_inputs()

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

         __UpperCamelCase						:						Optional[int]		     =	sd_pipe(**_lowerCamelCase     ).images
         __UpperCamelCase						:						List[Any]		     =	lora_images[0, -3:, -3:, -1]

         __UpperCamelCase						:						List[str]		     =	sd_pipe(**_lowerCamelCase  ,      cross_attention_kwargs={'scale': 0.5}     ).images
         __UpperCamelCase						:						Union[str, Any]		     =	lora_images_with_scale[0, -3:, -3:, -1]

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







     def lowerCAmelCase  (		self     ):
         with tempfile.TemporaryDirectory() as tmpdirname:
             self.create_lora_weight_file(_lowerCamelCase     )

             __UpperCamelCase     ,       __UpperCamelCase						:						Dict		     =	self.get_dummy_components()
             __UpperCamelCase						:						Union[str, Any]		     =	StableDiffusionPipeline(**_lowerCamelCase     )
             __UpperCamelCase						:						Tuple		     =	sd_pipe.to(_lowerCamelCase     )
             sd_pipe.set_progress_bar_config(disable=_lowerCamelCase     )

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

            # load LoRA weight file
             sd_pipe.load_lora_weights(_lowerCamelCase     )

             # check if lora attention processors are used
             for _, module in sd_pipe.unet.named_modules():
                 if isinstance(_lowerCamelCase  ,      _lowerCamelCase     ):
                     __UpperCamelCase						:						Union[str, Any]		     =	(
                         LoRAAttnProcessor2_0 if hasattr(_lowerCamelCase  ,      'scaled_dot_product_attention'     ) else LoRAAttnProcessor
                     )
                     self.assertIsInstance(module.processor  ,      _lowerCamelCase     )







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

             __UpperCamelCase     ,       __UpperCamelCase						:						Optional[Any]		     =	self.get_dummy_components()
             __UpperCamelCase						:						Dict		     =	StableDiffusionPipeline(**_lowerCamelCase     )
             __UpperCamelCase						:						str		     =	sd_pipe.to(_lowerCamelCase     )
             sd_pipe.set_progress_bar_config(disable=_lowerCamelCase     )

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

            # load LoRA weight file
             sd_pipe.load_lora_weights(_lowerCamelCase     )

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







     @unittest.skipIf(torch_device != 'cuda'  ,      'This test is supposed to run on GPU'     )
     def lowerCAmelCase  (		self     ):
         __UpperCamelCase     ,       __UpperCamelCase						:						Union[str, Any]		     =	self.get_dummy_components()
         __UpperCamelCase						:						Dict		     =	StableDiffusionPipeline(**_lowerCamelCase     )
         __UpperCamelCase						:						str		     =	sd_pipe.to(_lowerCamelCase     )
         sd_pipe.set_progress_bar_config(disable=_lowerCamelCase     )

         __UpperCamelCase     ,       __UpperCamelCase     ,       __UpperCamelCase						:						Optional[int]		     =	self.get_dummy_inputs()

         # enable XFormers
         sd_pipe.enable_xformers_memory_efficient_attention()

         __UpperCamelCase						:						Optional[Any]		     =	sd_pipe(**_lowerCamelCase     ).images
         __UpperCamelCase						:						Optional[int]		     =	original_images[0, -3:, -3:, -1]

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

         __UpperCamelCase						:						Union[str, Any]		     =	sd_pipe(**_lowerCamelCase     ).images
         __UpperCamelCase						:						List[Any]		     =	lora_images[0, -3:, -3:, -1]

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



