import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DeformableDetrConfig, DeformableDetrForObjectDetection, DeformableDetrImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase      :				List[str]     			=logging.get_logger(__name__)







def     A__	(       __A						):







    '''simple docstring'''
    if "backbone.0.body" in orig_key:
        _lowerCamelCase  :		Tuple			    =    orig_key.replace("""backbone.0.body""" ,			"""backbone.conv_encoder.model"""						)
    if "transformer" in orig_key:
        _lowerCamelCase  :		str			    =    orig_key.replace("""transformer.""" ,			""""""						)
    if "norm1" in orig_key:
        if "encoder" in orig_key:
            _lowerCamelCase  :		List[Any]			    =    orig_key.replace("""norm1""" ,			"""self_attn_layer_norm"""						)
        else:
            _lowerCamelCase  :		str			    =    orig_key.replace("""norm1""" ,			"""encoder_attn_layer_norm"""						)
    if "norm2" in orig_key:
        if "encoder" in orig_key:
            _lowerCamelCase  :		Union[str, Any]			    =    orig_key.replace("""norm2""" ,			"""final_layer_norm"""						)
        else:
            _lowerCamelCase  :		Optional[int]			    =    orig_key.replace("""norm2""" ,			"""self_attn_layer_norm"""						)
    if "norm3" in orig_key:
        _lowerCamelCase  :		Dict			    =    orig_key.replace("""norm3""" ,			"""final_layer_norm"""						)
    if "linear1" in orig_key:
        _lowerCamelCase  :		Tuple			    =    orig_key.replace("""linear1""" ,			"""fc1"""						)
    if "linear2" in orig_key:
        _lowerCamelCase  :		Tuple			    =    orig_key.replace("""linear2""" ,			"""fc2"""						)
    if "query_embed" in orig_key:
        _lowerCamelCase  :		str			    =    orig_key.replace("""query_embed""" ,			"""query_position_embeddings"""						)
    if "cross_attn" in orig_key:
        _lowerCamelCase  :		Tuple			    =    orig_key.replace("""cross_attn""" ,			"""encoder_attn"""						)

    return orig_key







def     A__	(       __A						):







    '''simple docstring'''
    # transformer decoder self-attention layers
    for i in range(6						):
        # read in weights + bias of input projection layer of self-attention
        _lowerCamelCase  :		str			    =    state_dict.pop(F"""decoder.layers.{i}.self_attn.in_proj_weight"""						)
        _lowerCamelCase  :		str			    =    state_dict.pop(F"""decoder.layers.{i}.self_attn.in_proj_bias"""						)
        # next, add query, keys and values (in that order) to the state dict
        _lowerCamelCase  :		Tuple			    =    in_proj_weight[:256, :]
        _lowerCamelCase  :		Optional[int]			    =    in_proj_bias[:256]
        _lowerCamelCase  :		int			    =    in_proj_weight[256:512, :]
        _lowerCamelCase  :		Optional[int]			    =    in_proj_bias[256:512]
        _lowerCamelCase  :		Any			    =    in_proj_weight[-256:, :]
        _lowerCamelCase  :		str			    =    in_proj_bias[-256:]







def     A__	(       ):







    '''simple docstring'''
    _lowerCamelCase  :		List[str]			    =    '''http://images.cocodataset.org/val2017/000000039769.jpg'''
    _lowerCamelCase  :		Dict			    =    Image.open(requests.get(_snake_case ,			stream=_snake_case						).raw						)

    return im







@torch.no_grad()
def     A__	(       __A ,			__A ,			__A ,			__A ,			__A ,			__A ,			__A ,			):







    '''simple docstring'''
    _lowerCamelCase  :		List[str]			    =    DeformableDetrConfig()
    # set config attributes
    if single_scale:
        _lowerCamelCase  :		Dict			    =    1
    _lowerCamelCase  :		Optional[int]			    =    dilation
    _lowerCamelCase  :		str			    =    with_box_refine
    _lowerCamelCase  :		str			    =    two_stage
    # set labels
    _lowerCamelCase  :		Dict			    =    91
    _lowerCamelCase  :		Any			    =    '''huggingface/label-files'''
    _lowerCamelCase  :		Any			    =    '''coco-detection-id2label.json'''
    _lowerCamelCase  :		Optional[int]			    =    json.load(open(cached_download(hf_hub_url(_snake_case ,			_snake_case ,			repo_type="""dataset"""						)						) ,			"""r"""						)						)
    _lowerCamelCase  :		Optional[Any]			    =    {int(_snake_case						): v for k, v in idalabel.items()}
    _lowerCamelCase  :		int			    =    idalabel
    _lowerCamelCase  :		str			    =    {v: k for k, v in idalabel.items()}

    # load image processor
    _lowerCamelCase  :		str			    =    DeformableDetrImageProcessor(format="""coco_detection"""						)

    # prepare image
    _lowerCamelCase  :		Tuple			    =    prepare_img()
    _lowerCamelCase  :		int			    =    image_processor(images=_snake_case ,			return_tensors="""pt"""						)
    _lowerCamelCase  :		int			    =    encoding['''pixel_values''']

    logger.info("""Converting model..."""						)

    # load original state dict
    _lowerCamelCase  :		Tuple			    =    torch.load(_snake_case ,			map_location="""cpu"""						)['''model''']
    # rename keys
    for key in state_dict.copy().keys():
        _lowerCamelCase  :		Dict			    =    state_dict.pop(_snake_case						)
        _lowerCamelCase  :		Optional[Any]			    =    val
    # query, key and value matrices need special treatment
    read_in_q_k_v(_snake_case						)
    # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
    _lowerCamelCase  :		List[str]			    =    '''model.'''
    for key in state_dict.copy().keys():
        if not key.startswith("""class_embed"""						) and not key.startswith("""bbox_embed"""						):
            _lowerCamelCase  :		Union[str, Any]			    =    state_dict.pop(_snake_case						)
            _lowerCamelCase  :		str			    =    val
    # finally, create HuggingFace model and load state dict
    _lowerCamelCase  :		Any			    =    DeformableDetrForObjectDetection(_snake_case						)
    model.load_state_dict(_snake_case						)
    model.eval()

    _lowerCamelCase  :		List[str]			    =    '''cuda''' if torch.cuda.is_available() else '''cpu'''
    model.to(_snake_case						)
    # verify our conversion
    _lowerCamelCase  :		Dict			    =    model(pixel_values.to(_snake_case						)						)

    _lowerCamelCase  :		Union[str, Any]			    =    torch.tensor(
        [[-9.6_645, -4.3_449, -5.8_705], [-9.7_035, -3.8_504, -5.0_724], [-10.5_634, -5.3_379, -7.5_116]]						)
    _lowerCamelCase  :		Dict			    =    torch.tensor([[0.8_693, 0.2_289, 0.2_492], [0.3_150, 0.5_489, 0.5_845], [0.5_563, 0.7_580, 0.8_518]]						)

    if single_scale:
        _lowerCamelCase  :		List[Any]			    =    torch.tensor(
            [[-9.9_051, -4.2_541, -6.4_852], [-9.6_947, -4.0_854, -6.8_033], [-10.0_665, -5.8_470, -7.7_003]]						)
        _lowerCamelCase  :		int			    =    torch.tensor([[0.7_292, 0.4_991, 0.5_532], [0.7_959, 0.2_426, 0.4_236], [0.7_582, 0.3_518, 0.4_451]]						)

    if single_scale and dilation:
        _lowerCamelCase  :		str			    =    torch.tensor(
            [[-8.9_652, -4.1_074, -5.6_635], [-9.0_596, -4.9_447, -6.6_075], [-10.1_178, -4.5_275, -6.2_671]]						)
        _lowerCamelCase  :		Tuple			    =    torch.tensor([[0.7_665, 0.4_130, 0.4_769], [0.8_364, 0.1_841, 0.3_391], [0.6_261, 0.3_895, 0.7_978]]						)

    if with_box_refine:
        _lowerCamelCase  :		Optional[Any]			    =    torch.tensor(
            [[-8.8_895, -5.4_187, -6.8_153], [-8.4_706, -6.1_668, -7.6_184], [-9.0_042, -5.5_359, -6.9_141]]						)
        _lowerCamelCase  :		List[str]			    =    torch.tensor([[0.7_828, 0.2_208, 0.4_323], [0.0_892, 0.5_996, 0.1_319], [0.5_524, 0.6_389, 0.8_914]]						)

    if with_box_refine and two_stage:
        _lowerCamelCase  :		Dict			    =    torch.tensor(
            [[-6.7_108, -4.3_213, -6.3_777], [-8.9_014, -6.1_799, -6.7_240], [-6.9_315, -4.4_735, -6.2_298]]						)
        _lowerCamelCase  :		Union[str, Any]			    =    torch.tensor([[0.2_583, 0.5_499, 0.4_683], [0.7_652, 0.9_068, 0.4_882], [0.5_490, 0.2_763, 0.0_564]]						)

    print("""Logits:""" ,			outputs.logits[0, :3, :3]						)

    assert torch.allclose(outputs.logits[0, :3, :3] ,			expected_logits.to(_snake_case						) ,			atol=1E-4						)
    assert torch.allclose(outputs.pred_boxes[0, :3, :3] ,			expected_boxes.to(_snake_case						) ,			atol=1E-4						)

    print("""Everything ok!"""						)

    # Save model and image processor
    logger.info(F"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."""						)
    Path(_snake_case						).mkdir(exist_ok=_snake_case						)
    model.save_pretrained(_snake_case						)
    image_processor.save_pretrained(_snake_case						)

    # Push to hub
    if push_to_hub:
        _lowerCamelCase  :		Optional[Any]			    =    '''deformable-detr'''
        model_name += "-single-scale" if single_scale else ""
        model_name += "-dc5" if dilation else ""
        model_name += "-with-box-refine" if with_box_refine else ""
        model_name += "-two-stage" if two_stage else ""
        print("""Pushing model to hub..."""						)
        model.push_to_hub(repo_path_or_name=_snake_case ,			organization="""nielsr""" ,			commit_message="""Add model"""						)


if __name__ == "__main__":
    lowerCAmelCase      :				Union[str, Any]     			=argparse.ArgumentParser()

    parser.add_argument(
        "--checkpoint_path",
        type=str,
        default="/home/niels/checkpoints/deformable_detr/r50_deformable_detr-checkpoint.pth",
        help="Path to Pytorch checkpoint (.pth file) you\'d like to convert.",
    )
    parser.add_argument("--single_scale", action="store_true", help="Whether to set config.num_features_levels = 1.")
    parser.add_argument("--dilation", action="store_true", help="Whether to set config.dilation=True.")
    parser.add_argument("--with_box_refine", action="store_true", help="Whether to set config.with_box_refine=True.")
    parser.add_argument("--two_stage", action="store_true", help="Whether to set config.two_stage=True.")
    parser.add_argument(
        "--pytorch_dump_folder_path",
        default=None,
        type=str,
        required=True,
        help="Path to the folder to output PyTorch model.",
    )
    parser.add_argument(
        "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
    )
    lowerCAmelCase      :				Union[str, Any]     			=parser.parse_args()
    convert_deformable_detr_checkpoint(
        args.checkpoint_path,
        args.single_scale,
        args.dilation,
        args.with_box_refine,
        args.two_stage,
        args.pytorch_dump_folder_path,
        args.push_to_hub,
    )








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


def   _snake_case    (    _snake_case					:      nn.Module      ):
      lowerCAmelCase       :						List[str]							= {}
      lowerCAmelCase       :						Optional[Any]							= (
          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       :						Dict							= lora_attn_processor_class(
                      hidden_size=module.out_proj.out_features			, cross_attention_dim=_snake_case      )
      return text_lora_attn_procs


def   _snake_case    (    _snake_case					:      nn.Module      ):
      lowerCAmelCase       :						Tuple							= create_text_encoder_lora_attn_procs(_snake_case      )
      lowerCAmelCase       :						Optional[int]							= AttnProcsLayers(_snake_case      )
      return text_encoder_lora_layers


def   _snake_case    (    _snake_case					:      Optional[Any]			, _snake_case					:      Any=False      ):
      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       :						Dict							= (
                            torch.randn_like(layer_module.up.weight      )
                            if randn_weight
                            else torch.zeros_like(layer_module.up.weight      )
                        )
                        lowerCAmelCase       :						Optional[int]							= torch.nn.Parameter(_snake_case      )






class 		snake_case_(							unittest.TestCase      ):






     def   lowerCamelCase__  (						self	:       int					):
           torch.manual_seed(0					)
           lowerCAmelCase       :						str							= 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       ,					)
           lowerCAmelCase       :						Dict							= DDIMScheduler(
               beta_start=0.00_085       ,					beta_end=0.012       ,					beta_schedule='''scaled_linear'''       ,					clip_sample=UpperCamelCase_       ,					set_alpha_to_one=UpperCamelCase_       ,					steps_offset=1       ,					)
           torch.manual_seed(0					)
           lowerCAmelCase       :						Tuple							= 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       ,					)
           lowerCAmelCase       :						str							= CLIPTextConfig(
               bos_token_id=0       ,					eos_token_id=2       ,					hidden_size=3_2       ,					intermediate_size=3_7       ,					layer_norm_eps=1E-05       ,					num_attention_heads=4       ,					num_hidden_layers=5       ,					pad_token_id=1       ,					vocab_size=1_0_0_0       ,					)
           lowerCAmelCase       :						List[Any]							= CLIPTextModel(UpperCamelCase_					)
           lowerCAmelCase       :						Dict							= CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''					)

           lowerCAmelCase,   lowerCAmelCase       :						List[str]							= create_unet_lora_layers(UpperCamelCase_					)
           lowerCAmelCase       :						int							= create_text_encoder_lora_layers(UpperCamelCase_					)

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

           lowerCAmelCase       :						Optional[Any]							= torch.manual_seed(0					)
           lowerCAmelCase       :						Any							= floats_tensor((batch_size, num_channels) + sizes					)
           lowerCAmelCase       :						Tuple							= torch.randint(1       ,					UpperCamelCase_       ,					size=(batch_size, sequence_length)       ,					generator=UpperCamelCase_					)

           lowerCAmelCase       :						Optional[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	:       Optional[Any]					):
           lowerCAmelCase       :						str							= 7_7

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

           lowerCAmelCase       :						Union[str, Any]							= {}
           lowerCAmelCase       :						List[str]							= inputs
           return prepared_inputs






     def   lowerCamelCase__  (						self	:       int       ,					UpperCamelCase_	:       Optional[Any]					):
           lowerCAmelCase,   lowerCAmelCase       :						Tuple							= self.get_dummy_components()
           LoraLoaderMixin.save_lora_weights(
               save_directory=UpperCamelCase_       ,					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(UpperCamelCase_       ,					'''pytorch_lora_weights.bin'''					)					)					)






     def   lowerCamelCase__  (						self	:       Optional[int]					):
           lowerCAmelCase,   lowerCAmelCase       :						Union[str, Any]							= self.get_dummy_components()
           lowerCAmelCase       :						str							= StableDiffusionPipeline(**UpperCamelCase_					)
           lowerCAmelCase       :						Union[str, Any]							= sd_pipe.to(UpperCamelCase_					)
           sd_pipe.set_progress_bar_config(disable=UpperCamelCase_					)

           lowerCAmelCase,   lowerCAmelCase,   lowerCAmelCase       :						Any							= self.get_dummy_inputs()

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

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

           lowerCAmelCase       :						Optional[Any]							= sd_pipe(**UpperCamelCase_					).images
           lowerCAmelCase       :						Tuple							= lora_images[0, -3:, -3:, -1]

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






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

           lowerCAmelCase,   lowerCAmelCase,   lowerCAmelCase       :						Any							= self.get_dummy_inputs()

           lowerCAmelCase       :						Union[str, Any]							= sd_pipe(**UpperCamelCase_					).images
           lowerCAmelCase       :						Union[str, Any]							= original_images[0, -3:, -3:, -1]

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

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

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






     def   lowerCamelCase__  (						self	:       Union[str, Any]					):
           lowerCAmelCase,   lowerCAmelCase       :						Tuple							= self.get_dummy_components()
           lowerCAmelCase       :						Dict							= lora_components['''unet_lora_attn_procs''']
           lowerCAmelCase       :						str							= StableDiffusionPipeline(**UpperCamelCase_					)
           lowerCAmelCase       :						str							= sd_pipe.to(UpperCamelCase_					)
           sd_pipe.set_progress_bar_config(disable=UpperCamelCase_					)

           lowerCAmelCase,   lowerCAmelCase,   lowerCAmelCase       :						List[str]							= self.get_dummy_inputs()

           lowerCAmelCase       :						List[str]							= sd_pipe(**UpperCamelCase_					).images
           lowerCAmelCase       :						Optional[Any]							= original_images[0, -3:, -3:, -1]

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

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

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






     def   lowerCamelCase__  (						self	:       List[Any]					):
           lowerCAmelCase,   lowerCAmelCase       :						str							= self.get_dummy_components()
           lowerCAmelCase       :						Optional[int]							= StableDiffusionPipeline(**UpperCamelCase_					)

           lowerCAmelCase       :						Union[str, Any]							= self.get_dummy_tokens()

           # inference without lora
           lowerCAmelCase       :						Optional[int]							= pipe.text_encoder(**UpperCamelCase_					)[0]
           assert outputs_without_lora.shape == (1, 7_7, 3_2)

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

           # monkey patch
           pipe._modify_text_encoder(UpperCamelCase_					)

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

           assert torch.allclose(
               UpperCamelCase_       ,					UpperCamelCase_					), "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       :						Any							= create_text_encoder_lora_attn_procs(pipe.text_encoder					)
           set_lora_up_weights(UpperCamelCase_       ,					randn_weight=UpperCamelCase_					)

           # monkey patch
           pipe._modify_text_encoder(UpperCamelCase_					)

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

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






     def   lowerCamelCase__  (						self	:       Optional[Any]					):
           lowerCAmelCase,   lowerCAmelCase       :						Optional[Any]							= self.get_dummy_components()
           lowerCAmelCase       :						Optional[int]							= StableDiffusionPipeline(**UpperCamelCase_					)

           lowerCAmelCase       :						Dict							= self.get_dummy_tokens()

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

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

           # monkey patch
           pipe._modify_text_encoder(UpperCamelCase_					)

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

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

           # remove monkey patch
           pipe._remove_text_encoder_monkey_patch()

           # inference with removed lora
           lowerCAmelCase       :						List[str]							= pipe.text_encoder(**UpperCamelCase_					)[0]
           assert outputs_without_lora_removed.shape == (1, 7_7, 3_2)

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






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

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

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

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

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

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






     def   lowerCamelCase__  (						self	:       List[str]					):
           with tempfile.TemporaryDirectory() as tmpdirname:
                 self.create_lora_weight_file(UpperCamelCase_					)

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

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

            # load LoRA weight file
                 sd_pipe.load_lora_weights(UpperCamelCase_					)

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






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

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

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

            # load LoRA weight file
                 sd_pipe.load_lora_weights(UpperCamelCase_					)

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






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

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

           # enable XFormers
           sd_pipe.enable_xformers_memory_efficient_attention()

           lowerCAmelCase       :						List[Any]							= sd_pipe(**UpperCamelCase_					).images
           lowerCAmelCase       :						Optional[int]							= original_images[0, -3:, -3:, -1]

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

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

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


