





'''simple docstring'''

import argparse
import json
import sys
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import torch
import torch.nn as nn
from huggingface_hub import cached_download, hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, VanConfig, VanForImageClassification
from transformers.models.deprecated.van.modeling_van import VanLayerScaling
from transformers.utils import logging


logging.set_verbosity_info()
lowercase__		:    Optional[int]  =       logging.get_logger(__name__)
@dataclass
class        __lowerCAmelCase  :






       """simple docstring"""
       _snake_case			:  nn.Module
       _snake_case			:  List[nn.Module]		       =					field(default_factory=lowercase_				)
       _snake_case			:  list		       =					field(default_factory=lowercase_				)





       def snake_case__		(							self     :	Optional[int] ,				lowerCAmelCase__     :	Optional[Any] ,				lowerCAmelCase__     :	Tensor ,				lowerCAmelCase__     :	Tensor  )					->  Optional[int]:

            '''simple docstring'''




            _UpperCamelCase       					= len(list(m.modules()  )  ) == 1 or isinstance(lowerCAmelCase__ ,				nn.Convad  ) or isinstance(lowerCAmelCase__ ,				nn.BatchNormad  )
            if has_not_submodules:
                 if not isinstance(lowerCAmelCase__ ,				lowerCAmelCase__  ):
                      self.traced.append(lowerCAmelCase__  )





       def __call__(							self     :	Optional[int] ,				lowerCAmelCase__     :	Tensor  )					->  str:

            '''simple docstring'''




            for m in self.module.modules():
                 self.handles.append(m.register_forward_hook(self._forward_hook  )  )
            self.module(lowerCAmelCase__  )
            [x.remove() for x in self.handles]
            return self







       @property
       def snake_case__		(							self     :	Dict  )					->  str:

            '''simple docstring'''




            return list(filter(lambda lowerCAmelCase__  : len(list(x.state_dict().keys()  )  ) > 0 ,				self.traced  )  )

@dataclass
class        __lowerCAmelCase  :






       """simple docstring"""
       _snake_case			:  nn.Module
       _snake_case			:  nn.Module
       _snake_case			:  int		       =					0
       _snake_case			:  List		       =					field(default_factory=lowercase_				)
       _snake_case			:  List		       =					field(default_factory=lowercase_				)





       def __call__(							self     :	int ,				lowerCAmelCase__     :	Tensor  )					->  Tuple:

            '''simple docstring'''




            _UpperCamelCase       					= Tracker(self.dest  )(lowerCAmelCase__  ).parametrized
            _UpperCamelCase       					= Tracker(self.src  )(lowerCAmelCase__  ).parametrized

            _UpperCamelCase       					= list(filter(lambda lowerCAmelCase__  : type(lowerCAmelCase__  ) not in self.src_skip ,				lowerCAmelCase__  )  )
            _UpperCamelCase       					= list(filter(lambda lowerCAmelCase__  : type(lowerCAmelCase__  ) not in self.dest_skip ,				lowerCAmelCase__  )  )

            if len(lowerCAmelCase__  ) != len(lowerCAmelCase__  ):
                 raise Exception(
                     f"""Numbers of operations are different. Source module has {len(lowerCAmelCase__  )} operations while"""
                     f""" destination module has {len(lowerCAmelCase__  )}."""  )

            for dest_m, src_m in zip(lowerCAmelCase__ ,				lowerCAmelCase__  ):
                 dest_m.load_state_dict(src_m.state_dict()  )
                 if self.verbose == 1:
                      print(f"""Transfered from={src_m} to={dest_m}"""  )







def       a__       (       lowercase      :  Optional[int],      lowercase      :  Optional[int]   )	->	nn.Module:





     """simple docstring"""

     _UpperCamelCase       					= from_model.state_dict()
     _UpperCamelCase       					= our_model.state_dict()
     _UpperCamelCase       					= our_model.config
     _UpperCamelCase       					= []
     for stage_idx in range(len(config.hidden_sizes   )   ):
          for block_id in range(config.depths[stage_idx]   ):
               _UpperCamelCase       					= F"""block{stage_idx + 1}.{block_id}.layer_scale_1"""
               _UpperCamelCase       					= F"""van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight"""

               all_keys.append((from_key, to_key)   )
               _UpperCamelCase       					= F"""block{stage_idx + 1}.{block_id}.layer_scale_2"""
               _UpperCamelCase       					= F"""van.encoder.stages.{stage_idx}.layers.{block_id}.mlp_scaling.weight"""

               all_keys.append((from_key, to_key)   )

     for from_key, to_key in all_keys:
          _UpperCamelCase       					= from_state_dict.pop(lowercase   )

     our_model.load_state_dict(lowercase   )
     return our_model







def       a__       (       lowercase      :  List[Any],      lowercase      :  Tuple,      lowercase      :  Optional[int],      lowercase      :  Optional[Any],      lowercase      :  List[str],      lowercase      :  str = True,      )	->	Union[str, Any]:





     """simple docstring"""

     print(F"""Downloading weights for {name}..."""   )
     _UpperCamelCase       					= cached_download(lowercase   )
     print(F"""Converting {name}..."""   )
     _UpperCamelCase       					= torch.load(lowercase   )['''state_dict''']
     from_model.load_state_dict(lowercase   )
     from_model.eval()
     with torch.no_grad():
          _UpperCamelCase       					= VanForImageClassification(lowercase   ).eval()
          _UpperCamelCase       					= ModuleTransfer(src=lowercase,      dest=lowercase   )
          _UpperCamelCase       					= torch.randn((1, 3, 224, 224)   )
          module_transfer(lowercase   )
          _UpperCamelCase       					= copy_parameters(lowercase,      lowercase   )

     if not torch.allclose(from_model(lowercase   ),      our_model(lowercase   ).logits   ):
          raise ValueError('''The model logits don\'t match the original one.'''   )

     _UpperCamelCase       					= name
     print(lowercase   )

     if push_to_hub:
          our_model.push_to_hub(
              repo_path_or_name=save_directory / checkpoint_name,      commit_message='''Add model''',      use_temp_dir=lowercase,      )

          # we can use the convnext one
          _UpperCamelCase       					= AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k'''   )
          image_processor.push_to_hub(
              repo_path_or_name=save_directory / checkpoint_name,      commit_message='''Add image processor''',      use_temp_dir=lowercase,      )

          print(F"""Pushed {checkpoint_name}"""   )







def       a__       (       lowercase      :  Dict,      lowercase      :  Optional[Any] = None,      lowercase      :  List[Any] = True   )	->	Tuple:





     """simple docstring"""

     _UpperCamelCase       					= '''imagenet-1k-id2label.json'''
     _UpperCamelCase       					= 1000

     _UpperCamelCase       					= '''huggingface/label-files'''
     _UpperCamelCase       					= num_labels
     _UpperCamelCase       					= json.load(open(hf_hub_download(lowercase,      lowercase,      repo_type='''dataset'''   ),      '''r'''   )   )
     _UpperCamelCase       					= {int(lowercase   ): v for k, v in idalabel.items()}

     _UpperCamelCase       					= idalabel
     _UpperCamelCase       					= {v: k for k, v in idalabel.items()}

     _UpperCamelCase       					= partial(lowercase,      num_labels=lowercase,      idalabel=lowercase,      labelaid=lowercase   )

     _UpperCamelCase       					= {
         '''van-tiny''': ImageNetPreTrainedConfig(
             hidden_sizes=[32, 64, 160, 256],      depths=[3, 3, 5, 2],      mlp_ratios=[8, 8, 4, 4],      ),
         '''van-small''': ImageNetPreTrainedConfig(
             hidden_sizes=[64, 128, 320, 512],      depths=[2, 2, 4, 2],      mlp_ratios=[8, 8, 4, 4],      ),
         '''van-base''': ImageNetPreTrainedConfig(
             hidden_sizes=[64, 128, 320, 512],      depths=[3, 3, 12, 3],      mlp_ratios=[8, 8, 4, 4],      ),
         '''van-large''': ImageNetPreTrainedConfig(
             hidden_sizes=[64, 128, 320, 512],      depths=[3, 5, 27, 3],      mlp_ratios=[8, 8, 4, 4],      ),
     }

     _UpperCamelCase       					= {
         '''van-tiny''': van_tiny,
         '''van-small''': van_small,
         '''van-base''': van_base,
         '''van-large''': van_large,
     }

     _UpperCamelCase       					= {
         '''van-tiny''': (
             '''https://huggingface.co/Visual-Attention-Network/VAN-Tiny-original/resolve/main/van_tiny_754.pth.tar'''
         ),
         '''van-small''': (
             '''https://huggingface.co/Visual-Attention-Network/VAN-Small-original/resolve/main/van_small_811.pth.tar'''
         ),
         '''van-base''': (
             '''https://huggingface.co/Visual-Attention-Network/VAN-Base-original/resolve/main/van_base_828.pth.tar'''
         ),
         '''van-large''': (
             '''https://huggingface.co/Visual-Attention-Network/VAN-Large-original/resolve/main/van_large_839.pth.tar'''
         ),
     }

     if model_name:
          convert_weight_and_push(
              lowercase,      names_to_config[model_name],      checkpoint=names_to_original_checkpoints[model_name],      from_model=names_to_original_models[model_name](),      save_directory=lowercase,      push_to_hub=lowercase,      )
     else:
          for model_name, config in names_to_config.items():
               convert_weight_and_push(
                   lowercase,      lowercase,      checkpoint=names_to_original_checkpoints[model_name],      from_model=names_to_original_models[model_name](),      save_directory=lowercase,      push_to_hub=lowercase,      )


if __name__ == "__main__":
   lowercase__		:    str  =       argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '--model-name',
       default=None,
       type=str,
       help=(
           'The name of the model you wish to convert, it must be one of the supported resnet* architecture,'
           ' currently: van-tiny/small/base/large. If `None`, all of them will the converted.'
       ),
   )
   parser.add_argument(
       '--pytorch_dump_folder_path',
       default=None,
       type=Path,
       required=True,
       help='Path to the output PyTorch model directory.',
   )
   parser.add_argument(
       '--van_dir',
       required=True,
       type=Path,
       help=(
           'A path to VAN\'s original implementation directory. You can download from here:'
           ' https://github.com/Visual-Attention-Network/VAN-Classification'
       ),
   )
   parser.add_argument(
       '--push_to_hub',
       default=True,
       type=bool,
       required=False,
       help='If True, push model and image processor to the hub.',
   )

   lowercase__		:    Union[str, Any]  =       parser.parse_args()
   lowercase__		:    Path  =       args.pytorch_dump_folder_path
   pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
   lowercase__		:    List[str]  =       args.van_dir
   # append the path to the parents to maskformer dir
   sys.path.append(str(van_dir.parent))
   from van.models.van import van_base, van_large, van_small, van_tiny

   convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)










'''simple docstring'''

import os
import tempfile
import unittest

import numpy as np
import torch

from diffusers.models import ModelMixin, UNetaDConditionModel
from diffusers.models.attention_processor import AttnProcessor, LoRAAttnProcessor
from diffusers.utils import (
    floats_tensor,
    logging,
    skip_mps,
    torch_device,
)
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()

lowercase__		:    Union[str, Any] =       logging.get_logger(__name__)







def       a__       (       lowercase      :  Dict,      lowercase      :  bool = True   )	->	int:





     """simple docstring"""

     _UpperCamelCase       					= {}
     for name in model.attn_processors.keys():
          _UpperCamelCase       					= name.endswith('''attn2.processor'''   ) and not (
              name.startswith('''transformer_in'''   ) or '''temp_attentions''' in name.split('''.'''   )
          )
          _UpperCamelCase       					= model.config.cross_attention_dim if has_cross_attention else None
          if name.startswith('''mid_block'''   ):
               _UpperCamelCase       					= model.config.block_out_channels[-1]
          elif name.startswith('''up_blocks'''   ):
               _UpperCamelCase       					= int(name[len('''up_blocks.'''   )]   )
               _UpperCamelCase       					= list(reversed(model.config.block_out_channels   )   )[block_id]
          elif name.startswith('''down_blocks'''   ):
               _UpperCamelCase       					= int(name[len('''down_blocks.'''   )]   )
               _UpperCamelCase       					= model.config.block_out_channels[block_id]
          elif name.startswith('''transformer_in'''   ):
               # Note that the `8 * ...` comes from: https://github.com/huggingface/diffusers/blob/7139f0e874f10b2463caa8cbd585762a309d12d6/src/diffusers/models/unet_3d_condition.py#L148
               _UpperCamelCase       					= 8 * model.config.attention_head_dim

          _UpperCamelCase       					= LoRAAttnProcessor(hidden_size=lowercase,      cross_attention_dim=lowercase   )
          _UpperCamelCase       					= lora_attn_procs[name].to(model.device   )

          if mock_weights:
               # add 1 to weights to mock trained weights
               with torch.no_grad():
                    lora_attn_procs[name].to_q_lora.up.weight += 1
                    lora_attn_procs[name].to_k_lora.up.weight += 1
                    lora_attn_procs[name].to_v_lora.up.weight += 1
                    lora_attn_procs[name].to_out_lora.up.weight += 1

     return lora_attn_procs




@skip_mps
class        __lowerCAmelCase  (					__magic_name__		,		__magic_name__		,		unittest.TestCase				):






       """simple docstring"""
       _snake_case			:  int		       =					UNetaDConditionModel
       _snake_case			:  List[str]		       =					'sample'





       @property
       def snake_case__		(							self     :	Optional[int]  )					->  Any:

            '''simple docstring'''




            _UpperCamelCase       					= 4
            _UpperCamelCase       					= 4
            _UpperCamelCase       					= 4
            _UpperCamelCase       					= (32, 32)

            _UpperCamelCase       					= floats_tensor((batch_size, num_channels, num_frames) + sizes  ).to(lowerCAmelCase__  )
            _UpperCamelCase       					= torch.tensor([10]  ).to(lowerCAmelCase__  )
            _UpperCamelCase       					= floats_tensor((batch_size, 4, 32)  ).to(lowerCAmelCase__  )

            return {"sample": noise, "timestep": time_step, "encoder_hidden_states": encoder_hidden_states}





       @property
       def snake_case__		(							self     :	Tuple  )					->  Optional[int]:

            '''simple docstring'''




            return (4, 4, 32, 32)





       @property
       def snake_case__		(							self     :	Optional[Any]  )					->  Union[str, Any]:

            '''simple docstring'''




            return (4, 4, 32, 32)





       def snake_case__		(							self     :	Optional[Any]  )					->  Tuple:

            '''simple docstring'''




            _UpperCamelCase       					= {
                '''block_out_channels''': (32, 64),
                '''down_block_types''': (
                    '''CrossAttnDownBlock3D''',
                    '''DownBlock3D''',
                ),
                '''up_block_types''': ('''UpBlock3D''', '''CrossAttnUpBlock3D'''),
                '''cross_attention_dim''': 32,
                '''attention_head_dim''': 8,
                '''out_channels''': 4,
                '''in_channels''': 4,
                '''layers_per_block''': 1,
                '''sample_size''': 32,
            }
            _UpperCamelCase       					= self.dummy_input
            return init_dict, inputs_dict





       @unittest.skipIf(
           torch_device != '''cuda''' or not is_xformers_available() ,				reason='''XFormers attention is only available with CUDA and `xformers` installed''' ,				)
       def snake_case__		(							self     :	Union[str, Any]  )					->  int:

            '''simple docstring'''




            _UpperCamelCase					,       _UpperCamelCase       					= self.prepare_init_args_and_inputs_for_common()
            _UpperCamelCase       					= self.model_class(**lowerCAmelCase__  )

            model.enable_xformers_memory_efficient_attention()

            assert (
                model.mid_block.attentions[0].transformer_blocks[0].attna.processor.__class__.__name__
                == "XFormersAttnProcessor"
            ), "xformers is not enabled"





       def snake_case__		(							self     :	Tuple  )					->  Tuple:

            '''simple docstring'''




            _UpperCamelCase					,       _UpperCamelCase       					= self.prepare_init_args_and_inputs_for_common()

            _UpperCamelCase       					= 32

            _UpperCamelCase       					= self.model_class(**lowerCAmelCase__  )
            model.to(lowerCAmelCase__  )
            model.eval()

            with torch.no_grad():
                 _UpperCamelCase       					= model(**lowerCAmelCase__  )

                 if isinstance(lowerCAmelCase__ ,				lowerCAmelCase__  ):
                      _UpperCamelCase       					= output.sample

            self.assertIsNotNone(lowerCAmelCase__  )
            _UpperCamelCase       					= inputs_dict['''sample'''].shape
            self.assertEqual(output.shape ,				lowerCAmelCase__ ,				'''Input and output shapes do not match'''  )





       def snake_case__		(							self     :	str  )					->  Optional[Any]:

            '''simple docstring'''




            _UpperCamelCase					,       _UpperCamelCase       					= self.prepare_init_args_and_inputs_for_common()
            _UpperCamelCase       					= self.model_class(**lowerCAmelCase__  )
            model.to(lowerCAmelCase__  )
            model.eval()

            with torch.no_grad():
                 # Warmup pass when using mps (see #372)
                 if torch_device == "mps" and isinstance(lowerCAmelCase__ ,				lowerCAmelCase__  ):
                      model(**self.dummy_input  )

                 _UpperCamelCase       					= model(**lowerCAmelCase__  )
                 if isinstance(lowerCAmelCase__ ,				lowerCAmelCase__  ):
                      _UpperCamelCase       					= first.sample

                 _UpperCamelCase       					= model(**lowerCAmelCase__  )
                 if isinstance(lowerCAmelCase__ ,				lowerCAmelCase__  ):
                      _UpperCamelCase       					= second.sample

            _UpperCamelCase       					= first.cpu().numpy()
            _UpperCamelCase       					= second.cpu().numpy()
            _UpperCamelCase       					= out_a[~np.isnan(lowerCAmelCase__  )]
            _UpperCamelCase       					= out_a[~np.isnan(lowerCAmelCase__  )]
            _UpperCamelCase       					= np.amax(np.abs(out_a - out_a  )  )
            self.assertLessEqual(lowerCAmelCase__ ,				1e-5  )





       def snake_case__		(							self     :	Optional[Any]  )					->  str:

            '''simple docstring'''




            _UpperCamelCase					,       _UpperCamelCase       					= self.prepare_init_args_and_inputs_for_common()

            _UpperCamelCase       					= 8

            _UpperCamelCase       					= self.model_class(**lowerCAmelCase__  )
            model.to(lowerCAmelCase__  )
            model.eval()

            model.set_attention_slice('''auto'''  )
            with torch.no_grad():
                 _UpperCamelCase       					= model(**lowerCAmelCase__  )
            assert output is not None

            model.set_attention_slice('''max'''  )
            with torch.no_grad():
                 _UpperCamelCase       					= model(**lowerCAmelCase__  )
            assert output is not None

            model.set_attention_slice(2  )
            with torch.no_grad():
                 _UpperCamelCase       					= model(**lowerCAmelCase__  )
            assert output is not None





       def snake_case__		(							self     :	Optional[int]  )					->  Optional[int]:

            '''simple docstring'''




            _UpperCamelCase					,       _UpperCamelCase       					= self.prepare_init_args_and_inputs_for_common()

            _UpperCamelCase       					= 8

            _UpperCamelCase       					= self.model_class(**lowerCAmelCase__  )
            model.to(lowerCAmelCase__  )

            with torch.no_grad():
                 _UpperCamelCase       					= model(**lowerCAmelCase__  ).sample

            _UpperCamelCase       					= create_lora_layers(lowerCAmelCase__  )

            # make sure we can set a list of attention processors
            model.set_attn_processor(lowerCAmelCase__  )
            model.to(lowerCAmelCase__  )

            # test that attn processors can be set to itself
            model.set_attn_processor(model.attn_processors  )

            with torch.no_grad():
                 _UpperCamelCase       					= model(**lowerCAmelCase__ ,				cross_attention_kwargs={'''scale''': 0.0}  ).sample
                 _UpperCamelCase       					= model(**lowerCAmelCase__ ,				cross_attention_kwargs={'''scale''': 0.5}  ).sample
                 _UpperCamelCase       					= model(**lowerCAmelCase__ ,				cross_attention_kwargs={'''scale''': 0.5}  ).sample

            assert (samplea - samplea).abs().max() < 3e-3
            assert (samplea - samplea).abs().max() < 3e-3

            # sample 2 and sample 3 should be different
            assert (samplea - samplea).abs().max() > 3e-3





       def snake_case__		(							self     :	str  )					->  Tuple:

            '''simple docstring'''




            _UpperCamelCase					,       _UpperCamelCase       					= self.prepare_init_args_and_inputs_for_common()

            _UpperCamelCase       					= 8

            torch.manual_seed(0  )
            _UpperCamelCase       					= self.model_class(**lowerCAmelCase__  )
            model.to(lowerCAmelCase__  )

            with torch.no_grad():
                 _UpperCamelCase       					= model(**lowerCAmelCase__  ).sample

            _UpperCamelCase       					= create_lora_layers(lowerCAmelCase__  )
            model.set_attn_processor(lowerCAmelCase__  )

            with torch.no_grad():
                 _UpperCamelCase       					= model(**lowerCAmelCase__ ,				cross_attention_kwargs={'''scale''': 0.5}  ).sample

            with tempfile.TemporaryDirectory() as tmpdirname:
                 model.save_attn_procs(lowerCAmelCase__  )
                 self.assertTrue(os.path.isfile(os.path.join(lowerCAmelCase__ ,				'''pytorch_lora_weights.bin'''  )  )  )
                 torch.manual_seed(0  )
                 _UpperCamelCase       					= self.model_class(**lowerCAmelCase__  )
                 new_model.to(lowerCAmelCase__  )
                 new_model.load_attn_procs(lowerCAmelCase__  )

            with torch.no_grad():
                 _UpperCamelCase       					= new_model(**lowerCAmelCase__ ,				cross_attention_kwargs={'''scale''': 0.5}  ).sample

            assert (sample - new_sample).abs().max() < 1e-3

            # LoRA and no LoRA should NOT be the same
            assert (sample - old_sample).abs().max() > 1e-4





       def snake_case__		(							self     :	str  )					->  List[Any]:

            '''simple docstring'''




            _UpperCamelCase					,       _UpperCamelCase       					= self.prepare_init_args_and_inputs_for_common()

            _UpperCamelCase       					= 8

            torch.manual_seed(0  )
            _UpperCamelCase       					= self.model_class(**lowerCAmelCase__  )
            model.to(lowerCAmelCase__  )

            with torch.no_grad():
                 _UpperCamelCase       					= model(**lowerCAmelCase__  ).sample

            _UpperCamelCase       					= create_lora_layers(lowerCAmelCase__  )
            model.set_attn_processor(lowerCAmelCase__  )

            with torch.no_grad():
                 _UpperCamelCase       					= model(**lowerCAmelCase__ ,				cross_attention_kwargs={'''scale''': 0.5}  ).sample

            with tempfile.TemporaryDirectory() as tmpdirname:
                 model.save_attn_procs(lowerCAmelCase__ ,				safe_serialization=lowerCAmelCase__  )
                 self.assertTrue(os.path.isfile(os.path.join(lowerCAmelCase__ ,				'''pytorch_lora_weights.safetensors'''  )  )  )
                 torch.manual_seed(0  )
                 _UpperCamelCase       					= self.model_class(**lowerCAmelCase__  )
                 new_model.to(lowerCAmelCase__  )
                 new_model.load_attn_procs(lowerCAmelCase__  )

            with torch.no_grad():
                 _UpperCamelCase       					= new_model(**lowerCAmelCase__ ,				cross_attention_kwargs={'''scale''': 0.5}  ).sample

            assert (sample - new_sample).abs().max() < 3e-3

            # LoRA and no LoRA should NOT be the same
            assert (sample - old_sample).abs().max() > 1e-4





       def snake_case__		(							self     :	Tuple  )					->  List[Any]:

            '''simple docstring'''




            _UpperCamelCase					,       _UpperCamelCase       					= self.prepare_init_args_and_inputs_for_common()

            _UpperCamelCase       					= 8

            torch.manual_seed(0  )
            _UpperCamelCase       					= self.model_class(**lowerCAmelCase__  )
            model.to(lowerCAmelCase__  )

            _UpperCamelCase       					= create_lora_layers(lowerCAmelCase__ ,				mock_weights=lowerCAmelCase__  )
            model.set_attn_processor(lowerCAmelCase__  )
            # Saving as torch, properly reloads with directly filename
            with tempfile.TemporaryDirectory() as tmpdirname:
                 model.save_attn_procs(lowerCAmelCase__  )
                 self.assertTrue(os.path.isfile(os.path.join(lowerCAmelCase__ ,				'''pytorch_lora_weights.bin'''  )  )  )
                 torch.manual_seed(0  )
                 _UpperCamelCase       					= self.model_class(**lowerCAmelCase__  )
                 new_model.to(lowerCAmelCase__  )
                 new_model.load_attn_procs(lowerCAmelCase__ ,				weight_name='''pytorch_lora_weights.bin'''  )





       def snake_case__		(							self     :	Union[str, Any]  )					->  str:

            '''simple docstring'''




            _UpperCamelCase					,       _UpperCamelCase       					= self.prepare_init_args_and_inputs_for_common()

            _UpperCamelCase       					= 8

            torch.manual_seed(0  )
            _UpperCamelCase       					= self.model_class(**lowerCAmelCase__  )
            model.to(lowerCAmelCase__  )

            _UpperCamelCase       					= create_lora_layers(lowerCAmelCase__ ,				mock_weights=lowerCAmelCase__  )
            model.set_attn_processor(lowerCAmelCase__  )
            # Saving as torch, properly reloads with directly filename
            with tempfile.TemporaryDirectory() as tmpdirname:
                 model.save_attn_procs(lowerCAmelCase__  )
                 self.assertTrue(os.path.isfile(os.path.join(lowerCAmelCase__ ,				'''pytorch_lora_weights.bin'''  )  )  )
                 torch.manual_seed(0  )
                 _UpperCamelCase       					= self.model_class(**lowerCAmelCase__  )
                 new_model.to(lowerCAmelCase__  )
                 with self.assertRaises(lowerCAmelCase__  ) as e:
                      new_model.load_attn_procs(lowerCAmelCase__ ,				use_safetensors=lowerCAmelCase__  )
                 self.assertIn('''Error no file named pytorch_lora_weights.safetensors''' ,				str(e.exception  )  )





       def snake_case__		(							self     :	Tuple  )					->  List[Any]:

            '''simple docstring'''




            _UpperCamelCase					,       _UpperCamelCase       					= self.prepare_init_args_and_inputs_for_common()

            _UpperCamelCase       					= 8

            torch.manual_seed(0  )
            _UpperCamelCase       					= self.model_class(**lowerCAmelCase__  )
            model.to(lowerCAmelCase__  )

            with torch.no_grad():
                 _UpperCamelCase       					= model(**lowerCAmelCase__  ).sample

            _UpperCamelCase       					= create_lora_layers(lowerCAmelCase__  )
            model.set_attn_processor(lowerCAmelCase__  )

            with torch.no_grad():
                 _UpperCamelCase       					= model(**lowerCAmelCase__ ,				cross_attention_kwargs={'''scale''': 0.0}  ).sample

            model.set_attn_processor(AttnProcessor()  )

            with torch.no_grad():
                 _UpperCamelCase       					= model(**lowerCAmelCase__  ).sample

            assert (sample - new_sample).abs().max() < 1e-4
            assert (sample - old_sample).abs().max() < 3e-3





       @unittest.skipIf(
           torch_device != '''cuda''' or not is_xformers_available() ,				reason='''XFormers attention is only available with CUDA and `xformers` installed''' ,				)
       def snake_case__		(							self     :	List[str]  )					->  Tuple:

            '''simple docstring'''




            _UpperCamelCase					,       _UpperCamelCase       					= self.prepare_init_args_and_inputs_for_common()

            _UpperCamelCase       					= 4

            torch.manual_seed(0  )
            _UpperCamelCase       					= self.model_class(**lowerCAmelCase__  )
            model.to(lowerCAmelCase__  )
            _UpperCamelCase       					= create_lora_layers(lowerCAmelCase__  )
            model.set_attn_processor(lowerCAmelCase__  )

            # default
            with torch.no_grad():
                 _UpperCamelCase       					= model(**lowerCAmelCase__  ).sample

                 model.enable_xformers_memory_efficient_attention()
                 _UpperCamelCase       					= model(**lowerCAmelCase__  ).sample

                 model.disable_xformers_memory_efficient_attention()
                 _UpperCamelCase       					= model(**lowerCAmelCase__  ).sample

            assert (sample - on_sample).abs().max() < 1e-4
            assert (sample - off_sample).abs().max() < 1e-4







       def snake_case__		(							self     :	Union[str, Any]  )					->  Optional[Any]:

            '''simple docstring'''




            _UpperCamelCase					,       _UpperCamelCase       					= self.prepare_init_args_and_inputs_for_common()
            _UpperCamelCase       					= 32

            _UpperCamelCase       					= self.model_class(**lowerCAmelCase__  )
            model.to(lowerCAmelCase__  )
            model.eval()

            with torch.no_grad():
                 _UpperCamelCase       					= model(**lowerCAmelCase__  )[0]

            model.enable_forward_chunking()
            with torch.no_grad():
                 _UpperCamelCase       					= model(**lowerCAmelCase__  )[0]

            self.assertEqual(output.shape ,				output_a.shape ,				'''Shape doesn\'t match'''  )
            assert np.abs(output.cpu() - output_a.cpu()  ).max() < 1e-2


# (todo: sayakpaul) implement SLOW tests.



