
'''simple docstring'''



import argparse
import collections

import jax
import jax.numpy as jnp
import torch
import torch.nn as nn
from clip.model import CLIP
from flax.training import checkpoints
from huggingface_hub import Repository

from transformers import (
    CLIPTokenizer,
    OwlViTConfig,
    OwlViTForObjectDetection,
    OwlViTImageProcessor,
    OwlViTModel,
    OwlViTProcessor,
)


__A          =    {
    "vit_b32": {
        "embed_dim": 512,
        "image_resolution": 768,
        "context_length": 16,
        "vocab_size": 49_408,
        "vision_layers": 12,
        "vision_width": 768,
        "vision_patch_size": 32,
        "transformer_width": 512,
        "transformer_heads": 8,
        "transformer_layers": 12,
    },
    "vit_b16": {
        "embed_dim": 512,
        "image_resolution": 768,
        "context_length": 16,
        "vocab_size": 49_408,
        "vision_layers": 12,
        "vision_width": 768,
        "vision_patch_size": 16,
        "transformer_width": 512,
        "transformer_heads": 8,
        "transformer_layers": 12,
    },
    "vit_l14": {
        "embed_dim": 768,
        "image_resolution": 840,
        "context_length": 16,
        "vocab_size": 49_408,
        "vision_layers": 24,
        "vision_width": 1_024,
        "vision_patch_size": 14,
        "transformer_width": 768,
        "transformer_heads": 12,
        "transformer_layers": 12,
    },
}





def  _A		(							lowercase__		,		lowercase__=""		,		lowercase__="/"	):
						lowercase__							 =       []

						for k, v in params.items():
												lowercase__							 =       parent_key + sep + k if parent_key else k

												if isinstance(lowercase__		,		collections.MutableMapping	):
																		items.extend(flatten_nested_dict(lowercase__		,		lowercase__		,		sep=lowercase__	).items()	)
												else:
																		items.append((new_key, v)	)
						return dict(lowercase__	)





def  _A		(							lowercase__	):
						return jax.tree_util.tree_map(lambda lowercase__	: x.astype(jnp.floataa	) if x.dtype == jnp.bfloataa else x		,		lowercase__	)





def  _A		(							lowercase__		,		lowercase__	):
						lowercase__ ,						lowercase__ ,						lowercase__							 =       pt_attn_layer.in_proj_weight.chunk(3		,		dim=0	)
						lowercase__ ,						lowercase__ ,						lowercase__							 =       pt_attn_layer.in_proj_bias.chunk(3		,		dim=0	)

						lowercase__							 =       pt_attn_layer.out_proj.weight
						lowercase__							 =       pt_attn_layer.out_proj.bias

						lowercase__							 =       q_proj
						lowercase__							 =       q_proj_bias

						lowercase__							 =       k_proj
						lowercase__							 =       k_proj_bias

						lowercase__							 =       v_proj
						lowercase__							 =       v_proj_bias

						lowercase__							 =       out_proj_weights
						lowercase__							 =       out_proj_bias





def  _A		(							lowercase__		,		lowercase__	):
						copy_linear(hf_mlp.fca		,		pt_mlp.c_fc	)
						copy_linear(hf_mlp.fca		,		pt_mlp.c_proj	)





def  _A		(							lowercase__		,		lowercase__	):
						lowercase__							 =       pt_linear.weight
						lowercase__							 =       pt_linear.bias





def  _A		(							lowercase__		,		lowercase__	):
						# copy layer norms
						copy_linear(hf_layer.layer_norma		,		pt_layer.ln_a	)
						copy_linear(hf_layer.layer_norma		,		pt_layer.ln_a	)

						# copy MLP
						copy_mlp(hf_layer.mlp		,		pt_layer.mlp	)

						# copy attn
						copy_attn_layer(hf_layer.self_attn		,		pt_layer.attn	)





def  _A		(							lowercase__		,		lowercase__	):
						for hf_layer, pt_layer in zip(lowercase__		,		lowercase__	):
												copy_layer(lowercase__		,		lowercase__	)





def  _A		(							lowercase__		,		lowercase__	):
						# copy  embeds
						lowercase__							 =       pt_model.token_embedding.weight
						lowercase__							 =       pt_model.positional_embedding

						# copy layer norm
						copy_linear(hf_encoder.final_layer_norm		,		pt_model.ln_final	)

						# copy hidden layers
						copy_layers(hf_encoder.encoder.layers		,		pt_model.transformer.resblocks	)





def  _A		(							lowercase__		,		lowercase__	):
						# copy projection
						lowercase__							 =       pt_model.text_projection.data.T

						# copy text encoder
						copy_encoder(hf_model.text_model		,		lowercase__	)





def  _A		(							lowercase__		,		lowercase__	):
						# copy projection
						lowercase__							 =       pt_model.visual.proj.data.T

						# copy layer norms
						copy_linear(hf_model.vision_model.pre_layernorm		,		pt_model.visual.ln_pre	)
						copy_linear(hf_model.vision_model.post_layernorm		,		pt_model.visual.ln_post	)

						# copy embeds
						lowercase__							 =       pt_model.visual.conva.weight.data
						lowercase__							 =       pt_model.visual.class_embedding
						lowercase__							 =       pt_model.visual.positional_embedding.data

						# copy encoder
						copy_layers(hf_model.vision_model.encoder.layers		,		pt_model.visual.transformer.resblocks	)





def  _A		(							lowercase__		,		lowercase__	):
						lowercase__							 =       flatten_nested_dict(flax_params["""backbone"""]["""merged_class_token"""]	)

						lowercase__							 =       torch.from_numpy(flax_class_token_params["""scale"""]	)
						lowercase__							 =       torch.from_numpy(flax_class_token_params["""bias"""]	)
						lowercase__							 =       nn.Parameter(lowercase__	)
						lowercase__							 =       nn.Parameter(lowercase__	)





def  _A		(							lowercase__		,		lowercase__	):
						lowercase__							 =       hf_model.state_dict()
						lowercase__							 =       {}

						# Rename class prediction head flax params to pytorch HF
						lowercase__							 =       flatten_nested_dict(flax_params["""class_head"""]	)

						for flax_key, v in flax_class_params.items():
												lowercase__							 =       flax_key.replace("""/"""		,		"""."""	)
												lowercase__							 =       torch_key.replace(""".kernel"""		,		""".weight"""	)
												lowercase__							 =       torch_key.replace("""Dense_0"""		,		"""dense0"""	)
												lowercase__							 =       """class_head.""" + torch_key

												if "weight" in torch_key and v.ndim == 2:
																		lowercase__							 =       v.T

												lowercase__							 =       nn.Parameter(torch.from_numpy(lowercase__	)	)

						# Rename box prediction box flax params to pytorch HF
						lowercase__							 =       flatten_nested_dict(flax_params["""obj_box_head"""]	)

						for flax_key, v in flax_box_params.items():
												lowercase__							 =       flax_key.replace("""/"""		,		"""."""	)
												lowercase__							 =       torch_key.replace(""".kernel"""		,		""".weight"""	)
												lowercase__							 =       torch_key.replace("""_"""		,		""""""	).lower()
												lowercase__							 =       """box_head.""" + torch_key

												if "weight" in torch_key and v.ndim == 2:
																		lowercase__							 =       v.T

												lowercase__							 =       nn.Parameter(torch.from_numpy(lowercase__	)	)

						# Copy flax params to PyTorch params
						for name, param in new_params.items():
												if name in pt_params.keys():
																		pt_params[name].copy_(lowercase__	)





def  _A		(							lowercase__		,		lowercase__	):
						for k, v in flax_attn_params.items():
												if k.startswith("""transformer"""	):
																		lowercase__							 =       k.replace("""transformer.resblocks"""		,		"""text_model.encoder.layers"""	)
												else:
																		lowercase__							 =       k.replace("""visual.transformer.resblocks"""		,		"""vision_model.encoder.layers"""	)

												lowercase__							 =       torch_key.replace("""attn"""		,		"""self_attn"""	)
												lowercase__							 =       torch_key.replace("""key"""		,		"""k_proj"""	)
												lowercase__							 =       torch_key.replace("""value"""		,		"""v_proj"""	)
												lowercase__							 =       torch_key.replace("""query"""		,		"""q_proj"""	)
												lowercase__							 =       torch_key.replace("""out"""		,		"""out_proj"""	)

												if "bias" in torch_key and v.ndim == 2:
																		lowercase__							 =       v.shape[0] * v.shape[1]
																		lowercase__							 =       v.reshape(lowercase__	)

												if "weight" in torch_key and "out" in torch_key:
																		lowercase__							 =       (v.shape[0] * v.shape[1], v.shape[2])
																		lowercase__							 =       v.reshape(lowercase__	).T

												if "weight" in torch_key and "out" not in torch_key:
																		lowercase__							 =       (v.shape[0], v.shape[1] * v.shape[2])
																		lowercase__							 =       v.reshape(lowercase__	).T

												# Copy flax CLIP attn params to HF PyTorch params
												lowercase__							 =       torch.from_numpy(lowercase__	)
												hf_backbone.state_dict()[torch_key].copy_(lowercase__	)





def  _A		(							lowercase__	):
						lowercase__							 =       {}
						lowercase__							 =       []

						for k, v in params.items():
												if "attn." in k:
																		lowercase__							 =       k[: k.rindex("""attn."""	) + 5]
																		if base in processed_attn_layers:
																								continue

																		processed_attn_layers.append(lowercase__	)
																		lowercase__							 =       params[base + """out.weight"""].shape[-1]
																		lowercase__							 =       params[base + """out.weight"""].reshape(lowercase__		,		lowercase__	).T
																		lowercase__							 =       params[base + """out.bias"""]
												else:
																		lowercase__							 =       v
						return new_params





def  _A		(							lowercase__		,		lowercase__	):
						lowercase__							 =       CLIP(**lowercase__	)
						torch_model.eval()
						lowercase__							 =       torch_model.state_dict()

						lowercase__							 =       flatten_nested_dict(flax_params["""backbone"""]["""clip"""]	)
						lowercase__							 =       {}

						for flax_key, v in flax_clip_params.items():
												lowercase__							 =       flax_key.replace("""/"""		,		"""."""	)
												lowercase__							 =       torch_key.replace("""text.token_embedding.embedding"""		,		"""token_embedding.kernel"""	)

												if (
												    torch_key.startswith("""text.transformer"""	)
												    or torch_key.startswith("""text.text_projection"""	)
												    or torch_key.startswith("""text.ln_final"""	)
												    or torch_key.startswith("""text.positional_embedding"""	)
												):
																		lowercase__							 =       torch_key[5:]

												lowercase__							 =       torch_key.replace("""text_projection.kernel"""		,		"""text_projection"""	)
												lowercase__							 =       torch_key.replace("""visual.proj.kernel"""		,		"""visual.proj"""	)
												lowercase__							 =       torch_key.replace(""".scale"""		,		""".weight"""	)
												lowercase__							 =       torch_key.replace(""".kernel"""		,		""".weight"""	)

												if "conv" in torch_key or "downsample.0.weight" in torch_key:
																		lowercase__							 =       v.transpose(3		,		2		,		0		,		1	)

												elif "weight" in torch_key and v.ndim == 2 and "embedding" not in torch_key:
																		# Fully connected layers are transposed, embeddings are not
																		lowercase__							 =       v.T

												lowercase__							 =       v

						lowercase__							 =       _convert_attn_layers(lowercase__	)
						new_torch_params.update(lowercase__	)
						lowercase__							 =       {}

						# Copy flax CLIP backbone params to PyTorch params
						for name, param in new_torch_params.items():
												if name in torch_clip_params.keys():
																		lowercase__							 =       torch.from_numpy(new_torch_params[name]	)
																		torch_clip_params[name].copy_(lowercase__	)
												else:
																		lowercase__							 =       param

						return torch_clip_params, torch_model, attn_params





@torch.no_grad()
def  _A		(							lowercase__		,		lowercase__		,		lowercase__		,		lowercase__		,		lowercase__=None	):
						lowercase__							 =       Repository(lowercase__		,		clone_from=f'''google/{pytorch_dump_folder_path}'''	)
						repo.git_pull()

						if config_path is not None:
												lowercase__							 =       OwlViTConfig.from_pretrained(lowercase__	)
						else:
												lowercase__							 =       OwlViTConfig()

						lowercase__							 =       OwlViTModel(lowercase__	).eval()
						lowercase__							 =       OwlViTForObjectDetection(lowercase__	).eval()

						copy_text_model_and_projection(lowercase__		,		lowercase__	)
						copy_vision_model_and_projection(lowercase__		,		lowercase__	)
						lowercase__							 =       pt_backbone.logit_scale
						copy_flax_attn_params(lowercase__		,		lowercase__	)

						lowercase__							 =       hf_backbone
						copy_class_merge_token(lowercase__		,		lowercase__	)
						copy_class_box_heads(lowercase__		,		lowercase__	)

						# Save HF model
						hf_model.save_pretrained(repo.local_dir	)

						# Initialize image processor
						lowercase__							 =       OwlViTImageProcessor(
						    size=config.vision_config.image_size		,		crop_size=config.vision_config.image_size	)
						# Initialize tokenizer
						lowercase__							 =       CLIPTokenizer.from_pretrained("""openai/clip-vit-base-patch32"""		,		pad_token="""!"""		,		model_max_length=16	)

						# Initialize processor
						lowercase__							 =       OwlViTProcessor(image_processor=lowercase__		,		tokenizer=lowercase__	)
						image_processor.save_pretrained(repo.local_dir	)
						processor.save_pretrained(repo.local_dir	)

						repo.git_add()
						repo.git_commit("""Upload model and processor"""	)
						repo.git_push()


if __name__ == "__main__":
				__A          =    argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    "--owlvit_version",
				    default=None,
				    type=str,
				    required=True,
				    help="OWL-ViT model name [clip_b16, clip_b32, clip_l14].",
				)
				parser.add_argument(
				    "--owlvit_checkpoint", default=None, type=str, required=True, help="Path to flax model checkpoint."
				)
				parser.add_argument("--hf_config", default=None, type=str, required=True, help="Path to HF model config.")
				parser.add_argument(
				    "--pytorch_dump_folder_path", default="hf_model", type=str, help="Path to the output PyTorch model."
				)
				__A          =    parser.parse_args()

				# Initialize PyToch clip model
				__A          =    args.owlvit_version
				if model_name == "clip_b16":
								__A          =    CONFIGS["vit_b16"]
				elif model_name == "clip_b32":
								__A          =    CONFIGS["vit_b32"]
				elif model_name == "clip_l14":
								__A          =    CONFIGS["vit_l14"]

				# Load from checkpoint and convert params to float-32
				__A          =    checkpoints.restore_checkpoint(args.owlvit_checkpoint, target=None)["optimizer"]["target"]
				__A          =    jax.tree_util.tree_map(lambda x: x.astype(jnp.floataa) if x.dtype == jnp.bfloataa else x, variables)
				del variables

				# Convert CLIP backbone
				__A    ,       __A    ,       __A          =    convert_clip_backbone(flax_params, torch_config)

				convert_owlvit_checkpoint(clip_pt, flax_params, attn_params, args.pytorch_dump_folder_path, args.hf_config)








'''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()

__A          =    logging.get_logger(__name__)





def  _A		(							lowercase__		,		lowercase__ = True	):
						lowercase__							 =       {}
						for name in model.attn_processors.keys():
												lowercase__							 =       name.endswith("""attn2.processor"""	) and not (
												    name.startswith("""transformer_in"""	) or """temp_attentions""" in name.split("""."""	)
												)
												lowercase__							 =       model.config.cross_attention_dim if has_cross_attention else None
												if name.startswith("""mid_block"""	):
																		lowercase__							 =       model.config.block_out_channels[-1]
												elif name.startswith("""up_blocks"""	):
																		lowercase__							 =       int(name[len("""up_blocks."""	)]	)
																		lowercase__							 =       list(reversed(model.config.block_out_channels	)	)[block_id]
												elif name.startswith("""down_blocks"""	):
																		lowercase__							 =       int(name[len("""down_blocks."""	)]	)
																		lowercase__							 =       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
																		lowercase__							 =       8 * model.config.attention_head_dim

												lowercase__							 =       LoRAAttnProcessor(hidden_size=lowercase__		,		cross_attention_dim=lowercase__	)
												lowercase__							 =       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 			A							(       __UpperCAmelCase		,						__UpperCAmelCase		,						unittest.TestCase				):
			lowerCamelCase							:							List[Any]       		=  UNetaDConditionModel
			lowerCamelCase							:							int       		=  """sample"""



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




									'''simple docstring'''



									lowercase__							 =       4
									lowercase__							 =       4
									lowercase__							 =       4
									lowercase__							 =       (32, 32)

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

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



			@property
			def A__ (					self       )			->		Any:




									'''simple docstring'''



									return (4, 4, 32, 32)



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




									'''simple docstring'''



									return (4, 4, 32, 32)



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




									'''simple docstring'''



									lowercase__							 =       {
									    """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,
									}
									lowercase__							 =       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 A__ (					self       )			->		str:




									'''simple docstring'''



									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()
									lowercase__							 =       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 A__ (					self       )			->		Dict:




									'''simple docstring'''



									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()

									lowercase__							 =       32

									lowercase__							 =       self.model_class(**lowerCamelCase__       )
									model.to(lowerCamelCase__       )
									model.eval()

									with torch.no_grad():
															lowercase__							 =       model(**lowerCamelCase__       )

															if isinstance(lowerCamelCase__       ,     lowerCamelCase__       ):
																					lowercase__							 =       output.sample

									self.assertIsNotNone(lowerCamelCase__       )
									lowercase__							 =       inputs_dict["""sample"""].shape
									self.assertEqual(output.shape       ,     lowerCamelCase__       ,     """Input and output shapes do not match"""       )



			def A__ (					self       )			->		List[Any]:




									'''simple docstring'''



									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()
									lowercase__							 =       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       )

															lowercase__							 =       model(**lowerCamelCase__       )
															if isinstance(lowerCamelCase__       ,     lowerCamelCase__       ):
																					lowercase__							 =       first.sample

															lowercase__							 =       model(**lowerCamelCase__       )
															if isinstance(lowerCamelCase__       ,     lowerCamelCase__       ):
																					lowercase__							 =       second.sample

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



			def A__ (					self       )			->		Any:




									'''simple docstring'''



									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()

									lowercase__							 =       8

									lowercase__							 =       self.model_class(**lowerCamelCase__       )
									model.to(lowerCamelCase__       )
									model.eval()

									model.set_attention_slice("""auto"""       )
									with torch.no_grad():
															lowercase__							 =       model(**lowerCamelCase__       )
									assert output is not None

									model.set_attention_slice("""max"""       )
									with torch.no_grad():
															lowercase__							 =       model(**lowerCamelCase__       )
									assert output is not None

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



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




									'''simple docstring'''



									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()

									lowercase__							 =       8

									lowercase__							 =       self.model_class(**lowerCamelCase__       )
									model.to(lowerCamelCase__       )

									with torch.no_grad():
															lowercase__							 =       model(**lowerCamelCase__       ).sample

									lowercase__							 =       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():
															lowercase__							 =       model(**lowerCamelCase__       ,     cross_attention_kwargs={"""scale""": 0.0}       ).sample
															lowercase__							 =       model(**lowerCamelCase__       ,     cross_attention_kwargs={"""scale""": 0.5}       ).sample
															lowercase__							 =       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 A__ (					self       )			->		Optional[int]:




									'''simple docstring'''



									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()

									lowercase__							 =       8

									torch.manual_seed(0       )
									lowercase__							 =       self.model_class(**lowerCamelCase__       )
									model.to(lowerCamelCase__       )

									with torch.no_grad():
															lowercase__							 =       model(**lowerCamelCase__       ).sample

									lowercase__							 =       create_lora_layers(lowerCamelCase__       )
									model.set_attn_processor(lowerCamelCase__       )

									with torch.no_grad():
															lowercase__							 =       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       )
															lowercase__							 =       self.model_class(**lowerCamelCase__       )
															new_model.to(lowerCamelCase__       )
															new_model.load_attn_procs(lowerCamelCase__       )

									with torch.no_grad():
															lowercase__							 =       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 A__ (					self       )			->		Any:




									'''simple docstring'''



									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()

									lowercase__							 =       8

									torch.manual_seed(0       )
									lowercase__							 =       self.model_class(**lowerCamelCase__       )
									model.to(lowerCamelCase__       )

									with torch.no_grad():
															lowercase__							 =       model(**lowerCamelCase__       ).sample

									lowercase__							 =       create_lora_layers(lowerCamelCase__       )
									model.set_attn_processor(lowerCamelCase__       )

									with torch.no_grad():
															lowercase__							 =       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       )
															lowercase__							 =       self.model_class(**lowerCamelCase__       )
															new_model.to(lowerCamelCase__       )
															new_model.load_attn_procs(lowerCamelCase__       )

									with torch.no_grad():
															lowercase__							 =       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 A__ (					self       )			->		Dict:




									'''simple docstring'''



									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()

									lowercase__							 =       8

									torch.manual_seed(0       )
									lowercase__							 =       self.model_class(**lowerCamelCase__       )
									model.to(lowerCamelCase__       )

									lowercase__							 =       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       )
															lowercase__							 =       self.model_class(**lowerCamelCase__       )
															new_model.to(lowerCamelCase__       )
															new_model.load_attn_procs(lowerCamelCase__       ,     weight_name="""pytorch_lora_weights.bin"""       )



			def A__ (					self       )			->		List[Any]:




									'''simple docstring'''



									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()

									lowercase__							 =       8

									torch.manual_seed(0       )
									lowercase__							 =       self.model_class(**lowerCamelCase__       )
									model.to(lowerCamelCase__       )

									lowercase__							 =       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       )
															lowercase__							 =       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 A__ (					self       )			->		Any:




									'''simple docstring'''



									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()

									lowercase__							 =       8

									torch.manual_seed(0       )
									lowercase__							 =       self.model_class(**lowerCamelCase__       )
									model.to(lowerCamelCase__       )

									with torch.no_grad():
															lowercase__							 =       model(**lowerCamelCase__       ).sample

									lowercase__							 =       create_lora_layers(lowerCamelCase__       )
									model.set_attn_processor(lowerCamelCase__       )

									with torch.no_grad():
															lowercase__							 =       model(**lowerCamelCase__       ,     cross_attention_kwargs={"""scale""": 0.0}       ).sample

									model.set_attn_processor(AttnProcessor()       )

									with torch.no_grad():
															lowercase__							 =       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 A__ (					self       )			->		Optional[int]:




									'''simple docstring'''



									lowercase__ ,						lowercase__							 =       self.prepare_init_args_and_inputs_for_common()

									lowercase__							 =       4

									torch.manual_seed(0       )
									lowercase__							 =       self.model_class(**lowerCamelCase__       )
									model.to(lowerCamelCase__       )
									lowercase__							 =       create_lora_layers(lowerCamelCase__       )
									model.set_attn_processor(lowerCamelCase__       )

									# default
									with torch.no_grad():
															lowercase__							 =       model(**lowerCamelCase__       ).sample

															model.enable_xformers_memory_efficient_attention()
															lowercase__							 =       model(**lowerCamelCase__       ).sample

															model.disable_xformers_memory_efficient_attention()
															lowercase__							 =       model(**lowerCamelCase__       ).sample

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



			def A__ (					self       )			->		List[Any]:




									'''simple docstring'''



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

									lowercase__							 =       self.model_class(**lowerCamelCase__       )
									model.to(lowerCamelCase__       )
									model.eval()

									with torch.no_grad():
															lowercase__							 =       model(**lowerCamelCase__       )[0]

									model.enable_forward_chunking()
									with torch.no_grad():
															lowercase__							 =       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.






