





'''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()

__UpperCamelCase										=  logging.get_logger(__name__)







def 				_a	(					_lowerCamelCase						,  _lowerCamelCase = True   )   ->							Dict:



			"""simple docstring"""


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

						__snake_case				:			Dict       			=    LoRAAttnProcessor(hidden_size=_lowerCamelCase						,  cross_attention_dim=_lowerCamelCase   )
						__snake_case				:			Any       			=    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   (				__lowercase	,				__lowercase	,				unittest.TestCase		):
		lowercase__:      int							    =      UNetaDConditionModel
		lowercase__:      str							    =      '''sample'''

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


					"""simple docstring"""

					__snake_case				:			List[Any]       			=    4
					__snake_case				:			Tuple       			=    4
					__snake_case				:			Optional[int]       			=    4
					__snake_case				:			List[Any]       			=    (32, 32)

					__snake_case				:			List[str]       			=    floats_tensor((batch_size, num_channels, num_frames) + sizes ).to(__magic_name__ )
					__snake_case				:			str       			=    torch.tensor([10] ).to(__magic_name__ )
					__snake_case				:			Optional[Any]       			=    floats_tensor((batch_size, 4, 32) ).to(__magic_name__ )

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

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


					"""simple docstring"""

					return (4, 4, 32, 32)

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


					"""simple docstring"""

					return (4, 4, 32, 32)

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


					"""simple docstring"""

					__snake_case				:			Tuple       			=    {
					    """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,
					}
					__snake_case				:			Optional[int]       			=    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   lowercase__    (      self				:      Tuple )				->						Tuple:


					"""simple docstring"""

					__snake_case			,    __snake_case				:			Tuple       			=    self.prepare_init_args_and_inputs_for_common()
					__snake_case				:			int       			=    self.model_class(**__magic_name__ )

					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   lowercase__    (      self				:      str )				->						Tuple:


					"""simple docstring"""

					__snake_case			,    __snake_case				:			Union[str, Any]       			=    self.prepare_init_args_and_inputs_for_common()

					__snake_case				:			Optional[Any]       			=    32

					__snake_case				:			Optional[int]       			=    self.model_class(**__magic_name__ )
					model.to(__magic_name__ )
					model.eval()

					with torch.no_grad():
								__snake_case				:			List[Any]       			=    model(**__magic_name__ )

								if isinstance(__magic_name__     ,			__magic_name__ ):
											__snake_case				:			Optional[Any]       			=    output.sample

					self.assertIsNotNone(__magic_name__ )
					__snake_case				:			int       			=    inputs_dict["""sample"""].shape
					self.assertEqual(output.shape     ,			__magic_name__     ,			"""Input and output shapes do not match""" )

		def   lowercase__    (      self				:      str )				->						int:


					"""simple docstring"""

					__snake_case			,    __snake_case				:			int       			=    self.prepare_init_args_and_inputs_for_common()
					__snake_case				:			Union[str, Any]       			=    self.model_class(**__magic_name__ )
					model.to(__magic_name__ )
					model.eval()

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

								__snake_case				:			List[Any]       			=    model(**__magic_name__ )
								if isinstance(__magic_name__     ,			__magic_name__ ):
											__snake_case				:			List[Any]       			=    first.sample

								__snake_case				:			Any       			=    model(**__magic_name__ )
								if isinstance(__magic_name__     ,			__magic_name__ ):
											__snake_case				:			Union[str, Any]       			=    second.sample

					__snake_case				:			List[str]       			=    first.cpu().numpy()
					__snake_case				:			List[str]       			=    second.cpu().numpy()
					__snake_case				:			Dict       			=    out_a[~np.isnan(__magic_name__ )]
					__snake_case				:			List[Any]       			=    out_a[~np.isnan(__magic_name__ )]
					__snake_case				:			List[Any]       			=    np.amax(np.abs(out_a - out_a ) )
					self.assertLessEqual(__magic_name__     ,			1E-5 )

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


					"""simple docstring"""

					__snake_case			,    __snake_case				:			Union[str, Any]       			=    self.prepare_init_args_and_inputs_for_common()

					__snake_case				:			int       			=    8

					__snake_case				:			Tuple       			=    self.model_class(**__magic_name__ )
					model.to(__magic_name__ )
					model.eval()

					model.set_attention_slice("""auto""" )
					with torch.no_grad():
								__snake_case				:			Any       			=    model(**__magic_name__ )
					assert output is not None

					model.set_attention_slice("""max""" )
					with torch.no_grad():
								__snake_case				:			List[Any]       			=    model(**__magic_name__ )
					assert output is not None

					model.set_attention_slice(2 )
					with torch.no_grad():
								__snake_case				:			Union[str, Any]       			=    model(**__magic_name__ )
					assert output is not None

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


					"""simple docstring"""

					__snake_case			,    __snake_case				:			Dict       			=    self.prepare_init_args_and_inputs_for_common()

					__snake_case				:			List[Any]       			=    8

					__snake_case				:			Any       			=    self.model_class(**__magic_name__ )
					model.to(__magic_name__ )

					with torch.no_grad():
								__snake_case				:			Optional[Any]       			=    model(**__magic_name__ ).sample

					__snake_case				:			Optional[Any]       			=    create_lora_layers(__magic_name__ )

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

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

					with torch.no_grad():
								__snake_case				:			Dict       			=    model(**__magic_name__     ,			cross_attention_kwargs={"""scale""": 0.0} ).sample
								__snake_case				:			List[Any]       			=    model(**__magic_name__     ,			cross_attention_kwargs={"""scale""": 0.5} ).sample
								__snake_case				:			Tuple       			=    model(**__magic_name__     ,			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   lowercase__    (      self				:      List[str] )				->						List[Any]:


					"""simple docstring"""

					__snake_case			,    __snake_case				:			Optional[int]       			=    self.prepare_init_args_and_inputs_for_common()

					__snake_case				:			Union[str, Any]       			=    8

					torch.manual_seed(0 )
					__snake_case				:			List[Any]       			=    self.model_class(**__magic_name__ )
					model.to(__magic_name__ )

					with torch.no_grad():
								__snake_case				:			int       			=    model(**__magic_name__ ).sample

					__snake_case				:			Optional[int]       			=    create_lora_layers(__magic_name__ )
					model.set_attn_processor(__magic_name__ )

					with torch.no_grad():
								__snake_case				:			List[str]       			=    model(**__magic_name__     ,			cross_attention_kwargs={"""scale""": 0.5} ).sample

					with tempfile.TemporaryDirectory() as tmpdirname:
								model.save_attn_procs(__magic_name__ )
								self.assertTrue(os.path.isfile(os.path.join(__magic_name__     ,			"""pytorch_lora_weights.bin""" ) ) )
								torch.manual_seed(0 )
								__snake_case				:			Optional[int]       			=    self.model_class(**__magic_name__ )
								new_model.to(__magic_name__ )
								new_model.load_attn_procs(__magic_name__ )

					with torch.no_grad():
								__snake_case				:			Dict       			=    new_model(**__magic_name__     ,			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   lowercase__    (      self				:      Optional[Any] )				->						List[Any]:


					"""simple docstring"""

					__snake_case			,    __snake_case				:			str       			=    self.prepare_init_args_and_inputs_for_common()

					__snake_case				:			Optional[Any]       			=    8

					torch.manual_seed(0 )
					__snake_case				:			str       			=    self.model_class(**__magic_name__ )
					model.to(__magic_name__ )

					with torch.no_grad():
								__snake_case				:			int       			=    model(**__magic_name__ ).sample

					__snake_case				:			Optional[int]       			=    create_lora_layers(__magic_name__ )
					model.set_attn_processor(__magic_name__ )

					with torch.no_grad():
								__snake_case				:			Any       			=    model(**__magic_name__     ,			cross_attention_kwargs={"""scale""": 0.5} ).sample

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

					with torch.no_grad():
								__snake_case				:			int       			=    new_model(**__magic_name__     ,			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   lowercase__    (      self				:      List[str] )				->						Optional[Any]:


					"""simple docstring"""

					__snake_case			,    __snake_case				:			str       			=    self.prepare_init_args_and_inputs_for_common()

					__snake_case				:			Optional[Any]       			=    8

					torch.manual_seed(0 )
					__snake_case				:			Tuple       			=    self.model_class(**__magic_name__ )
					model.to(__magic_name__ )

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

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


					"""simple docstring"""

					__snake_case			,    __snake_case				:			Union[str, Any]       			=    self.prepare_init_args_and_inputs_for_common()

					__snake_case				:			Optional[Any]       			=    8

					torch.manual_seed(0 )
					__snake_case				:			Optional[int]       			=    self.model_class(**__magic_name__ )
					model.to(__magic_name__ )

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

		def   lowercase__    (      self				:      Any )				->						Dict:


					"""simple docstring"""

					__snake_case			,    __snake_case				:			List[Any]       			=    self.prepare_init_args_and_inputs_for_common()

					__snake_case				:			Optional[Any]       			=    8

					torch.manual_seed(0 )
					__snake_case				:			List[Any]       			=    self.model_class(**__magic_name__ )
					model.to(__magic_name__ )

					with torch.no_grad():
								__snake_case				:			List[str]       			=    model(**__magic_name__ ).sample

					__snake_case				:			Optional[int]       			=    create_lora_layers(__magic_name__ )
					model.set_attn_processor(__magic_name__ )

					with torch.no_grad():
								__snake_case				:			Tuple       			=    model(**__magic_name__     ,			cross_attention_kwargs={"""scale""": 0.0} ).sample

					model.set_attn_processor(AttnProcessor() )

					with torch.no_grad():
								__snake_case				:			Optional[int]       			=    model(**__magic_name__ ).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   lowercase__    (      self				:      List[Any] )				->						List[str]:


					"""simple docstring"""

					__snake_case			,    __snake_case				:			str       			=    self.prepare_init_args_and_inputs_for_common()

					__snake_case				:			Union[str, Any]       			=    4

					torch.manual_seed(0 )
					__snake_case				:			Union[str, Any]       			=    self.model_class(**__magic_name__ )
					model.to(__magic_name__ )
					__snake_case				:			Optional[Any]       			=    create_lora_layers(__magic_name__ )
					model.set_attn_processor(__magic_name__ )

					# default
					with torch.no_grad():
								__snake_case				:			Dict       			=    model(**__magic_name__ ).sample

								model.enable_xformers_memory_efficient_attention()
								__snake_case				:			Union[str, Any]       			=    model(**__magic_name__ ).sample

								model.disable_xformers_memory_efficient_attention()
								__snake_case				:			Union[str, Any]       			=    model(**__magic_name__ ).sample

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

		def   lowercase__    (      self				:      Optional[int] )				->						str:


					"""simple docstring"""

					__snake_case			,    __snake_case				:			Tuple       			=    self.prepare_init_args_and_inputs_for_common()
					__snake_case				:			Tuple       			=    32

					__snake_case				:			Dict       			=    self.model_class(**__magic_name__ )
					model.to(__magic_name__ )
					model.eval()

					with torch.no_grad():
								__snake_case				:			Optional[int]       			=    model(**__magic_name__ )[0]

					model.enable_forward_chunking()
					with torch.no_grad():
								__snake_case				:			Tuple       			=    model(**__magic_name__ )[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.









'''simple docstring'''






from __future__ import annotations

from typing import Any




class    _A   :

		def __init__(      self				:      Optional[int]     ,			__magic_name__				:      Any     ,			__magic_name__				:      Any )				->						None:  # noqa: A002


					"""simple docstring"""

					__snake_case				:			List[Any]       			=    item
					__snake_case				:			List[Any]       			=    next





class    _A   :

		def __init__(      self				:      str )				->						None:


					"""simple docstring"""

					__snake_case				:			Node | None       			=    None
					__snake_case				:			Optional[Any]       			=    0

		def   lowercase__    (      self				:      Optional[Any]     ,			__magic_name__				:      Any )				->						None:


					"""simple docstring"""

					__snake_case				:			int       			=    Node(__magic_name__     ,			self.head )
					self.size += 1

		def   lowercase__    (      self				:      Tuple )				->						Any:


					"""simple docstring"""

					if self.head is None:
								return None
					else:
								__snake_case				:			Union[str, Any]       			=    self.head.item
								__snake_case				:			Union[str, Any]       			=    self.head.next
								self.size -= 1
								return item

		def   lowercase__    (      self				:      str )				->						bool:


					"""simple docstring"""

					return self.head is None

		def __str__(      self				:      str )				->						str:


					"""simple docstring"""

					if self.is_empty():
								return ""
					else:
								__snake_case				:			Dict       			=    self.head
								__snake_case				:			Optional[int]       			=    """"""
								__snake_case				:			list[str]       			=    []
								while iterate:
											item_list.append(str(iterate.item ) )
											__snake_case				:			int       			=    iterate.next

								__snake_case				:			Union[str, Any]       			=    """ --> """.join(__magic_name__ )

								return item_str

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


					"""simple docstring"""

					return self.size


