

'''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()

lowerCAmelCase_     =      logging.get_logger(__name__)

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

		snake_case							=					LoRAAttnProcessor(hidden_size=A       , cross_attention_dim=A				)
		snake_case							=					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   (    __lowerCAmelCase				,				__lowerCAmelCase				,				unittest.TestCase				):
				snake_case_							     = UNetaDConditionModel
				snake_case_							     = '''sample'''

				@property
				def 					_lowerCamelCase    (       self )    ->							Tuple:
					snake_case							=					4
					snake_case							=					4
					snake_case							=					4
					snake_case							=					(32, 32)

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

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

				@property
				def 					_lowerCamelCase    (       self )    ->							Dict:
					return (4, 4, 32, 32)

				@property
				def 					_lowerCamelCase    (       self )    ->							List[str]:
					return (4, 4, 32, 32)

				def 					_lowerCamelCase    (       self )    ->							Dict:
					snake_case							=					{
					    '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							=					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 					_lowerCamelCase    (       self )    ->							Optional[Any]:
					snake_case			,			snake_case							=					self.prepare_init_args_and_inputs_for_common()
					snake_case							=					self.model_class(**lowercase_ )

					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 					_lowerCamelCase    (       self )    ->							Union[str, Any]:
					snake_case			,			snake_case							=					self.prepare_init_args_and_inputs_for_common()

					snake_case							=					32

					snake_case							=					self.model_class(**lowercase_ )
					model.to(lowercase_ )
					model.eval()

					with torch.no_grad():
						snake_case							=					model(**lowercase_ )

						if isinstance(lowercase_,						lowercase_ ):
							snake_case							=					output.sample

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

				def 					_lowerCamelCase    (       self )    ->							Tuple:
					snake_case			,			snake_case							=					self.prepare_init_args_and_inputs_for_common()
					snake_case							=					self.model_class(**lowercase_ )
					model.to(lowercase_ )
					model.eval()

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

						snake_case							=					model(**lowercase_ )
						if isinstance(lowercase_,						lowercase_ ):
							snake_case							=					first.sample

						snake_case							=					model(**lowercase_ )
						if isinstance(lowercase_,						lowercase_ ):
							snake_case							=					second.sample

					snake_case							=					first.cpu().numpy()
					snake_case							=					second.cpu().numpy()
					snake_case							=					out_a[~np.isnan(lowercase_ )]
					snake_case							=					out_a[~np.isnan(lowercase_ )]
					snake_case							=					np.amax(np.abs(out_a - out_a ) )
					self.assertLessEqual(lowercase_,						1E-5 )

				def 					_lowerCamelCase    (       self )    ->							Optional[Any]:
					snake_case			,			snake_case							=					self.prepare_init_args_and_inputs_for_common()

					snake_case							=					8

					snake_case							=					self.model_class(**lowercase_ )
					model.to(lowercase_ )
					model.eval()

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

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

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

				def 					_lowerCamelCase    (       self )    ->							str:
					snake_case			,			snake_case							=					self.prepare_init_args_and_inputs_for_common()

					snake_case							=					8

					snake_case							=					self.model_class(**lowercase_ )
					model.to(lowercase_ )

					with torch.no_grad():
						snake_case							=					model(**lowercase_ ).sample

					snake_case							=					create_lora_layers(lowercase_ )

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

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

					with torch.no_grad():
						snake_case							=					model(**lowercase_,						cross_attention_kwargs={'scale': 0.0} ).sample
						snake_case							=					model(**lowercase_,						cross_attention_kwargs={'scale': 0.5} ).sample
						snake_case							=					model(**lowercase_,						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 					_lowerCamelCase    (       self )    ->							Tuple:
					snake_case			,			snake_case							=					self.prepare_init_args_and_inputs_for_common()

					snake_case							=					8

					torch.manual_seed(0 )
					snake_case							=					self.model_class(**lowercase_ )
					model.to(lowercase_ )

					with torch.no_grad():
						snake_case							=					model(**lowercase_ ).sample

					snake_case							=					create_lora_layers(lowercase_ )
					model.set_attn_processor(lowercase_ )

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

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

					with torch.no_grad():
						snake_case							=					new_model(**lowercase_,						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 					_lowerCamelCase    (       self )    ->							Optional[Any]:
					snake_case			,			snake_case							=					self.prepare_init_args_and_inputs_for_common()

					snake_case							=					8

					torch.manual_seed(0 )
					snake_case							=					self.model_class(**lowercase_ )
					model.to(lowercase_ )

					with torch.no_grad():
						snake_case							=					model(**lowercase_ ).sample

					snake_case							=					create_lora_layers(lowercase_ )
					model.set_attn_processor(lowercase_ )

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

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

					with torch.no_grad():
						snake_case							=					new_model(**lowercase_,						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 					_lowerCamelCase    (       self )    ->							Optional[Any]:
					# enable deterministic behavior for gradient checkpointing
					snake_case			,			snake_case							=					self.prepare_init_args_and_inputs_for_common()

					snake_case							=					8

					torch.manual_seed(0 )
					snake_case							=					self.model_class(**lowercase_ )
					model.to(lowercase_ )

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

				def 					_lowerCamelCase    (       self )    ->							str:
					snake_case			,			snake_case							=					self.prepare_init_args_and_inputs_for_common()

					snake_case							=					8

					torch.manual_seed(0 )
					snake_case							=					self.model_class(**lowercase_ )
					model.to(lowercase_ )

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

				def 					_lowerCamelCase    (       self )    ->							Optional[Any]:
					snake_case			,			snake_case							=					self.prepare_init_args_and_inputs_for_common()

					snake_case							=					8

					torch.manual_seed(0 )
					snake_case							=					self.model_class(**lowercase_ )
					model.to(lowercase_ )

					with torch.no_grad():
						snake_case							=					model(**lowercase_ ).sample

					snake_case							=					create_lora_layers(lowercase_ )
					model.set_attn_processor(lowercase_ )

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

					model.set_attn_processor(AttnProcessor() )

					with torch.no_grad():
						snake_case							=					model(**lowercase_ ).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 					_lowerCamelCase    (       self )    ->							int:
					# enable deterministic behavior for gradient checkpointing
					snake_case			,			snake_case							=					self.prepare_init_args_and_inputs_for_common()

					snake_case							=					4

					torch.manual_seed(0 )
					snake_case							=					self.model_class(**lowercase_ )
					model.to(lowercase_ )
					snake_case							=					create_lora_layers(lowercase_ )
					model.set_attn_processor(lowercase_ )

					# default
					with torch.no_grad():
						snake_case							=					model(**lowercase_ ).sample

						model.enable_xformers_memory_efficient_attention()
						snake_case							=					model(**lowercase_ ).sample

						model.disable_xformers_memory_efficient_attention()
						snake_case							=					model(**lowercase_ ).sample

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






				def 					_lowerCamelCase    (       self )    ->							List[str]:
					snake_case			,			snake_case							=					self.prepare_init_args_and_inputs_for_common()
					snake_case							=					32

					snake_case							=					self.model_class(**lowercase_ )
					model.to(lowercase_ )
					model.eval()

					with torch.no_grad():
						snake_case							=					model(**lowercase_ )[0]

					model.enable_forward_chunking()
					with torch.no_grad():
						snake_case							=					model(**lowercase_ )[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







class lowerCamelCase   :

				def __init__(       self,						lowercase_ )    ->							None:
					snake_case							=					value
					snake_case							=					None
					snake_case							=					None







class lowerCamelCase   :
				snake_case_							     = 42

				def __init__(       self )    ->							None:
					snake_case							=					0

				def 					_lowerCamelCase    (       self,						lowercase_,						lowercase_ )    ->							None:
					if node is None:
						return

					if path_sum == self.target:
						self.paths += 1

					if node.left:
						self.depth_first_search(node.left,						path_sum + node.left.value )
					if node.right:
						self.depth_first_search(node.right,						path_sum + node.right.value )






				def 					_lowerCamelCase    (       self,						lowercase_,						lowercase_ = None )    ->							int:
					if node is None:
						return 0
					if target is not None:
						snake_case							=					target

					self.depth_first_search(lowercase_,						node.value )
					self.path_sum(node.left )
					self.path_sum(node.right )

					return self.paths


if __name__ == "__main__":
						import doctest

						doctest.testmod()





