

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        _lowercase  (					a_					:       Dict       ,a_					:       bool = True					)					->						List[str]:




					'''simple docstring'''







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

										__magic_name__				=     LoRAAttnProcessor(hidden_size=a_       ,cross_attention_dim=a_					)
										__magic_name__				=     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 					__UpperCamelCase					(       SCREAMING_SNAKE_CASE	,    SCREAMING_SNAKE_CASE	,    unittest.TestCase      ):
	_lowercase	: List[Any]												=	UNetaDConditionModel
	_lowercase	: Optional[int]												=	"sample"







	@property
	def 						_SCREAMING_SNAKE_CASE				( self:			List[str]   ):

						'''simple docstring'''





						__magic_name__				=     4
						__magic_name__				=     4
						__magic_name__				=     4
						__magic_name__				=     (32, 32)

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

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







	@property
	def 						_SCREAMING_SNAKE_CASE				( self:			Any   ):

						'''simple docstring'''





						return (4, 4, 32, 32)







	@property
	def 						_SCREAMING_SNAKE_CASE				( self:			Union[str, Any]   ):

						'''simple docstring'''





						return (4, 4, 32, 32)







	def 						_SCREAMING_SNAKE_CASE				( self:			Dict   ):

						'''simple docstring'''





						__magic_name__				=     {
						    '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,
						}
						__magic_name__				=     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 						_SCREAMING_SNAKE_CASE				( self:			List[Any]   ):

						'''simple docstring'''





						__magic_name__,						__magic_name__				=     self.prepare_init_args_and_inputs_for_common()
						__magic_name__				=     self.model_class(**__UpperCamelCase   )

						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 						_SCREAMING_SNAKE_CASE				( self:			int   ):

						'''simple docstring'''





						__magic_name__,						__magic_name__				=     self.prepare_init_args_and_inputs_for_common()

						__magic_name__				=     32

						__magic_name__				=     self.model_class(**__UpperCamelCase   )
						model.to(__UpperCamelCase   )
						model.eval()

						with torch.no_grad():
											__magic_name__				=     model(**__UpperCamelCase   )

											if isinstance(__UpperCamelCase	,			__UpperCamelCase   ):
																__magic_name__				=     output.sample

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







	def 						_SCREAMING_SNAKE_CASE				( self:			int   ):

						'''simple docstring'''





						__magic_name__,						__magic_name__				=     self.prepare_init_args_and_inputs_for_common()
						__magic_name__				=     self.model_class(**__UpperCamelCase   )
						model.to(__UpperCamelCase   )
						model.eval()

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

											__magic_name__				=     model(**__UpperCamelCase   )
											if isinstance(__UpperCamelCase	,			__UpperCamelCase   ):
																__magic_name__				=     first.sample

											__magic_name__				=     model(**__UpperCamelCase   )
											if isinstance(__UpperCamelCase	,			__UpperCamelCase   ):
																__magic_name__				=     second.sample

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







	def 						_SCREAMING_SNAKE_CASE				( self:			str   ):

						'''simple docstring'''





						__magic_name__,						__magic_name__				=     self.prepare_init_args_and_inputs_for_common()

						__magic_name__				=     8

						__magic_name__				=     self.model_class(**__UpperCamelCase   )
						model.to(__UpperCamelCase   )
						model.eval()

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

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

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







	def 						_SCREAMING_SNAKE_CASE				( self:			List[str]   ):

						'''simple docstring'''





						__magic_name__,						__magic_name__				=     self.prepare_init_args_and_inputs_for_common()

						__magic_name__				=     8

						__magic_name__				=     self.model_class(**__UpperCamelCase   )
						model.to(__UpperCamelCase   )

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

						__magic_name__				=     create_lora_layers(__UpperCamelCase   )

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

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

						with torch.no_grad():
											__magic_name__				=     model(**__UpperCamelCase	,			cross_attention_kwargs={'scale': 0.0}   ).sample
											__magic_name__				=     model(**__UpperCamelCase	,			cross_attention_kwargs={'scale': 0.5}   ).sample
											__magic_name__				=     model(**__UpperCamelCase	,			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 						_SCREAMING_SNAKE_CASE				( self:			int   ):

						'''simple docstring'''





						__magic_name__,						__magic_name__				=     self.prepare_init_args_and_inputs_for_common()

						__magic_name__				=     8

						torch.manual_seed(0   )
						__magic_name__				=     self.model_class(**__UpperCamelCase   )
						model.to(__UpperCamelCase   )

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

						__magic_name__				=     create_lora_layers(__UpperCamelCase   )
						model.set_attn_processor(__UpperCamelCase   )

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

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

						with torch.no_grad():
											__magic_name__				=     new_model(**__UpperCamelCase	,			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 						_SCREAMING_SNAKE_CASE				( self:			int   ):

						'''simple docstring'''





						__magic_name__,						__magic_name__				=     self.prepare_init_args_and_inputs_for_common()

						__magic_name__				=     8

						torch.manual_seed(0   )
						__magic_name__				=     self.model_class(**__UpperCamelCase   )
						model.to(__UpperCamelCase   )

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

						__magic_name__				=     create_lora_layers(__UpperCamelCase   )
						model.set_attn_processor(__UpperCamelCase   )

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

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

						with torch.no_grad():
											__magic_name__				=     new_model(**__UpperCamelCase	,			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 						_SCREAMING_SNAKE_CASE				( self:			str   ):

						'''simple docstring'''





						__magic_name__,						__magic_name__				=     self.prepare_init_args_and_inputs_for_common()

						__magic_name__				=     8

						torch.manual_seed(0   )
						__magic_name__				=     self.model_class(**__UpperCamelCase   )
						model.to(__UpperCamelCase   )

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







	def 						_SCREAMING_SNAKE_CASE				( self:			Optional[Any]   ):

						'''simple docstring'''





						__magic_name__,						__magic_name__				=     self.prepare_init_args_and_inputs_for_common()

						__magic_name__				=     8

						torch.manual_seed(0   )
						__magic_name__				=     self.model_class(**__UpperCamelCase   )
						model.to(__UpperCamelCase   )

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







	def 						_SCREAMING_SNAKE_CASE				( self:			int   ):

						'''simple docstring'''





						__magic_name__,						__magic_name__				=     self.prepare_init_args_and_inputs_for_common()

						__magic_name__				=     8

						torch.manual_seed(0   )
						__magic_name__				=     self.model_class(**__UpperCamelCase   )
						model.to(__UpperCamelCase   )

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

						__magic_name__				=     create_lora_layers(__UpperCamelCase   )
						model.set_attn_processor(__UpperCamelCase   )

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

						model.set_attn_processor(AttnProcessor()   )

						with torch.no_grad():
											__magic_name__				=     model(**__UpperCamelCase   ).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 						_SCREAMING_SNAKE_CASE				( self:			Optional[Any]   ):

						'''simple docstring'''





						__magic_name__,						__magic_name__				=     self.prepare_init_args_and_inputs_for_common()

						__magic_name__				=     4

						torch.manual_seed(0   )
						__magic_name__				=     self.model_class(**__UpperCamelCase   )
						model.to(__UpperCamelCase   )
						__magic_name__				=     create_lora_layers(__UpperCamelCase   )
						model.set_attn_processor(__UpperCamelCase   )

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

											model.enable_xformers_memory_efficient_attention()
											__magic_name__				=     model(**__UpperCamelCase   ).sample

											model.disable_xformers_memory_efficient_attention()
											__magic_name__				=     model(**__UpperCamelCase   ).sample

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







	def 						_SCREAMING_SNAKE_CASE				( self:			Any   ):

						'''simple docstring'''





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

						__magic_name__				=     self.model_class(**__UpperCamelCase   )
						model.to(__UpperCamelCase   )
						model.eval()

						with torch.no_grad():
											__magic_name__				=     model(**__UpperCamelCase   )[0]

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







from hashlib import mda, shaaaa

A__							=				(shaaaa, mda)

class 					__UpperCamelCase					:







	def __init__( self:			Optional[int]	,			__UpperCamelCase:			int = 8   ):

						'''simple docstring'''





						__magic_name__				=     0b0
						__magic_name__				=     size







	def 						_SCREAMING_SNAKE_CASE				( self:			Tuple	,			__UpperCamelCase:			str   ):

						'''simple docstring'''





						__magic_name__				=     self.hash_(__UpperCamelCase   )
						self.bitarray |= h







	def 						_SCREAMING_SNAKE_CASE				( self:			List[str]	,			__UpperCamelCase:			str   ):

						'''simple docstring'''





						__magic_name__				=     self.hash_(__UpperCamelCase   )
						return (h & self.bitarray) == h







	def __contains__( self:			Optional[int]	,			__UpperCamelCase:			str   ):

						'''simple docstring'''





						return self.exists(__UpperCamelCase   )







	def 						_SCREAMING_SNAKE_CASE				( self:			int	,			__UpperCamelCase:			int   ):

						'''simple docstring'''





						__magic_name__				=     bin(__UpperCamelCase   )[2:]
						return res.zfill(self.size   )







	@property
	def 						_SCREAMING_SNAKE_CASE				( self:			str   ):

						'''simple docstring'''





						return self.format_bin(self.bitarray   )







	def 						_SCREAMING_SNAKE_CASE				( self:			str	,			__UpperCamelCase:			str   ):

						'''simple docstring'''





						__magic_name__				=     0b0
						for func in HASH_FUNCTIONS:
											__magic_name__				=     (
											    int.from_bytes(func(value.encode()   ).digest()	,			'little'   ) % self.size
											)
											res |= 2**position
						return res







	def 						_SCREAMING_SNAKE_CASE				( self:			Dict	,			__UpperCamelCase:			str   ):

						'''simple docstring'''





						return self.format_bin(self.hash_(__UpperCamelCase   )   )







	@property
	def 						_SCREAMING_SNAKE_CASE				( self:			Optional[int]   ):

						'''simple docstring'''





						__magic_name__				=     bin(self.bitarray   ).count('1'   )
						return (n_ones / self.size) ** len(__UpperCamelCase   )




