
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()

__snake_case					:							Optional[Any]           =							logging.get_logger(__name__)




def 		A						(  SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE = True							):




			"""simple docstring"""


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

						UpperCAmelCase__		:Tuple 		=    LoRAAttnProcessor(hidden_size=SCREAMING_SNAKE_CASE     ,						cross_attention_dim=SCREAMING_SNAKE_CASE							)
						UpperCAmelCase__		:List[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 	UpperCamelCase__       (  UpperCAmelCase__						,			UpperCAmelCase__						,			unittest.TestCase):

						'''simple docstring'''



						__a     :    Optional[int]												=			UNetaDConditionModel
						__a     :    Dict												=			"""sample"""


						@property
						def    A__       (  self	)  ->Union[str, Any]:
									UpperCAmelCase__		:Any 		=    4
									UpperCAmelCase__		:Optional[Any] 		=    4
									UpperCAmelCase__		:Optional[int] 		=    4
									UpperCAmelCase__		:Tuple 		=    (32, 32)

									UpperCAmelCase__		:str 		=    floats_tensor((batch_size, num_channels, num_frames) + sizes	).to(A	)
									UpperCAmelCase__		:Union[str, Any] 		=    torch.tensor([10]	).to(A	)
									UpperCAmelCase__		:Tuple 		=    floats_tensor((batch_size, 4, 32)	).to(A	)

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


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


						@property
						def    A__       (  self	)  ->str:
									return (4, 4, 32, 32)


						def    A__       (  self	)  ->Optional[int]:
									UpperCAmelCase__		:int 		=    {
									    '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__		:List[str] 		=    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	)  ->Dict:
									UpperCAmelCase__		,       UpperCAmelCase__		:Union[str, Any] 		=    self.prepare_init_args_and_inputs_for_common()
									UpperCAmelCase__		:Optional[Any] 		=    self.model_class(**A	)

									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	)  ->int:
									UpperCAmelCase__		,       UpperCAmelCase__		:Optional[int] 		=    self.prepare_init_args_and_inputs_for_common()

									UpperCAmelCase__		:Optional[Any] 		=    32

									UpperCAmelCase__		:int 		=    self.model_class(**A	)
									model.to(A	)
									model.eval()

									with torch.no_grad():
												UpperCAmelCase__		:List[Any] 		=    model(**A	)

												if isinstance(A  , A	):
															UpperCAmelCase__		:str 		=    output.sample

									self.assertIsNotNone(A	)
									UpperCAmelCase__		:Optional[Any] 		=    inputs_dict['sample'].shape
									self.assertEqual(output.shape  , A  , 'Input and output shapes do not match'	)


						def    A__       (  self	)  ->Any:
									UpperCAmelCase__		,       UpperCAmelCase__		:List[Any] 		=    self.prepare_init_args_and_inputs_for_common()
									UpperCAmelCase__		:str 		=    self.model_class(**A	)
									model.to(A	)
									model.eval()

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

												UpperCAmelCase__		:Union[str, Any] 		=    model(**A	)
												if isinstance(A  , A	):
															UpperCAmelCase__		:int 		=    first.sample

												UpperCAmelCase__		:List[str] 		=    model(**A	)
												if isinstance(A  , A	):
															UpperCAmelCase__		:List[str] 		=    second.sample

									UpperCAmelCase__		:Dict 		=    first.cpu().numpy()
									UpperCAmelCase__		:int 		=    second.cpu().numpy()
									UpperCAmelCase__		:Optional[Any] 		=    out_a[~np.isnan(A	)]
									UpperCAmelCase__		:Optional[int] 		=    out_a[~np.isnan(A	)]
									UpperCAmelCase__		:Optional[int] 		=    np.amax(np.abs(out_a - out_a	)	)
									self.assertLessEqual(A  , 1e-5	)


						def    A__       (  self	)  ->List[Any]:
									UpperCAmelCase__		,       UpperCAmelCase__		:List[str] 		=    self.prepare_init_args_and_inputs_for_common()

									UpperCAmelCase__		:Optional[Any] 		=    8

									UpperCAmelCase__		:List[Any] 		=    self.model_class(**A	)
									model.to(A	)
									model.eval()

									model.set_attention_slice('auto'	)
									with torch.no_grad():
												UpperCAmelCase__		:Any 		=    model(**A	)
									assert output is not None

									model.set_attention_slice('max'	)
									with torch.no_grad():
												UpperCAmelCase__		:Optional[Any] 		=    model(**A	)
									assert output is not None

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


						def    A__       (  self	)  ->Dict:
									UpperCAmelCase__		,       UpperCAmelCase__		:Optional[Any] 		=    self.prepare_init_args_and_inputs_for_common()

									UpperCAmelCase__		:Tuple 		=    8

									UpperCAmelCase__		:Tuple 		=    self.model_class(**A	)
									model.to(A	)

									with torch.no_grad():
												UpperCAmelCase__		:List[str] 		=    model(**A	).sample

									UpperCAmelCase__		:Dict 		=    create_lora_layers(A	)

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

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

									with torch.no_grad():
												UpperCAmelCase__		:Dict 		=    model(**A  , cross_attention_kwargs={'scale': 0.0}	).sample
												UpperCAmelCase__		:Dict 		=    model(**A  , cross_attention_kwargs={'scale': 0.5}	).sample
												UpperCAmelCase__		:Dict 		=    model(**A  , 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]:
									UpperCAmelCase__		,       UpperCAmelCase__		:Tuple 		=    self.prepare_init_args_and_inputs_for_common()

									UpperCAmelCase__		:int 		=    8

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

									with torch.no_grad():
												UpperCAmelCase__		:Optional[Any] 		=    model(**A	).sample

									UpperCAmelCase__		:str 		=    create_lora_layers(A	)
									model.set_attn_processor(A	)

									with torch.no_grad():
												UpperCAmelCase__		:List[Any] 		=    model(**A  , cross_attention_kwargs={'scale': 0.5}	).sample

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

									with torch.no_grad():
												UpperCAmelCase__		:Dict 		=    new_model(**A  , 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	)  ->List[Any]:
									UpperCAmelCase__		,       UpperCAmelCase__		:str 		=    self.prepare_init_args_and_inputs_for_common()

									UpperCAmelCase__		:int 		=    8

									torch.manual_seed(0	)
									UpperCAmelCase__		:int 		=    self.model_class(**A	)
									model.to(A	)

									with torch.no_grad():
												UpperCAmelCase__		:List[Any] 		=    model(**A	).sample

									UpperCAmelCase__		:List[Any] 		=    create_lora_layers(A	)
									model.set_attn_processor(A	)

									with torch.no_grad():
												UpperCAmelCase__		:str 		=    model(**A  , cross_attention_kwargs={'scale': 0.5}	).sample

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

									with torch.no_grad():
												UpperCAmelCase__		:Tuple 		=    new_model(**A  , 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	)  ->Optional[Any]:
									# enable deterministic behavior for gradient checkpointing
									UpperCAmelCase__		,       UpperCAmelCase__		:Optional[int] 		=    self.prepare_init_args_and_inputs_for_common()

									UpperCAmelCase__		:int 		=    8

									torch.manual_seed(0	)
									UpperCAmelCase__		:int 		=    self.model_class(**A	)
									model.to(A	)

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


						def    A__       (  self	)  ->List[Any]:
									UpperCAmelCase__		,       UpperCAmelCase__		:Optional[int] 		=    self.prepare_init_args_and_inputs_for_common()

									UpperCAmelCase__		:Tuple 		=    8

									torch.manual_seed(0	)
									UpperCAmelCase__		:Any 		=    self.model_class(**A	)
									model.to(A	)

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


						def    A__       (  self	)  ->List[Any]:
									UpperCAmelCase__		,       UpperCAmelCase__		:int 		=    self.prepare_init_args_and_inputs_for_common()

									UpperCAmelCase__		:Tuple 		=    8

									torch.manual_seed(0	)
									UpperCAmelCase__		:Optional[Any] 		=    self.model_class(**A	)
									model.to(A	)

									with torch.no_grad():
												UpperCAmelCase__		:Optional[Any] 		=    model(**A	).sample

									UpperCAmelCase__		:Optional[Any] 		=    create_lora_layers(A	)
									model.set_attn_processor(A	)

									with torch.no_grad():
												UpperCAmelCase__		:Optional[Any] 		=    model(**A  , cross_attention_kwargs={'scale': 0.0}	).sample

									model.set_attn_processor(AttnProcessor()	)

									with torch.no_grad():
												UpperCAmelCase__		:Union[str, Any] 		=    model(**A	).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	)  ->List[str]:
									# enable deterministic behavior for gradient checkpointing
									UpperCAmelCase__		,       UpperCAmelCase__		:Any 		=    self.prepare_init_args_and_inputs_for_common()

									UpperCAmelCase__		:Dict 		=    4

									torch.manual_seed(0	)
									UpperCAmelCase__		:str 		=    self.model_class(**A	)
									model.to(A	)
									UpperCAmelCase__		:Any 		=    create_lora_layers(A	)
									model.set_attn_processor(A	)

									# default
									with torch.no_grad():
												UpperCAmelCase__		:Union[str, Any] 		=    model(**A	).sample

												model.enable_xformers_memory_efficient_attention()
												UpperCAmelCase__		:Tuple 		=    model(**A	).sample

												model.disable_xformers_memory_efficient_attention()
												UpperCAmelCase__		:str 		=    model(**A	).sample

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


						def    A__       (  self	)  ->Union[str, Any]:
									UpperCAmelCase__		,       UpperCAmelCase__		:Any 		=    self.prepare_init_args_and_inputs_for_common()
									UpperCAmelCase__		:List[str] 		=    32

									UpperCAmelCase__		:int 		=    self.model_class(**A	)
									model.to(A	)
									model.eval()

									with torch.no_grad():
												UpperCAmelCase__		:str 		=    model(**A	)[0]

									model.enable_forward_chunking()
									with torch.no_grad():
												UpperCAmelCase__		:List[Any] 		=    model(**A	)[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.







import bisect
import copy
from collections import defaultdict

import numpy as np
from torch.utils.data import BatchSampler, Sampler

from utils import logger




def 		A						(  SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE							):




			"""simple docstring"""


			UpperCAmelCase__		:int 		=    copy.deepcopy(SCREAMING_SNAKE_CASE							)
			UpperCAmelCase__		:List[Any] 		=    sorted(SCREAMING_SNAKE_CASE							)
			UpperCAmelCase__		:Dict 		=    [bisect.bisect_right(SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE							) for y in x]
			return quantized




def 		A						(  SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE=0							):




			"""simple docstring"""


			UpperCAmelCase__		:Tuple 		=    np.arange(start=3     ,						stop=SCREAMING_SNAKE_CASE     ,						step=4							).tolist() if k > 0 else [10]
			UpperCAmelCase__		:List[Any] 		=    _quantize(SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE							)
			# count number of elements per group
			UpperCAmelCase__		:Dict 		=    np.unique(SCREAMING_SNAKE_CASE     ,						return_counts=SCREAMING_SNAKE_CASE							)[1]
			UpperCAmelCase__		:Any 		=    [0] + bins + [np.inf]
			logger.info('Using {} as bins for aspect lengths quantization'.format(SCREAMING_SNAKE_CASE							)							)
			logger.info('Count of instances per bin: {}'.format(SCREAMING_SNAKE_CASE							)							)
			return groups




class 	UpperCamelCase__       (  UpperCAmelCase__):

						'''simple docstring'''



						def __init__(  self  , A  , A  , A	)  ->Union[str, Any]:
									if not isinstance(A  , A	):
												raise ValueError(
												    'sampler should be an instance of torch.utils.data.Sampler, but got sampler={}'.format(A	)	)
									UpperCAmelCase__		:Tuple 		=    sampler
									UpperCAmelCase__		:Tuple 		=    group_ids
									UpperCAmelCase__		:str 		=    batch_size


						def __iter__(  self	)  ->Dict:
									UpperCAmelCase__		:List[str] 		=    defaultdict(A	)
									UpperCAmelCase__		:List[str] 		=    defaultdict(A	)

									UpperCAmelCase__		:Any 		=    0
									for idx in self.sampler:
												UpperCAmelCase__		:List[Any] 		=    self.group_ids[idx]
												buffer_per_group[group_id].append(A	)
												samples_per_group[group_id].append(A	)
												if len(buffer_per_group[group_id]	) == self.batch_size:
															yield buffer_per_group[group_id]  # TODO
															num_batches += 1
															del buffer_per_group[group_id]
												assert len(buffer_per_group[group_id]	) < self.batch_size

									# now we have run out of elements that satisfy
									# the group criteria, let's return the remaining
									# elements so that the size of the sampler is
									# deterministic
									UpperCAmelCase__		:Union[str, Any] 		=    len(self	)
									UpperCAmelCase__		:Tuple 		=    expected_num_batches - num_batches
									if num_remaining > 0:
												# for the remaining batches, group the batches by similar lengths
												UpperCAmelCase__		:str 		=    []
												for group_id, idxs in sorted(buffer_per_group.items()  , key=lambda A	: x[0]	):
															batch_idx.extend(A	)
															if len(A	) >= self.batch_size:
																		yield batch_idx[: self.batch_size]
																		UpperCAmelCase__		:int 		=    batch_idx[self.batch_size :]
																		num_remaining -= 1
												if len(A	) > 0:
															yield batch_idx
															num_remaining -= 1
									assert num_remaining == 0


						def __len__(  self	)  ->Union[str, Any]:
									return (len(self.sampler	) + self.batch_size - 1) // self.batch_size





