
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  :			List[Any]     				=				logging.get_logger(__name__)


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

								__a    							=					LoRAAttnProcessor(hidden_size=a__		,					cross_attention_dim=a__						)
								__a    							=					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(				a						,     a						,     unittest.TestCase    ):
				snake_case_		     =					UNetaDConditionModel
				snake_case_		     =					'''sample'''




				@property
				def        SCREAMING_SNAKE_CASE_	(  self       )					->   List[str]:


								'''simple docstring'''




								__a    							=					4
								__a    							=					4
								__a    							=					4
								__a    							=					(32, 32)

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

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




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


								'''simple docstring'''




								return (4, 4, 32, 32)




				@property
				def        SCREAMING_SNAKE_CASE_	(  self       )					->   Tuple:


								'''simple docstring'''




								return (4, 4, 32, 32)




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   Tuple:


								'''simple docstring'''




								__a    							=					{
								    '''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,
								}
								__a    							=					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       )					->   Dict:


								'''simple docstring'''




								__a							,  __a    							=					self.prepare_init_args_and_inputs_for_common()
								__a    							=					self.model_class(**_snake_case       )

								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       )					->   str:


								'''simple docstring'''




								__a							,  __a    							=					self.prepare_init_args_and_inputs_for_common()

								__a    							=					32

								__a    							=					self.model_class(**_snake_case       )
								model.to(_snake_case       )
								model.eval()

								with torch.no_grad():
												__a    							=					model(**_snake_case       )

												if isinstance(_snake_case	,  _snake_case       ):
																__a    							=					output.sample

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




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


								'''simple docstring'''




								__a							,  __a    							=					self.prepare_init_args_and_inputs_for_common()
								__a    							=					self.model_class(**_snake_case       )
								model.to(_snake_case       )
								model.eval()

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

												__a    							=					model(**_snake_case       )
												if isinstance(_snake_case	,  _snake_case       ):
																__a    							=					first.sample

												__a    							=					model(**_snake_case       )
												if isinstance(_snake_case	,  _snake_case       ):
																__a    							=					second.sample

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




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   int:


								'''simple docstring'''




								__a							,  __a    							=					self.prepare_init_args_and_inputs_for_common()

								__a    							=					8

								__a    							=					self.model_class(**_snake_case       )
								model.to(_snake_case       )
								model.eval()

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

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

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




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


								'''simple docstring'''




								__a							,  __a    							=					self.prepare_init_args_and_inputs_for_common()

								__a    							=					8

								__a    							=					self.model_class(**_snake_case       )
								model.to(_snake_case       )

								with torch.no_grad():
												__a    							=					model(**_snake_case       ).sample

								__a    							=					create_lora_layers(_snake_case       )

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

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

								with torch.no_grad():
												__a    							=					model(**_snake_case	,  cross_attention_kwargs={'''scale''': 0.0}       ).sample
												__a    							=					model(**_snake_case	,  cross_attention_kwargs={'''scale''': 0.5}       ).sample
												__a    							=					model(**_snake_case	,  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       )					->   Union[str, Any]:


								'''simple docstring'''




								__a							,  __a    							=					self.prepare_init_args_and_inputs_for_common()

								__a    							=					8

								torch.manual_seed(0       )
								__a    							=					self.model_class(**_snake_case       )
								model.to(_snake_case       )

								with torch.no_grad():
												__a    							=					model(**_snake_case       ).sample

								__a    							=					create_lora_layers(_snake_case       )
								model.set_attn_processor(_snake_case       )

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

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

								with torch.no_grad():
												__a    							=					new_model(**_snake_case	,  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       )					->   Any:


								'''simple docstring'''




								__a							,  __a    							=					self.prepare_init_args_and_inputs_for_common()

								__a    							=					8

								torch.manual_seed(0       )
								__a    							=					self.model_class(**_snake_case       )
								model.to(_snake_case       )

								with torch.no_grad():
												__a    							=					model(**_snake_case       ).sample

								__a    							=					create_lora_layers(_snake_case       )
								model.set_attn_processor(_snake_case       )

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

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

								with torch.no_grad():
												__a    							=					new_model(**_snake_case	,  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       )					->   Dict:


								'''simple docstring'''




								__a							,  __a    							=					self.prepare_init_args_and_inputs_for_common()

								__a    							=					8

								torch.manual_seed(0       )
								__a    							=					self.model_class(**_snake_case       )
								model.to(_snake_case       )

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




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


								'''simple docstring'''




								__a							,  __a    							=					self.prepare_init_args_and_inputs_for_common()

								__a    							=					8

								torch.manual_seed(0       )
								__a    							=					self.model_class(**_snake_case       )
								model.to(_snake_case       )

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




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   Tuple:


								'''simple docstring'''




								__a							,  __a    							=					self.prepare_init_args_and_inputs_for_common()

								__a    							=					8

								torch.manual_seed(0       )
								__a    							=					self.model_class(**_snake_case       )
								model.to(_snake_case       )

								with torch.no_grad():
												__a    							=					model(**_snake_case       ).sample

								__a    							=					create_lora_layers(_snake_case       )
								model.set_attn_processor(_snake_case       )

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

								model.set_attn_processor(AttnProcessor()       )

								with torch.no_grad():
												__a    							=					model(**_snake_case       ).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[int]:


								'''simple docstring'''




								__a							,  __a    							=					self.prepare_init_args_and_inputs_for_common()

								__a    							=					4

								torch.manual_seed(0       )
								__a    							=					self.model_class(**_snake_case       )
								model.to(_snake_case       )
								__a    							=					create_lora_layers(_snake_case       )
								model.set_attn_processor(_snake_case       )

								# default
								with torch.no_grad():
												__a    							=					model(**_snake_case       ).sample

												model.enable_xformers_memory_efficient_attention()
												__a    							=					model(**_snake_case       ).sample

												model.disable_xformers_memory_efficient_attention()
												__a    							=					model(**_snake_case       ).sample

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




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   Optional[Any]:


								'''simple docstring'''




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

								__a    							=					self.model_class(**_snake_case       )
								model.to(_snake_case       )
								model.eval()

								with torch.no_grad():
												__a    							=					model(**_snake_case       )[0]

								model.enable_forward_chunking()
								with torch.no_grad():
												__a    							=					model(**_snake_case       )[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 itertools import compress, repeat
from math import ceil, sqrt


def 					__lowerCAmelCase    (    a__						) ->						list[int]:

				if num <= 2:
								return []
				if num == 3:
								return [2]

				# Odd sieve for numbers in range [3, num - 1]
				__a    							=					bytearray(B'''\x01'''						) * ((num >> 1) - 1)

				for i in range(3		,					int(sqrt(a__						)						) + 1		,					2						):
								if sieve[(i >> 1) - 1]:
												__a    							=					i**2
												__a    							=					repeat(
												    0		,					ceil((num - i_squared) / (i << 1)						)						)

				return [2] + list(compress(range(3		,					a__		,					2						)		,					a__						)						)


if __name__ == "__main__":
					import doctest

					doctest.testmod()