
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     a						(							snake_case__: str						,       snake_case__: bool = True ):


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

								lowercase_					  = LoRAAttnProcessor(hidden_size=snake_case__						,       cross_attention_dim=snake_case__ )
								lowercase_					  = 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 			lowercase__(    UpperCAmelCase			,						UpperCAmelCase			,						unittest.TestCase							):



	"""simple docstring"""




	a      :List[Any]   					=     UNetaDConditionModel
	a      :Dict   					=     'sample'
	@property
	def 						_lowercase  (	self  :      List[str]	)	->					Optional[Any]:
					lowercase_					  = 4
					lowercase_					  = 4
					lowercase_					  = 4
					lowercase_					  = (3_2, 3_2)

					lowercase_					  = floats_tensor((batch_size, num_channels, num_frames) + sizes	).to(SCREAMING_SNAKE_CASE_	)
					lowercase_					  = torch.tensor([1_0]	).to(SCREAMING_SNAKE_CASE_	)
					lowercase_					  = floats_tensor((batch_size, 4, 3_2)	).to(SCREAMING_SNAKE_CASE_	)

					return {"sample": noise, "timestep": time_step, "encoder_hidden_states": encoder_hidden_states}
	@property
	def 						_lowercase  (	self  :      Dict	)	->					int:
					return (4, 4, 3_2, 3_2)
	@property
	def 						_lowercase  (	self  :      List[str]	)	->					Any:
					return (4, 4, 3_2, 3_2)
	def 						_lowercase  (	self  :      List[Any]	)	->					int:
					lowercase_					  = {
					    '''block_out_channels''': (3_2, 6_4),
					    '''down_block_types''': (
					        '''CrossAttnDownBlock3D''',
					        '''DownBlock3D''',
					    ),
					    '''up_block_types''': ('''UpBlock3D''', '''CrossAttnUpBlock3D'''),
					    '''cross_attention_dim''': 3_2,
					    '''attention_head_dim''': 8,
					    '''out_channels''': 4,
					    '''in_channels''': 4,
					    '''layers_per_block''': 1,
					    '''sample_size''': 3_2,
					}
					lowercase_					  = 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	)	->					List[Any]:
					lowercase_  ,     lowercase_					  = self.prepare_init_args_and_inputs_for_common()
					lowercase_					  = self.model_class(**SCREAMING_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 						_lowercase  (	self  :      List[str]	)	->					Optional[Any]:
					lowercase_  ,     lowercase_					  = self.prepare_init_args_and_inputs_for_common()

					lowercase_					  = 3_2

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

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

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

					self.assertIsNotNone(SCREAMING_SNAKE_CASE_	)
					lowercase_					  = inputs_dict['''sample'''].shape
					self.assertEqual(output.shape	,							SCREAMING_SNAKE_CASE_	,							'''Input and output shapes do not match'''	)
	def 						_lowercase  (	self  :      str	)	->					Union[str, Any]:
					lowercase_  ,     lowercase_					  = self.prepare_init_args_and_inputs_for_common()
					lowercase_					  = self.model_class(**SCREAMING_SNAKE_CASE_	)
					model.to(SCREAMING_SNAKE_CASE_	)
					model.eval()

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

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

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

					lowercase_					  = first.cpu().numpy()
					lowercase_					  = second.cpu().numpy()
					lowercase_					  = out_a[~np.isnan(SCREAMING_SNAKE_CASE_	)]
					lowercase_					  = out_a[~np.isnan(SCREAMING_SNAKE_CASE_	)]
					lowercase_					  = np.amax(np.abs(out_a - out_a	)	)
					self.assertLessEqual(SCREAMING_SNAKE_CASE_	,							1e-5	)
	def 						_lowercase  (	self  :      Optional[Any]	)	->					Any:
					lowercase_  ,     lowercase_					  = self.prepare_init_args_and_inputs_for_common()

					lowercase_					  = 8

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

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

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

					model.set_attention_slice(2	)
					with torch.no_grad():
									lowercase_					  = model(**SCREAMING_SNAKE_CASE_	)
					assert output is not None
	def 						_lowercase  (	self  :      int	)	->					Union[str, Any]:
					lowercase_  ,     lowercase_					  = self.prepare_init_args_and_inputs_for_common()

					lowercase_					  = 8

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

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

					lowercase_					  = create_lora_layers(SCREAMING_SNAKE_CASE_	)

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

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

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

					lowercase_					  = 8

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

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

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

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

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

					with torch.no_grad():
									lowercase_					  = new_model(**SCREAMING_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 						_lowercase  (	self  :      Tuple	)	->					str:
					lowercase_  ,     lowercase_					  = self.prepare_init_args_and_inputs_for_common()

					lowercase_					  = 8

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

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

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

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

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

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

					lowercase_					  = 8

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

					lowercase_					  = create_lora_layers(SCREAMING_SNAKE_CASE_	,							mock_weights=SCREAMING_SNAKE_CASE_	)
					model.set_attn_processor(SCREAMING_SNAKE_CASE_	)
					# Saving as torch, properly reloads with directly filename
					with tempfile.TemporaryDirectory() as tmpdirname:
									model.save_attn_procs(SCREAMING_SNAKE_CASE_	)
									self.assertTrue(os.path.isfile(os.path.join(SCREAMING_SNAKE_CASE_	,							'''pytorch_lora_weights.bin'''	)	)	)
									torch.manual_seed(0	)
									lowercase_					  = self.model_class(**SCREAMING_SNAKE_CASE_	)
									new_model.to(SCREAMING_SNAKE_CASE_	)
									new_model.load_attn_procs(SCREAMING_SNAKE_CASE_	,							weight_name='''pytorch_lora_weights.bin'''	)
	def 						_lowercase  (	self  :      Union[str, Any]	)	->					List[str]:
					lowercase_  ,     lowercase_					  = self.prepare_init_args_and_inputs_for_common()

					lowercase_					  = 8

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

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

					lowercase_					  = 8

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

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

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

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

					model.set_attn_processor(AttnProcessor()	)

					with torch.no_grad():
									lowercase_					  = model(**SCREAMING_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 						_lowercase  (	self  :      int	)	->					Optional[Any]:
					# enable deterministic behavior for gradient checkpointing
					lowercase_  ,     lowercase_					  = self.prepare_init_args_and_inputs_for_common()

					lowercase_					  = 4

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

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

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

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

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




	def 						_lowercase  (	self  :      List[Any]	)	->					str:
					lowercase_  ,     lowercase_					  = self.prepare_init_args_and_inputs_for_common()
					lowercase_					  = 3_2

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

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

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






class 			lowercase__:



	"""simple docstring"""




	def __init__(	self  :      str	)	->					None:
					lowercase_					  = [0, 1]




	def 						_lowercase  (	self  :      str	,							SCREAMING_SNAKE_CASE_  :      int	)	->					list:
					if (difference := index - (len(self.sequence	) - 2)) >= 1:
									for _ in range(SCREAMING_SNAKE_CASE_	):
													self.sequence.append(self.sequence[-1] + self.sequence[-2]	)
					return self.sequence[:index]




def     a						(							):


				'''simple docstring'''
				print(
				    '''Fibonacci Series Using Dynamic Programming\n'''						,       '''Enter the index of the Fibonacci number you want to calculate '''						,       '''in the prompt below. (To exit enter exit or Ctrl-C)\n'''						,       sep=''''''						,       )
				lowercase_					  = Fibonacci()

				while True:
								lowercase_					  = input('''>> ''' )
								if prompt in {"exit", "quit"}:
												break

								try:
												lowercase_					  = int(snake_case__ )
								except ValueError:
												print('''Enter a number or \'exit\'''' )
												continue

								print(fibonacci.get(snake_case__ ) )


if __name__ == "__main__":
						main()




