

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()

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







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

						__UpperCamelCase    :    List[Any]								=      LoRAAttnProcessor(hidden_size=_lowerCAmelCase     ,			cross_attention_dim=_lowerCAmelCase		)
						__UpperCamelCase    :    int								=      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 		SCREAMING_SNAKE_CASE__					(		lowerCamelCase__						,	lowerCamelCase__						,	unittest.TestCase   ):







						"""simple docstring"""




						lowercase  :				int											=							UNetaDConditionModel
						lowercase  :				Optional[Any]											=							'sample'

						@property
						def __lowerCamelCase					(				self    )							->	Tuple:




									'''simple docstring'''


									__UpperCamelCase    :    Union[str, Any]								=      4
									__UpperCamelCase    :    Dict								=      4
									__UpperCamelCase    :    int								=      4
									__UpperCamelCase    :    int								=      (32, 32)

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

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

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




									'''simple docstring'''


									return (4, 4, 32, 32)

						@property
						def __lowerCamelCase					(				self    )							->	Union[str, Any]:




									'''simple docstring'''


									return (4, 4, 32, 32)

						def __lowerCamelCase					(				self    )							->	Tuple:




									'''simple docstring'''


									__UpperCamelCase    :    Tuple								=      {
									    "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    :    Optional[int]								=      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    )							->	Any:




									'''simple docstring'''


									__UpperCamelCase							,				__UpperCamelCase    :    Tuple								=      self.prepare_init_args_and_inputs_for_common()
									__UpperCamelCase    :    Optional[Any]								=      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 __lowerCamelCase					(				self    )							->	List[str]:




									'''simple docstring'''


									__UpperCamelCase							,				__UpperCamelCase    :    Dict								=      self.prepare_init_args_and_inputs_for_common()

									__UpperCamelCase    :    Dict								=      32

									__UpperCamelCase    :    Optional[Any]								=      self.model_class(**__UpperCamelCase    )
									model.to(__UpperCamelCase    )
									model.eval()

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

												if isinstance(__UpperCamelCase , __UpperCamelCase    ):
															__UpperCamelCase    :    Union[str, Any]								=      output.sample

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

						def __lowerCamelCase					(				self    )							->	Optional[int]:




									'''simple docstring'''


									__UpperCamelCase							,				__UpperCamelCase    :    List[str]								=      self.prepare_init_args_and_inputs_for_common()
									__UpperCamelCase    :    Optional[Any]								=      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    )

												__UpperCamelCase    :    List[str]								=      model(**__UpperCamelCase    )
												if isinstance(__UpperCamelCase , __UpperCamelCase    ):
															__UpperCamelCase    :    List[Any]								=      first.sample

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

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

						def __lowerCamelCase					(				self    )							->	Any:




									'''simple docstring'''


									__UpperCamelCase							,				__UpperCamelCase    :    List[Any]								=      self.prepare_init_args_and_inputs_for_common()

									__UpperCamelCase    :    int								=      8

									__UpperCamelCase    :    Any								=      self.model_class(**__UpperCamelCase    )
									model.to(__UpperCamelCase    )
									model.eval()

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

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

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

						def __lowerCamelCase					(				self    )							->	str:




									'''simple docstring'''


									__UpperCamelCase							,				__UpperCamelCase    :    Optional[Any]								=      self.prepare_init_args_and_inputs_for_common()

									__UpperCamelCase    :    List[str]								=      8

									__UpperCamelCase    :    Any								=      self.model_class(**__UpperCamelCase    )
									model.to(__UpperCamelCase    )

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

									__UpperCamelCase    :    int								=      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():
												__UpperCamelCase    :    Union[str, Any]								=      model(**__UpperCamelCase , cross_attention_kwargs={"scale": 0.0}    ).sample
												__UpperCamelCase    :    str								=      model(**__UpperCamelCase , cross_attention_kwargs={"scale": 0.5}    ).sample
												__UpperCamelCase    :    Optional[int]								=      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 __lowerCamelCase					(				self    )							->	List[Any]:




									'''simple docstring'''


									__UpperCamelCase							,				__UpperCamelCase    :    List[str]								=      self.prepare_init_args_and_inputs_for_common()

									__UpperCamelCase    :    Optional[int]								=      8

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

									with torch.no_grad():
												__UpperCamelCase    :    Optional[int]								=      model(**__UpperCamelCase    ).sample

									__UpperCamelCase    :    int								=      create_lora_layers(__UpperCamelCase    )
									model.set_attn_processor(__UpperCamelCase    )

									with torch.no_grad():
												__UpperCamelCase    :    int								=      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    )
												__UpperCamelCase    :    Any								=      self.model_class(**__UpperCamelCase    )
												new_model.to(__UpperCamelCase    )
												new_model.load_attn_procs(__UpperCamelCase    )

									with torch.no_grad():
												__UpperCamelCase    :    List[Any]								=      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 __lowerCamelCase					(				self    )							->	Optional[int]:




									'''simple docstring'''


									__UpperCamelCase							,				__UpperCamelCase    :    Any								=      self.prepare_init_args_and_inputs_for_common()

									__UpperCamelCase    :    str								=      8

									torch.manual_seed(0    )
									__UpperCamelCase    :    List[Any]								=      self.model_class(**__UpperCamelCase    )
									model.to(__UpperCamelCase    )

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

									__UpperCamelCase    :    Any								=      create_lora_layers(__UpperCamelCase    )
									model.set_attn_processor(__UpperCamelCase    )

									with torch.no_grad():
												__UpperCamelCase    :    Optional[Any]								=      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    )
												__UpperCamelCase    :    Tuple								=      self.model_class(**__UpperCamelCase    )
												new_model.to(__UpperCamelCase    )
												new_model.load_attn_procs(__UpperCamelCase    )

									with torch.no_grad():
												__UpperCamelCase    :    Optional[int]								=      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 __lowerCamelCase					(				self    )							->	Dict:




									'''simple docstring'''


									__UpperCamelCase							,				__UpperCamelCase    :    List[Any]								=      self.prepare_init_args_and_inputs_for_common()

									__UpperCamelCase    :    Optional[Any]								=      8

									torch.manual_seed(0    )
									__UpperCamelCase    :    Union[str, Any]								=      self.model_class(**__UpperCamelCase    )
									model.to(__UpperCamelCase    )

									__UpperCamelCase    :    Optional[int]								=      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    )
												__UpperCamelCase    :    List[str]								=      self.model_class(**__UpperCamelCase    )
												new_model.to(__UpperCamelCase    )
												new_model.load_attn_procs(__UpperCamelCase , weight_name="pytorch_lora_weights.bin"    )

						def __lowerCamelCase					(				self    )							->	Any:




									'''simple docstring'''


									__UpperCamelCase							,				__UpperCamelCase    :    Optional[Any]								=      self.prepare_init_args_and_inputs_for_common()

									__UpperCamelCase    :    List[Any]								=      8

									torch.manual_seed(0    )
									__UpperCamelCase    :    List[Any]								=      self.model_class(**__UpperCamelCase    )
									model.to(__UpperCamelCase    )

									__UpperCamelCase    :    int								=      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    )
												__UpperCamelCase    :    Optional[Any]								=      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 __lowerCamelCase					(				self    )							->	Optional[Any]:




									'''simple docstring'''


									__UpperCamelCase							,				__UpperCamelCase    :    List[str]								=      self.prepare_init_args_and_inputs_for_common()

									__UpperCamelCase    :    List[str]								=      8

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

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

									__UpperCamelCase    :    Dict								=      create_lora_layers(__UpperCamelCase    )
									model.set_attn_processor(__UpperCamelCase    )

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

									model.set_attn_processor(AttnProcessor()    )

									with torch.no_grad():
												__UpperCamelCase    :    Optional[int]								=      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 __lowerCamelCase					(				self    )							->	Optional[Any]:




									'''simple docstring'''


									__UpperCamelCase							,				__UpperCamelCase    :    Any								=      self.prepare_init_args_and_inputs_for_common()

									__UpperCamelCase    :    List[Any]								=      4

									torch.manual_seed(0    )
									__UpperCamelCase    :    Optional[Any]								=      self.model_class(**__UpperCamelCase    )
									model.to(__UpperCamelCase    )
									__UpperCamelCase    :    int								=      create_lora_layers(__UpperCamelCase    )
									model.set_attn_processor(__UpperCamelCase    )

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

												model.enable_xformers_memory_efficient_attention()
												__UpperCamelCase    :    List[Any]								=      model(**__UpperCamelCase    ).sample

												model.disable_xformers_memory_efficient_attention()
												__UpperCamelCase    :    str								=      model(**__UpperCamelCase    ).sample

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




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




									'''simple docstring'''


									__UpperCamelCase							,				__UpperCamelCase    :    List[str]								=      self.prepare_init_args_and_inputs_for_common()
									__UpperCamelCase    :    Optional[int]								=      32

									__UpperCamelCase    :    List[str]								=      self.model_class(**__UpperCamelCase    )
									model.to(__UpperCamelCase    )
									model.eval()

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

									model.enable_forward_chunking()
									with torch.no_grad():
												__UpperCamelCase    :    List[Any]								=      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 collections.abc import Callable

lowercase     :       Optional[Any]        =       Callable[[float], float]  # type alias for a real -> real function







def  UpperCAmelCase_   (_lowerCAmelCase :		RealFunc     ,			_lowerCAmelCase :		RealFunc     ,			_lowerCAmelCase :		int     ,			):
			__UpperCamelCase    :    Any								=      float(_lowerCAmelCase		)
			while True:
						try:
									__UpperCamelCase    :    List[Any]								=      prev_guess - function(_lowerCAmelCase		) / derivative(_lowerCAmelCase		)
						except ZeroDivisionError:
									raise ZeroDivisionError("Could not find root"		) from None
						if abs(prev_guess - next_guess		) < 10**-5:
									return next_guess
						__UpperCamelCase    :    Tuple								=      next_guess







def  UpperCAmelCase_   (_lowerCAmelCase :		float		):
			return (x**3) - (2 * x) - 5







def  UpperCAmelCase_   (_lowerCAmelCase :		float		):
			return 3 * (x**2) - 2


if __name__ == "__main__":
					print(newton(f, fa, 3))