


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()

SCREAMING_SNAKE_CASE : Any          =    logging.get_logger(__name__)





def 						_lowerCamelCase		(							SCREAMING_SNAKE_CASE_				: int	,	SCREAMING_SNAKE_CASE_				: bool = True      ):






							"""simple docstring"""



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

														a_       :   List[str]			=	LoRAAttnProcessor(hidden_size=SCREAMING_SNAKE_CASE_	,	cross_attention_dim=SCREAMING_SNAKE_CASE_      )
														a_       :   Tuple			=	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 					snake_case__  ( __A      ,    __A      ,    unittest.TestCase       ):
	UpperCAmelCase   :							Dict         = UNetaDConditionModel
	UpperCAmelCase   :							Optional[Any]         = """sample"""





	@property
	def     A				(  self							)		->			List[Any]:
								"""simple docstring"""






								a_       :   Any			=	4
								a_       :   List[str]			=	4
								a_       :   int			=	4
								a_       :   List[str]			=	(32, 32)

								a_       :   List[Any]			=	floats_tensor((batch_size, num_channels, num_frames) + sizes							).to(UpperCamelCase_							)
								a_       :   Optional[Any]			=	torch.tensor([10]							).to(UpperCamelCase_							)
								a_       :   Tuple			=	floats_tensor((batch_size, 4, 32)							).to(UpperCamelCase_							)

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





	@property
	def     A				(  self							)		->			List[Any]:
								"""simple docstring"""






								return (4, 4, 32, 32)





	@property
	def     A				(  self							)		->			str:
								"""simple docstring"""






								return (4, 4, 32, 32)





	def     A				(  self							)		->			List[str]:
								"""simple docstring"""






								a_       :   Any			=	{
								    """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_       :   Union[str, Any]			=	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							)		->			List[str]:
								"""simple docstring"""






								a_		,				a_       :   Dict			=	self.prepare_init_args_and_inputs_for_common()
								a_       :   List[str]			=	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     A				(  self							)		->			List[Any]:
								"""simple docstring"""






								a_		,				a_       :   Union[str, Any]			=	self.prepare_init_args_and_inputs_for_common()

								a_       :   int			=	32

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

								with torch.no_grad():
															a_       :   int			=	model(**UpperCamelCase_							)

															if isinstance(UpperCamelCase_				,    UpperCamelCase_							):
																						a_       :   Dict			=	output.sample

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





	def     A				(  self							)		->			Any:
								"""simple docstring"""






								a_		,				a_       :   Any			=	self.prepare_init_args_and_inputs_for_common()
								a_       :   str			=	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							)

															a_       :   Tuple			=	model(**UpperCamelCase_							)
															if isinstance(UpperCamelCase_				,    UpperCamelCase_							):
																						a_       :   int			=	first.sample

															a_       :   Optional[int]			=	model(**UpperCamelCase_							)
															if isinstance(UpperCamelCase_				,    UpperCamelCase_							):
																						a_       :   Optional[Any]			=	second.sample

								a_       :   Union[str, Any]			=	first.cpu().numpy()
								a_       :   List[Any]			=	second.cpu().numpy()
								a_       :   Dict			=	out_a[~np.isnan(UpperCamelCase_							)]
								a_       :   List[str]			=	out_a[~np.isnan(UpperCamelCase_							)]
								a_       :   Dict			=	np.amax(np.abs(out_a - out_a							)							)
								self.assertLessEqual(UpperCamelCase_				,    1e-5							)





	def     A				(  self							)		->			Tuple:
								"""simple docstring"""






								a_		,				a_       :   Dict			=	self.prepare_init_args_and_inputs_for_common()

								a_       :   List[Any]			=	8

								a_       :   str			=	self.model_class(**UpperCamelCase_							)
								model.to(UpperCamelCase_							)
								model.eval()

								model.set_attention_slice("""auto"""							)
								with torch.no_grad():
															a_       :   str			=	model(**UpperCamelCase_							)
								assert output is not None

								model.set_attention_slice("""max"""							)
								with torch.no_grad():
															a_       :   Any			=	model(**UpperCamelCase_							)
								assert output is not None

								model.set_attention_slice(2							)
								with torch.no_grad():
															a_       :   int			=	model(**UpperCamelCase_							)
								assert output is not None





	def     A				(  self							)		->			Optional[Any]:
								"""simple docstring"""






								a_		,				a_       :   Union[str, Any]			=	self.prepare_init_args_and_inputs_for_common()

								a_       :   Optional[int]			=	8

								a_       :   str			=	self.model_class(**UpperCamelCase_							)
								model.to(UpperCamelCase_							)

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

								a_       :   List[Any]			=	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():
															a_       :   int			=	model(**UpperCamelCase_				,    cross_attention_kwargs={"""scale""": 0.0}							).sample
															a_       :   List[str]			=	model(**UpperCamelCase_				,    cross_attention_kwargs={"""scale""": 0.5}							).sample
															a_       :   Any			=	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     A				(  self							)		->			Optional[Any]:
								"""simple docstring"""






								a_		,				a_       :   int			=	self.prepare_init_args_and_inputs_for_common()

								a_       :   int			=	8

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

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

								a_       :   List[str]			=	create_lora_layers(UpperCamelCase_							)
								model.set_attn_processor(UpperCamelCase_							)

								with torch.no_grad():
															a_       :   str			=	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							)
															a_       :   Dict			=	self.model_class(**UpperCamelCase_							)
															new_model.to(UpperCamelCase_							)
															new_model.load_attn_procs(UpperCamelCase_							)

								with torch.no_grad():
															a_       :   Optional[int]			=	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     A				(  self							)		->			str:
								"""simple docstring"""






								a_		,				a_       :   Tuple			=	self.prepare_init_args_and_inputs_for_common()

								a_       :   Optional[int]			=	8

								torch.manual_seed(0							)
								a_       :   Dict			=	self.model_class(**UpperCamelCase_							)
								model.to(UpperCamelCase_							)

								with torch.no_grad():
															a_       :   Optional[Any]			=	model(**UpperCamelCase_							).sample

								a_       :   Tuple			=	create_lora_layers(UpperCamelCase_							)
								model.set_attn_processor(UpperCamelCase_							)

								with torch.no_grad():
															a_       :   Optional[int]			=	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							)
															a_       :   Dict			=	self.model_class(**UpperCamelCase_							)
															new_model.to(UpperCamelCase_							)
															new_model.load_attn_procs(UpperCamelCase_							)

								with torch.no_grad():
															a_       :   str			=	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     A				(  self							)		->			Optional[int]:
								"""simple docstring"""






								a_		,				a_       :   Dict			=	self.prepare_init_args_and_inputs_for_common()

								a_       :   List[str]			=	8

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

								a_       :   Dict			=	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							)
															a_       :   Dict			=	self.model_class(**UpperCamelCase_							)
															new_model.to(UpperCamelCase_							)
															new_model.load_attn_procs(UpperCamelCase_				,    weight_name="""pytorch_lora_weights.bin"""							)





	def     A				(  self							)		->			str:
								"""simple docstring"""






								a_		,				a_       :   Union[str, Any]			=	self.prepare_init_args_and_inputs_for_common()

								a_       :   Dict			=	8

								torch.manual_seed(0							)
								a_       :   Dict			=	self.model_class(**UpperCamelCase_							)
								model.to(UpperCamelCase_							)

								a_       :   List[Any]			=	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							)
															a_       :   Tuple			=	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     A				(  self							)		->			Tuple:
								"""simple docstring"""






								a_		,				a_       :   Tuple			=	self.prepare_init_args_and_inputs_for_common()

								a_       :   Optional[int]			=	8

								torch.manual_seed(0							)
								a_       :   Any			=	self.model_class(**UpperCamelCase_							)
								model.to(UpperCamelCase_							)

								with torch.no_grad():
															a_       :   Dict			=	model(**UpperCamelCase_							).sample

								a_       :   Dict			=	create_lora_layers(UpperCamelCase_							)
								model.set_attn_processor(UpperCamelCase_							)

								with torch.no_grad():
															a_       :   int			=	model(**UpperCamelCase_				,    cross_attention_kwargs={"""scale""": 0.0}							).sample

								model.set_attn_processor(AttnProcessor()							)

								with torch.no_grad():
															a_       :   Any			=	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     A				(  self							)		->			Tuple:
								"""simple docstring"""






								a_		,				a_       :   str			=	self.prepare_init_args_and_inputs_for_common()

								a_       :   List[Any]			=	4

								torch.manual_seed(0							)
								a_       :   Optional[Any]			=	self.model_class(**UpperCamelCase_							)
								model.to(UpperCamelCase_							)
								a_       :   Tuple			=	create_lora_layers(UpperCamelCase_							)
								model.set_attn_processor(UpperCamelCase_							)

								# default
								with torch.no_grad():
															a_       :   Tuple			=	model(**UpperCamelCase_							).sample

															model.enable_xformers_memory_efficient_attention()
															a_       :   Tuple			=	model(**UpperCamelCase_							).sample

															model.disable_xformers_memory_efficient_attention()
															a_       :   Dict			=	model(**UpperCamelCase_							).sample

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





	def     A				(  self							)		->			List[str]:
								"""simple docstring"""






								a_		,				a_       :   int			=	self.prepare_init_args_and_inputs_for_common()
								a_       :   List[Any]			=	32

								a_       :   str			=	self.model_class(**UpperCamelCase_							)
								model.to(UpperCamelCase_							)
								model.eval()

								with torch.no_grad():
															a_       :   List[Any]			=	model(**UpperCamelCase_							)[0]

								model.enable_forward_chunking()
								with torch.no_grad():
															a_       :   Tuple			=	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.






import copy
from typing import Dict, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


SCREAMING_SNAKE_CASE : List[str]          =    logging.get_logger(__name__)

SCREAMING_SNAKE_CASE : List[Any]          =    {
    "shi-labs/oneformer_ade20k_swin_tiny": (
        "https://huggingface.co/shi-labs/oneformer_ade20k_swin_tiny/blob/main/config.json"
    ),
    # See all OneFormer models at https://huggingface.co/models?filter=oneformer
}





class 					snake_case__  ( __A       ):
	UpperCAmelCase   :							Tuple         = """oneformer"""
	UpperCAmelCase   :							Union[str, Any]         = {"""hidden_size""": """hidden_dim"""}





	def __init__(  self				,    UpperCamelCase_ = None				,    UpperCamelCase_ = 255				,    UpperCamelCase_ = 150				,    UpperCamelCase_ = 0.1				,    UpperCamelCase_ = 2.0				,    UpperCamelCase_ = 5.0				,    UpperCamelCase_ = 5.0				,    UpperCamelCase_ = 0.5				,    UpperCamelCase_ = 0.07				,    UpperCamelCase_ = 12544				,    UpperCamelCase_ = 3.0				,    UpperCamelCase_ = 0.75				,    UpperCamelCase_ = 0.02				,    UpperCamelCase_ = 1.0				,    UpperCamelCase_ = 1e-05				,    UpperCamelCase_ = False				,    UpperCamelCase_ = True				,    UpperCamelCase_ = True				,    UpperCamelCase_ = [4, 8, 16, 32]				,    UpperCamelCase_ = 77				,    UpperCamelCase_ = 256				,    UpperCamelCase_ = 77				,    UpperCamelCase_ = 6				,    UpperCamelCase_ = 49408				,    UpperCamelCase_ = 2				,    UpperCamelCase_ = 16				,    UpperCamelCase_ = 256				,    UpperCamelCase_ = 256				,    UpperCamelCase_ = 256				,    UpperCamelCase_ = 1024				,    UpperCamelCase_ = "GN"				,    UpperCamelCase_ = 6				,    UpperCamelCase_ = 10				,    UpperCamelCase_ = True				,    UpperCamelCase_ = 8				,    UpperCamelCase_ = 0.1				,    UpperCamelCase_ = 2048				,    UpperCamelCase_ = False				,    UpperCamelCase_ = False				,    UpperCamelCase_ = 2				,    UpperCamelCase_ = 4				,    **UpperCamelCase_				,    )		->			List[Any]:
								"""simple docstring"""






								if backbone_config is None:
															logger.info("""`backbone_config` is unset. Initializing the config with the default `Swin` backbone."""							)
															a_       :   Tuple			=	CONFIG_MAPPING["""swin"""](
															    image_size=224				,    in_channels=3				,    patch_size=4				,    embed_dim=96				,    depths=[2, 2, 6, 2]				,    num_heads=[3, 6, 12, 24]				,    window_size=7				,    drop_path_rate=0.3				,    use_absolute_embeddings=UpperCamelCase_				,    out_features=["""stage1""", """stage2""", """stage3""", """stage4"""]				,    )
								elif isinstance(UpperCamelCase_				,    UpperCamelCase_							):
															a_       :   str			=	backbone_config.get("""model_type"""							)
															a_       :   Optional[Any]			=	CONFIG_MAPPING[backbone_model_type]
															a_       :   List[Any]			=	config_class.from_dict(UpperCamelCase_							)

								a_       :   str			=	backbone_config

								a_       :   List[str]			=	ignore_value
								a_       :   Optional[int]			=	num_queries
								a_       :   Optional[Any]			=	no_object_weight
								a_       :   List[Any]			=	class_weight
								a_       :   Union[str, Any]			=	mask_weight
								a_       :   Union[str, Any]			=	dice_weight
								a_       :   Optional[Any]			=	contrastive_weight
								a_       :   Any			=	contrastive_temperature
								a_       :   Optional[int]			=	train_num_points
								a_       :   Tuple			=	oversample_ratio
								a_       :   str			=	importance_sample_ratio
								a_       :   List[Any]			=	init_std
								a_       :   int			=	init_xavier_std
								a_       :   List[Any]			=	layer_norm_eps
								a_       :   Optional[int]			=	is_training
								a_       :   List[Any]			=	use_auxiliary_loss
								a_       :   str			=	output_auxiliary_logits
								a_       :   Dict			=	strides
								a_       :   List[Any]			=	task_seq_len
								a_       :   int			=	text_encoder_width
								a_       :   List[Any]			=	text_encoder_context_length
								a_       :   List[str]			=	text_encoder_num_layers
								a_       :   Union[str, Any]			=	text_encoder_vocab_size
								a_       :   Optional[Any]			=	text_encoder_proj_layers
								a_       :   Optional[int]			=	text_encoder_n_ctx
								a_       :   Optional[Any]			=	conv_dim
								a_       :   str			=	mask_dim
								a_       :   Optional[Any]			=	hidden_dim
								a_       :   Any			=	encoder_feedforward_dim
								a_       :   Dict			=	norm
								a_       :   List[Any]			=	encoder_layers
								a_       :   int			=	decoder_layers
								a_       :   Any			=	use_task_norm
								a_       :   Union[str, Any]			=	num_attention_heads
								a_       :   List[str]			=	dropout
								a_       :   List[Any]			=	dim_feedforward
								a_       :   Union[str, Any]			=	pre_norm
								a_       :   Dict			=	enforce_input_proj
								a_       :   Tuple			=	query_dec_layers
								a_       :   Any			=	common_stride
								a_       :   List[str]			=	decoder_layers

								super().__init__(**UpperCamelCase_							)





	def     A				(  self							)		->			Dict[str, any]:
								"""simple docstring"""






								a_       :   Dict			=	copy.deepcopy(self.__dict__							)
								a_       :   Any			=	self.backbone_config.to_dict()
								a_       :   Union[str, Any]			=	self.__class__.model_type
								return output


