
import copy
from typing import Dict, Optional

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


snake_case				:	Dict							   =						logging.get_logger(__name__)

snake_case				:	str							   =						{
    '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      (  snake_case       ):
			UpperCamelCase__					   =					'oneformer'
			UpperCamelCase__					   =					{'hidden_size': 'hidden_dim'}







			def __init__(       self		,     _a = None		,     _a = 255		,     _a = 150		,     _a = 0.1		,     _a = 2.0		,     _a = 5.0		,     _a = 5.0		,     _a = 0.5		,     _a = 0.07		,     _a = 12_544		,     _a = 3.0		,     _a = 0.75		,     _a = 0.02		,     _a = 1.0		,     _a = 1e-05		,     _a = False		,     _a = True		,     _a = True		,     _a = [4, 8, 16, 32]		,     _a = 77		,     _a = 256		,     _a = 77		,     _a = 6		,     _a = 49_408		,     _a = 2		,     _a = 16		,     _a = 256		,     _a = 256		,     _a = 256		,     _a = 1_024		,     _a = "GN"		,     _a = 6		,     _a = 10		,     _a = True		,     _a = 8		,     _a = 0.1		,     _a = 2_048		,     _a = False		,     _a = False		,     _a = 2		,     _a = 4		,     **_a		,     ):
				if backbone_config is None:
					logger.info("`backbone_config` is unset. Initializing the config with the default `Swin` backbone."	)
					__magic_name__	:	Union[str, Any]			     =  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=A_		,     out_features=["stage1", "stage2", "stage3", "stage4"]		,     )
				elif isinstance(A_		,     A_	):
					__magic_name__	:	Optional[int]			     =  backbone_config.get("model_type"	)
					__magic_name__	:	str			     =  CONFIG_MAPPING[backbone_model_type]
					__magic_name__	:	List[Any]			     =  config_class.from_dict(A_	)

				__magic_name__	:	Any			     =  backbone_config

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

				super().__init__(**A_	)







			def     SCREAMING_SNAKE_CASE					(       self	):
				__magic_name__	:	List[str]			     =  copy.deepcopy(self.__dict__	)
				__magic_name__	:	Union[str, Any]			     =  self.backbone_config.to_dict()
				__magic_name__	:	Optional[int]			     =  self.__class__.model_type
				return output





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				:	int							   =						logging.get_logger(__name__)






def 						lowerCAmelCase_		(      _snake_case   :						List[Any]   ,     _snake_case   :						bool = True     )       ->	Tuple:







	'''simple docstring'''


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

		__magic_name__	:	Union[str, Any]			     =  LoRAAttnProcessor(hidden_size=_snake_case   ,     cross_attention_dim=_snake_case     )
		__magic_name__	:	str			     =  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      (  snake_case       ,		snake_case       ,		unittest.TestCase       ):
			UpperCamelCase__					   =					UNetaDConditionModel
			UpperCamelCase__					   =					'sample'







			@property
			def     SCREAMING_SNAKE_CASE					(       self	):
				__magic_name__	:	Optional[Any]			     =  4
				__magic_name__	:	str			     =  4
				__magic_name__	:	List[str]			     =  4
				__magic_name__	:	Optional[int]			     =  (32, 32)

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

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







			@property
			def     SCREAMING_SNAKE_CASE					(       self	):
				return (4, 4, 32, 32)







			@property
			def     SCREAMING_SNAKE_CASE					(       self	):
				return (4, 4, 32, 32)







			def     SCREAMING_SNAKE_CASE					(       self	):
				__magic_name__	:	Optional[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,
				}
				__magic_name__	:	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     SCREAMING_SNAKE_CASE					(       self	):
				__magic_name__     ,					__magic_name__	:	Any			     =  self.prepare_init_args_and_inputs_for_common()
				__magic_name__	:	Union[str, 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     SCREAMING_SNAKE_CASE					(       self	):
				__magic_name__     ,					__magic_name__	:	Tuple			     =  self.prepare_init_args_and_inputs_for_common()

				__magic_name__	:	Any			     =  32

				__magic_name__	:	Dict			     =  self.model_class(**_a	)
				model.to(_a	)
				model.eval()

				with torch.no_grad():
					__magic_name__	:	Any			     =  model(**_a	)

					if isinstance(_a		,     _a	):
						__magic_name__	:	Any			     =  output.sample

				self.assertIsNotNone(_a	)
				__magic_name__	:	Union[str, Any]			     =  inputs_dict["sample"].shape
				self.assertEqual(output.shape		,     _a		,     "Input and output shapes do not match"	)







			def     SCREAMING_SNAKE_CASE					(       self	):
				__magic_name__     ,					__magic_name__	:	Optional[int]			     =  self.prepare_init_args_and_inputs_for_common()
				__magic_name__	:	int			     =  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	)

					__magic_name__	:	str			     =  model(**_a	)
					if isinstance(_a		,     _a	):
						__magic_name__	:	str			     =  first.sample

					__magic_name__	:	Tuple			     =  model(**_a	)
					if isinstance(_a		,     _a	):
						__magic_name__	:	str			     =  second.sample

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







			def     SCREAMING_SNAKE_CASE					(       self	):
				__magic_name__     ,					__magic_name__	:	Optional[int]			     =  self.prepare_init_args_and_inputs_for_common()

				__magic_name__	:	Union[str, Any]			     =  8

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

				model.set_attention_slice("auto"	)
				with torch.no_grad():
					__magic_name__	:	List[Any]			     =  model(**_a	)
				assert output is not None

				model.set_attention_slice("max"	)
				with torch.no_grad():
					__magic_name__	:	Tuple			     =  model(**_a	)
				assert output is not None

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







			def     SCREAMING_SNAKE_CASE					(       self	):
				__magic_name__     ,					__magic_name__	:	str			     =  self.prepare_init_args_and_inputs_for_common()

				__magic_name__	:	List[Any]			     =  8

				__magic_name__	:	Dict			     =  self.model_class(**_a	)
				model.to(_a	)

				with torch.no_grad():
					__magic_name__	:	Union[str, Any]			     =  model(**_a	).sample

				__magic_name__	:	int			     =  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():
					__magic_name__	:	int			     =  model(**_a		,     cross_attention_kwargs={"scale": 0.0}	).sample
					__magic_name__	:	Optional[Any]			     =  model(**_a		,     cross_attention_kwargs={"scale": 0.5}	).sample
					__magic_name__	:	Union[str, Any]			     =  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     SCREAMING_SNAKE_CASE					(       self	):
				__magic_name__     ,					__magic_name__	:	Union[str, Any]			     =  self.prepare_init_args_and_inputs_for_common()

				__magic_name__	:	Optional[int]			     =  8

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

				with torch.no_grad():
					__magic_name__	:	Tuple			     =  model(**_a	).sample

				__magic_name__	:	Union[str, Any]			     =  create_lora_layers(_a	)
				model.set_attn_processor(_a	)

				with torch.no_grad():
					__magic_name__	:	Optional[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	)
					__magic_name__	:	Union[str, Any]			     =  self.model_class(**_a	)
					new_model.to(_a	)
					new_model.load_attn_procs(_a	)

				with torch.no_grad():
					__magic_name__	:	int			     =  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     SCREAMING_SNAKE_CASE					(       self	):
				__magic_name__     ,					__magic_name__	:	Optional[Any]			     =  self.prepare_init_args_and_inputs_for_common()

				__magic_name__	:	Optional[int]			     =  8

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

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

				__magic_name__	:	Union[str, Any]			     =  create_lora_layers(_a	)
				model.set_attn_processor(_a	)

				with torch.no_grad():
					__magic_name__	:	Dict			     =  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	)
					__magic_name__	:	List[str]			     =  self.model_class(**_a	)
					new_model.to(_a	)
					new_model.load_attn_procs(_a	)

				with torch.no_grad():
					__magic_name__	:	List[Any]			     =  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     SCREAMING_SNAKE_CASE					(       self	):
				# enable deterministic behavior for gradient checkpointing
				__magic_name__     ,					__magic_name__	:	Union[str, Any]			     =  self.prepare_init_args_and_inputs_for_common()

				__magic_name__	:	List[str]			     =  8

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

				__magic_name__	:	Any			     =  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	)
					__magic_name__	:	List[Any]			     =  self.model_class(**_a	)
					new_model.to(_a	)
					new_model.load_attn_procs(_a		,     weight_name="pytorch_lora_weights.bin"	)







			def     SCREAMING_SNAKE_CASE					(       self	):
				__magic_name__     ,					__magic_name__	:	str			     =  self.prepare_init_args_and_inputs_for_common()

				__magic_name__	:	List[str]			     =  8

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

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

				__magic_name__	:	Any			     =  8

				torch.manual_seed(0	)
				__magic_name__	:	List[str]			     =  self.model_class(**_a	)
				model.to(_a	)

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

				__magic_name__	:	int			     =  create_lora_layers(_a	)
				model.set_attn_processor(_a	)

				with torch.no_grad():
					__magic_name__	:	Dict			     =  model(**_a		,     cross_attention_kwargs={"scale": 0.0}	).sample

				model.set_attn_processor(AttnProcessor()	)

				with torch.no_grad():
					__magic_name__	:	Optional[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     SCREAMING_SNAKE_CASE					(       self	):
				# enable deterministic behavior for gradient checkpointing
				__magic_name__     ,					__magic_name__	:	int			     =  self.prepare_init_args_and_inputs_for_common()

				__magic_name__	:	Optional[int]			     =  4

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

				# default
				with torch.no_grad():
					__magic_name__	:	int			     =  model(**_a	).sample

					model.enable_xformers_memory_efficient_attention()
					__magic_name__	:	str			     =  model(**_a	).sample

					model.disable_xformers_memory_efficient_attention()
					__magic_name__	:	Optional[Any]			     =  model(**_a	).sample

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







			def     SCREAMING_SNAKE_CASE					(       self	):
				__magic_name__     ,					__magic_name__	:	int			     =  self.prepare_init_args_and_inputs_for_common()
				__magic_name__	:	str			     =  32

				__magic_name__	:	int			     =  self.model_class(**_a	)
				model.to(_a	)
				model.eval()

				with torch.no_grad():
					__magic_name__	:	Optional[Any]			     =  model(**_a	)[0]

				model.enable_forward_chunking()
				with torch.no_grad():
					__magic_name__	:	int			     =  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.
