






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()

__UpperCamelCase:  Tuple   =   logging.get_logger(__name__)


def 			SCREAMING_SNAKE_CASE__     (					_lowercase    :   Dict      ,						_lowercase    :   bool = True )      ->       Union[str, Any]:




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

				lowercase__				:			str            =		LoRAAttnProcessor(hidden_size=_lowercase      ,						cross_attention_dim=_lowercase )
				lowercase__				:			Optional[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 			__lowerCAmelCase				(					_UpperCamelCase      ,						_UpperCamelCase      ,						unittest.TestCase			):


	'''simple docstring'''





	_A       	=							UNetaDConditionModel
	_A       	=							"sample"

	@property
	def       snake_case__(	self:	Dict ):
			lowercase__				:			Any            =		4
			lowercase__				:			Any            =		4
			lowercase__				:			str            =		4
			lowercase__				:			List[str]            =		(32, 32)

			lowercase__				:			Optional[int]            =		floats_tensor((batch_size, num_channels, num_frames) + sizes ).to(lowerCamelCase_ )
			lowercase__				:			Dict            =		torch.tensor([10] ).to(lowerCamelCase_ )
			lowercase__				:			str            =		floats_tensor((batch_size, 4, 32) ).to(lowerCamelCase_ )

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

	@property
	def       snake_case__(	self:	Dict ):
			return (4, 4, 32, 32)

	@property
	def       snake_case__(	self:	Any ):
			return (4, 4, 32, 32)

	def       snake_case__(	self:	Union[str, Any] ):
			lowercase__				:			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,
			}
			lowercase__				:			List[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       snake_case__(	self:	List[str] ):
			lowercase__				:			Any            =		self.prepare_init_args_and_inputs_for_common()
			lowercase__				:			Union[str, Any]            =		self.model_class(**lowerCamelCase_ )

			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       snake_case__(	self:	List[str] ):
			lowercase__				:			Any            =		self.prepare_init_args_and_inputs_for_common()

			lowercase__				:			List[Any]            =		32

			lowercase__				:			Optional[int]            =		self.model_class(**lowerCamelCase_ )
			model.to(lowerCamelCase_ )
			model.eval()

			with torch.no_grad():
					lowercase__				:			Optional[Any]            =		model(**lowerCamelCase_ )

					if isinstance(lowerCamelCase_,				lowerCamelCase_ ):
							lowercase__				:			int            =		output.sample

			self.assertIsNotNone(lowerCamelCase_ )
			lowercase__				:			Dict            =		inputs_dict['sample'].shape
			self.assertEqual(output.shape,				lowerCamelCase_,				'Input and output shapes do not match' )

	def       snake_case__(	self:	List[str] ):
			lowercase__				:			Tuple            =		self.prepare_init_args_and_inputs_for_common()
			lowercase__				:			int            =		self.model_class(**lowerCamelCase_ )
			model.to(lowerCamelCase_ )
			model.eval()

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

					lowercase__				:			int            =		model(**lowerCamelCase_ )
					if isinstance(lowerCamelCase_,				lowerCamelCase_ ):
							lowercase__				:			Union[str, Any]            =		first.sample

					lowercase__				:			Union[str, Any]            =		model(**lowerCamelCase_ )
					if isinstance(lowerCamelCase_,				lowerCamelCase_ ):
							lowercase__				:			Tuple            =		second.sample

			lowercase__				:			Tuple            =		first.cpu().numpy()
			lowercase__				:			List[str]            =		second.cpu().numpy()
			lowercase__				:			str            =		out_a[~np.isnan(lowerCamelCase_ )]
			lowercase__				:			str            =		out_a[~np.isnan(lowerCamelCase_ )]
			lowercase__				:			int            =		np.amax(np.abs(out_a - out_a ) )
			self.assertLessEqual(lowerCamelCase_,				1E-5 )

	def       snake_case__(	self:	Tuple ):
			lowercase__				:			Any            =		self.prepare_init_args_and_inputs_for_common()

			lowercase__				:			Optional[int]            =		8

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

			model.set_attention_slice('auto' )
			with torch.no_grad():
					lowercase__				:			List[Any]            =		model(**lowerCamelCase_ )
			assert output is not None

			model.set_attention_slice('max' )
			with torch.no_grad():
					lowercase__				:			Tuple            =		model(**lowerCamelCase_ )
			assert output is not None

			model.set_attention_slice(2 )
			with torch.no_grad():
					lowercase__				:			Any            =		model(**lowerCamelCase_ )
			assert output is not None

	def       snake_case__(	self:	Optional[int] ):
			lowercase__				:			Union[str, Any]            =		self.prepare_init_args_and_inputs_for_common()

			lowercase__				:			Tuple            =		8

			lowercase__				:			Dict            =		self.model_class(**lowerCamelCase_ )
			model.to(lowerCamelCase_ )

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

			lowercase__				:			List[Any]            =		create_lora_layers(lowerCamelCase_ )

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

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

			with torch.no_grad():
					lowercase__				:			Dict            =		model(**lowerCamelCase_,				cross_attention_kwargs={'scale': 0.0} ).sample
					lowercase__				:			Any            =		model(**lowerCamelCase_,				cross_attention_kwargs={'scale': 0.5} ).sample
					lowercase__				:			int            =		model(**lowerCamelCase_,				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       snake_case__(	self:	List[str] ):
			lowercase__				:			Optional[int]            =		self.prepare_init_args_and_inputs_for_common()

			lowercase__				:			Optional[int]            =		8

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

			with torch.no_grad():
					lowercase__				:			Optional[Any]            =		model(**lowerCamelCase_ ).sample

			lowercase__				:			str            =		create_lora_layers(lowerCamelCase_ )
			model.set_attn_processor(lowerCamelCase_ )

			with torch.no_grad():
					lowercase__				:			List[Any]            =		model(**lowerCamelCase_,				cross_attention_kwargs={'scale': 0.5} ).sample

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

			with torch.no_grad():
					lowercase__				:			Union[str, Any]            =		new_model(**lowerCamelCase_,				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       snake_case__(	self:	Tuple ):
			lowercase__				:			str            =		self.prepare_init_args_and_inputs_for_common()

			lowercase__				:			Tuple            =		8

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

			with torch.no_grad():
					lowercase__				:			Optional[Any]            =		model(**lowerCamelCase_ ).sample

			lowercase__				:			List[Any]            =		create_lora_layers(lowerCamelCase_ )
			model.set_attn_processor(lowerCamelCase_ )

			with torch.no_grad():
					lowercase__				:			List[Any]            =		model(**lowerCamelCase_,				cross_attention_kwargs={'scale': 0.5} ).sample

			with tempfile.TemporaryDirectory() as tmpdirname:
					model.save_attn_procs(lowerCamelCase_,				safe_serialization=lowerCamelCase_ )
					self.assertTrue(os.path.isfile(os.path.join(lowerCamelCase_,				'pytorch_lora_weights.safetensors' ) ) )
					torch.manual_seed(0 )
					lowercase__				:			Union[str, Any]            =		self.model_class(**lowerCamelCase_ )
					new_model.to(lowerCamelCase_ )
					new_model.load_attn_procs(lowerCamelCase_ )

			with torch.no_grad():
					lowercase__				:			List[str]            =		new_model(**lowerCamelCase_,				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       snake_case__(	self:	int ):
			# enable deterministic behavior for gradient checkpointing
			lowercase__				:			Any            =		self.prepare_init_args_and_inputs_for_common()

			lowercase__				:			Dict            =		8

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

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

	def       snake_case__(	self:	int ):
			lowercase__				:			int            =		self.prepare_init_args_and_inputs_for_common()

			lowercase__				:			int            =		8

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

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

	def       snake_case__(	self:	Optional[Any] ):
			lowercase__				:			Dict            =		self.prepare_init_args_and_inputs_for_common()

			lowercase__				:			List[str]            =		8

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

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

			lowercase__				:			List[Any]            =		create_lora_layers(lowerCamelCase_ )
			model.set_attn_processor(lowerCamelCase_ )

			with torch.no_grad():
					lowercase__				:			str            =		model(**lowerCamelCase_,				cross_attention_kwargs={'scale': 0.0} ).sample

			model.set_attn_processor(AttnProcessor() )

			with torch.no_grad():
					lowercase__				:			List[Any]            =		model(**lowerCamelCase_ ).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       snake_case__(	self:	List[Any] ):
			# enable deterministic behavior for gradient checkpointing
			lowercase__				:			Tuple            =		self.prepare_init_args_and_inputs_for_common()

			lowercase__				:			Optional[int]            =		4

			torch.manual_seed(0 )
			lowercase__				:			Union[str, Any]            =		self.model_class(**lowerCamelCase_ )
			model.to(lowerCamelCase_ )
			lowercase__				:			Optional[Any]            =		create_lora_layers(lowerCamelCase_ )
			model.set_attn_processor(lowerCamelCase_ )

			# default
			with torch.no_grad():
					lowercase__				:			List[str]            =		model(**lowerCamelCase_ ).sample

					model.enable_xformers_memory_efficient_attention()
					lowercase__				:			Union[str, Any]            =		model(**lowerCamelCase_ ).sample

					model.disable_xformers_memory_efficient_attention()
					lowercase__				:			Optional[int]            =		model(**lowerCamelCase_ ).sample

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





	def       snake_case__(	self:	List[Any] ):
			lowercase__				:			Union[str, Any]            =		self.prepare_init_args_and_inputs_for_common()
			lowercase__				:			Union[str, Any]            =		32

			lowercase__				:			str            =		self.model_class(**lowerCamelCase_ )
			model.to(lowerCamelCase_ )
			model.eval()

			with torch.no_grad():
					lowercase__				:			List[Any]            =		model(**lowerCamelCase_ )[0]

			model.enable_forward_chunking()
			with torch.no_grad():
					lowercase__				:			int            =		model(**lowerCamelCase_ )[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 enum
import inspect
from typing import Iterable, List, Optional, Tuple, Union


class 			__lowerCAmelCase				(					enum.Enum			):


	'''simple docstring'''





	_A       	=							"timm"
	_A       	=							"transformers"


def 			SCREAMING_SNAKE_CASE__     (					_lowercase    :   Optional[Iterable[str]]      ,						_lowercase    :   Optional[Iterable[int]]      ,						_lowercase    :   Optional[Iterable[str]] )      ->       Optional[int]:




		'''simple docstring'''
		if stage_names is None:
				raise ValueError('Stage_names must be set for transformers backbones' )

		if out_features is not None:
				if not isinstance(_lowercase      ,						(list,) ):
						raise ValueError(f"""out_features must be a list {type(_lowercase )}""" )
				if any(feat not in stage_names for feat in out_features ):
						raise ValueError(f"""out_features must be a subset of stage_names: {stage_names} got {out_features}""" )

		if out_indices is not None:
				if not isinstance(_lowercase      ,						(list, tuple) ):
						raise ValueError(f"""out_indices must be a list or tuple, got {type(_lowercase )}""" )
				if any(idx >= len(_lowercase ) for idx in out_indices ):
						raise ValueError('out_indices must be valid indices for stage_names {stage_names}, got {out_indices}' )

		if out_features is not None and out_indices is not None:
				if len(_lowercase ) != len(_lowercase ):
						raise ValueError('out_features and out_indices should have the same length if both are set' )
				if out_features != [stage_names[idx] for idx in out_indices]:
						raise ValueError('out_features and out_indices should correspond to the same stages if both are set' )


def 			SCREAMING_SNAKE_CASE__     (					_lowercase    :   Optional[List[str]]      ,						_lowercase    :   Optional[Union[List[int], Tuple[int]]]      ,						_lowercase    :   List[str]      ,						)      ->       Dict:




		'''simple docstring'''
		if out_indices is None and out_features is None:
				lowercase__				:			str            =		[len(_lowercase ) - 1]
				lowercase__				:			Union[str, Any]            =		[stage_names[-1]]
		elif out_indices is None and out_features is not None:
				lowercase__				:			Optional[int]            =		[stage_names.index(_lowercase ) for layer in out_features]
		elif out_features is None and out_indices is not None:
				lowercase__				:			str            =		[stage_names[idx] for idx in out_indices]
		return out_features, out_indices


def 			SCREAMING_SNAKE_CASE__     (					_lowercase    :   Optional[List[str]]      ,						_lowercase    :   Optional[Union[List[int], Tuple[int]]]      ,						_lowercase    :   List[str]      ,						)      ->       Tuple[List[str], List[int]]:




		'''simple docstring'''
		verify_out_features_out_indices(out_features=_lowercase      ,						out_indices=_lowercase      ,						stage_names=_lowercase )
		lowercase__      ,							lowercase__				:			List[str]            =		_align_output_features_output_indices(
		    out_features=_lowercase      ,						out_indices=_lowercase      ,						stage_names=_lowercase )
		# Verify that the aligned out_features and out_indices are valid
		verify_out_features_out_indices(out_features=_lowercase      ,						out_indices=_lowercase      ,						stage_names=_lowercase )
		return output_features, output_indices


class 			__lowerCAmelCase				:


	'''simple docstring'''





	_A       	=							None

	def       snake_case__(	self:	Dict,				lowerCamelCase_:	Optional[Any] ):
			if getattr(self,				'_backbone',				lowerCamelCase_ ) is None:
					raise ValueError('self._backbone must be set before calling _init_timm_backbone' )

			# These will diagree with the defaults for the transformers models e.g. for resnet50
			# the transformer model has out_features = ['stem', 'stage1', 'stage2', 'stage3', 'stage4']
			# the timm model has out_features = ['act', 'layer1', 'layer2', 'layer3', 'layer4']
			lowercase__				:			Tuple            =		[stage['module'] for stage in self._backbone.feature_info.info]
			lowercase__				:			Union[str, Any]            =		[stage['num_chs'] for stage in self._backbone.feature_info.info]
			lowercase__				:			Dict            =		self._backbone.feature_info.out_indices
			lowercase__				:			Tuple            =		self._backbone.feature_info.module_name()

			# We verify the out indices and out features are valid
			verify_out_features_out_indices(
			    out_features=lowerCamelCase_,				out_indices=lowerCamelCase_,				stage_names=self.stage_names )
			lowercase__      ,							lowercase__				:			List[Any]            =		out_features, out_indices

	def       snake_case__(	self:	Any,				lowerCamelCase_:	List[str] ):
			lowercase__				:			Dict            =		getattr(lowerCamelCase_,				'stage_names' )
			lowercase__				:			List[str]            =		getattr(lowerCamelCase_,				'out_features',				lowerCamelCase_ )
			lowercase__				:			Optional[int]            =		getattr(lowerCamelCase_,				'out_indices',				lowerCamelCase_ )

			lowercase__				:			Optional[int]            =		stage_names
			lowercase__      ,							lowercase__				:			Union[str, Any]            =		get_aligned_output_features_output_indices(
			    out_features=lowerCamelCase_,				out_indices=lowerCamelCase_,				stage_names=lowerCamelCase_ )
			# Number of channels for each stage. This is set in the transformer backbone model init
			lowercase__				:			Dict            =		None

	def       snake_case__(	self:	List[Any],				lowerCamelCase_:	List[str] ):
			lowercase__				:			List[Any]            =		config

			lowercase__				:			Optional[int]            =		getattr(lowerCamelCase_,				'use_timm_backbone',				lowerCamelCase_ )
			lowercase__				:			List[Any]            =		BackboneType.TIMM if self.use_timm_backbone else BackboneType.TRANSFORMERS

			if self.backbone_type == BackboneType.TIMM:
					self._init_timm_backbone(lowerCamelCase_ )
			elif self.backbone_type == BackboneType.TRANSFORMERS:
					self._init_transformers_backbone(lowerCamelCase_ )
			else:
					raise ValueError(F"""backbone_type {self.backbone_type} not supported.""" )

	@property
	def       snake_case__(	self:	List[str] ):
			return self._out_features

	@out_features.setter
	def       snake_case__(	self:	Union[str, Any],				lowerCamelCase_:	List[str] ):
			lowercase__      ,							lowercase__				:			Optional[int]            =		get_aligned_output_features_output_indices(
			    out_features=lowerCamelCase_,				out_indices=lowerCamelCase_,				stage_names=self.stage_names )

	@property
	def       snake_case__(	self:	List[Any] ):
			return self._out_indices

	@out_indices.setter
	def       snake_case__(	self:	Tuple,				lowerCamelCase_:	Union[Tuple[int], List[int]] ):
			lowercase__      ,							lowercase__				:			List[str]            =		get_aligned_output_features_output_indices(
			    out_features=lowerCamelCase_,				out_indices=lowerCamelCase_,				stage_names=self.stage_names )

	@property
	def       snake_case__(	self:	str ):
			# the current backbones will output the number of channels for each stage
			# even if that stage is not in the out_features list.
			return {stage: self.num_features[i] for i, stage in enumerate(self.stage_names )}

	@property
	def       snake_case__(	self:	Optional[int] ):
			return [self.out_feature_channels[name] for name in self.out_features]

	def       snake_case__(	self:	List[Any],				*lowerCamelCase_:	List[Any],				**lowerCamelCase_:	Optional[Any] ):
			lowercase__				:			Any            =		dict(inspect.signature(self.forward ).parameters )
			lowercase__				:			List[Any]            =		{k: v for k, v in kwargs.items() if k in signature}
			return self(*lowerCamelCase_,				**lowerCamelCase_ )

	def       snake_case__(	self:	int,				lowerCamelCase_:	str,				lowerCamelCase_:	Optional[bool] = None,				lowerCamelCase_:	Optional[bool] = None,				lowerCamelCase_:	Optional[bool] = None,				):
			raise NotImplementedError('This method should be implemented by the derived class.' )





	def       snake_case__(	self:	Dict ):
			lowercase__				:			List[Any]            =		super().to_dict()
			lowercase__				:			Union[str, Any]            =		output.pop('_out_features' )
			lowercase__				:			Any            =		output.pop('_out_indices' )
			return output


class 			__lowerCAmelCase				:


	'''simple docstring'''





	@property
	def       snake_case__(	self:	Optional[int] ):
			return self._out_features

	@out_features.setter
	def       snake_case__(	self:	List[Any],				lowerCamelCase_:	List[str] ):
			lowercase__      ,							lowercase__				:			List[Any]            =		get_aligned_output_features_output_indices(
			    out_features=lowerCamelCase_,				out_indices=lowerCamelCase_,				stage_names=self.stage_names )

	@property
	def       snake_case__(	self:	Dict ):
			return self._out_indices

	@out_indices.setter
	def       snake_case__(	self:	Optional[int],				lowerCamelCase_:	Union[Tuple[int], List[int]] ):
			lowercase__      ,							lowercase__				:			Any            =		get_aligned_output_features_output_indices(
			    out_features=lowerCamelCase_,				out_indices=lowerCamelCase_,				stage_names=self.stage_names )





	def       snake_case__(	self:	Union[str, Any] ):
			lowercase__				:			List[str]            =		super().to_dict()
			lowercase__				:			str            =		output.pop('_out_features' )
			lowercase__				:			str            =		output.pop('_out_indices' )
			return output


