





import csv
import json
import logging
import os
import re
import subprocess
import tempfile
import unittest
import zipfile
from pathlib import Path
from typing import Optional
from unittest import mock

import torch

# We use TF to parse the logs
from accelerate import Accelerator
from accelerate.test_utils.testing import (
    MockingTestCase,
    TempDirTestCase,
    require_comet_ml,
    require_tensorboard,
    require_wandb,
    skip,
)
from accelerate.tracking import CometMLTracker, GeneralTracker
from accelerate.utils import ProjectConfiguration, is_comet_ml_available, is_tensorboard_available


if is_comet_ml_available():
			from comet_ml import OfflineExperiment

if is_tensorboard_available():
			import struct

			import tensorboard.compat.proto.event_pba as event_pba

__UpperCamelCase:  Optional[Any]   =   logging.getLogger(__name__)


@require_tensorboard
class 			__lowerCAmelCase				(					unittest.TestCase			):


	'''simple docstring'''





	def       snake_case__(	self:	Optional[int] ):
			lowercase__				:			List[str]            =		'test_project_with_config'
			with tempfile.TemporaryDirectory() as dirpath:
					lowercase__				:			List[Any]            =		Accelerator(log_with='tensorboard',				project_dir=lowerCamelCase_ )
					lowercase__				:			int            =		{'num_iterations': 12, 'learning_rate': 1E-2, 'some_boolean': False, 'some_string': 'some_value'}
					accelerator.init_trackers(lowerCamelCase_,				lowerCamelCase_ )
					accelerator.end_training()
					for child in Path(F"""{dirpath}/{project_name}""" ).glob('*/**' ):
							lowercase__				:			int            =		list(filter(lambda lowerCamelCase_ : x.is_file(),				child.iterdir() ) )[0]
					self.assertNotEqual(str(lowerCamelCase_ ),				'' )

	def       snake_case__(	self:	Union[str, Any] ):
			lowercase__				:			Union[str, Any]            =		'test_project_with_log'
			with tempfile.TemporaryDirectory() as dirpath:
					lowercase__				:			Optional[Any]            =		Accelerator(log_with='tensorboard',				project_dir=lowerCamelCase_ )
					accelerator.init_trackers(lowerCamelCase_ )
					lowercase__				:			Tuple            =		{'total_loss': 0.1, 'iteration': 1, 'my_text': 'some_value'}
					accelerator.log(lowerCamelCase_,				step=0 )
					accelerator.end_training()
					# Logged values are stored in the outermost-tfevents file and can be read in as a TFRecord
					# Names are randomly generated each time
					lowercase__				:			Union[str, Any]            =		list(filter(lambda lowerCamelCase_ : x.is_file(),				Path(F"""{dirpath}/{project_name}""" ).iterdir() ) )[0]
					self.assertNotEqual(str(lowerCamelCase_ ),				'' )

	def       snake_case__(	self:	Any ):
			lowercase__				:			str            =		'test_project_with_log'
			with tempfile.TemporaryDirectory() as dirpath:
					lowercase__				:			Tuple            =		Accelerator(log_with='tensorboard',				project_dir=lowerCamelCase_ )
					accelerator.init_trackers(lowerCamelCase_ )
					lowercase__				:			List[str]            =		{'tensor': torch.tensor(1 )}
					accelerator.log(lowerCamelCase_,				step=0 )
					accelerator.end_training()
					# Logged values are stored in the outermost-tfevents file and can be read in as a TFRecord
					# Names are randomly generated each time
					lowercase__				:			Optional[Any]            =		list(filter(lambda lowerCamelCase_ : x.is_file(),				Path(F"""{dirpath}/{project_name}""" ).iterdir() ) )[0]
					# Reading implementation based on https://github.com/pytorch/pytorch/issues/45327#issuecomment-703757685
					with open(lowerCamelCase_,				'rb' ) as f:
							lowercase__				:			Union[str, Any]            =		f.read()
					lowercase__				:			str            =		False
					while data:
							lowercase__				:			List[str]            =		struct.unpack('Q',				data[:8] )

							lowercase__				:			Union[str, Any]            =		data[12 : 12 + int(header[0] )]  # 8+4
							lowercase__				:			Tuple            =		data[12 + int(header[0] ) + 4 :]
							lowercase__				:			List[str]            =		event_pba.Event()

							event.ParseFromString(lowerCamelCase_ )
							if event.HasField('summary' ):
									for value in event.summary.value:
											if value.simple_value == 1.0 and value.tag == "tensor":
													lowercase__				:			List[Any]            =		True
					self.assertTrue(lowerCamelCase_,				'Converted tensor was not found in the log file!' )

	def       snake_case__(	self:	List[Any] ):
			with self.assertRaisesRegex(lowerCamelCase_,				'Logging with `tensorboard` requires a `logging_dir`' ):
					lowercase__				:			Optional[int]            =		Accelerator(log_with='tensorboard' )
			with tempfile.TemporaryDirectory() as dirpath:
					lowercase__				:			Dict            =		Accelerator(log_with='tensorboard',				project_dir=lowerCamelCase_ )





	def       snake_case__(	self:	Union[str, Any] ):
			lowercase__				:			Optional[Any]            =		ProjectConfiguration(total_limit=30 )
			with tempfile.TemporaryDirectory() as dirpath:
					lowercase__				:			List[str]            =		Accelerator(log_with='tensorboard',				project_dir=lowerCamelCase_,				project_config=lowerCamelCase_ )


@require_wandb
@mock.patch.dict(os.environ      ,						{"WANDB_MODE": "offline"}			)
class 			__lowerCAmelCase				(					_UpperCamelCase      ,						_UpperCamelCase			):


	'''simple docstring'''





	def       snake_case__(	self:	Union[str, Any] ):
			super().setUp()
			# wandb let's us override where logs are stored to via the WANDB_DIR env var
			self.add_mocks(mock.patch.dict(os.environ,				{'WANDB_DIR': self.tmpdir} ) )

	@staticmethod
	def       snake_case__(	lowerCamelCase_:	str,				lowerCamelCase_:	str,				lowerCamelCase_:	bool = True ):
			lowercase__				:			List[str]            =		rF"""{section} ([\S\s]*?)\n\n"""
			if record:
					lowercase__				:			List[Any]            =		rF"""Record: {pattern}"""
			lowercase__				:			int            =		re.findall(lowerCamelCase_,				lowerCamelCase_ )[0]
			# A config
			if section == "config" or section == "history":
					lowercase__				:			Union[str, Any]            =		re.findall(r'"([a-zA-Z0-9_.,]+)',				lowerCamelCase_ )
					return {key: val for key, val in zip(cleaned_record[0::2],				cleaned_record[1::2] )}
			# Everything else
			else:
					return dict(re.findall(r'(\w+): "([^\s]+)"',				lowerCamelCase_ ) )





	@skip
	def       snake_case__(	self:	List[Any] ):
			lowercase__				:			str            =		'test_project_with_config'
			lowercase__				:			str            =		Accelerator(log_with='wandb' )
			lowercase__				:			Optional[int]            =		{'num_iterations': 12, 'learning_rate': 1E-2, 'some_boolean': False, 'some_string': 'some_value'}
			lowercase__				:			Optional[int]            =		{'wandb': {'tags': ['my_tag']}}
			accelerator.init_trackers(lowerCamelCase_,				lowerCamelCase_,				lowerCamelCase_ )
			lowercase__				:			List[Any]            =		{'total_loss': 0.1, 'iteration': 1, 'my_text': 'some_value'}
			accelerator.log(lowerCamelCase_,				step=0 )
			accelerator.end_training()
			# The latest offline log is stored at wandb/latest-run/*.wandb
			for child in Path(F"""{self.tmpdir}/wandb/latest-run""" ).glob('*' ):
					if child.is_file() and child.suffix == ".wandb":
							lowercase__				:			List[str]            =		subprocess.check_output(
							    ['wandb', 'sync', '--view', '--verbose', str(lowerCamelCase_ )],				env=os.environ.copy() ).decode('utf8',				'ignore' )
							break

        # Check HPS through careful parsing and cleaning
			lowercase__				:			List[Any]            =		self.parse_log(lowerCamelCase_,				'config' )
			self.assertEqual(logged_items['num_iterations'],				'12' )
			self.assertEqual(logged_items['learning_rate'],				'0.01' )
			self.assertEqual(logged_items['some_boolean'],				'false' )
			self.assertEqual(logged_items['some_string'],				'some_value' )
			self.assertEqual(logged_items['some_string'],				'some_value' )

			# Run tags
			lowercase__				:			Tuple            =		self.parse_log(lowerCamelCase_,				'run',				lowerCamelCase_ )
			self.assertEqual(logged_items['tags'],				'my_tag' )

			# Actual logging
			lowercase__				:			List[Any]            =		self.parse_log(lowerCamelCase_,				'history' )
			self.assertEqual(logged_items['total_loss'],				'0.1' )
			self.assertEqual(logged_items['iteration'],				'1' )
			self.assertEqual(logged_items['my_text'],				'some_value' )
			self.assertEqual(logged_items['_step'],				'0' )


def 			SCREAMING_SNAKE_CASE__     (					self    :   str      ,						_lowercase    :   str      ,						_lowercase    :   str )      ->       Any:




		'''simple docstring'''
		lowercase__				:			str            =		run_name
		lowercase__				:			str            =		OfflineExperiment(project_name=_lowercase      ,						offline_directory=_lowercase )
		logger.info(f"""Initialized offline CometML project {self.run_name}""" )
		logger.info('Make sure to log any initial configurations with `self.store_init_configuration` before training!' )


@require_comet_ml
@mock.patch.object(_UpperCamelCase      ,						"__init__"      ,						_UpperCamelCase			)
class 			__lowerCAmelCase				(					unittest.TestCase			):


	'''simple docstring'''





	@staticmethod
	def       snake_case__(	lowerCamelCase_:	int,				lowerCamelCase_:	str,				lowerCamelCase_:	bool = False ):
			for log in log_list:
					lowercase__				:			Tuple            =		json.loads(lowerCamelCase_ )['payload']
					if is_param and "param" in j.keys():
							if j["param"]["paramName"] == key:
									return j["param"]["paramValue"]
					if "log_other" in j.keys():
							if j["log_other"]["key"] == key:
									return j["log_other"]["val"]
					if "metric" in j.keys():
							if j["metric"]["metricName"] == key:
									return j["metric"]["metricValue"]

	def       snake_case__(	self:	Optional[Any] ):
			with tempfile.TemporaryDirectory() as d:
					lowercase__				:			Any            =		CometMLTracker('test_project_with_config',				lowerCamelCase_ )
					lowercase__				:			str            =		Accelerator(log_with=lowerCamelCase_ )
					lowercase__				:			str            =		{'num_iterations': 12, 'learning_rate': 1E-2, 'some_boolean': False, 'some_string': 'some_value'}
					accelerator.init_trackers(lowerCamelCase_,				lowerCamelCase_ )
					accelerator.end_training()
					lowercase__				:			List[Any]            =		os.listdir(lowerCamelCase_ )[0]  # Comet is nice, it's just a zip file here
					# We parse the raw logs
					lowercase__				:			int            =		os.path.join(lowerCamelCase_,				lowerCamelCase_ )
					lowercase__				:			List[str]            =		zipfile.ZipFile(lowerCamelCase_,				'r' )
					lowercase__				:			List[Any]            =		archive.open('messages.json' ).read().decode('utf-8' )
			lowercase__				:			Optional[int]            =		log.split('\n' )[:-1]
			self.assertEqual(self.get_value_from_key(lowerCamelCase_,				'num_iterations',				lowerCamelCase_ ),				12 )
			self.assertEqual(self.get_value_from_key(lowerCamelCase_,				'learning_rate',				lowerCamelCase_ ),				0.0_1 )
			self.assertEqual(self.get_value_from_key(lowerCamelCase_,				'some_boolean',				lowerCamelCase_ ),				lowerCamelCase_ )
			self.assertEqual(self.get_value_from_key(lowerCamelCase_,				'some_string',				lowerCamelCase_ ),				'some_value' )





	def       snake_case__(	self:	int ):
			with tempfile.TemporaryDirectory() as d:
					lowercase__				:			str            =		CometMLTracker('test_project_with_config',				lowerCamelCase_ )
					lowercase__				:			str            =		Accelerator(log_with=lowerCamelCase_ )
					accelerator.init_trackers(lowerCamelCase_ )
					lowercase__				:			Any            =		{'total_loss': 0.1, 'iteration': 1, 'my_text': 'some_value'}
					accelerator.log(lowerCamelCase_,				step=0 )
					accelerator.end_training()
					lowercase__				:			Optional[int]            =		os.listdir(lowerCamelCase_ )[0]  # Comet is nice, it's just a zip file here
					# We parse the raw logs
					lowercase__				:			Optional[int]            =		os.path.join(lowerCamelCase_,				lowerCamelCase_ )
					lowercase__				:			str            =		zipfile.ZipFile(lowerCamelCase_,				'r' )
					lowercase__				:			Any            =		archive.open('messages.json' ).read().decode('utf-8' )
			lowercase__				:			Dict            =		log.split('\n' )[:-1]
			self.assertEqual(self.get_value_from_key(lowerCamelCase_,				'curr_step',				lowerCamelCase_ ),				0 )
			self.assertEqual(self.get_value_from_key(lowerCamelCase_,				'total_loss' ),				0.1 )
			self.assertEqual(self.get_value_from_key(lowerCamelCase_,				'iteration' ),				1 )
			self.assertEqual(self.get_value_from_key(lowerCamelCase_,				'my_text' ),				'some_value' )


class 			__lowerCAmelCase				(					_UpperCamelCase			):


	'''simple docstring'''





	_A       	=							[
	    "total_loss",
	    "iteration",
	    "my_text",
	    "learning_rate",
	    "num_iterations",
	    "some_boolean",
	    "some_string",
	]

	_A       	=							"my_custom_tracker"
	_A       	=							False

	def __init__(	self:	str,				lowerCamelCase_:	str ):
			lowercase__				:			int            =		open(F"""{dir}/log.csv""",				'w+' )
			lowercase__				:			Optional[Any]            =		csv.DictWriter(self.f,				fieldnames=self._col_names )
			self.writer.writeheader()

	@property
	def       snake_case__(	self:	Tuple ):
			return self.writer

	def       snake_case__(	self:	List[Any],				lowerCamelCase_:	dict ):
			logger.info('Call init' )
			self.writer.writerow(lowerCamelCase_ )

	def       snake_case__(	self:	Dict,				lowerCamelCase_:	dict,				lowerCamelCase_:	Optional[int] ):
			logger.info('Call log' )
			self.writer.writerow(lowerCamelCase_ )





	def       snake_case__(	self:	Optional[int] ):
			self.f.close()


class 			__lowerCAmelCase				(					unittest.TestCase			):


	'''simple docstring'''





	def       snake_case__(	self:	Tuple ):
			with tempfile.TemporaryDirectory() as d:
					lowercase__				:			Optional[int]            =		MyCustomTracker(lowerCamelCase_ )
					lowercase__				:			Any            =		Accelerator(log_with=lowerCamelCase_ )
					lowercase__				:			List[Any]            =		{'num_iterations': 12, 'learning_rate': 1E-2, 'some_boolean': False, 'some_string': 'some_value'}
					accelerator.init_trackers('Some name',				lowerCamelCase_ )
					accelerator.end_training()
					with open(F"""{d}/log.csv""",				'r' ) as f:
							lowercase__				:			Dict            =		csv.DictReader(lowerCamelCase_ )
							lowercase__				:			List[Any]            =		next(lowerCamelCase_ )
							lowercase__				:			List[Any]            =		{
							    'total_loss': '',
							    'iteration': '',
							    'my_text': '',
							    'learning_rate': '0.01',
							    'num_iterations': '12',
							    'some_boolean': 'False',
							    'some_string': 'some_value',
							}
							self.assertDictEqual(lowerCamelCase_,				lowerCamelCase_ )





	def       snake_case__(	self:	Tuple ):
			with tempfile.TemporaryDirectory() as d:
					lowercase__				:			int            =		MyCustomTracker(lowerCamelCase_ )
					lowercase__				:			Union[str, Any]            =		Accelerator(log_with=lowerCamelCase_ )
					accelerator.init_trackers('Some name' )
					lowercase__				:			str            =		{'total_loss': 0.1, 'iteration': 1, 'my_text': 'some_value'}
					accelerator.log(lowerCamelCase_,				step=0 )
					accelerator.end_training()
					with open(F"""{d}/log.csv""",				'r' ) as f:
							lowercase__				:			List[Any]            =		csv.DictReader(lowerCamelCase_ )
							lowercase__				:			Optional[int]            =		next(lowerCamelCase_ )
							lowercase__				:			Dict            =		{
							    'total_loss': '0.1',
							    'iteration': '1',
							    'my_text': 'some_value',
							    'learning_rate': '',
							    'num_iterations': '',
							    'some_boolean': '',
							    'some_string': '',
							}
							self.assertDictEqual(lowerCamelCase_,				lowerCamelCase_ )









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


