



from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


UpperCAmelCase_		 =					logging.get_logger(__name__)

UpperCAmelCase_		 =					{
    """microsoft/focalnet-tiny""": """https://huggingface.co/microsoft/focalnet-tiny/resolve/main/config.json""",
}


class 	lowerCamelCase__	(       _A							,  _A):



 """simple docstring"""

 a__					: List[Any] =  "focalnet"

 def __init__(    self					:					Union[str, Any]			,						__lowerCAmelCase					:					str=2_24			,						__lowerCAmelCase					:					List[Any]=4			,						__lowerCAmelCase					:					List[str]=3			,						__lowerCAmelCase					:					Tuple=96			,						__lowerCAmelCase					:					List[Any]=False			,						__lowerCAmelCase					:					Tuple=[1_92, 3_84, 7_68, 7_68]			,						__lowerCAmelCase					:					int=[2, 2, 6, 2]			,						__lowerCAmelCase					:					List[str]=[2, 2, 2, 2]			,						__lowerCAmelCase					:					str=[3, 3, 3, 3]			,						__lowerCAmelCase					:					Dict="gelu"			,						__lowerCAmelCase					:					int=4.0			,						__lowerCAmelCase					:					Optional[Any]=0.0			,						__lowerCAmelCase					:					int=0.1			,						__lowerCAmelCase					:					int=False			,						__lowerCAmelCase					:					str=1E-4			,						__lowerCAmelCase					:					str=False			,						__lowerCAmelCase					:					Union[str, Any]=False			,						__lowerCAmelCase					:					str=False			,						__lowerCAmelCase					:					List[str]=0.02			,						__lowerCAmelCase					:					Optional[int]=1E-5			,						__lowerCAmelCase					:					Tuple=32			,						__lowerCAmelCase					:					Optional[int]=None			,						__lowerCAmelCase					:					Dict=None			,						**__lowerCAmelCase					:					Union[str, Any]			,						)   ->	Union[str, Any]:
    super().__init__(**__lowerCAmelCase      )

    _A      =       image_size
    _A      =       patch_size
    _A      =       num_channels
    _A      =       embed_dim
    _A      =       use_conv_embed
    _A      =       hidden_sizes
    _A      =       depths
    _A      =       focal_levels
    _A      =       focal_windows
    _A      =       hidden_act
    _A      =       mlp_ratio
    _A      =       hidden_dropout_prob
    _A      =       drop_path_rate
    _A      =       use_layerscale
    _A      =       layerscale_value
    _A      =       use_post_layernorm
    _A      =       use_post_layernorm_in_modulation
    _A      =       normalize_modulator
    _A      =       initializer_range
    _A      =       layer_norm_eps
    _A      =       encoder_stride
    _A      =       ['''stem'''] + [f'''stage{idx}''' for idx in range(1			,						len(self.depths      ) + 1      )]
    _A  ,    _A      =       get_aligned_output_features_output_indices(
        out_features=__lowerCAmelCase			,						out_indices=__lowerCAmelCase			,						stage_names=self.stage_names      )











import importlib.util
import json
import os
import warnings
from dataclasses import dataclass, field

import torch

from ..training_args import TrainingArguments
from ..utils import cached_property, is_sagemaker_dp_enabled, logging


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



def  a__ ()		-> Any:
				# Get the sagemaker specific mp parameters from smp_options variable.
				_A	:				Any   =      os.getenv('''SM_HP_MP_PARAMETERS'''					,  '''{}'''   )
				try:
								# Parse it and check the field "partitions" is included, it is required for model parallel.
								_A	:				Dict   =      json.loads(__lowercase   )
								if "partitions" not in smp_options:
												return False
				except json.JSONDecodeError:
								return False

				# Get the sagemaker specific framework parameters from mpi_options variable.
				_A	:				Optional[int]   =      os.getenv('''SM_FRAMEWORK_PARAMS'''					,  '''{}'''   )
				try:
								# Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
								_A	:				Any   =      json.loads(__lowercase   )
								if not mpi_options.get('''sagemaker_mpi_enabled'''					,  __lowercase   ):
												return False
				except json.JSONDecodeError:
								return False
				# Lastly, check if the `smdistributed` module is present.
				return importlib.util.find_spec('''smdistributed'''   ) is not None


if is_sagemaker_model_parallel_available():
	import smdistributed.modelparallel.torch as smp

	smp.init()






@dataclass
class 						UpperCAmelCase__ (							__snake_case							):
		__snake_case      :       str     =   field(
		    default=""     ,				metadata={"help": "Used by the SageMaker launcher to send mp-specific args. Ignored in SageMakerTrainer"}     ,				)







		def  A__      (					self		):
						super().__post_init__()
						warnings.warn(
						    '''`SageMakerTrainingArguments` is deprecated and will be removed in v5 of Transformers. You can use '''
						    '''`TrainingArguments` instead.'''       ,A__       ,)







		@cached_property
		def  A__      (					self		):
						logger.info('''PyTorch: setting up devices'''		)
						if torch.distributed.is_available() and torch.distributed.is_initialized() and self.local_rank == -1:
										logger.warning(
										    '''torch.distributed process group is initialized, but local_rank == -1. '''
										    '''In order to use Torch DDP, launch your script with `python -m torch.distributed.launch'''		)
						if self.no_cuda:
										_A	:				Union[str, Any]   =      torch.device('''cpu'''		)
										_A	:				List[str]   =      0
						elif is_sagemaker_model_parallel_available():
										_A	:				Dict   =      smp.local_rank()
										_A	:				Union[str, Any]   =      torch.device('''cuda'''       ,A__		)
										_A	:				Tuple   =      1
						elif is_sagemaker_dp_enabled():
										import smdistributed.dataparallel.torch.torch_smddp  # noqa: F401

										torch.distributed.init_process_group(backend='''smddp'''       ,timeout=self.ddp_timeout_delta		)
										_A	:				int   =      int(os.getenv('''SMDATAPARALLEL_LOCAL_RANK'''		)		)
										_A	:				int   =      torch.device('''cuda'''       ,self.local_rank		)
										_A	:				Union[str, Any]   =      1
						elif self.local_rank == -1:
										# if n_gpu is > 1 we'll use nn.DataParallel.
										# If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
										# Explicitly set CUDA to the first (index 0) CUDA device, otherwise `set_device` will
										# trigger an error that a device index is missing. Index 0 takes into account the
										# GPUs available in the environment, so `CUDA_VISIBLE_DEVICES=1,2` with `cuda:0`
										# will use the first GPU in that env, i.e. GPU#1
										_A	:				Dict   =      torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu'''		)
										# Sometimes the line in the postinit has not been run before we end up here, so just checking we're not at
										# the default value.
										_A	:				Union[str, Any]   =      torch.cuda.device_count()
						else:
										# Here, we'll use torch.distributed.
										# Initializes the distributed backend which will take care of synchronizing nodes/GPUs
										if not torch.distributed.is_initialized():
														torch.distributed.init_process_group(backend='''nccl'''       ,timeout=self.ddp_timeout_delta		)
										_A	:				Union[str, Any]   =      torch.device('''cuda'''       ,self.local_rank		)
										_A	:				List[Any]   =      1

						if device.type == "cuda":
										torch.cuda.set_device(A__		)

						return device







		@property
		def  A__      (					self		):
						if is_sagemaker_model_parallel_available():
										return smp.dp_size()

						return super().world_size







		@property
		def  A__      (					self		):
						return not is_sagemaker_model_parallel_available()







		@property
		def  A__      (					self		):
						return False



