



import os
import socket
from contextlib import contextmanager

import torch

from ..commands.config.default import write_basic_config  # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine import convert_model
from .versions import is_torch_version


if is_deepspeed_available():
						from deepspeed import DeepSpeedEngine

if is_tpu_available(check_device=False):
						import torch_xla.core.xla_model as xm


def 			UpperCamelCase(  __UpperCamelCase							:		Dict ):
		if is_torch_version('''<'''     ,'''2.0.0''' ) or not hasattr(__UpperCamelCase     ,'''_dynamo''' ):
				return False
		return isinstance(__UpperCamelCase     ,torch._dynamo.eval_frame.OptimizedModule )


def 			UpperCamelCase(  __UpperCamelCase							:		Any     ,__UpperCamelCase							:		bool = True ):
		lowerCAmelCase_			:     List[Any]									= (torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)

		lowerCAmelCase_			:     Optional[Any]									= is_compiled_module(__UpperCamelCase )
		if is_compiled:
				lowerCAmelCase_			:     Optional[Any]									= model
				lowerCAmelCase_			:     str									= model._orig_mod

		if is_deepspeed_available():
				options += (DeepSpeedEngine,)

		while isinstance(__UpperCamelCase     ,__UpperCamelCase ):
				lowerCAmelCase_			:     int									= model.module

		if not keep_fpaa_wrapper:
				lowerCAmelCase_			:     List[Any]									= getattr(__UpperCamelCase     ,'''forward''' )
				lowerCAmelCase_			:     Any									= model.__dict__.pop('''_original_forward'''     ,__UpperCamelCase )
				if original_forward is not None:
						while hasattr(__UpperCamelCase     ,'''__wrapped__''' ):
								lowerCAmelCase_			:     Tuple									= forward.__wrapped__
								if forward == original_forward:
										break
						lowerCAmelCase_			:     str									= forward
				if getattr(__UpperCamelCase     ,'''_converted_to_transformer_engine'''     ,__UpperCamelCase ):
						convert_model(__UpperCamelCase     ,to_transformer_engine=__UpperCamelCase )

		if is_compiled:
				lowerCAmelCase_			:     List[str]									= model
				lowerCAmelCase_			:     str									= compiled_model

		return model


def 			UpperCamelCase(  ):
		PartialState().wait_for_everyone()


def 			UpperCamelCase(  __UpperCamelCase							:		Optional[Any]     ,__UpperCamelCase							:		Dict ):
		if PartialState().distributed_type == DistributedType.TPU:
				xm.save(__UpperCamelCase     ,__UpperCamelCase )
		elif PartialState().local_process_index == 0:
				torch.save(__UpperCamelCase     ,__UpperCamelCase )


@contextmanager
def 			UpperCamelCase(  **__UpperCamelCase							:		Optional[int] ):
		for key, value in kwargs.items():
				lowerCAmelCase_			:     Dict									= str(__UpperCamelCase )

		yield

		for key in kwargs:
				if key.upper() in os.environ:
						del os.environ[key.upper()]


def 			UpperCamelCase(  __UpperCamelCase							:		Optional[int] ):
		if not hasattr(__UpperCamelCase     ,'''__qualname__''' ) and not hasattr(__UpperCamelCase     ,'''__name__''' ):
				lowerCAmelCase_			:     Dict									= getattr(__UpperCamelCase     ,'''__class__'''     ,__UpperCamelCase )
		if hasattr(__UpperCamelCase     ,'''__qualname__''' ):
				return obj.__qualname__
		if hasattr(__UpperCamelCase     ,'''__name__''' ):
				return obj.__name__
		return str(__UpperCamelCase )


def 			UpperCamelCase(  __UpperCamelCase							:		Any     ,__UpperCamelCase							:		int ):
		for key, value in source.items():
				if isinstance(__UpperCamelCase     ,__UpperCamelCase ):
						lowerCAmelCase_			:     Optional[int]									= destination.setdefault(__UpperCamelCase     ,{} )
						merge_dicts(__UpperCamelCase     ,__UpperCamelCase )
				else:
						lowerCAmelCase_			:     Tuple									= value

		return destination





def 			UpperCamelCase(  __UpperCamelCase							:		int = None ):
		if port is None:
				lowerCAmelCase_			:     str									= 29500
		with socket.socket(socket.AF_INET     ,socket.SOCK_STREAM ) as s:
				return s.connect_ex(('''localhost''', port) ) == 0










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


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



class 				__snake_case						(     UpperCamelCase_						,UpperCamelCase_						):
							_a			      = '''maskformer-swin'''

							_a			      = {
							    '''num_attention_heads''': '''num_heads''',
							    '''num_hidden_layers''': '''num_layers''',
							}




							def __init__(       self     :       Union[str, Any]				,				A_     :       Dict=2_2_4				,				A_     :       Optional[Any]=4				,				A_     :       List[str]=3				,				A_     :       str=9_6				,				A_     :       Optional[Any]=[2, 2, 6, 2]				,				A_     :       Tuple=[3, 6, 1_2, 2_4]				,				A_     :       List[Any]=7				,				A_     :       List[Any]=4.0				,				A_     :       List[str]=True				,				A_     :       Dict=0.0				,				A_     :       int=0.0				,				A_     :       str=0.1				,				A_     :       Optional[int]="gelu"				,				A_     :       List[Any]=False				,				A_     :       int=0.02				,				A_     :       int=1e-5				,				A_     :       Optional[int]=None				,				A_     :       List[str]=None				,				**A_     :       List[Any]				,				):
									super().__init__(**A_)

									lowerCAmelCase_			:     Dict									= image_size
									lowerCAmelCase_			:     Optional[Any]									= patch_size
									lowerCAmelCase_			:     Tuple									= num_channels
									lowerCAmelCase_			:     Any									= embed_dim
									lowerCAmelCase_			:     List[str]									= depths
									lowerCAmelCase_			:     Union[str, Any]									= len(A_)
									lowerCAmelCase_			:     List[str]									= num_heads
									lowerCAmelCase_			:     Dict									= window_size
									lowerCAmelCase_			:     Optional[int]									= mlp_ratio
									lowerCAmelCase_			:     Dict									= qkv_bias
									lowerCAmelCase_			:     str									= hidden_dropout_prob
									lowerCAmelCase_			:     List[str]									= attention_probs_dropout_prob
									lowerCAmelCase_			:     Optional[int]									= drop_path_rate
									lowerCAmelCase_			:     Any									= hidden_act
									lowerCAmelCase_			:     str									= use_absolute_embeddings
									lowerCAmelCase_			:     List[str]									= layer_norm_eps
									lowerCAmelCase_			:     int									= initializer_range
									# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
									# this indicates the channel dimension after the last stage of the model
									lowerCAmelCase_			:     str									= int(embed_dim * 2 ** (len(A_) - 1))
									lowerCAmelCase_			:     Optional[Any]									= ['''stem'''] + [F"""stage{idx}""" for idx in range(1				,				len(A_) + 1)]
									lowerCAmelCase_					,  lowerCAmelCase_			:     int									= get_aligned_output_features_output_indices(
									    out_features=A_				,				out_indices=A_				,				stage_names=self.stage_names)





