


'''simple docstring'''



from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder, VectorQuantizer





@dataclass
class 							__UpperCamelCase    (		lowerCAmelCase_ ):
	A_           =   42





class 							__UpperCamelCase    (		lowerCAmelCase_		,				lowerCAmelCase_ ):




	@register_to_config
	def __init__(       self						,    __a = 3						,    __a = 3						,    __a = ("DownEncoderBlock2D",)						,    __a = ("UpDecoderBlock2D",)						,    __a = (64,)						,    __a = 1						,    __a = "silu"						,    __a = 3						,    __a = 32						,    __a = 256						,    __a = 32						,    __a = None						,    __a = 0.18215						,    __a = "group"						,    ):

						'''simple docstring'''





						super().__init__()

						# pass init params to Encoder
						__a      :	Any    =  Encoder(
						    in_channels=__a						,    out_channels=__a						,    down_block_types=__a						,    block_out_channels=__a						,    layers_per_block=__a						,    act_fn=__a						,    norm_num_groups=__a						,    double_z=__a						,    )

						__a      :	Optional[int]    =  vq_embed_dim if vq_embed_dim is not None else latent_channels

						__a      :	Dict    =  nn.Convad(__a						,    __a						,    1							)
						__a      :	Any    =  VectorQuantizer(__a						,    __a						,    beta=0.25						,    remap=__a						,    sane_index_shape=__a							)
						__a      :	Dict    =  nn.Convad(__a						,    __a						,    1							)

						# pass init params to Decoder
						__a      :	Optional[Any]    =  Decoder(
						    in_channels=__a						,    out_channels=__a						,    up_block_types=__a						,    block_out_channels=__a						,    layers_per_block=__a						,    act_fn=__a						,    norm_num_groups=__a						,    norm_type=__a						,    )




	@apply_forward_hook
	def 				__UpperCAmelCase							(       self						,    __a						,    __a = True							):

						'''simple docstring'''





						__a      :	Optional[int]    =  self.encoder(__a							)
						__a      :	Optional[int]    =  self.quant_conv(__a							)

						if not return_dict:
											return (h,)

						return VQEncoderOutput(latents=__a							)




	@apply_forward_hook
	def 				__UpperCAmelCase							(       self						,    __a						,    __a = False						,    __a = True							):

						'''simple docstring'''





						if not force_not_quantize:
											__a   ,    __a   ,    __a      :	Optional[Any]    =  self.quantize(__a							)
						else:
											__a      :	Union[str, Any]    =  h
						__a      :	List[str]    =  self.post_quant_conv(__a							)
						__a      :	Dict    =  self.decoder(__a						,    quant if self.config.norm_type == 'spatial' else None							)

						if not return_dict:
											return (dec,)

						return DecoderOutput(sample=__a							)





	def 				__UpperCAmelCase							(       self						,    __a						,    __a = True							):

						'''simple docstring'''





						__a      :	Dict    =  sample
						__a      :	List[Any]    =  self.encode(__a							).latents
						__a      :	List[str]    =  self.decode(__a							).sample

						if not return_dict:
											return (dec,)

						return DecoderOutput(sample=__a							)







'''simple docstring'''



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   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       Optional[Any]       ):
					if is_torch_version('<'						,				'2.0.0'       ) or not hasattr(_SCREAMING_SNAKE_CASE						,				'_dynamo'       ):
										return False
					return isinstance(_SCREAMING_SNAKE_CASE						,				torch._dynamo.eval_frame.OptimizedModule       )






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       Optional[Any]						,				_SCREAMING_SNAKE_CASE				:       bool = True       ):
					__a      :	int    =  (torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)

					__a      :	Any    =  is_compiled_module(_SCREAMING_SNAKE_CASE       )
					if is_compiled:
										__a      :	List[Any]    =  model
										__a      :	Union[str, Any]    =  model._orig_mod

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

					while isinstance(_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE       ):
										__a      :	Union[str, Any]    =  model.module

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

					if is_compiled:
										__a      :	List[str]    =  model
										__a      :	Optional[int]    =  compiled_model

					return model






def   lowerCamelCase							():
					PartialState().wait_for_everyone()






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       int						,				_SCREAMING_SNAKE_CASE				:       Tuple       ):
					if PartialState().distributed_type == DistributedType.TPU:
										xm.save(_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE       )
					elif PartialState().local_process_index == 0:
										torch.save(_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE       )






@contextmanager
def   lowerCamelCase							(**_SCREAMING_SNAKE_CASE				:       Tuple       ):
					for key, value in kwargs.items():
										__a      :	Optional[int]    =  str(_SCREAMING_SNAKE_CASE       )

					yield

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






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       Dict       ):
					if not hasattr(_SCREAMING_SNAKE_CASE						,				'__qualname__'       ) and not hasattr(_SCREAMING_SNAKE_CASE						,				'__name__'       ):
										__a      :	List[Any]    =  getattr(_SCREAMING_SNAKE_CASE						,				'__class__'						,				_SCREAMING_SNAKE_CASE       )
					if hasattr(_SCREAMING_SNAKE_CASE						,				'__qualname__'       ):
										return obj.__qualname__
					if hasattr(_SCREAMING_SNAKE_CASE						,				'__name__'       ):
										return obj.__name__
					return str(_SCREAMING_SNAKE_CASE       )






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       int						,				_SCREAMING_SNAKE_CASE				:       List[str]       ):
					for key, value in source.items():
										if isinstance(_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE       ):
															__a      :	int    =  destination.setdefault(_SCREAMING_SNAKE_CASE						,				{}       )
															merge_dicts(_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE       )
										else:
															__a      :	Tuple    =  value

					return destination






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       int = None       ):
					if port is None:
										__a      :	List[str]    =  29_500
					with socket.socket(socket.AF_INET						,				socket.SOCK_STREAM       ) as s:
										return s.connect_ex(('localhost', port)       ) == 0



