





"""simple docstring"""




import argparse

import huggingface_hub
import k_diffusion as K
import torch

from diffusers import UNetaDConditionModel


UpperCamelCase_	 ="""pcuenq/k-upscaler"""





def 			a_							(				_lowercase			,		_lowercase			,		*, _lowercase			,		_lowercase  ):
						_UpperCamelCase     :   Tuple      =       {
						    # norm1
						    F"""{diffusers_resnet_prefix}.norm1.linear.weight""": checkpoint[F"""{resnet_prefix}.main.0.mapper.weight"""],
						    F"""{diffusers_resnet_prefix}.norm1.linear.bias""": checkpoint[F"""{resnet_prefix}.main.0.mapper.bias"""],
						    # conv1
						    F"""{diffusers_resnet_prefix}.conv1.weight""": checkpoint[F"""{resnet_prefix}.main.2.weight"""],
						    F"""{diffusers_resnet_prefix}.conv1.bias""": checkpoint[F"""{resnet_prefix}.main.2.bias"""],
						    # norm2
						    F"""{diffusers_resnet_prefix}.norm2.linear.weight""": checkpoint[F"""{resnet_prefix}.main.4.mapper.weight"""],
						    F"""{diffusers_resnet_prefix}.norm2.linear.bias""": checkpoint[F"""{resnet_prefix}.main.4.mapper.bias"""],
						    # conv2
						    F"""{diffusers_resnet_prefix}.conv2.weight""": checkpoint[F"""{resnet_prefix}.main.6.weight"""],
						    F"""{diffusers_resnet_prefix}.conv2.bias""": checkpoint[F"""{resnet_prefix}.main.6.bias"""],
						}

						if resnet.conv_shortcut is not None:
												rv.update(
												    {
												        F"""{diffusers_resnet_prefix}.conv_shortcut.weight""": checkpoint[F"""{resnet_prefix}.skip.weight"""],
												    }  )

						return rv





def 			a_							(				_lowercase			,		*, _lowercase			,		_lowercase  ):
						_UpperCamelCase  ,				_UpperCamelCase  ,				_UpperCamelCase     :   str      =       checkpoint[F"""{attention_prefix}.qkv_proj.weight"""].chunk(3			,		dim=0  )
						_UpperCamelCase  ,				_UpperCamelCase  ,				_UpperCamelCase     :   str      =       checkpoint[F"""{attention_prefix}.qkv_proj.bias"""].chunk(3			,		dim=0  )
						_UpperCamelCase     :   List[str]      =       {
						    # norm
						    F"""{diffusers_attention_prefix}.norm1.linear.weight""": checkpoint[F"""{attention_prefix}.norm_in.mapper.weight"""],
						    F"""{diffusers_attention_prefix}.norm1.linear.bias""": checkpoint[F"""{attention_prefix}.norm_in.mapper.bias"""],
						    # to_q
						    F"""{diffusers_attention_prefix}.attn1.to_q.weight""": weight_q.squeeze(-1  ).squeeze(-1  ),
						    F"""{diffusers_attention_prefix}.attn1.to_q.bias""": bias_q,
						    # to_k
						    F"""{diffusers_attention_prefix}.attn1.to_k.weight""": weight_k.squeeze(-1  ).squeeze(-1  ),
						    F"""{diffusers_attention_prefix}.attn1.to_k.bias""": bias_k,
						    # to_v
						    F"""{diffusers_attention_prefix}.attn1.to_v.weight""": weight_v.squeeze(-1  ).squeeze(-1  ),
						    F"""{diffusers_attention_prefix}.attn1.to_v.bias""": bias_v,
						    # to_out
						    F"""{diffusers_attention_prefix}.attn1.to_out.0.weight""": checkpoint[F"""{attention_prefix}.out_proj.weight"""]
						    .squeeze(-1  )
						    .squeeze(-1  ),
						    F"""{diffusers_attention_prefix}.attn1.to_out.0.bias""": checkpoint[F"""{attention_prefix}.out_proj.bias"""],
						}

						return rv





def 			a_							(				_lowercase			,		*, _lowercase			,		_lowercase			,		_lowercase  ):
						_UpperCamelCase  ,				_UpperCamelCase     :   Optional[Any]      =       checkpoint[F"""{attention_prefix}.kv_proj.weight"""].chunk(2			,		dim=0  )
						_UpperCamelCase  ,				_UpperCamelCase     :   Tuple      =       checkpoint[F"""{attention_prefix}.kv_proj.bias"""].chunk(2			,		dim=0  )

						_UpperCamelCase     :   Tuple      =       {
						    # norm2 (ada groupnorm)
						    F"""{diffusers_attention_prefix}.norm{diffusers_attention_index}.linear.weight""": checkpoint[
						        F"""{attention_prefix}.norm_dec.mapper.weight"""
						    ],
						    F"""{diffusers_attention_prefix}.norm{diffusers_attention_index}.linear.bias""": checkpoint[
						        F"""{attention_prefix}.norm_dec.mapper.bias"""
						    ],
						    # layernorm on encoder_hidden_state
						    F"""{diffusers_attention_prefix}.attn{diffusers_attention_index}.norm_cross.weight""": checkpoint[
						        F"""{attention_prefix}.norm_enc.weight"""
						    ],
						    F"""{diffusers_attention_prefix}.attn{diffusers_attention_index}.norm_cross.bias""": checkpoint[
						        F"""{attention_prefix}.norm_enc.bias"""
						    ],
						    # to_q
						    F"""{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_q.weight""": checkpoint[
						        F"""{attention_prefix}.q_proj.weight"""
						    ]
						    .squeeze(-1  )
						    .squeeze(-1  ),
						    F"""{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_q.bias""": checkpoint[
						        F"""{attention_prefix}.q_proj.bias"""
						    ],
						    # to_k
						    F"""{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_k.weight""": weight_k.squeeze(-1  ).squeeze(-1  ),
						    F"""{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_k.bias""": bias_k,
						    # to_v
						    F"""{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_v.weight""": weight_v.squeeze(-1  ).squeeze(-1  ),
						    F"""{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_v.bias""": bias_v,
						    # to_out
						    F"""{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_out.0.weight""": checkpoint[
						        F"""{attention_prefix}.out_proj.weight"""
						    ]
						    .squeeze(-1  )
						    .squeeze(-1  ),
						    F"""{diffusers_attention_prefix}.attn{diffusers_attention_index}.to_out.0.bias""": checkpoint[
						        F"""{attention_prefix}.out_proj.bias"""
						    ],
						}

						return rv





def 			a_							(				_lowercase			,		_lowercase			,		_lowercase			,		_lowercase  ):
						_UpperCamelCase     :   Optional[int]      =       '''inner_model.u_net.u_blocks''' if block_type == '''up''' else '''inner_model.u_net.d_blocks'''
						_UpperCamelCase     :   Any      =       F"""{block_prefix}.{block_idx}"""

						_UpperCamelCase     :   Union[str, Any]      =       {}

						if not hasattr(_lowercase			,		'''attentions'''  ):
												_UpperCamelCase     :   Optional[int]      =       1  # resnet only
						elif not block.attentions[0].add_self_attention:
												_UpperCamelCase     :   Optional[Any]      =       2  # resnet -> cross-attention
						else:
												_UpperCamelCase     :   Union[str, Any]      =       3  # resnet -> self-attention -> cross-attention)

						for resnet_idx, resnet in enumerate(block.resnets  ):
												# diffusers_resnet_prefix = f"{diffusers_up_block_prefix}.resnets.{resnet_idx}"
												_UpperCamelCase     :   Union[str, Any]      =       F"""{block_type}_blocks.{block_idx}.resnets.{resnet_idx}"""
												_UpperCamelCase     :   Tuple      =       n * resnet_idx if block_type == '''up''' else n * resnet_idx + 1
												_UpperCamelCase     :   List[Any]      =       F"""{block_prefix}.{idx}""" if block_type == '''up''' else F"""{block_prefix}.{idx}"""

												diffusers_checkpoint.update(
												    resnet_to_diffusers_checkpoint(
												        _lowercase			,		_lowercase			,		diffusers_resnet_prefix=_lowercase			,		resnet_prefix=_lowercase  )  )

						if hasattr(_lowercase			,		'''attentions'''  ):
												for attention_idx, attention in enumerate(block.attentions  ):
																		_UpperCamelCase     :   List[Any]      =       F"""{block_type}_blocks.{block_idx}.attentions.{attention_idx}"""
																		_UpperCamelCase     :   List[Any]      =       n * attention_idx + 1 if block_type == '''up''' else n * attention_idx + 2
																		_UpperCamelCase     :   Optional[int]      =       F"""{block_prefix}.{idx}"""
																		_UpperCamelCase     :   Optional[int]      =       F"""{block_prefix}.{idx }"""
																		_UpperCamelCase     :   List[Any]      =       1 if not attention.add_self_attention else 2
																		_UpperCamelCase     :   Optional[int]      =       (
																		    n * attention_idx + cross_attention_index
																		    if block_type == '''up'''
																		    else n * attention_idx + cross_attention_index + 1
																		)
																		_UpperCamelCase     :   Tuple      =       F"""{block_prefix}.{idx }"""

																		diffusers_checkpoint.update(
																		    cross_attn_to_diffusers_checkpoint(
																		        _lowercase			,		diffusers_attention_prefix=_lowercase			,		diffusers_attention_index=2			,		attention_prefix=_lowercase			,		)  )

																		if attention.add_self_attention is True:
																								diffusers_checkpoint.update(
																								    self_attn_to_diffusers_checkpoint(
																								        _lowercase			,		diffusers_attention_prefix=_lowercase			,		attention_prefix=_lowercase			,		)  )

						return diffusers_checkpoint





def 			a_							(				_lowercase			,		_lowercase  ):
						_UpperCamelCase     :   List[Any]      =       {}

						# pre-processing
						diffusers_checkpoint.update(
						    {
						        '''conv_in.weight''': checkpoint['''inner_model.proj_in.weight'''],
						        '''conv_in.bias''': checkpoint['''inner_model.proj_in.bias'''],
						    }  )

						# timestep and class embedding
						diffusers_checkpoint.update(
						    {
						        '''time_proj.weight''': checkpoint['''inner_model.timestep_embed.weight'''].squeeze(-1  ),
						        '''time_embedding.linear_1.weight''': checkpoint['''inner_model.mapping.0.weight'''],
						        '''time_embedding.linear_1.bias''': checkpoint['''inner_model.mapping.0.bias'''],
						        '''time_embedding.linear_2.weight''': checkpoint['''inner_model.mapping.2.weight'''],
						        '''time_embedding.linear_2.bias''': checkpoint['''inner_model.mapping.2.bias'''],
						        '''time_embedding.cond_proj.weight''': checkpoint['''inner_model.mapping_cond.weight'''],
						    }  )

						# down_blocks
						for down_block_idx, down_block in enumerate(model.down_blocks  ):
												diffusers_checkpoint.update(block_to_diffusers_checkpoint(_lowercase			,		_lowercase			,		_lowercase			,		'''down'''  )  )

						# up_blocks
						for up_block_idx, up_block in enumerate(model.up_blocks  ):
												diffusers_checkpoint.update(block_to_diffusers_checkpoint(_lowercase			,		_lowercase			,		_lowercase			,		'''up'''  )  )

						# post-processing
						diffusers_checkpoint.update(
						    {
						        '''conv_out.weight''': checkpoint['''inner_model.proj_out.weight'''],
						        '''conv_out.bias''': checkpoint['''inner_model.proj_out.bias'''],
						    }  )

						return diffusers_checkpoint





def 			a_							(				_lowercase  ):
						_UpperCamelCase     :   int      =       original_config['''input_channels'''] + original_config['''unet_cond_dim''']
						_UpperCamelCase     :   Any      =       original_config['''input_channels'''] + (1 if original_config['''has_variance'''] else 0)

						_UpperCamelCase     :   Tuple      =       original_config['''channels''']

						assert (
						    len(set(original_config['''depths''']  )  ) == 1
						), "UNet2DConditionModel currently do not support blocks with different number of layers"
						_UpperCamelCase     :   int      =       original_config['''depths'''][0]

						_UpperCamelCase     :   Optional[int]      =       original_config['''mapping_cond_dim''']
						_UpperCamelCase     :   Optional[int]      =       original_config['''cross_cond_dim''']

						_UpperCamelCase     :   Tuple      =       []
						_UpperCamelCase     :   List[str]      =       []
						for s, c in zip(original_config['''self_attn_depths''']			,		original_config['''cross_attn_depths''']  ):
												if s:
																		_UpperCamelCase     :   str      =       '''self'''
																		_UpperCamelCase     :   List[Any]      =       '''cross''' if c else None
												elif c:
																		_UpperCamelCase     :   Union[str, Any]      =       '''cross'''
																		_UpperCamelCase     :   int      =       None
												else:
																		_UpperCamelCase     :   int      =       None
																		_UpperCamelCase     :   List[Any]      =       None
												attna_types.append(_lowercase  )
												attna_types.append(_lowercase  )

						_UpperCamelCase     :   Dict      =       UNetaDConditionModel(
						    in_channels=_lowercase			,		out_channels=_lowercase			,		down_block_types=('''KDownBlock2D''', '''KCrossAttnDownBlock2D''', '''KCrossAttnDownBlock2D''', '''KCrossAttnDownBlock2D''')			,		mid_block_type=_lowercase			,		up_block_types=('''KCrossAttnUpBlock2D''', '''KCrossAttnUpBlock2D''', '''KCrossAttnUpBlock2D''', '''KUpBlock2D''')			,		block_out_channels=_lowercase			,		layers_per_block=_lowercase			,		act_fn='''gelu'''			,		norm_num_groups=_lowercase			,		cross_attention_dim=_lowercase			,		attention_head_dim=64			,		time_cond_proj_dim=_lowercase			,		resnet_time_scale_shift='''scale_shift'''			,		time_embedding_type='''fourier'''			,		timestep_post_act='''gelu'''			,		conv_in_kernel=1			,		conv_out_kernel=1			,		)

						return unet





def 			a_							(				_lowercase  ):
						_UpperCamelCase     :   Tuple      =       torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''  )

						_UpperCamelCase     :   str      =       huggingface_hub.hf_hub_download(_lowercase			,		'''config_laion_text_cond_latent_upscaler_2.json'''  )
						_UpperCamelCase     :   List[Any]      =       huggingface_hub.hf_hub_download(
						    _lowercase			,		'''laion_text_cond_latent_upscaler_2_1_00470000_slim.pth'''  )
						print(F"""loading original model configuration from {orig_config_path}"""  )
						print(F"""loading original model checkpoint from {orig_weights_path}"""  )

						print('''converting to diffusers unet'''  )
						_UpperCamelCase     :   Any      =       K.config.load_config(open(_lowercase  )  )['''model''']
						_UpperCamelCase     :   Tuple      =       unet_model_from_original_config(_lowercase  )

						_UpperCamelCase     :   List[Any]      =       torch.load(_lowercase			,		map_location=_lowercase  )['''model_ema''']
						_UpperCamelCase     :   Optional[int]      =       unet_to_diffusers_checkpoint(_lowercase			,		_lowercase  )

						model.load_state_dict(_lowercase			,		strict=_lowercase  )
						model.save_pretrained(args.dump_path  )
						print(F"""saving converted unet model in {args.dump_path}"""  )


if __name__ == "__main__":
						UpperCamelCase_	 =argparse.ArgumentParser()

						parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")
						UpperCamelCase_	 =parser.parse_args()

						main(args)










# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import json
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import yaml

from ...utils import ComputeEnvironment, DistributedType, SageMakerDistributedType
from ...utils.constants import SAGEMAKER_PYTHON_VERSION, SAGEMAKER_PYTORCH_VERSION, SAGEMAKER_TRANSFORMERS_VERSION


UpperCAmelCase_   				=  os.path.expanduser(
    os.getenv("""HF_HOME""", os.path.join(os.getenv("""XDG_CACHE_HOME""", """~/.cache"""), """huggingface"""))
)
UpperCAmelCase_   				=  os.path.join(hf_cache_home, """accelerate""")
UpperCAmelCase_   				=  os.path.join(cache_dir, """default_config.yaml""")
UpperCAmelCase_   				=  os.path.join(cache_dir, """default_config.yaml""")

# For backward compatibility: the default config is the json one if it's the only existing file.
if os.path.isfile(default_yaml_config_file) or not os.path.isfile(default_json_config_file):
					UpperCAmelCase_   				=  default_yaml_config_file
else:
					UpperCAmelCase_   				=  default_json_config_file





def     lowerCamelCase__  (  UpperCamelCase__      :							List[str]    )      ->			str:



					'''simple docstring'''




					if config_file is not None:
										if not os.path.isfile(UpperCamelCase__    ):
															raise FileNotFoundError(
															    F'''The passed configuration file `{config_file}` does not exist. '''
															    'Please pass an existing file to `accelerate launch`, or use the the default one '
															    'created through `accelerate config` and run `accelerate launch` '
															    'without the `--config_file` argument.'    )
					else:
										_snake_case						=					default_config_file
					with open(UpperCamelCase__							,     'r'							,     encoding='utf-8'    ) as f:
										if config_file.endswith('.json'    ):
															if (
															    json.load(UpperCamelCase__    ).get('compute_environment'							,     ComputeEnvironment.LOCAL_MACHINE    )
															    == ComputeEnvironment.LOCAL_MACHINE
															):
																				_snake_case						=					ClusterConfig
															else:
																				_snake_case						=					SageMakerConfig
															return config_class.from_json_file(json_file=UpperCamelCase__    )
										else:
															if (
															    yaml.safe_load(UpperCamelCase__    ).get('compute_environment'							,     ComputeEnvironment.LOCAL_MACHINE    )
															    == ComputeEnvironment.LOCAL_MACHINE
															):
																				_snake_case						=					ClusterConfig
															else:
																				_snake_case						=					SageMakerConfig
															return config_class.from_yaml_file(yaml_file=UpperCamelCase__    )
@dataclass
class       UpperCamelCase_		:
					lowerCAmelCase_				=						42
					lowerCAmelCase_				=						42
					lowerCAmelCase_				=						42
					lowerCAmelCase_				=						42




					def 						lowerCAmelCase      (		self	)		->				Optional[Any]:
										_snake_case						=					self.__dict__
										# For serialization, it's best to convert Enums to strings (or their underlying value type).
										for key, value in result.items():
															if isinstance(lowerCAmelCase_					,    lowerCAmelCase_	):
																				_snake_case						=					value.value
															if isinstance(lowerCAmelCase_					,    lowerCAmelCase_	) and not bool(lowerCAmelCase_	):
																				_snake_case						=					None
										_snake_case						=					{k: v for k, v in result.items() if v is not None}
										return result




					@classmethod
					def 						lowerCAmelCase      (		cls					,    lowerCAmelCase_=None	)		->				List[Any]:
										_snake_case						=					default_json_config_file if json_file is None else json_file
										with open(lowerCAmelCase_					,    'r'					,    encoding='utf-8'	) as f:
															_snake_case						=					json.load(lowerCAmelCase_	)
										if "compute_environment" not in config_dict:
															_snake_case						=					ComputeEnvironment.LOCAL_MACHINE
										if "mixed_precision" not in config_dict:
															_snake_case						=					'fp16' if ('fp16' in config_dict and config_dict['fp16']) else None
										if "fp16" in config_dict:  # Convert the config to the new format.
															del config_dict["fp16"]
										if "dynamo_backend" in config_dict:  # Convert the config to the new format.
															_snake_case						=					config_dict.pop('dynamo_backend'	)
															_snake_case						=					{} if dynamo_backend == 'NO' else {'dynamo_backend': dynamo_backend}
										if "use_cpu" not in config_dict:
															_snake_case						=					False
										return cls(**lowerCAmelCase_	)




					def 						lowerCAmelCase      (		self					,    lowerCAmelCase_	)		->				List[str]:
										with open(lowerCAmelCase_					,    'w'					,    encoding='utf-8'	) as f:
															_snake_case						=					json.dumps(self.to_dict()					,    indent=2					,    sort_keys=lowerCAmelCase_	) + '\n'
															f.write(lowerCAmelCase_	)




					@classmethod
					def 						lowerCAmelCase      (		cls					,    lowerCAmelCase_=None	)		->				int:
										_snake_case						=					default_yaml_config_file if yaml_file is None else yaml_file
										with open(lowerCAmelCase_					,    'r'					,    encoding='utf-8'	) as f:
															_snake_case						=					yaml.safe_load(lowerCAmelCase_	)
										if "compute_environment" not in config_dict:
															_snake_case						=					ComputeEnvironment.LOCAL_MACHINE

										if "mixed_precision" not in config_dict:
															_snake_case						=					'fp16' if ('fp16' in config_dict and config_dict['fp16']) else None
										if isinstance(config_dict['mixed_precision']					,    lowerCAmelCase_	) and not config_dict["mixed_precision"]:
															_snake_case						=					'no'
										if "fp16" in config_dict:  # Convert the config to the new format.
															del config_dict["fp16"]
										if "dynamo_backend" in config_dict:  # Convert the config to the new format.
															_snake_case						=					config_dict.pop('dynamo_backend'	)
															_snake_case						=					{} if dynamo_backend == 'NO' else {'dynamo_backend': dynamo_backend}
										if "use_cpu" not in config_dict:
															_snake_case						=					False
										return cls(**lowerCAmelCase_	)




					def 						lowerCAmelCase      (		self					,    lowerCAmelCase_	)		->				List[str]:
										with open(lowerCAmelCase_					,    'w'					,    encoding='utf-8'	) as f:
															yaml.safe_dump(self.to_dict()					,    lowerCAmelCase_	)






					def 						lowerCAmelCase      (		self	)		->				Tuple:
										if isinstance(self.compute_environment					,    lowerCAmelCase_	):
															_snake_case						=					ComputeEnvironment(self.compute_environment	)
										if isinstance(self.distributed_type					,    lowerCAmelCase_	):
															if self.compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
																				_snake_case						=					SageMakerDistributedType(self.distributed_type	)
															else:
																				_snake_case						=					DistributedType(self.distributed_type	)
										if self.dynamo_config is None:
															_snake_case						=					{}





@dataclass
class       UpperCamelCase_		(	_lowerCamelCase       ):
					lowerCAmelCase_				=						42
					lowerCAmelCase_				=						0
					lowerCAmelCase_				=						1
					lowerCAmelCase_				=						None
					lowerCAmelCase_				=						None
					lowerCAmelCase_				=						None
					lowerCAmelCase_				=						"static"
					lowerCAmelCase_				=						False
					lowerCAmelCase_				=						"main"

					# args for deepspeed_plugin
					lowerCAmelCase_				=						None
					# args for fsdp
					lowerCAmelCase_				=						None
					# args for megatron_lm
					lowerCAmelCase_				=						None
					# args for ipex
					lowerCAmelCase_				=						None
					# args for TPU
					lowerCAmelCase_				=						False

					# args for TPU pods
					lowerCAmelCase_				=						None
					lowerCAmelCase_				=						None
					lowerCAmelCase_				=						False
					lowerCAmelCase_				=						False
					lowerCAmelCase_				=						None
					lowerCAmelCase_				=						None
					lowerCAmelCase_				=						None
					lowerCAmelCase_				=						None

					# args for dynamo
					lowerCAmelCase_				=						None




					def 						lowerCAmelCase      (		self	)		->				Optional[Any]:
										if self.deepspeed_config is None:
															_snake_case						=					{}
										if self.fsdp_config is None:
															_snake_case						=					{}
										if self.megatron_lm_config is None:
															_snake_case						=					{}
										if self.ipex_config is None:
															_snake_case						=					{}
										return super().__post_init__()





@dataclass
class       UpperCamelCase_		(	_lowerCamelCase       ):
					lowerCAmelCase_				=						42
					lowerCAmelCase_				=						42
					lowerCAmelCase_				=						None
					lowerCAmelCase_				=						None
					lowerCAmelCase_				=						"us-east-1"
					lowerCAmelCase_				=						1
					lowerCAmelCase_				=						"all"
					lowerCAmelCase_				=						f"accelerate-sagemaker-{num_machines}"
					lowerCAmelCase_				=						SAGEMAKER_PYTORCH_VERSION
					lowerCAmelCase_				=						SAGEMAKER_TRANSFORMERS_VERSION
					lowerCAmelCase_				=						SAGEMAKER_PYTHON_VERSION
					lowerCAmelCase_				=						None
					lowerCAmelCase_				=						None
					lowerCAmelCase_				=						None
					lowerCAmelCase_				=						None



