from pathlib import Path

import torch

from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter


UpperCAmelCase_						="Create a default config file for Accelerate with only a few flags set."



def       UpperCAmelCase       (     _snake_case="no"						, _snake_case = default_json_config_file						, _snake_case = False							):
							lowerCAmelCase   =		Path(__lowerCAmelCase							)
							path.parent.mkdir(parents=__lowerCAmelCase						, exist_ok=__lowerCAmelCase							)
							if path.exists():
														print(
														    F"""Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`."""							)
														return False
							lowerCAmelCase   =		mixed_precision.lower()
							if mixed_precision not in ["no", "fp16", "bf16", "fp8"]:
														raise ValueError(
														    F"""`mixed_precision` should be one of \'no\', \'fp16\', \'bf16\', or \'fp8\'. Received {mixed_precision}"""							)
							lowerCAmelCase   =		{
							    """compute_environment""": """LOCAL_MACHINE""",
							    """mixed_precision""": mixed_precision,
							}
							if torch.cuda.is_available():
														lowerCAmelCase   =		torch.cuda.device_count()
														lowerCAmelCase   =		num_gpus
														lowerCAmelCase   =		False
														if num_gpus > 1:
																					lowerCAmelCase   =		"""MULTI_GPU"""
														else:
																					lowerCAmelCase   =		"""NO"""
							elif is_xpu_available() and use_xpu:
														lowerCAmelCase   =		torch.xpu.device_count()
														lowerCAmelCase   =		num_xpus
														lowerCAmelCase   =		False
														if num_xpus > 1:
																					lowerCAmelCase   =		"""MULTI_XPU"""
														else:
																					lowerCAmelCase   =		"""NO"""
							elif is_npu_available():
														lowerCAmelCase   =		torch.npu.device_count()
														lowerCAmelCase   =		num_npus
														lowerCAmelCase   =		False
														if num_npus > 1:
																					lowerCAmelCase   =		"""MULTI_NPU"""
														else:
																					lowerCAmelCase   =		"""NO"""
							else:
														lowerCAmelCase   =		0
														lowerCAmelCase   =		True
														lowerCAmelCase   =		1
														lowerCAmelCase   =		"""NO"""
							lowerCAmelCase   =		ClusterConfig(**__lowerCAmelCase							)
							config.to_json_file(__lowerCAmelCase							)
							return path



def       UpperCAmelCase       (     _snake_case						, _snake_case							):
							lowerCAmelCase   =		parser.add_parser('''default'''						, parents=__lowerCAmelCase						, help=__lowerCAmelCase						, formatter_class=__lowerCAmelCase							)
							parser.add_argument(
							    '''--config_file'''						, default=__lowerCAmelCase						, help=(
							        '''The path to use to store the config file. Will default to a file named default_config.yaml in the cache '''
							        '''location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '''
							        '''such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '''
							        '''with \'huggingface\'.'''
							    )						, dest='''save_location'''						, )

							parser.add_argument(
							    '''--mixed_precision'''						, choices=['''no''', '''fp16''', '''bf16''']						, type=__lowerCAmelCase						, help='''Whether or not to use mixed precision training. '''
							    '''Choose between FP16 and BF16 (bfloat16) training. '''
							    '''BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later.'''						, default='''no'''						, )
							parser.set_defaults(func=__lowerCAmelCase							)
							return parser



def       UpperCAmelCase       (     _snake_case							):
							lowerCAmelCase   =		write_basic_config(args.mixed_precision						, args.save_location							)
							if config_file:
														print(F"""accelerate configuration saved at {config_file}"""							)









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__)


class 							__UpperCamelCase			( __UpperCAmelCase     ,					__UpperCAmelCase  ):





				'''simple docstring'''



				__a							: Optional[Any]										="""maskformer-swin"""

				__a							: Optional[int]										={
				    """num_attention_heads""": """num_heads""",
				    """num_hidden_layers""": """num_layers""",
				}



				def __init__( self       , UpperCAmelCase_=2_24       , UpperCAmelCase_=4       , UpperCAmelCase_=3       , UpperCAmelCase_=96       , UpperCAmelCase_=[2, 2, 6, 2]       , UpperCAmelCase_=[3, 6, 12, 24]       , UpperCAmelCase_=7       , UpperCAmelCase_=4.0       , UpperCAmelCase_=True       , UpperCAmelCase_=0.0       , UpperCAmelCase_=0.0       , UpperCAmelCase_=0.1       , UpperCAmelCase_="gelu"       , UpperCAmelCase_=False       , UpperCAmelCase_=0.02       , UpperCAmelCase_=1E-5       , UpperCAmelCase_=None       , UpperCAmelCase_=None       , **UpperCAmelCase_       , ):
											super().__init__(**UpperCAmelCase_						)

											lowerCAmelCase   =		image_size
											lowerCAmelCase   =		patch_size
											lowerCAmelCase   =		num_channels
											lowerCAmelCase   =		embed_dim
											lowerCAmelCase   =		depths
											lowerCAmelCase   =		len(UpperCAmelCase_						)
											lowerCAmelCase   =		num_heads
											lowerCAmelCase   =		window_size
											lowerCAmelCase   =		mlp_ratio
											lowerCAmelCase   =		qkv_bias
											lowerCAmelCase   =		hidden_dropout_prob
											lowerCAmelCase   =		attention_probs_dropout_prob
											lowerCAmelCase   =		drop_path_rate
											lowerCAmelCase   =		hidden_act
											lowerCAmelCase   =		use_absolute_embeddings
											lowerCAmelCase   =		layer_norm_eps
											lowerCAmelCase   =		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   =		int(embed_dim * 2 ** (len(UpperCAmelCase_						) - 1)						)
											lowerCAmelCase   =		['''stem'''] + [F"""stage{idx}""" for idx in range(1       , len(UpperCAmelCase_						) + 1						)]
											lowerCAmelCase   ,     lowerCAmelCase   =		get_aligned_output_features_output_indices(
											    out_features=UpperCAmelCase_       , out_indices=UpperCAmelCase_       , stage_names=self.stage_names						)



