






import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image

from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase        =				logging.get_logger(__name__)




def 				A		(					lowercase__  :  int  )		-> Optional[Any]:
	UpperCamelCase__ :str						=      SwinConfig(
	    embed_dim=192 ,		depths=(2, 2, 18, 2) ,		num_heads=(6, 12, 24, 48) ,		window_size=12 ,		out_features=["""stage2""", """stage3""", """stage4"""] ,		)

	UpperCamelCase__ :Tuple						=      DetaConfig(
	    backbone_config=snake_case__ ,		num_queries=900 ,		encoder_ffn_dim=2048 ,		decoder_ffn_dim=2048 ,		num_feature_levels=5 ,		assign_first_stage=snake_case__ ,		with_box_refine=snake_case__ ,		two_stage=snake_case__ ,		)

	# set labels
	UpperCamelCase__ :Tuple						=      """huggingface/label-files"""
	if "o365" in model_name:
		UpperCamelCase__ :List[str]						=      366
		UpperCamelCase__ :List[Any]						=      """object365-id2label.json"""
	else:
		UpperCamelCase__ :List[Any]						=      91
		UpperCamelCase__ :Dict						=      """coco-detection-id2label.json"""

	UpperCamelCase__ :int						=      num_labels
	UpperCamelCase__ :Union[str, Any]						=      json.load(open(cached_download(hf_hub_url(snake_case__ ,		snake_case__ ,		repo_type="""dataset"""  )  ) ,		"""r"""  )  )
	UpperCamelCase__ :Dict						=      {int(snake_case__  ): v for k, v in idalabel.items()}
	UpperCamelCase__ :Optional[int]						=      idalabel
	UpperCamelCase__ :List[Any]						=      {v: k for k, v in idalabel.items()}

	return config




def 				A		(					lowercase__  :  Any  )		-> List[str]:
	UpperCamelCase__ :List[str]						=      []

	# stem
	# fmt: off
	rename_keys.append(("""backbone.0.body.patch_embed.proj.weight""", """model.backbone.model.embeddings.patch_embeddings.projection.weight""")  )
	rename_keys.append(("""backbone.0.body.patch_embed.proj.bias""", """model.backbone.model.embeddings.patch_embeddings.projection.bias""")  )
	rename_keys.append(("""backbone.0.body.patch_embed.norm.weight""", """model.backbone.model.embeddings.norm.weight""")  )
	rename_keys.append(("""backbone.0.body.patch_embed.norm.bias""", """model.backbone.model.embeddings.norm.bias""")  )
	# stages
	for i in range(len(config.backbone_config.depths  )  ):
		for j in range(config.backbone_config.depths[i]  ):
			rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.norm1.weight""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight""")  )
			rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.norm1.bias""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias""")  )
			rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_bias_table""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table""")  )
			rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.attn.relative_position_index""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index""")  )
			rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.attn.proj.weight""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight""")  )
			rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.attn.proj.bias""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias""")  )
			rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.norm2.weight""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight""")  )
			rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.norm2.bias""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias""")  )
			rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.weight""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight""")  )
			rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.mlp.fc1.bias""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias""")  )
			rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.weight""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.weight""")  )
			rename_keys.append((f"""backbone.0.body.layers.{i}.blocks.{j}.mlp.fc2.bias""", f"""model.backbone.model.encoder.layers.{i}.blocks.{j}.output.dense.bias""")  )

		if i < 3:
			rename_keys.append((f"""backbone.0.body.layers.{i}.downsample.reduction.weight""", f"""model.backbone.model.encoder.layers.{i}.downsample.reduction.weight""")  )
			rename_keys.append((f"""backbone.0.body.layers.{i}.downsample.norm.weight""", f"""model.backbone.model.encoder.layers.{i}.downsample.norm.weight""")  )
			rename_keys.append((f"""backbone.0.body.layers.{i}.downsample.norm.bias""", f"""model.backbone.model.encoder.layers.{i}.downsample.norm.bias""")  )

	rename_keys.append(("""backbone.0.body.norm1.weight""", """model.backbone.model.hidden_states_norms.stage2.weight""")  )
	rename_keys.append(("""backbone.0.body.norm1.bias""", """model.backbone.model.hidden_states_norms.stage2.bias""")  )
	rename_keys.append(("""backbone.0.body.norm2.weight""", """model.backbone.model.hidden_states_norms.stage3.weight""")  )
	rename_keys.append(("""backbone.0.body.norm2.bias""", """model.backbone.model.hidden_states_norms.stage3.bias""")  )
	rename_keys.append(("""backbone.0.body.norm3.weight""", """model.backbone.model.hidden_states_norms.stage4.weight""")  )
	rename_keys.append(("""backbone.0.body.norm3.bias""", """model.backbone.model.hidden_states_norms.stage4.bias""")  )

	# transformer encoder
	for i in range(config.encoder_layers  ):
		rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.sampling_offsets.weight""", f"""model.encoder.layers.{i}.self_attn.sampling_offsets.weight""")  )
		rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.sampling_offsets.bias""", f"""model.encoder.layers.{i}.self_attn.sampling_offsets.bias""")  )
		rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.attention_weights.weight""", f"""model.encoder.layers.{i}.self_attn.attention_weights.weight""")  )
		rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.attention_weights.bias""", f"""model.encoder.layers.{i}.self_attn.attention_weights.bias""")  )
		rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.value_proj.weight""", f"""model.encoder.layers.{i}.self_attn.value_proj.weight""")  )
		rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.value_proj.bias""", f"""model.encoder.layers.{i}.self_attn.value_proj.bias""")  )
		rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.output_proj.weight""", f"""model.encoder.layers.{i}.self_attn.output_proj.weight""")  )
		rename_keys.append((f"""transformer.encoder.layers.{i}.self_attn.output_proj.bias""", f"""model.encoder.layers.{i}.self_attn.output_proj.bias""")  )
		rename_keys.append((f"""transformer.encoder.layers.{i}.norm1.weight""", f"""model.encoder.layers.{i}.self_attn_layer_norm.weight""")  )
		rename_keys.append((f"""transformer.encoder.layers.{i}.norm1.bias""", f"""model.encoder.layers.{i}.self_attn_layer_norm.bias""")  )
		rename_keys.append((f"""transformer.encoder.layers.{i}.linear1.weight""", f"""model.encoder.layers.{i}.fc1.weight""")  )
		rename_keys.append((f"""transformer.encoder.layers.{i}.linear1.bias""", f"""model.encoder.layers.{i}.fc1.bias""")  )
		rename_keys.append((f"""transformer.encoder.layers.{i}.linear2.weight""", f"""model.encoder.layers.{i}.fc2.weight""")  )
		rename_keys.append((f"""transformer.encoder.layers.{i}.linear2.bias""", f"""model.encoder.layers.{i}.fc2.bias""")  )
		rename_keys.append((f"""transformer.encoder.layers.{i}.norm2.weight""", f"""model.encoder.layers.{i}.final_layer_norm.weight""")  )
		rename_keys.append((f"""transformer.encoder.layers.{i}.norm2.bias""", f"""model.encoder.layers.{i}.final_layer_norm.bias""")  )

	# transformer decoder
	for i in range(config.decoder_layers  ):
		rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.sampling_offsets.weight""", f"""model.decoder.layers.{i}.encoder_attn.sampling_offsets.weight""")  )
		rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.sampling_offsets.bias""", f"""model.decoder.layers.{i}.encoder_attn.sampling_offsets.bias""")  )
		rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.attention_weights.weight""", f"""model.decoder.layers.{i}.encoder_attn.attention_weights.weight""")  )
		rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.attention_weights.bias""", f"""model.decoder.layers.{i}.encoder_attn.attention_weights.bias""")  )
		rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.value_proj.weight""", f"""model.decoder.layers.{i}.encoder_attn.value_proj.weight""")  )
		rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.value_proj.bias""", f"""model.decoder.layers.{i}.encoder_attn.value_proj.bias""")  )
		rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.output_proj.weight""", f"""model.decoder.layers.{i}.encoder_attn.output_proj.weight""")  )
		rename_keys.append((f"""transformer.decoder.layers.{i}.cross_attn.output_proj.bias""", f"""model.decoder.layers.{i}.encoder_attn.output_proj.bias""")  )
		rename_keys.append((f"""transformer.decoder.layers.{i}.norm1.weight""", f"""model.decoder.layers.{i}.encoder_attn_layer_norm.weight""")  )
		rename_keys.append((f"""transformer.decoder.layers.{i}.norm1.bias""", f"""model.decoder.layers.{i}.encoder_attn_layer_norm.bias""")  )
		rename_keys.append((f"""transformer.decoder.layers.{i}.self_attn.out_proj.weight""", f"""model.decoder.layers.{i}.self_attn.out_proj.weight""")  )
		rename_keys.append((f"""transformer.decoder.layers.{i}.self_attn.out_proj.bias""", f"""model.decoder.layers.{i}.self_attn.out_proj.bias""")  )
		rename_keys.append((f"""transformer.decoder.layers.{i}.norm2.weight""", f"""model.decoder.layers.{i}.self_attn_layer_norm.weight""")  )
		rename_keys.append((f"""transformer.decoder.layers.{i}.norm2.bias""", f"""model.decoder.layers.{i}.self_attn_layer_norm.bias""")  )
		rename_keys.append((f"""transformer.decoder.layers.{i}.linear1.weight""", f"""model.decoder.layers.{i}.fc1.weight""")  )
		rename_keys.append((f"""transformer.decoder.layers.{i}.linear1.bias""", f"""model.decoder.layers.{i}.fc1.bias""")  )
		rename_keys.append((f"""transformer.decoder.layers.{i}.linear2.weight""", f"""model.decoder.layers.{i}.fc2.weight""")  )
		rename_keys.append((f"""transformer.decoder.layers.{i}.linear2.bias""", f"""model.decoder.layers.{i}.fc2.bias""")  )
		rename_keys.append((f"""transformer.decoder.layers.{i}.norm3.weight""", f"""model.decoder.layers.{i}.final_layer_norm.weight""")  )
		rename_keys.append((f"""transformer.decoder.layers.{i}.norm3.bias""", f"""model.decoder.layers.{i}.final_layer_norm.bias""")  )

	# fmt: on

	return rename_keys




def 				A		(					lowercase__  :  Tuple ,		lowercase__  :  List[Any] ,		lowercase__  :  Any  )		-> Optional[int]:
	UpperCamelCase__ :int						=      dct.pop(snake_case__  )
	UpperCamelCase__ :Dict						=      val




def 				A		(					lowercase__  :  Any ,		lowercase__  :  Optional[int]  )		-> Union[str, Any]:
	UpperCamelCase__ :Union[str, Any]						=      [int(backbone_config.embed_dim * 2**i  ) for i in range(len(backbone_config.depths  )  )]
	for i in range(len(backbone_config.depths  )  ):
		UpperCamelCase__ :List[str]						=      num_features[i]
		for j in range(backbone_config.depths[i]  ):
			# fmt: off
			# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
			UpperCamelCase__ :List[str]						=      state_dict.pop(f"""backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.weight"""  )
			UpperCamelCase__ :Optional[Any]						=      state_dict.pop(f"""backbone.0.body.layers.{i}.blocks.{j}.attn.qkv.bias"""  )
			# next, add query, keys and values (in that order) to the state dict
			UpperCamelCase__ :str						=      in_proj_weight[:dim, :]
			UpperCamelCase__ :str						=      in_proj_bias[: dim]
			UpperCamelCase__ :Optional[int]						=      in_proj_weight[
			    dim : dim * 2, :
			]
			UpperCamelCase__ :int						=      in_proj_bias[
			    dim : dim * 2
			]
			UpperCamelCase__ :Any						=      in_proj_weight[
			    -dim :, :
			]
			UpperCamelCase__ :List[str]						=      in_proj_bias[-dim :]
			# fmt: on




def 				A		(					lowercase__  :  Tuple ,		lowercase__  :  Union[str, Any]  )		-> Optional[int]:
	# transformer decoder self-attention layers
	UpperCamelCase__ :Optional[Any]						=      config.d_model
	for i in range(config.decoder_layers  ):
		# read in weights + bias of input projection layer of self-attention
		UpperCamelCase__ :Optional[int]						=      state_dict.pop(f"""transformer.decoder.layers.{i}.self_attn.in_proj_weight"""  )
		UpperCamelCase__ :str						=      state_dict.pop(f"""transformer.decoder.layers.{i}.self_attn.in_proj_bias"""  )
		# next, add query, keys and values (in that order) to the state dict
		UpperCamelCase__ :Tuple						=      in_proj_weight[:hidden_size, :]
		UpperCamelCase__ :Optional[Any]						=      in_proj_bias[:hidden_size]
		UpperCamelCase__ :Dict						=      in_proj_weight[
		    hidden_size : hidden_size * 2, :
		]
		UpperCamelCase__ :List[str]						=      in_proj_bias[hidden_size : hidden_size * 2]
		UpperCamelCase__ :Dict						=      in_proj_weight[-hidden_size:, :]
		UpperCamelCase__ :List[Any]						=      in_proj_bias[-hidden_size:]




def 				A		(					)		-> Optional[int]:
	UpperCamelCase__ :int						=      """http://images.cocodataset.org/val2017/000000039769.jpg"""
	UpperCamelCase__ :Optional[int]						=      Image.open(requests.get(snake_case__ ,		stream=snake_case__  ).raw  )

	return im




@torch.no_grad()
def 				A		(					lowercase__  :  Dict ,		lowercase__  :  Dict ,		lowercase__  :  List[str]  )		-> str:
	UpperCamelCase__ :Dict						=      get_deta_config(snake_case__  )

	# load original state dict
	if model_name == "deta-swin-large":
		UpperCamelCase__ :Tuple						=      hf_hub_download(repo_id="""nielsr/deta-checkpoints""" ,		filename="""adet_swin_ft.pth"""  )
	elif model_name == "deta-swin-large-o365":
		UpperCamelCase__ :Union[str, Any]						=      hf_hub_download(repo_id="""jozhang97/deta-swin-l-o365""" ,		filename="""deta_swin_pt_o365.pth"""  )
	else:
		raise ValueError(f"""Model name {model_name} not supported"""  )

	UpperCamelCase__ :str						=      torch.load(snake_case__ ,		map_location="""cpu"""  )["""model"""]

	# original state dict
	for name, param in state_dict.items():
		print(snake_case__ ,		param.shape  )

	# rename keys
	UpperCamelCase__ :Any						=      create_rename_keys(snake_case__  )
	for src, dest in rename_keys:
		rename_key(snake_case__ ,		snake_case__ ,		snake_case__  )
	read_in_swin_q_k_v(snake_case__ ,		config.backbone_config  )
	read_in_decoder_q_k_v(snake_case__ ,		snake_case__  )

	# fix some prefixes
	for key in state_dict.copy().keys():
		if "transformer.decoder.class_embed" in key or "transformer.decoder.bbox_embed" in key:
			UpperCamelCase__ :Optional[int]						=      state_dict.pop(snake_case__  )
			UpperCamelCase__ :List[Any]						=      val
		if "input_proj" in key:
			UpperCamelCase__ :Optional[Any]						=      state_dict.pop(snake_case__  )
			UpperCamelCase__ :Optional[Any]						=      val
		if "level_embed" in key or "pos_trans" in key or "pix_trans" in key or "enc_output" in key:
			UpperCamelCase__ :List[str]						=      state_dict.pop(snake_case__  )
			UpperCamelCase__ :List[Any]						=      val

    # finally, create HuggingFace model and load state dict
	UpperCamelCase__ :List[Any]						=      DetaForObjectDetection(snake_case__  )
	model.load_state_dict(snake_case__  )
	model.eval()

	UpperCamelCase__ :Optional[Any]						=      """cuda""" if torch.cuda.is_available() else """cpu"""
	model.to(snake_case__  )

	# load image processor
	UpperCamelCase__ :Dict						=      DetaImageProcessor(format="""coco_detection"""  )

	# verify our conversion on image
	UpperCamelCase__ :int						=      prepare_img()
	UpperCamelCase__ :int						=      processor(images=snake_case__ ,		return_tensors="""pt"""  )
	UpperCamelCase__ :Tuple						=      encoding["""pixel_values"""]
	UpperCamelCase__ :str						=      model(pixel_values.to(snake_case__  )  )

	# verify logits
	print("""Logits:""" ,		outputs.logits[0, :3, :3]  )
	print("""Boxes:""" ,		outputs.pred_boxes[0, :3, :3]  )
	if model_name == "deta-swin-large":
		UpperCamelCase__ :Any						=      torch.tensor(
		    [[-7.6308, -2.8485, -5.3737], [-7.2037, -4.5505, -4.8027], [-7.2943, -4.2611, -4.6617]]  )
		UpperCamelCase__ :List[str]						=      torch.tensor([[0.4987, 0.4969, 0.9999], [0.2549, 0.5498, 0.4805], [0.5498, 0.2757, 0.0569]]  )
	elif model_name == "deta-swin-large-o365":
		UpperCamelCase__ :int						=      torch.tensor(
		    [[-8.0122, -3.5720, -4.9717], [-8.1547, -3.6886, -4.6389], [-7.6610, -3.6194, -5.0134]]  )
		UpperCamelCase__ :Union[str, Any]						=      torch.tensor([[0.2523, 0.5549, 0.4881], [0.7715, 0.4149, 0.4601], [0.5503, 0.2753, 0.0575]]  )
	assert torch.allclose(outputs.logits[0, :3, :3] ,		expected_logits.to(snake_case__  ) ,		atol=1E-4  )
	assert torch.allclose(outputs.pred_boxes[0, :3, :3] ,		expected_boxes.to(snake_case__  ) ,		atol=1E-4  )
	print("""Everything ok!"""  )

	if pytorch_dump_folder_path:
		# Save model and processor
		logger.info(f"""Saving PyTorch model and processor to {pytorch_dump_folder_path}..."""  )
		Path(snake_case__  ).mkdir(exist_ok=snake_case__  )
		model.save_pretrained(snake_case__  )
		processor.save_pretrained(snake_case__  )

	# Push to hub
	if push_to_hub:
		print("""Pushing model and processor to hub..."""  )
		model.push_to_hub(f"""jozhang97/{model_name}"""  )
		processor.push_to_hub(f"""jozhang97/{model_name}"""  )


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

							parser.add_argument(
							    "--model_name",
							    type=str,
							    default="deta-swin-large",
							    choices=["deta-swin-large", "deta-swin-large-o365"],
							    help="Name of the model you\'d like to convert.",
							)
							parser.add_argument(
							    "--pytorch_dump_folder_path",
							    default=None,
							    type=str,
							    help="Path to the folder to output PyTorch model.",
							)
							parser.add_argument(
							    "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
							)
							UpperCamelCase        =				parser.parse_args()
							convert_deta_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)

"""simple docstring"""


# 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 argparse
import os

from accelerate.utils import ComputeEnvironment

from .cluster import get_cluster_input
from .config_args import cache_dir, default_config_file, default_yaml_config_file, load_config_from_file  # noqa: F401
from .config_utils import _ask_field, _ask_options, _convert_compute_environment  # noqa: F401
from .sagemaker import get_sagemaker_input


_lowercase				 =					'''Launches a series of prompts to create and save a `default_config.yaml` configuration file for your training system. Should always be ran first on your machine'''






def    _snake_case					(		):
	A     = _ask_options(
	    'In which compute environment are you running?'						,   ['This machine', 'AWS (Amazon SageMaker)']						,   _convert_compute_environment						,   )
	if compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
		A     = get_sagemaker_input()
	else:
		A     = get_cluster_input()
	return config






def    _snake_case					(		snake_case__ :  Any=None  ):
	if subparsers is not None:
		A     = subparsers.add_parser('config'						,   description=snake_case__  )
	else:
		A     = argparse.ArgumentParser('Accelerate config command'						,   description=snake_case__  )

	parser.add_argument(
	    '--config_file'						,   default=snake_case__						,   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\'.'
	    )						,   )

	if subparsers is not None:
		parser.set_defaults(func=snake_case__  )
	return parser






def    _snake_case					(		snake_case__ :  Tuple  ):
	A     = get_user_input()
	if args.config_file is not None:
		A     = args.config_file
	else:
		if not os.path.isdir(snake_case__  ):
			os.makedirs(snake_case__  )
		A     = default_yaml_config_file

	if config_file.endswith('.json'  ):
		config.to_json_file(snake_case__  )
	else:
		config.to_yaml_file(snake_case__  )
	print(F'accelerate configuration saved at {config_file}'  )






def    _snake_case					(		):
	A     = config_command_parser()
	A     = parser.parse_args()
	config_command(snake_case__  )


if __name__ == "__main__":
		main()