





"""simple docstring"""




import argparse
import json
import pickle
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging


logging.set_verbosity_info()
a						: Dict     	=			logging.get_logger(__name__)






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    str					) ->Optional[Any]:






	'''simple docstring'''






	a      :		int	=				SwinConfig.from_pretrained(
	    "microsoft/swin-tiny-patch4-window7-224"    ,       out_features=["stage1", "stage2", "stage3", "stage4"]					)
	a      :		Dict	=				MaskFormerConfig(backbone_config=_lowercase					)

	a      :		int	=				"huggingface/label-files"
	if "ade20k-full" in model_name:
		# this should be ok
		a      :		Union[str, Any]	=				847
		a      :		Dict	=				"maskformer-ade20k-full-id2label.json"
	elif "ade" in model_name:
		# this should be ok
		a      :		List[str]	=				150
		a      :		List[str]	=				"ade20k-id2label.json"
	elif "coco-stuff" in model_name:
		# this should be ok
		a      :		Dict	=				171
		a      :		Dict	=				"maskformer-coco-stuff-id2label.json"
	elif "coco" in model_name:
		# TODO
		a      :		Any	=				133
		a      :		str	=				"coco-panoptic-id2label.json"
	elif "cityscapes" in model_name:
		# this should be ok
		a      :		List[str]	=				19
		a      :		Dict	=				"cityscapes-id2label.json"
	elif "vistas" in model_name:
		# this should be ok
		a      :		List[Any]	=				65
		a      :		int	=				"mapillary-vistas-id2label.json"

	a      :		int	=				json.load(open(hf_hub_download(_lowercase    ,       _lowercase    ,       repo_type="dataset"					)    ,       "r"					)					)
	a      :		Tuple	=				{int(_lowercase					): v for k, v in idalabel.items()}

	return config






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    Optional[int]					) ->List[str]:






	'''simple docstring'''






	a      :		Union[str, Any]	=				[]
	# stem
	# fmt: off
	rename_keys.append(("backbone.patch_embed.proj.weight", "model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight")					)
	rename_keys.append(("backbone.patch_embed.proj.bias", "model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias")					)
	rename_keys.append(("backbone.patch_embed.norm.weight", "model.pixel_level_module.encoder.model.embeddings.norm.weight")					)
	rename_keys.append(("backbone.patch_embed.norm.bias", "model.pixel_level_module.encoder.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.layers.{i}.blocks.{j}.norm1.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight""")					)
			rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.norm1.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias""")					)
			rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table""")					)
			rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.attn.relative_position_index""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index""")					)
			rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.attn.proj.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight""")					)
			rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.attn.proj.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias""")					)
			rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.norm2.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight""")					)
			rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.norm2.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias""")					)
			rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.mlp.fc1.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight""")					)
			rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.mlp.fc1.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias""")					)
			rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.mlp.fc2.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight""")					)
			rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.mlp.fc2.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias""")					)

		if i < 3:
			rename_keys.append((F"""backbone.layers.{i}.downsample.reduction.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight""")					)
			rename_keys.append((F"""backbone.layers.{i}.downsample.norm.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight""")					)
			rename_keys.append((F"""backbone.layers.{i}.downsample.norm.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias""")					)
		rename_keys.append((F"""backbone.norm{i}.weight""", F"""model.pixel_level_module.encoder.hidden_states_norms.{i}.weight""")					)
		rename_keys.append((F"""backbone.norm{i}.bias""", F"""model.pixel_level_module.encoder.hidden_states_norms.{i}.bias""")					)

	# FPN
	rename_keys.append(("sem_seg_head.layer_4.weight", "model.pixel_level_module.decoder.fpn.stem.0.weight")					)
	rename_keys.append(("sem_seg_head.layer_4.norm.weight", "model.pixel_level_module.decoder.fpn.stem.1.weight")					)
	rename_keys.append(("sem_seg_head.layer_4.norm.bias", "model.pixel_level_module.decoder.fpn.stem.1.bias")					)
	for source_index, target_index in zip(range(3    ,       0    ,       -1					)    ,       range(0    ,       3					)					):
		rename_keys.append((F"""sem_seg_head.adapter_{source_index}.weight""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight""")					)
		rename_keys.append((F"""sem_seg_head.adapter_{source_index}.norm.weight""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight""")					)
		rename_keys.append((F"""sem_seg_head.adapter_{source_index}.norm.bias""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias""")					)
		rename_keys.append((F"""sem_seg_head.layer_{source_index}.weight""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight""")					)
		rename_keys.append((F"""sem_seg_head.layer_{source_index}.norm.weight""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight""")					)
		rename_keys.append((F"""sem_seg_head.layer_{source_index}.norm.bias""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias""")					)
	rename_keys.append(("sem_seg_head.mask_features.weight", "model.pixel_level_module.decoder.mask_projection.weight")					)
	rename_keys.append(("sem_seg_head.mask_features.bias", "model.pixel_level_module.decoder.mask_projection.bias")					)

	# Transformer decoder
	for idx in range(config.decoder_config.decoder_layers					):
		# self-attention out projection
		rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight""", F"""model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight""")					)
		rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias""", F"""model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias""")					)
		# cross-attention out projection
		rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight""", F"""model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight""")					)
		rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias""", F"""model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias""")					)
		# MLP 1
		rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight""", F"""model.transformer_module.decoder.layers.{idx}.fc1.weight""")					)
		rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias""", F"""model.transformer_module.decoder.layers.{idx}.fc1.bias""")					)
		# MLP 2
		rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight""", F"""model.transformer_module.decoder.layers.{idx}.fc2.weight""")					)
		rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias""", F"""model.transformer_module.decoder.layers.{idx}.fc2.bias""")					)
		# layernorm 1 (self-attention layernorm)
		rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight""", F"""model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight""")					)
		rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias""", F"""model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias""")					)
		# layernorm 2 (cross-attention layernorm)
		rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight""", F"""model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight""")					)
		rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias""", F"""model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias""")					)
		# layernorm 3 (final layernorm)
		rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight""", F"""model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight""")					)
		rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias""", F"""model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias""")					)

	rename_keys.append(("sem_seg_head.predictor.transformer.decoder.norm.weight", "model.transformer_module.decoder.layernorm.weight")					)
	rename_keys.append(("sem_seg_head.predictor.transformer.decoder.norm.bias", "model.transformer_module.decoder.layernorm.bias")					)

	# heads on top
	rename_keys.append(("sem_seg_head.predictor.query_embed.weight", "model.transformer_module.queries_embedder.weight")					)

	rename_keys.append(("sem_seg_head.predictor.input_proj.weight", "model.transformer_module.input_projection.weight")					)
	rename_keys.append(("sem_seg_head.predictor.input_proj.bias", "model.transformer_module.input_projection.bias")					)

	rename_keys.append(("sem_seg_head.predictor.class_embed.weight", "class_predictor.weight")					)
	rename_keys.append(("sem_seg_head.predictor.class_embed.bias", "class_predictor.bias")					)

	for i in range(3					):
		rename_keys.append((F"""sem_seg_head.predictor.mask_embed.layers.{i}.weight""", F"""mask_embedder.{i}.0.weight""")					)
		rename_keys.append((F"""sem_seg_head.predictor.mask_embed.layers.{i}.bias""", F"""mask_embedder.{i}.0.bias""")					)
	# fmt: on

	return rename_keys






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    List[str]    ,       _lowercase						:    Dict    ,       _lowercase						:    Union[str, Any]					) ->Optional[Any]:






	'''simple docstring'''






	a      :		str	=				dct.pop(_lowercase					)
	a      :		Dict	=				val






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    str    ,       _lowercase						:    Union[str, Any]					) ->List[Any]:






	'''simple docstring'''






	a      :		int	=				[int(backbone_config.embed_dim * 2**i					) for i in range(len(backbone_config.depths					)					)]
	for i in range(len(backbone_config.depths					)					):
		a      :		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)
			a      :		List[Any]	=				state_dict.pop(F"""backbone.layers.{i}.blocks.{j}.attn.qkv.weight"""					)
			a      :		Optional[int]	=				state_dict.pop(F"""backbone.layers.{i}.blocks.{j}.attn.qkv.bias"""					)
			# next, add query, keys and values (in that order) to the state dict
			a      :		Optional[int]	=				in_proj_weight[:dim, :]
			a      :		Dict	=				in_proj_bias[: dim]
			a      :		Optional[int]	=				in_proj_weight[
			    dim : dim * 2, :
			]
			a      :		str	=				in_proj_bias[
			    dim : dim * 2
			]
			a      :		Union[str, Any]	=				in_proj_weight[
			    -dim :, :
			]
			a      :		Dict	=				in_proj_bias[-dim :]
			# fmt: on






def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    List[str]    ,       _lowercase						:    Optional[Any]					) ->Any:






	'''simple docstring'''






	a      :		Optional[int]	=				config.decoder_config.hidden_size
	for idx in range(config.decoder_config.decoder_layers					):
		# read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
		a      :		int	=				state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight"""					)
		a      :		List[str]	=				state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias"""					)
		# next, add query, keys and values (in that order) to the state dict
		a      :		List[str]	=				in_proj_weight[: hidden_size, :]
		a      :		Optional[int]	=				in_proj_bias[:config.hidden_size]
		a      :		Dict	=				in_proj_weight[hidden_size : hidden_size * 2, :]
		a      :		int	=				in_proj_bias[hidden_size : hidden_size * 2]
		a      :		List[Any]	=				in_proj_weight[-hidden_size :, :]
		a      :		Optional[int]	=				in_proj_bias[-hidden_size :]
		# read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
		a      :		Optional[Any]	=				state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight"""					)
		a      :		Union[str, Any]	=				state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias"""					)
		# next, add query, keys and values (in that order) to the state dict
		a      :		str	=				in_proj_weight[: hidden_size, :]
		a      :		List[Any]	=				in_proj_bias[:config.hidden_size]
		a      :		Dict	=				in_proj_weight[hidden_size : hidden_size * 2, :]
		a      :		Optional[Any]	=				in_proj_bias[hidden_size : hidden_size * 2]
		a      :		Union[str, Any]	=				in_proj_weight[-hidden_size :, :]
		a      :		str	=				in_proj_bias[-hidden_size :]
	# fmt: on






def       _SCREAMING_SNAKE_CASE		(	) ->torch.Tensor:






	'''simple docstring'''






	a      :		Tuple	=				"http://images.cocodataset.org/val2017/000000039769.jpg"
	a      :		Dict	=				Image.open(requests.get(_lowercase    ,       stream=_lowercase					).raw					)
	return im






@torch.no_grad()
def       _SCREAMING_SNAKE_CASE		(	_lowercase						:    str    ,       _lowercase						:    str    ,       _lowercase						:    str    ,       _lowercase						:    bool = False					) ->List[Any]:






	'''simple docstring'''






	a      :		List[Any]	=				get_maskformer_config(_lowercase					)

	# load original state_dict
	with open(_lowercase    ,       "rb"					) as f:
		a      :		Union[str, Any]	=				pickle.load(_lowercase					)
	a      :		Tuple	=				data["model"]

	# for name, param in state_dict.items():
	#     print(name, param.shape)

	# rename keys
	a      :		str	=				create_rename_keys(_lowercase					)
	for src, dest in rename_keys:
		rename_key(_lowercase    ,       _lowercase    ,       _lowercase					)
	read_in_swin_q_k_v(_lowercase    ,       config.backbone_config					)
	read_in_decoder_q_k_v(_lowercase    ,       _lowercase					)

	# update to torch tensors
	for key, value in state_dict.items():
		a      :		Dict	=				torch.from_numpy(_lowercase					)

	# load 🤗 model
	a      :		Optional[int]	=				MaskFormerForInstanceSegmentation(_lowercase					)
	model.eval()

	for name, param in model.named_parameters():
		print(_lowercase    ,       param.shape					)

	a,       a      :		Union[str, Any]	=				model.load_state_dict(_lowercase    ,       strict=_lowercase					)
	assert missing_keys == [
	    "model.pixel_level_module.encoder.model.layernorm.weight",
	    "model.pixel_level_module.encoder.model.layernorm.bias",
	]
	assert len(_lowercase					) == 0, F"""Unexpected keys: {unexpected_keys}"""

	# verify results
	a      :		Dict	=				prepare_img()
	if "vistas" in model_name:
		a      :		Union[str, Any]	=				65
	elif "cityscapes" in model_name:
		a      :		List[Any]	=				6_5535
	else:
		a      :		Dict	=				255
	a      :		Optional[int]	=				True if "ade" in model_name else False
	a      :		List[Any]	=				MaskFormerImageProcessor(ignore_index=_lowercase    ,       reduce_labels=_lowercase					)

	a      :		List[str]	=				image_processor(_lowercase    ,       return_tensors="pt"					)

	a      :		Union[str, Any]	=				model(**_lowercase					)

	print("Logits:"    ,       outputs.class_queries_logits[0, :3, :3]					)

	if model_name == "maskformer-swin-tiny-ade":
		a      :		int	=				torch.tensor(
		    [[3.6353, -4.4770, -2.6065], [0.5081, -4.2394, -3.5343], [2.1909, -5.0353, -1.9323]]					)
	assert torch.allclose(outputs.class_queries_logits[0, :3, :3]    ,       _lowercase    ,       atol=1E-4					)
	print("Looks ok!"					)

	if pytorch_dump_folder_path is not None:
		print(F"""Saving model and image processor to {pytorch_dump_folder_path}"""					)
		Path(_lowercase					).mkdir(exist_ok=_lowercase					)
		model.save_pretrained(_lowercase					)
		image_processor.save_pretrained(_lowercase					)

	if push_to_hub:
		print("Pushing model and image processor to the hub..."					)
		model.push_to_hub(F"""nielsr/{model_name}"""					)
		image_processor.push_to_hub(F"""nielsr/{model_name}"""					)


if __name__ == "__main__":
		a						: Dict     	=			argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--model_name''',
		    default='''maskformer-swin-tiny-ade''',
		    type=str,
		    help=('''Name of the MaskFormer model you\'d like to convert''',),
		)
		parser.add_argument(
		    '''--checkpoint_path''',
		    default='''/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl''',
		    type=str,
		    help='''Path to the original state dict (.pth file).''',
		)
		parser.add_argument(
		    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
		)
		parser.add_argument(
		    '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
		)

		a						: str     	=			parser.parse_args()
		convert_maskformer_checkpoint(
		    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
		)










"""simple docstring"""



def 						lowercase    (				__snake_case				: Optional[int]	):
		lowercase_ : int							       =   0
		lowercase_ : Optional[Any]							       =   len(__snake_case	)

		for i in range(n - 1	):
				for j in range(i + 1		,					__snake_case	):
						if arr[i] > arr[j]:
								num_inversions += 1

		return num_inversions



def 						lowercase    (				__snake_case				: str	):
		if len(__snake_case	) <= 1:
				return arr, 0
		lowercase_ : Optional[Any]							       =   len(__snake_case	) // 2
		lowercase_ : List[Any]							       =   arr[0:mid]
		lowercase_ : Union[str, Any]							       =   arr[mid:]

		lowercase_       ,					lowercase_ : Tuple							       =   count_inversions_recursive(__snake_case	)
		lowercase_       ,					lowercase_ : List[Any]							       =   count_inversions_recursive(__snake_case	)
		lowercase_       ,					lowercase_ : List[Any]							       =   _count_cross_inversions(__snake_case		,					__snake_case	)

		lowercase_ : List[Any]							       =   inversion_p + inversions_q + cross_inversions
		return c, num_inversions



def 						lowercase    (				__snake_case				: str		,					__snake_case				: Optional[int]	):
		lowercase_ : Optional[Any]							       =   []
		lowercase_ : Any							       =   0
		while i < len(__snake_case	) and j < len(__snake_case	):
				if p[i] > q[j]:
						# if P[1] > Q[j], then P[k] > Q[k] for all  i < k <= len(P)
						# These are all inversions. The claim emerges from the
						# property that P is sorted.
						num_inversion += len(__snake_case	) - i
						r.append(q[j]	)
						j += 1
				else:
						r.append(p[i]	)
						i += 1

		if i < len(__snake_case	):
				r.extend(p[i:]	)
		else:
				r.extend(q[j:]	)

		return r, num_inversion



def 						lowercase    (				):
		lowercase_ : Union[str, Any]							       =   [1_0, 2, 1, 5, 5, 2, 1_1]

		# this arr has 8 inversions:
		# (10, 2), (10, 1), (10, 5), (10, 5), (10, 2), (2, 1), (5, 2), (5, 2)

		lowercase_ : int							       =   count_inversions_bf(__snake_case	)
		lowercase_       ,					lowercase_ : Dict							       =   count_inversions_recursive(__snake_case	)

		assert num_inversions_bf == num_inversions_recursive == 8

		print('''number of inversions = '''		,					__snake_case	)

		# testing an array with zero inversion (a sorted arr_1)

		arr_a.sort()
		lowercase_ : Dict							       =   count_inversions_bf(__snake_case	)
		lowercase_       ,					lowercase_ : Dict							       =   count_inversions_recursive(__snake_case	)

		assert num_inversions_bf == num_inversions_recursive == 0
		print('''number of inversions = '''		,					__snake_case	)

		# an empty list should also have zero inversions
		lowercase_ : List[Any]							       =   []
		lowercase_ : Any							       =   count_inversions_bf(__snake_case	)
		lowercase_       ,					lowercase_ : List[str]							       =   count_inversions_recursive(__snake_case	)

		assert num_inversions_bf == num_inversions_recursive == 0
		print('''number of inversions = '''		,					__snake_case	)


if __name__ == "__main__":
						main()



