





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()
lowerCAmelCase_    =    logging.get_logger(__name__)






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						Union[str, Any]:






				"""simple docstring"""


				snake_case_						:   List[str]           =  SwinConfig.from_pretrained(
				    '''microsoft/swin-tiny-patch4-window7-224'''			,    out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']			)
				snake_case_						:   Any           =  MaskFormerConfig(backbone_config=_UpperCamelCase			)

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

				snake_case_						:   str           =  json.load(open(hf_hub_download(_UpperCamelCase			,    _UpperCamelCase			,    repo_type='''dataset'''			)			,    '''r'''			)			)
				snake_case_						:   Dict           =  {int(_UpperCamelCase			): v for k, v in idalabel.items()}

				return config






def 					lowerCamelCase_						(    _UpperCamelCase			)		->						Any:






				"""simple docstring"""


				snake_case_						:   List[str]           =  []
				# 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 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			)		->						Optional[Any]:






				"""simple docstring"""


				snake_case_						:   str           =  dct.pop(_UpperCamelCase			)
				snake_case_						:   int           =  val






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			)		->						Tuple:






				"""simple docstring"""


				snake_case_						:   int           =  [int(backbone_config.embed_dim * 2**i			) for i in range(len(backbone_config.depths			)			)]
				for i in range(len(backbone_config.depths			)			):
								snake_case_						:   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)
												snake_case_						:   Tuple           =  state_dict.pop(f'''backbone.layers.{i}.blocks.{j}.attn.qkv.weight'''			)
												snake_case_						:   str           =  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
												snake_case_						:   str           =  in_proj_weight[:dim, :]
												snake_case_						:   Optional[Any]           =  in_proj_bias[: dim]
												snake_case_						:   Optional[Any]           =  in_proj_weight[
												    dim : dim * 2, :
												]
												snake_case_						:   List[str]           =  in_proj_bias[
												    dim : dim * 2
												]
												snake_case_						:   Union[str, Any]           =  in_proj_weight[
												    -dim :, :
												]
												snake_case_						:   List[str]           =  in_proj_bias[-dim :]
												# fmt: on






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			)		->						List[Any]:






				"""simple docstring"""


				snake_case_						:   Optional[Any]           =  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)
								snake_case_						:   Any           =  state_dict.pop(f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight'''			)
								snake_case_						:   int           =  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
								snake_case_						:   Optional[Any]           =  in_proj_weight[: hidden_size, :]
								snake_case_						:   Optional[int]           =  in_proj_bias[:config.hidden_size]
								snake_case_						:   str           =  in_proj_weight[hidden_size : hidden_size * 2, :]
								snake_case_						:   Dict           =  in_proj_bias[hidden_size : hidden_size * 2]
								snake_case_						:   Dict           =  in_proj_weight[-hidden_size :, :]
								snake_case_						:   List[Any]           =  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)
								snake_case_						:   str           =  state_dict.pop(f'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight'''			)
								snake_case_						:   str           =  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
								snake_case_						:   Optional[Any]           =  in_proj_weight[: hidden_size, :]
								snake_case_						:   List[Any]           =  in_proj_bias[:config.hidden_size]
								snake_case_						:   Optional[int]           =  in_proj_weight[hidden_size : hidden_size * 2, :]
								snake_case_						:   Union[str, Any]           =  in_proj_bias[hidden_size : hidden_size * 2]
								snake_case_						:   List[str]           =  in_proj_weight[-hidden_size :, :]
								snake_case_						:   Dict           =  in_proj_bias[-hidden_size :]
				# fmt: on






def 					lowerCamelCase_						(    )		->						torch.Tensor:






				"""simple docstring"""


				snake_case_						:   Union[str, Any]           =  '''http://images.cocodataset.org/val2017/000000039769.jpg'''
				snake_case_						:   Any           =  Image.open(requests.get(_UpperCamelCase			,    stream=_UpperCamelCase			).raw			)
				return im






@torch.no_grad()
def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase = False			)		->						Optional[Any]:






				"""simple docstring"""


				snake_case_						:   Any           =  get_maskformer_config(_UpperCamelCase			)

				# load original state_dict
				with open(_UpperCamelCase			,    '''rb'''			) as f:
								snake_case_						:   List[str]           =  pickle.load(_UpperCamelCase			)
				snake_case_						:   Optional[Any]           =  data['''model''']

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

				# rename keys
				snake_case_						:   Optional[Any]           =  create_rename_keys(_UpperCamelCase			)
				for src, dest in rename_keys:
								rename_key(_UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			)
				read_in_swin_q_k_v(_UpperCamelCase			,    config.backbone_config			)
				read_in_decoder_q_k_v(_UpperCamelCase			,    _UpperCamelCase			)

				# update to torch tensors
				for key, value in state_dict.items():
								snake_case_						:   int           =  torch.from_numpy(_UpperCamelCase			)

				# load 🤗 model
				snake_case_						:   Optional[int]           =  MaskFormerForInstanceSegmentation(_UpperCamelCase			)
				model.eval()

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

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

				# verify results
				snake_case_						:   Any           =  prepare_img()
				if "vistas" in model_name:
								snake_case_						:   Optional[int]           =  65
				elif "cityscapes" in model_name:
								snake_case_						:   Dict           =  65_535
				else:
								snake_case_						:   Tuple           =  255
				snake_case_						:   List[str]           =  True if '''ade''' in model_name else False
				snake_case_						:   Any           =  MaskFormerImageProcessor(ignore_index=_UpperCamelCase			,    reduce_labels=_UpperCamelCase			)

				snake_case_						:   Union[str, Any]           =  image_processor(_UpperCamelCase			,    return_tensors='''pt'''			)

				snake_case_						:   List[Any]           =  model(**_UpperCamelCase			)

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

				if model_name == "maskformer-swin-tiny-ade":
								snake_case_						:   List[str]           =  torch.tensor(
								    [[3.6_353, -4.4_770, -2.6_065], [0.5_081, -4.2_394, -3.5_343], [2.1_909, -5.0_353, -1.9_323]]			)
				assert torch.allclose(outputs.class_queries_logits[0, :3, :3]			,    _UpperCamelCase			,    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(_UpperCamelCase			).mkdir(exist_ok=_UpperCamelCase			)
								model.save_pretrained(_UpperCamelCase			)
								image_processor.save_pretrained(_UpperCamelCase			)

				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__":
	lowerCAmelCase_    =    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.'''
	)

	lowerCAmelCase_    =    parser.parse_args()
	convert_maskformer_checkpoint(
	    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
	)







from math import isclose, sqrt






def 					lowerCamelCase_						(    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			)		->						tuple[float, float, float]:






				"""simple docstring"""


				snake_case_						:   Dict           =  point_y / 4 / point_x
				snake_case_						:   List[str]           =  2 * normal_gradient / (1 + normal_gradient * normal_gradient)
				snake_case_						:   Union[str, Any]           =  (1 - normal_gradient * normal_gradient) / (
				    1 + normal_gradient * normal_gradient
				)
				snake_case_						:   Tuple           =  (sa - ca * incoming_gradient) / (ca + sa * incoming_gradient)

				# to find the next point, solve the simultaeneous equations:
				# y^2 + 4x^2 = 100
				# y - b = m * (x - a)
				# ==> A x^2 + B x + C = 0
				snake_case_						:   Union[str, Any]           =  outgoing_gradient**2 + 4
				snake_case_						:   Tuple           =  2 * outgoing_gradient * (point_y - outgoing_gradient * point_x)
				snake_case_						:   Optional[Any]           =  (point_y - outgoing_gradient * point_x) ** 2 - 100

				snake_case_						:   Dict           =  (
				    -linear_term - sqrt(linear_term**2 - 4 * quadratic_term * constant_term			)
				) / (2 * quadratic_term)
				snake_case_						:   Optional[int]           =  (
				    -linear_term + sqrt(linear_term**2 - 4 * quadratic_term * constant_term			)
				) / (2 * quadratic_term)

				# two solutions, one of which is our input point
				snake_case_						:   Any           =  x_minus if isclose(_UpperCamelCase			,    _UpperCamelCase			) else x_plus
				snake_case_						:   int           =  point_y + outgoing_gradient * (next_x - point_x)

				return next_x, next_y, outgoing_gradient






def 					lowerCamelCase_						(    _UpperCamelCase = 1.4			,    _UpperCamelCase = -9.6			)		->						int:






				"""simple docstring"""


				snake_case_						:   int           =  0
				snake_case_						:   float           =  first_x_coord
				snake_case_						:   float           =  first_y_coord
				snake_case_						:   float           =  (10.1 - point_y) / (0.0 - point_x)

				while not (-0.01 <= point_x <= 0.01 and point_y > 0):
								snake_case_      ,							snake_case_      ,							snake_case_						:   str           =  next_point(_UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			)
								num_reflections += 1

				return num_reflections


if __name__ == "__main__":
	print(F'''{solution() = }''')
