

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      		=     logging.get_logger(__name__)



def 				snake_case_(_UpperCamelCase					)							->     Optional[Any]:



  """simple docstring"""






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

  _snake_case						=							'''huggingface/label-files'''
  if "ade20k-full" in model_name:
    # this should be ok
    _snake_case						=							847
    _snake_case						=							'''maskformer-ade20k-full-id2label.json'''
  elif "ade" in model_name:
    # this should be ok
    _snake_case						=							150
    _snake_case						=							'''ade20k-id2label.json'''
  elif "coco-stuff" in model_name:
    # this should be ok
    _snake_case						=							171
    _snake_case						=							'''maskformer-coco-stuff-id2label.json'''
  elif "coco" in model_name:
    # TODO
    _snake_case						=							133
    _snake_case						=							'''coco-panoptic-id2label.json'''
  elif "cityscapes" in model_name:
    # this should be ok
    _snake_case						=							19
    _snake_case						=							'''cityscapes-id2label.json'''
  elif "vistas" in model_name:
    # this should be ok
    _snake_case						=							65
    _snake_case						=							'''mapillary-vistas-id2label.json'''

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

  return config



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



  """simple docstring"""






  _snake_case						=							[]
  # 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 				snake_case_(_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase					)							->     Any:



  """simple docstring"""






  _snake_case						=							dct.pop(_UpperCamelCase					)
  _snake_case						=							val



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



  """simple docstring"""






  _snake_case						=							[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						=							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						=							state_dict.pop(F"""backbone.layers.{i}.blocks.{j}.attn.qkv.weight"""					)
      _snake_case						=							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						=							in_proj_weight[:dim, :]
      _snake_case						=							in_proj_bias[: dim]
      _snake_case						=							in_proj_weight[
          dim : dim * 2, :
      ]
      _snake_case						=							in_proj_bias[
          dim : dim * 2
      ]
      _snake_case						=							in_proj_weight[
          -dim :, :
      ]
      _snake_case						=							in_proj_bias[-dim :]
      # fmt: on



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase					)							->     Tuple:



  """simple docstring"""






  _snake_case						=							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						=							state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight"""					)
    _snake_case						=							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						=							in_proj_weight[: hidden_size, :]
    _snake_case						=							in_proj_bias[:config.hidden_size]
    _snake_case						=							in_proj_weight[hidden_size : hidden_size * 2, :]
    _snake_case						=							in_proj_bias[hidden_size : hidden_size * 2]
    _snake_case						=							in_proj_weight[-hidden_size :, :]
    _snake_case						=							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						=							state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight"""					)
    _snake_case						=							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						=							in_proj_weight[: hidden_size, :]
    _snake_case						=							in_proj_bias[:config.hidden_size]
    _snake_case						=							in_proj_weight[hidden_size : hidden_size * 2, :]
    _snake_case						=							in_proj_bias[hidden_size : hidden_size * 2]
    _snake_case						=							in_proj_weight[-hidden_size :, :]
    _snake_case						=							in_proj_bias[-hidden_size :]
  # fmt: on



def 				snake_case_()							->     torch.Tensor:



  """simple docstring"""






  _snake_case						=							'''http://images.cocodataset.org/val2017/000000039769.jpg'''
  _snake_case						=							Image.open(requests.get(_UpperCamelCase			,	stream=_UpperCamelCase					).raw					)
  return im



@torch.no_grad()
def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase = False					)							->     str:



  """simple docstring"""






  _snake_case						=							get_maskformer_config(_UpperCamelCase					)

  # load original state_dict
  with open(_UpperCamelCase			,	'''rb'''					) as f:
    _snake_case						=							pickle.load(_UpperCamelCase					)
  _snake_case						=							data['''model''']

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

  # rename keys
  _snake_case						=							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						=							torch.from_numpy(_UpperCamelCase					)

  # load 🤗 model
  _snake_case						=							MaskFormerForInstanceSegmentation(_UpperCamelCase					)
  model.eval()

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

  _snake_case,					_snake_case						=							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						=							prepare_img()
  if "vistas" in model_name:
    _snake_case						=							65
  elif "cityscapes" in model_name:
    _snake_case						=							65_535
  else:
    _snake_case						=							255
  _snake_case						=							True if '''ade''' in model_name else False
  _snake_case						=							MaskFormerImageProcessor(ignore_index=_UpperCamelCase			,	reduce_labels=_UpperCamelCase					)

  _snake_case						=							image_processor(_UpperCamelCase			,	return_tensors='''pt'''					)

  _snake_case						=							model(**_UpperCamelCase					)

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

  if model_name == "maskformer-swin-tiny-ade":
    _snake_case						=							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]			,	_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__":
 __A      		=     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      		=     parser.parse_args()
 convert_maskformer_checkpoint(
     args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
 )





from math import cos, sin, sqrt, tau

from audio_filters.iir_filter import IIRFilter



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase = 1 / sqrt(2					)					)							->     IIRFilter:



  """simple docstring"""






  _snake_case						=							tau * frequency / samplerate
  _snake_case						=							sin(_UpperCamelCase					)
  _snake_case						=							cos(_UpperCamelCase					)
  _snake_case						=							_sin / (2 * q_factor)

  _snake_case						=							(1 - _cos) / 2
  _snake_case						=							1 - _cos

  _snake_case						=							1 + alpha
  _snake_case						=							-2 * _cos
  _snake_case						=							1 - alpha

  _snake_case						=							IIRFilter(2					)
  filt.set_coefficients([aa, aa, aa]			,	[ba, ba, ba]					)
  return filt



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase = 1 / sqrt(2					)					)							->     IIRFilter:



  """simple docstring"""






  _snake_case						=							tau * frequency / samplerate
  _snake_case						=							sin(_UpperCamelCase					)
  _snake_case						=							cos(_UpperCamelCase					)
  _snake_case						=							_sin / (2 * q_factor)

  _snake_case						=							(1 + _cos) / 2
  _snake_case						=							-1 - _cos

  _snake_case						=							1 + alpha
  _snake_case						=							-2 * _cos
  _snake_case						=							1 - alpha

  _snake_case						=							IIRFilter(2					)
  filt.set_coefficients([aa, aa, aa]			,	[ba, ba, ba]					)
  return filt



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase = 1 / sqrt(2					)					)							->     IIRFilter:



  """simple docstring"""






  _snake_case						=							tau * frequency / samplerate
  _snake_case						=							sin(_UpperCamelCase					)
  _snake_case						=							cos(_UpperCamelCase					)
  _snake_case						=							_sin / (2 * q_factor)

  _snake_case						=							_sin / 2
  _snake_case						=							0
  _snake_case						=							-ba

  _snake_case						=							1 + alpha
  _snake_case						=							-2 * _cos
  _snake_case						=							1 - alpha

  _snake_case						=							IIRFilter(2					)
  filt.set_coefficients([aa, aa, aa]			,	[ba, ba, ba]					)
  return filt



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase = 1 / sqrt(2					)					)							->     IIRFilter:



  """simple docstring"""






  _snake_case						=							tau * frequency / samplerate
  _snake_case						=							sin(_UpperCamelCase					)
  _snake_case						=							cos(_UpperCamelCase					)
  _snake_case						=							_sin / (2 * q_factor)

  _snake_case						=							1 - alpha
  _snake_case						=							-2 * _cos
  _snake_case						=							1 + alpha

  _snake_case						=							IIRFilter(2					)
  filt.set_coefficients([ba, ba, ba]			,	[ba, ba, ba]					)
  return filt



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase = 1 / sqrt(2					)			,	)							->     IIRFilter:



  """simple docstring"""






  _snake_case						=							tau * frequency / samplerate
  _snake_case						=							sin(_UpperCamelCase					)
  _snake_case						=							cos(_UpperCamelCase					)
  _snake_case						=							_sin / (2 * q_factor)
  _snake_case						=							10 ** (gain_db / 40)

  _snake_case						=							1 + alpha * big_a
  _snake_case						=							-2 * _cos
  _snake_case						=							1 - alpha * big_a
  _snake_case						=							1 + alpha / big_a
  _snake_case						=							-2 * _cos
  _snake_case						=							1 - alpha / big_a

  _snake_case						=							IIRFilter(2					)
  filt.set_coefficients([aa, aa, aa]			,	[ba, ba, ba]					)
  return filt



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase = 1 / sqrt(2					)			,	)							->     IIRFilter:



  """simple docstring"""






  _snake_case						=							tau * frequency / samplerate
  _snake_case						=							sin(_UpperCamelCase					)
  _snake_case						=							cos(_UpperCamelCase					)
  _snake_case						=							_sin / (2 * q_factor)
  _snake_case						=							10 ** (gain_db / 40)
  _snake_case						=							(big_a + 1) - (big_a - 1) * _cos
  _snake_case						=							(big_a + 1) + (big_a - 1) * _cos
  _snake_case						=							(big_a - 1) - (big_a + 1) * _cos
  _snake_case						=							(big_a - 1) + (big_a + 1) * _cos
  _snake_case						=							2 * sqrt(_UpperCamelCase					) * alpha

  _snake_case						=							big_a * (pmc + aaa)
  _snake_case						=							2 * big_a * mpc
  _snake_case						=							big_a * (pmc - aaa)
  _snake_case						=							ppmc + aaa
  _snake_case						=							-2 * pmpc
  _snake_case						=							ppmc - aaa

  _snake_case						=							IIRFilter(2					)
  filt.set_coefficients([aa, aa, aa]			,	[ba, ba, ba]					)
  return filt



def 				snake_case_(_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase			,	_UpperCamelCase = 1 / sqrt(2					)			,	)							->     IIRFilter:



  """simple docstring"""






  _snake_case						=							tau * frequency / samplerate
  _snake_case						=							sin(_UpperCamelCase					)
  _snake_case						=							cos(_UpperCamelCase					)
  _snake_case						=							_sin / (2 * q_factor)
  _snake_case						=							10 ** (gain_db / 40)
  _snake_case						=							(big_a + 1) - (big_a - 1) * _cos
  _snake_case						=							(big_a + 1) + (big_a - 1) * _cos
  _snake_case						=							(big_a - 1) - (big_a + 1) * _cos
  _snake_case						=							(big_a - 1) + (big_a + 1) * _cos
  _snake_case						=							2 * sqrt(_UpperCamelCase					) * alpha

  _snake_case						=							big_a * (ppmc + aaa)
  _snake_case						=							-2 * big_a * pmpc
  _snake_case						=							big_a * (ppmc - aaa)
  _snake_case						=							pmc + aaa
  _snake_case						=							2 * mpc
  _snake_case						=							pmc - aaa

  _snake_case						=							IIRFilter(2					)
  filt.set_coefficients([aa, aa, aa]			,	[ba, ba, ba]					)
  return filt

